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Use of ASTER- and LANDSAT Images for the Determination of Soil Parameters Bastiaan Notebaert 2004

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1 Word of thanks This study could only be completed thanks to the help and support of many people. Therefore

I want to thank all these people for their contribution:

My promoters, Prof. Dr. Eric Van Ranst and Prof. Dr. Rudi Goossens, for their support and

because they made everything possible. I want to thank Prof. Dr Rudi Goossens in special for

all his advise and tips about the used procedures and software, and his concerns about the

advance of my studies. I want to thank Prof. Dr. Eric Van Ranst because he provided me a lot

of material from the archives of the University of Ghent like maps and aerial photo’s.

I also want to thank the people of the Royal Museum of Central Africa (Tervuren, Belgium),

and in special Dr. Luc Tack, Dr. Johan Lavreau and Philippe Trefois for the archive material

they putted at my disposal (photomosaics). I want to thank in special Dr. Luc Tack for the

time he spent with me searching for topographical maps.

I want to thank Prof. Dr. Geert Baert. For all his help in searching the archives of the

University of Ghent, for his practical information about the region in which I was working,

for providing me the digital soil maps and his personal copies of the explanatory texts of the

soil maps and all the support he gave me.

The research assistants of the geography department of the University of Ghent also earn my

gratitude. Lic. Tony Vanderstraete for teaching me how to work with Virtuozo and Ilwis and

helping me to solve problems with these software packets. Lic. Dennis Devriendt for teaching

me how to work with Virtuozo and trying to solve the frequent occurring problems I had with

this software.

Lic. Stijn Van Coillie for giving me practical advises for the construction of a DEM.

Lic. Joris Verbeken for putting his thesis at my disposal and for the practical advises in

classification.

My girlfriend, Lic. Tanja Miloti, for all the support and practical help.

My parents for their support.


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2 Table of contents

2.1 Table of contents 1 Word of thanks ................................................................................................ ................... 1

2 Table of contents ................................................................................................ ................ 2

2.1 Table of contents ................................................................................................ ........ 2

2.2 Table of figures ................................................................................................ .......... 7

2.3 Table of tables .......................................................................................................... 11

3 Introduction ...................................................................................................................... 13

4 Objectives ......................................................................................................................... 14

5 The study area .................................................................................................................. 16

5.1 Introduction .............................................................................................................. 16

5.2 Climate ..................................................................................................................... 17

5.3 Geological regions .................................................................................................... 21

5.4 The geological formations ........................................................................................ 24

5.4.1 The soft covering deposits ................................................................................ 24

5.4.1.1 Holocene alluvial deposits ........................................................................... 24

5.4.1.2 Pliocene and Pleistocene sand deposits ........................................................ 24

5.4.1.3 Ochre sands (Kalahari Sands) ...................................................................... 24

5.4.2 Tertiary formations ........................................................................................... 24

5.4.2.1 The series of ‘Polymorphic Sandstones’ ...................................................... 24

5.4.3 The Mesozoic consolidated formations ............................................................ 25

5.4.3.1 The Kwango Series (Upper Cretaceous) ...................................................... 25

5.4.3.2 The undifferentiated Cretaceous (Lower Cretaceous) .................................. 25

5.4.4 The Precambrian socle ..................................................................................... 25

5.4.4.1 The West-Congo supergroup ....................................................................... 26

5.4.4.1.1 The ‘Schisto-Gréseux’ group (the schist-sandstone group) ................... 26

5.4.4.1.2 The schist-limestone group .................................................................... 27

5.4.4.1.3 The Tillite Supérieure (Upper Till) of Bas-Congo ................................. 27

5.4.4.1.4 The group of Haute-Shiloango ............................................................... 27

5.4.4.1.5 The Tillite Inférieur of Bas-Congo ......................................................... 28

5.4.4.1.6 group of the Sansikwa ............................................................................ 28

5.4.4.2 The Zadinien supergroup ............................................................................. 28


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5.5 Geomorphology ........................................................................................................ 29

5.5.1 Introduction: alternation ................................................................................... 29

5.5.2 General geomorphological structure ................................................................ 35

5.5.3 The geomorphological units ............................................................................. 39

5.5.3.1 Plateau du Bangu .......................................................................................... 39

5.5.3.2 Crête de Mbanza Ngungu ............................................................................. 39

5.5.3.3 Plateau des Batekes – high plateau du Kwango ........................................... 40

5.5.3.4 Surfaces of the basin of the Haute Lukunga ................................................ 40

5.5.3.5 The intermediate surface (of the Batekes-Inkisi surfaces) ........................... 41

5.5.3.6 The lower surface (of the Batekes-Inkisi surfaces) ...................................... 41

5.5.3.7 Pool Malebo plain ........................................................................................ 41

5.5.3.8 The Schist-Limestone depression ................................................................. 41

5.5.3.9 Schist-Sandstone massif ............................................................................... 42

5.6 Vegetation ................................................................................................................ 43

5.6.1 Introduction ...................................................................................................... 43

5.6.2 Vegetation types ............................................................................................... 43

5.6.2.1 Aquatic and semi-aquatic vegetation ........................................................... 43

5.6.2.1.1 Aquatic vegetation .................................................................................. 43

5.6.2.1.2 The vegetation of inundated rocks and rapids ........................................ 44

5.6.2.1.3 Semi-aquatic and marsh vegetation ........................................................ 44

5.6.2.2 The pioneering vegetation of the loose landslides. ...................................... 44

5.6.2.3 Post-agricultural and nitrophilic vegetation ................................................. 44

5.6.2.4 The savannah vegetation .............................................................................. 44

5.6.2.4.1 Savannah in the valleys .......................................................................... 45

5.6.2.4.2 Savannah on heavy soils ........................................................................ 45

5.6.2.4.3 Savannah on light soils ........................................................................... 45

5.6.2.4.4 Steppe ..................................................................................................... 46

5.6.2.5 Forests .......................................................................................................... 46

5.6.2.5.1 Guinea forests on heavy soils ................................................................. 46

5.6.2.5.2 Forests on light soils ............................................................................... 47

5.6.2.5.3 The antropogenous forests ..................................................................... 47

5.6.3 Vegetation types appearing on the vegetation maps ........................................ 47

5.7 Soils .......................................................................................................................... 49

5.7.1 Introduction ...................................................................................................... 49


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5.7.2 The criteria to which the soils are grouped in series and phases ...................... 50

5.7.3 The maps .......................................................................................................... 51

5.7.4 The different series ........................................................................................... 52

5.7.4.1 Introduction .................................................................................................. 52

5.7.4.2 Soil series on micaschist .............................................................................. 52

5.7.4.3 Soil series on basic rocks ............................................................................. 53

5.7.4.4 Soil series on not or slightly metamorphic rocks and till ............................. 54

5.7.4.5 Soils on calcareous rocks ............................................................................. 55

5.7.4.6 Series on hard rocks with a quartz dominance ............................................. 57

5.7.4.7 Series of soils developed in soft sediments with a quartz dominance ......... 59

5.7.4.8 Soils of alluvia and colluvia ......................................................................... 60

6 Used data .......................................................................................................................... 63

6.1 ASTER images ......................................................................................................... 63

6.1.1 Introduction to ASTER .................................................................................... 63

6.1.1.1 The TERRA platform ................................................................................... 63

6.1.1.2 ASTER ......................................................................................................... 64

6.1.1.2.1 Visible and Near Infrared (VNIR) ......................................................... 64

6.1.1.2.2 Shortwave Infrared (SWIR) ................................................................... 66

6.1.1.2.3 Thermal Infrared (TIR) .......................................................................... 66

6.1.1.2.4 Overview ................................................................................................ 67

6.1.2 Selection of an image ....................................................................................... 68

6.1.3 Metadata of the chosen image .......................................................................... 68

6.1.4 Preparation of the image .................................................................................. 68

6.2 LANDSAT images ................................................................................................... 68

7 Introduction in teledetection and air photography ........................................................... 69

7.1 Introduction .............................................................................................................. 69

7.2 Basic principles of stereoscopy ................................................................................ 69

7.3 Photogrammetry ....................................................................................................... 72

7.4 Principles of teledetection ........................................................................................ 74

7.4.1 Introduction ...................................................................................................... 74

7.4.2 Electromagnetic radiation ................................................................................ 75

7.4.3 Images .............................................................................................................. 78

7.4.4 Image processing .............................................................................................. 78

7.4.4.1 Rectification and restoration ........................................................................ 78


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7.4.4.2 Image enhancement ...................................................................................... 79

7.4.4.3 Feature space and soil line ............................................................................ 82

7.4.4.4 Vegetation indices ........................................................................................ 82

7.4.4.5 Classification ................................................................................................ 84

8 Production of a DEM ....................................................................................................... 89

8.1 Introduction .............................................................................................................. 89

8.1.1 Concept ............................................................................................................. 89

8.1.2 Used software ................................................................................................... 89

8.2 Available data ........................................................................................................... 89

8.3 Preparing the images in Ilwis ................................................................................... 90

8.3.1 Stretching ......................................................................................................... 90

8.3.2 Georeferencing and making submaps .............................................................. 90

8.4 Production of the DEM in Virtuozo ......................................................................... 91

8.4.1 Preparative steps ............................................................................................... 91

8.4.1.1 Creating a block ........................................................................................... 91

8.4.1.2 Importing images .......................................................................................... 92

8.4.1.3 Turning the images ....................................................................................... 92

8.4.1.4 Creation of the Pass points file ..................................................................... 93

8.4.1.5 Creating a model .......................................................................................... 94

8.4.2 Production of the stereopair ............................................................................. 95

8.4.2.1 Relative orientation ...................................................................................... 95

8.4.2.2 Absolute orientation ..................................................................................... 97

8.4.2.3 Absolute orientation: finding the pass points and occurring problems ........ 98

8.4.3 Production of the DEM .................................................................................... 99

8.4.3.1 Image matching & editing the match ........................................................... 99

8.4.3.2 Creating a DEM ......................................................................................... 101

8.4.4 Overview of the exported filetypes from Virtuozo ........................................ 102

8.5 Problems encountered in Virtuozo ......................................................................... 103

8.5.1 Problems with the coordinates ....................................................................... 103

8.5.2 Problems during match edit ............................................................................ 103

8.5.3 Problem with the large cloud ......................................................................... 104

8.6 Converting the DEM to a grid ................................................................................ 105

8.7 Production of an orthophoto ................................................................................... 107

8.7.1 Production of the orthophoto .......................................................................... 107


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8.7.2 Preparing the orthophoto for use .................................................................... 108

8.7.3 Contourmap .................................................................................................... 109

8.7.4 Drape .............................................................................................................. 109

8.8 Quality report ......................................................................................................... 111

8.9 Discussion .............................................................................................................. 112

9 Digital soil map of the region ......................................................................................... 115

9.1 Introduction ............................................................................................................ 115

9.2 Correcting the map ................................................................................................. 115

9.3 Units of the soil map .............................................................................................. 116

10 Classification .............................................................................................................. 121

10.1 Introduction ............................................................................................................ 121

10.2 Importing the data in Ilwis ..................................................................................... 121

10.2.1 Importing the soilmap and DEM .................................................................... 121

10.2.1.1 Importing the soilmap ............................................................................ 121

10.2.1.2 Importing the DEM ................................................................................ 121

10.2.2 Preparing the satellite images in ILWIS ........................................................ 121

10.2.2.1 The orthophoto ....................................................................................... 121

10.2.2.2 The VNIR bands ..................................................................................... 122

10.2.2.3 The SWIR bands .................................................................................... 123

10.2.2.4 The TIR bands ........................................................................................ 123

10.2.2.5 Making the map lists .............................................................................. 124

10.2.3 Making a colour composite ............................................................................ 125

10.3 Unsupervised Vegetation classification ................................................................. 126

10.4 Supervised vegetation classification ...................................................................... 129

10.4.1 Vegetation indices .......................................................................................... 129

10.4.2 Vegetation types and selection of training pixels ........................................... 130

10.4.2.1 Forests .................................................................................................... 131

10.4.2.2 Savannah ................................................................................................ 135

10.4.2.3 Agricultural land .................................................................................... 140

10.4.2.4 Clouds ..................................................................................................... 141

10.4.2.5 Water ...................................................................................................... 142

10.4.3 Discussion of the results ................................................................................. 143

10.4.3.1 Choosing the maps that will be used further .......................................... 143

10.4.3.2 Preparing the maps ................................................................................. 144


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10.4.3.3 Comparison of the methods: confusion matrix ...................................... 145

10.4.3.4 Comparison of the methods: visual comparison .................................... 147

10.4.3.5 Smoothing .............................................................................................. 153

10.5 Supervised texture classification ............................................................................ 153

10.6 Supervised parent material classification ............................................................... 155

10.7 Discussion .............................................................................................................. 156

11 Practical application: erosion map ............................................................................. 160

11.1 Methods and results ................................................................................................ 160

11.2 Discussion .............................................................................................................. 161

12 Conclusions and advise for further research .............................................................. 163

13 Bibliography ............................................................................................................... 166

13.1 Written publications ............................................................................................... 166

13.1.1 Books .............................................................................................................. 166

13.1.2 Articles ........................................................................................................... 167

13.1.3 Non-published works ..................................................................................... 167

13.1.4 Cartographic material ..................................................................................... 168

13.1.5 Digital documents .......................................................................................... 168

13.1.6 Websites ......................................................................................................... 168

14 Appendix .................................................................................................................... 169

14.1 Digital files ............................................................................................................. 169

14.2 Maps ....................................................................................................................... 170

2.2 Table of figures Figure 1: Bas Congo. Indicated on the map are the different map sheets of Bas-Congo and in

red the extent of the used satellite image. Adapted from Baert 1991a. ........................... 16

Figure 2: situation of Bas Congo and its map sheets in the Democratic Republic Congo.

Source: Baert 1991a. ........................................................................................................ 17

Figure 3: precipitation in Bas-Congo. Based on Baert 1991a, Baert 1991b, Baert 1991c and

Baert 1991d. ..................................................................................................................... 19

Figure 4: precipitation and evapotranspiration for Kinshasa-Binza. Based on Baert 1991a,

Baert 1991b, Baert 1991c and Baert 1991d. .................................................................... 19

Figure 5: map of the average annual precipitation (in mm) in Bas-Congo. Source: Baert

1991a. ............................................................................................................................... 20

Figure 6: temperature for Kinshasa-Binza. Based on Baert 1991b. ......................................... 20


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Figure 7: variation of the evapotranspiration in Bas-Congo. Source: Baert 1991a. ................ 21

Figure 8: geological map of Bas-Congo. Source: Baert 1991a. ............................................... 23

Figure 9: Strakhov diagram. Neerslag= precipitation; temperatuur= temperature; toendra=

tundra; woestijn en halfwoestijn= dessert and semi-dessert; savanne= savannah; tropisch

woud= tropical forest; saproliet=saprolite; onverweerd gesteente= un-alternatd rock;

produktie plantenafval= production plant-waste. Source: De Dapper 1994. ................... 29

Figure 10: pediplanation: with the formation of inselbergs. “Parallelle dalwandregressie”

means parallel slope regression. Source: De Dapper 1994. ............................................. 30

Figure 11: peneplanation: 1 is the young relief with the initial surface, 2 is the mature

landscape and 3 is the old landscape with monadnoks and peneplaines. Dashed line

expresses the sealevel (“zeeniveau”). Source: De Dapper 1994. ..................................... 31

Figure 12: etchplanation. Two surfaces are recognised: the upper topographic surface

(“bovenste topografisch oppervlak”) and the lower alternation front with a hummocky

relief (“onderste basal verweringsfront met bultigeondergronds relief). A thick

saprolitecover (“dik saprolietdek”) is present in 1 and is partially stripped in 2, leaving

some bornhardts. Source: De Dapper 1994. ..................................................................... 32

Figure 13: ideal alternation profile for the tropics with the stone-line. Figure adapted to: De

Dapper 1994. .................................................................................................................... 34

Figure 14: general geomorphological structure. Explanation in the text. Source: Baert 1991a.

.......................................................................................................................................... 38

Figure 15: general geomorphological structure (continued). Explanation in the text. Source:

Baert 1991a. ..................................................................................................................... 38

Figure 16: VNIR band spectral wavelengths. Source: official Aster website,

http://asterweb.jpl.nasa.gov, consulted 30 May 2004. ..................................................... 65

Figure 17: SWIR band spectral wavelengths. Source: official Aster website,


Figure 18: SWIR band spectral wavelengths. Source: official Aster website,


Figure 19: ASTER spectral bands. Source: official Aster website, http://asterweb.jpl.nasa.gov,

consulted 30 May 2004. ................................................................................................... 67

Figure 20: explanation of a parallax. Source: Bossyns 2004, original source Bethel et al. 2001.

.......................................................................................................................................... 70

Figure 21: different steps of teledetection. Source: Lillesand and Kiefer 1994. ...................... 74

Figure 22: the electromagnetic spectrum. Source: Lillesand and Kiefer 1994. ....................... 75


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Figure 23: spectral characteristics of energy sources, atmospheric effects and common remote

sensing systems. Source: Lillesand and Kiefer 1994. ...................................................... 76

Figure 24: different reflectance types. Source: Lillesand and Kiefer 1994. ............................. 77

Figure 25: reflectances of different surfaces for a variety of wavelengths. Source: Tso and

Mather 2001. .................................................................................................................... 77

Figure 26: resampling using nearest neighbour method. Source: Ilwis Help. ......................... 79

Figure 27: resampling using bilinear resampling. Source: Ilwis Help. .................................... 79

Figure 28: resampling using Bicubic resampling. Source: Ilwis Help. .................................... 79

Figure 29: linear stretch. Source: Ilwis Help. ........................................................................... 80

Figure 30: histogram equalization stretch. Source: Ilwis Help. ............................................... 80

Figure 31: stretching methods. Source: Lillesand and Kiefer 1994. ........................................ 81

Figure 32: principles of classification. Source: Gibson and Power 2000. ............................... 85

Figure 33: different classification methods. Source: Gibson and Power 2000. ....................... 86

Figure 34: setup of a block in Virtuozo. Source: own research in Virtuozo. ........................... 92

Figure 35: turning of images in Virtuozo. In red is indicated how to turn the images over

270°. Source: own research in Virtuozo. .......................................................................... 93

Figure 36: setup window for the ground control points in Virtuozo. Source: own research in

Virtuozo. ........................................................................................................................... 94

Figure 37: setup of a model in Virtuozo. Source: own research in Virtuozo. .......................... 95

Figure 38: relative orientation window in Virtuozo. Source: own research in Virtuozo. ........ 96

Figure 39: absolute orientation window in Virtuozo. Source: own research in Virtuozo. ....... 98

Figure 40: match edit window in Virtuozo. Source: own research in Virtuozo. .................... 101

Figure 41: DEM setup window in Virtuozo. Source: own research in Virtuozo. .................. 102

Figure 42: setup window of the orthoimage. Source: own research in Virtuozo. .................. 107

Figure 43: turning the orthophoto and changing its coordinates. Source: own research. ...... 108

Figure 44: anaglyph image of the study area. To see this image in 3D a special pair of glasses

has to be used (with one red and one green eye). Source: own research in Ilwis and

Virtuozo. ......................................................................................................................... 110

Figure 45: anaglyph image of the study area. To see this image in 3D a special pair of glasses

has to be used (with one red and one green eye). Source: own research in Ilwis and

Virtuozo. ......................................................................................................................... 110

Figure 46: correlation matrix in Ilwis. Source : own research in Ilwis. ................................. 125

Figure 47: part of the map produced with the first cluster operation. More information in the

text. Source: own research in Ilwis. ............................................................................... 127


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Figure 48: part of the map produced with the second cluster operation. More information in

the text. Source: own research in Ilwis. .......................................................................... 127

Figure 49: part of the map produced with the third cluster operation. More information in the


Figure 50: part of the map produced with the fourth cluster operation. More information in the


Figure 51: screenshot showing a part of the study area around the Inkisi river. The aquatic

forests have a higher DN value for the vnir1 (green) band than the other forests. Within

the aquatic forests two types are recognised: one with a high density and one with a

lower. Source: own research in ILWIS. ......................................................................... 132

Figure 52: screenshot showing a part of the study area. In the colour composite the red colour

represents the vnir2 band, the green colour the vnir1 band and the blue colour the

calculated NDVI values. The forests in the valleys have the same feature space as the

forests in the valleys. Within these forests there is however still some variation and

therefore the forests were split in different classes. By visual interpretation the afforested

savannah can be recognised from the forests. Source: own research in ILWIS. ........... 133



calculated NDVI values. This figure illustrates the appearance of the class “open forest”.

In this case the selected pixels represent a less dense part in the middle of the forest.

Source: own research in ILWIS. .................................................................................... 134



calculated NDVI values. This figure illustrates the appearance of the class “open forest”.

In this case the selected pixels represent a denser part in the middle of the afforested

savannah. Source: own research in ILWIS. ................................................................... 135

Figure 55: feature space for the vnir2 (red) and vnir3 (near infrared) band. The blue dots

represent the different classes that are recognised in forests, the other dots represent the

different savannah classes. Source: own research in ILWIS. ........................................ 136

Figure 56: feature space for the vnir2 (red) band and calculated NDVI . The blue dots




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Figure 57: feature space for the vnir1 (green) and vnir3 (near infrared) band. The blue dots





calculated NDVI values. The burned area can be split in two classes: one with a very low

reflectance in the visible and near infrared wavelengths (dark grey on this figure) and

one that is less dark (light grey on the figure). Source: own research in Ilwis. ............. 138



calculated NDVI values. A road network can easily be recognised on the map.

Explanation in the text. Source: own research in Ilwis. ................................................. 139



calculated NDVI values. It can be noticed that the thickness of the cloud has an

important influence on the DN values of the selected pixels. All selected pixels are

representative for forests. Source: own research in Ilwis. .............................................. 142

Figure 61: legend of the several classification maps displayed in this paragraph. Source: own

research in Ilwis. ............................................................................................................ 148

Figure 62: zone 1: Minimum Distance classification. Source: own research in Ilwis. .......... 148

Figure 63: zone 1: Maximal Likelihood classification. Source: own research in Ilwis. ........ 149

Figure 64: zone 1: Minimum Mahalanobis Distance classification. Source: own research in

Ilwis. ............................................................................................................................... 149

Figure 65: zone 1: calculated NDVI. Red colours for positive values, green for values around

0 and blue for negative values. Source: own research in Ilwis. ..................................... 150

Figure 66: false colour composite of zone 1. The vnir2 band is represented in red, the vnir1

band in green and the calculated NDVI as blue. Source: own research in Ilwis. ........... 150

Figure 67: zone 2: Minimum Distance classification. Source : own research in Ilwis. ......... 152

Figure 68: zone 2: Maximal Likelihood classification. Source : own research in Ilwis. ....... 152

Maps are added as appendix.

2.3 Table of tables Table 1: climatic data for the INERA station in M'Vuazi (14°54’E, 5°27’S, latitude 505m).

Source: Baert 1991a. ........................................................................................................ 17


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Table 2: climatic data for Kinshasa-Binza (15°15’E, 4°22’S, latitude 440 m). Source: Baert

1991b. ............................................................................................................................... 18

Table 3: applanation levels. Source: Baert 1991a, Baert 1991b, Baert 1991c, Baert 1991d. . 35

Table 4: soil series on micaschist. Source: Baert 1991a, Baert 1991b, Baert 1991c, Baert

1991d. ............................................................................................................................... 52

Table 5: soil series on basic rocks. Source: Baert 1991a, Baert 1991b, Baert 1991c, Baert

1991d. ............................................................................................................................... 53

Table 6: soil series on not or slightly metamorphic rocks. Source: Baert 1991a, Baert 1991b,

Baert 1991c, Baert 1991d. ................................................................................................ 54

Table 7: soil series on calcareous rocks. Source: Baert 1991a, Baert 1991b, Baert 1991c, Baert

1991d. ............................................................................................................................... 56

Table 8: series of the soils on hard rock with a quartz dominance. Source: Baert 1991a, Baert

1991b, Baert 1991c, Baert 1991d. .................................................................................... 58

Table 9: series of soil developed in soft sediments with a quartz dominance. Source: Baert

1991a, Baert 1991b, Baert 1991c, Baert 1991d. .............................................................. 59

Table 10: series of mineral and organic soils with a bad drainage. Source: Baert 1991a, Baert

1991b, Baert 1991c, Baert 1991d. .................................................................................... 61

Table 11: series of soils with an excessive, normal or moderate drainage. Source: Baert

1991a, Baert 1991b, Baert 1991c, Baert 1991d. .............................................................. 62

Table 12: overview of the ASTER sensors. Source: official Aster website,


Table 13: overview of the exported filetypes from Virtuozo. Adapted from Van Coillie 2003.

........................................................................................................................................ 102


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3 Introduction When starting the courses in Physical Land Resources around the first of November 2003 at

the University of Ghent, one month after the beginning of the courses, I had to choose a

subject for a thesis. Thanks to the cooperation of Prof. Dr. Rudi Goossens and Prof. Dr. Erik

Van Ranst I found a subject that was possible to be finished before the end of the academic

year. In the beginning I had no idea about the procedures I would follow and the software

programs I had to use. During the production of this thesis I learned how to work with several

new software devices. I also learned about the advantages and disadvantages of the software,

the applicability of satellite images and image processing software in soil science.

Maybe more important for my development as a soil scientist is that I also learned about the

soils of Congo and the way these soils were mapped and classified, using other methods than

the ideal (but in time, labour and costs very expensive) methods that were teached us in the

courses of Pedology and Soil and Regolith Prospection. Thanks to this study I got a more

practical view about the items of the courses in tropical soils.

When I would now be at the start of this work I would for sure do things in another way. With

the experience I now have about the software everything would go much faster and I would

be able to do lots more in one year. I would also spend less time in solving problems than I

have spent because now I have an idea which problems can be solved and how. But more

important: when I would start over again I would like to get some more practical experiences

with soils and the terrain. Of course such a thing was not possible at the beginning of this

study, but as a Physical Geographer and student in Physical Land Resources I am very

attached to field work and it missed it a lot in the last year.

But overall I am satisfied about this work and about the things I learned in the last year. I am

sure that the knowledge I gathered in the last year will help me in a future job.


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4 Objectives The main objective from this study is incorporated in the title: “the use of ASTER- and

Landsat Images for the determination of soil parameters”. This means that it is in the

meaning of this work to determine if satellite images can be used in the determination of soil

parameters, and more in particular it was meant to study this for the region Bas-Congo. The

factors that determine the soil formation and the main properties of the soils were considered

as soil parameters. These factors determining the soil genesis are (Ameryckx et al. 1995):

• Parent material

• Climate

• Biological activity

• Human influence

• Topography and relief

• Time

The main properties of a soil are (Ameryckx et al. 1995):

• Texture

• Structure

• Compaction

• Colour

• Some chemical properties like base saturation, pH, humus content, exchange

properties, …

• Mineralogical properties

• Soil water properties

• Depth of the soil

• Profile development

• …

When studying these features with satellite images it should be taken into account that these

satellite images represent the spectral reflectance of the surface in several spectral ranges. Due

to this nature of the satellite images it is not possible to map things that have no influence on

the spectral reflectance. For this reason it will be difficult to detect properties that don’t

appear at the surface, like for instance the profile development. From these considerations, the

available sources (see further), the properties of the selected satellite images (see further), the


Page 15

properties of spectral reflectance (see further) and from the knowledge what was possible in

former studies, it was decided that the following items deserved special attention:

• Relief and topography of the region

• Vegetation (as part of the biological activity)

• Parent material

• Texture

The first item (relief) was studied using Virtuozo NT (see chapter 8), the other items where

studied via a classification in Ilwis (see chapter 10).


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5 The study area

5.1 Introduction The chosen study area was the Bas-Congo. This region was chosen for the presence of good

soil maps and other information at the University of Ghent. The exact delimitation of the

study area was done by means of the available satellite images (see paragraph 6.1.2). Before

the onset of this study it was mend to chose the study area as westwards as possible in the

Bas-Congo, as the more western parts show a larger variation in both geology and soils. But

as this seemed impossible (see paragraph 6.1.2) a study area more to the east was selected.

Figure 1: Bas Congo. Indicated on the map are the different map sheets of Bas-Congo and in red the

extent of the used satellite image. Adapted from Baert 1991a.


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Figure 2: situation of Bas Congo and its map sheets in the Democratic Republic Congo. Source: Baert

1991a.

5.2 Climate1

Table 1: climatic data for the INERA station in M'Vuazi (14°54’E, 5°27’S, latitude 505m). Source: Baert

1991a.

Period2 Jan. Feb. Ma. Apr. May June July Aug. Sept. Oct. Nov. Dec. Annual Precipitation

(mm) 41-80 138 141 181 265 142 7 1 3 23 102 256 205 1464

Days with rain 41-80 12 11 15 19 12 3 2 2 5 11 18 16 126 Mean T max.

(°C) 54-80 29.3 30.3 30.8 30.5 29.8 27.7 25.9 26.7 28.7 29.7 29.5 29.0 29.0

Mean T min. (°C) 54-80 20.3 20.3 20.5 20.4 20.1 17.5 15.8 16.6 18.6 20.0 20.3 20.3 19.2

Mean T (°C) 54-80 24.8 25.3 25.7 25.5 25.0 22.6 20.9 21.6 23.7 24.9 24.9 24.7 24.1 Amplitude

(°C) 54-80 9.0 10.0 10.3 10.1 9.7 10.2 10.1 10.1 10.1 9.7 9.2 8.7 9.8

Pressure (mb) 54-80 24.7 24.5 24.8 25.1 24.7 21.0 18.6 18.5 20.1 22.3 23.9 24.4 22.7 Rel. hum. 6 h

(%) 54-80 95 94 95 95 95 94 92 90 89 91 94 95 93

Rel. hum. 15 h (%) 54-80 66 62 62 66 65 60 59 56 54 58 65 67 62

Rel. hum. 18 h (%) 54-80 76 78 75 80 78 71 68 65 63 67 76 78 73

Rel. hum. mean (%) 54-80 79 78 77 80 79 75 73 70 69 72 78 80 76

Insolation h 54-78 129 140 158 149 158 159 142 135 124 121 119 117 1652 Duration of

insol. n /N % 54-78 34 40 42 41 43 45 38 36 33 33 32 31 37

Radiation (cal./cm²/g) 58-59 387 430 448 423 385 336 280 280 314 343 385 380 366

Windspeed (m/s) 55-74 1.1 1.2 1.2 1.1 1.0 1.1 1.2 1.6 1.8 1.5 1.3 1.1 1.3

ETP (mm) 55-74 111 107 118 103 97 85 86 101 112 119 108 107 1254

According to the Köppen classification the area of Bas-Congo has a humid tropical climate

with a distinct dry season of four months. In the dry season the temperature is also lower. The

climatic stations closest to the selected study area are those of Kinshasa (Kinshasa-N’Djili and

1 This paragraph is entirely based on Baert 1991a, Baert 1991b, Baert 1991c, Baert 1991d. 2 Period: expressed as years in the 20the century (e.g.: 41-80 is from 1941 until 1980).


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Kinshasa-Binza) and the station of the INERA in M’Vuazi. The climatic data of both stations

are represented in the tables. Beside these data also some less complete data were available

for Kisantu, Lemfu, Kimfula and Luozi.

Table 2: climatic data for Kinshasa-Binza (15°15’E, 4°22’S, latitude 440 m). Source: Baert 1991b.

Period3 Jan. Feb. Ma. Apr. May June July Aug. Sept. Oct. Nov. Dec. annual Precipitation

(mm) 56-73 133 113 178 214 130 4 2 2 33 107 253 159 1328

Days with rain 56-73 11 10 14 15 10 1 1 1 4 10 18 14 108 Mean T max.

(°C) 56-73 29.0 29.8 30.3 30.4 29.4 27.0 25.8 27.2 29.2 29.4 29.1 28.8 28.8

Mean T min. (°C) 56-73 20.7 20.8 20.9 20.9 20.8 18.7 17.2 17.8 19.3 20.4 20.5 20.6 19.9

Mean T (°C) 56-73 24.1 24.4 24.6 24.4 24.1 22.1 21.0 22.0 23.5 24.2 23.8 23.8 23.5 Amplitude

(°C) 56-73 8.3 9.0 9.4 9.5 8.6 8.3 8.6 9.4 9.9 9.0 8.6 8.2 8.9

Pressure (mb) 56-73 25.8 26.0 26.3 26.0 26.1 22.9 20.6 20.6 22.6 24.2 25.1 25.1 24.3 Rel. hum. max

(%) 56-73 100 100 100 100 100 100 100 100 100 100 100 100 100

Rel. hum. min (%) 56-73 45 42 34 37 46 48 46 39 33 32 38 38 32

Rel. hum. mean (%) 56-73 86 85 85 85 87 86 83 78 78 80 85 85 84

Insolation h 56-73 143 151 167 165 158 153 143 161 141 140 135 133 1790 Duration of

insol. n /N % 56-73 37 42 45 46 45 43 39 43 39 37 37 35 40

Radiation (cal./cm²/g) 56-73 402 428 435 415 381 356 349 387 399 404 402 390 395

Windspeed (m/s) 56-73 1.3 1.3 1.3 1.3 1.3 1.3 1.5 1.6 1.6 1.5 1.3 1.2 1.4

ETP (mm) 51-60 110 105 118 107 99 84 88 103 110 116 106 107 1253

The variation in precipitation is represented in Figure 3. One very dry period can be

distinguished very clearly: from mid May until the end of September. The duration of this

period is 115 to 135 days. A secondary dry period exists in January and February. The two

maxima are observed in April and November. Very heavy rains with a short duration

characterize the wet period. The amount of rain is also very variable between different years.

In the dry period some mists can appear in the afternoon.

Figure 4 represents the precipitation and evapotranspiration for Kinshasa-Binza. From June

until October there is a rain deficit, while from November to May there is a surplus of rain.

3 Period: expressed as years in the 20the century (e.g.: 41-80 is from 1941 until 1980).


Page 19

Figure 3: precipitation in Bas-Congo. Based on Baert 1991a, Baert 1991b, Baert 1991c and Baert 1991d.

precipitation and evapotranspiration for the station Kinshasa-Binza

0

50

100

150

200

250

300

Jan. Feb. Ma. Apr. May June July Aug. Sept. Oct. Nov. Dec.

mm Precipitation (mm)

ETP (mm)

Figure 4: precipitation and evapotranspiration for Kinshasa-Binza. Based on Baert 1991a, Baert 1991b,

Baert 1991c and Baert 1991d.


Page 20

Figure 5: map of the average annual precipitation (in mm) in Bas-Congo. Source: Baert 1991a.

The temperature of Bas-Congo is strongly influenced by the Benguela cold sea current. This

causes a rather low temperature, and that it is not corresponding with the temperature

expected at 5° latitude but with the temperature expected at 25° latitude. The maximal

temperatures are situated in March and April, towards the end of the wet season. In the dry

season the temperatures are the lowest.

Figure 6: temperature for Kinshasa-Binza. Based on Baert 1991b.


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There are only minor variations in the main humidity: during the whole year the humidity

oscillates around 80% for Kinshasa and 70% for M’Vuazi.. The largest values are obtained

during the night (mostly around 95%), while on the warmest moments of the day the humidity

can be as low as 50%. The high humidity during the dry months is due to the almost

permanent cloud cover.

Figure 7: variation of the evapotranspiration in Bas-Congo. Source: Baert 1991a.

The insolation is low and oscillates around 1650 h/year (M’Vuazi) and 1760 hours/year

(Kinshasa). This corresponds respectively with 37 and 40 % of the astronomical possible

insolation for this latitude. The highest values for the insolation are observed at the end of the

wet season and the beginning of the dry season.

The wind direction is dominantly southwest. In the wet season some very violent winds can

occur, announcing thunderstorms.

The evapotranspiration as represented in Figure 4 is calculated with the penman method

modified by Frère and Popov. The annual evapotranspiration is slightly lower then the

precipitation. The maxima are situated around the beginning (October) and end (March-April)

of the wet season.

5.3 Geological regions In Bas-Congo three main geological zones can be distinguished:

• The coastal zone existing of sediments, dating from the Cretaceous or younger, and

from eastern and marine origin, resting on the Precambrian socle. The layers have a

monocline position with a slight inclination towards the west. The geomorphology


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exists of plateaus (existing of Tertiary sediments). The rivers are incised as deep as in

the Cretaceous sediments.

• The axial zone. This zone exists of Precambrian deposits. The youngest deposits are

situated in the east. The folds have a SSE-NNW orientation. The morphology exists of

an abrupt landscape of hills, high plateaus and crests that have the same direction as

the folds. There are two exceptions on this scheme:

o The region with Appalachian relief. The Lufu Basin and partially the Kwilu

Basins make up this region. They are characterized by an alternation of

synclines of soft rocks (Schist and Limestone) and anticlines in which older

and more resistant sediments are at the surface.

o The tabular region: in the eastern part the formations are much less folded.

• The eastern zone: the zone east of the line between Sona-Bata and N’Gidinga. In this

zone there appear continental sediments which date from the Cretaceous or younger

and which rest on the Precambrian socle. The layers are monocline and have a slight

inclination towards the east. The landscape is characterised by slightly dissected

plateaus. It is in this region that the further defined study area is situated. The

Mesozoic deposits are mainly soft sandstones (Grès Tendres) and are for a large extent

covered by the ochre sands of the Bateke Plateau (the Kalahari Sands).

(Baert 1991a, Baert 1991b, Baert 1991c, Baert 1991d)


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Figure 8: geological map of Bas-Congo. Source: Baert 1991a.


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5.4 The geological formations

5.4.1

5.4.1.1 Holocene alluvial deposits

The soft covering deposits

The Holocene alluvial deposits are mainly situated around the valleys. Large concentrations

are found around the Congo River (Pool Malebo) and some other rivers, which however are

not located in the study area. The soils have mainly a sandy texture.


5.4.1.2 Pliocene and Pleistocene sand deposits

This group contains all sandy formations that are more recent then the Kalahari Sands. They

mainly consist of reworked Kalahari Sands mixed with alternation products of older deposits.

Commonly a lateritic zone can be found at the basis. They can be found on flatted areas and

old terraces.


5.4.1.3 Ochre sands (Kalahari Sands)

These deposits, with a maximal thickness off 80 meters, are largely present on the Batekes

Plateau and are also forming some residual islands along the N’Sele valley. They date from

the Neogene. They have a very fine sand texture and don’t have stratification. Their colour

varies from ochre to very pale yellow. At their base there appears locally a conglomeratic

horizon. Before they were considered as having an eolian origin, but now it is believed that

they are fluviatile.


5.4.2

5.4.2.1 The series of ‘Polymorphic Sandstones’

Tertiary formations

This system, with a maximal thickness of 75 meters, is of Palaeogene age. It rests on an end-

Cretaceous applanation surface. It can be subdivided into two subunits:

• The upper part: soft sandstone and white sands with locally red intercalations. At the

lower part there are silicated irregularities.


Page 25

• The lower part: hard and silicated rocks: quartzitic sandstone with chalcedony cement

and chalcedony. Locally a laterised surface at the base. This part can be found at the

surface at the flanks of the cliffs of the Bateke Plateau.

On the plateaus this series is covered by alluvial sandstones but a lot of large valleys are

incised until the level of the silicated rocks.


5.4.3

5.4.3.1 The Kwango Series (Upper Cretaceous)

The Mesozoic consolidated formations

This series exists of soft sandstone, sometimes silicated, with a red or red-violet colour. It

contains some conglomerates and red or green clays (which are locally calciferous). This

series only appears around Kimvula and dates from the upper Post-Wealdian. It has a

continental origin.


5.4.3.2 The undifferentiated Cretaceous (Lower Cretaceous)

This series is discordantly laying on the erosion surface of the Schisto-Gréseux. It is

constituted of soft sandstone with a fine to average texture and a red or mauve colour. It

contains cobbles of sandstone, chert and sandstone and schist of the Schisto-Gréseux. In the

north the facies exists of very soft sandstone with a white to pink colour and a very fine

texture.

This series occurs on the central part of the Kinshasa map, in the deep valleys of the Batekes

plateau, and also on the map Inkisi, east of the line from N’Gidinga to Sona-Bata. As there is

no discordance between this series and the Kwango Series, it probably is a part of this series.

The layers are monoclinal with a slight inclination towards the Central Basin. This indicates

that the cretaceous layers were only submitted to weak epeirogenetic forces.


5.4.4

The Precambrian socle is subdivided in two large units, the West-Congo Supergroup and the

Zadinien supergroup.

The Precambrian socle



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5.4.4.1 The West-Congo supergroup

This supergroup is composed of non or weakly metamorphic rocks. The intensity of the

metamorphism and tectonic forces diminishes towards the east. (Baert 1991a, Baert 1991b,

Baert 1991c, Baert 1991d)

5.4.4.1.1 The ‘Schisto-Gréseux’ group (the schist-sandstone group)

This group is the upper part of the Precambrian socle and rest on the schist-limestone group.

Between these groups there is a weakly developed discordance. This group is divided in two

series within the study area:

• Inkisi series. This series rests with a weakly developed discordance on the M’Pioka

series. The facies is in generally old red sandstone (vieux grès rouge) with a

continental character. This series has two layers:

o Etage II:

I2c: feldspatic quartzite, schist and sandstone containing schist of

Luvumbu with a red to violet red colour.

I2b: quartzitic arkoses of Zongo, red to mauve, locally greenish. With

cobbles of quartz, schist and feldspars.

o Etage I:

I2a: quartzite and schist of Morozi: schist, psammites and quartzite.

They have a red to violet red colour.

I1: quartzitic arkose of the Fulu, mauve to purple-red, locally greenish.

With quartz cobbles in an arkose matrix.

• M’Pioka series: this series rests on the different facies of the schist-limestone group

with a concordance or weakly developed discordance. The origin is subaquatic. It is

also divided in some layers:

o Etage II:

P3: schist and quartzite of Liansama: greenish grey schist and red, grey

or greenish grey sandstone containing schist. With red or grey

quartzitic intercalations.

P2: quartzite with a feldspar-like nature of Kubuzi.. With a pink to

purple red colour.

o Etage I:


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P1: schist and quartzite of Vampa: red schist with intercalations of

feldspar-like quartzite with a grey to greenish grey colour.

P0: conglomerates of Bangu and Niari: calcareous and chert cobbles

and blocs, sometimes angular.


5.4.4.1.2 The schist-limestone group

The schist-limestone group is composed of alternating layers of limestone and dolomites and

more or less calcareous schist. This group represents a complete sedimentation cycle in a

calcareous environment. After a glacial continental phase (Tillite Superieure, see further) a

lagunair phase came, followed by a marine transgression. This transgression stops with

shallow see with formation of oolites and stromatholithes. The following regression is

characterised by oscillations with transgressions.

These deposits are strongly folded in the west. There the older deposits are exposed in the

anticlines where the younger are exposed in the synclines. The anticlines are in depression

positions according to the synclines (Appalachian relief).


5.4.4.1.3 The Tillite Supérieure (Upper Till) of Bas-Congo

This till is a conglomerate with a greenish grey or violet grey cement, often calcareous. The

cobbles are subrounded to rounded, with a very varying diameter. They are of a very diverse

nature. Rarely there occur striated cobbles. This layer has probably a glacial origin. The layer

had some slight tectonic influences.


5.4.4.1.4 The group of Haute-Shiloango

This group represents a sedimentation cycle existing of a transgression followed by a

regression (etage of the small Bembezi), followed by another transgression (etage of

Sekelolo). This group lies discordantly on the underlying Tillite Inférieure. The layers are

weakly folded.



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5.4.4.1.5 The Tillite Inférieur of Bas-Congo

This conglomerate has a darker coloured cement then the upper till layer. The cement contains

mostly rounded quartz grains of 0,5 mm diameters. The cobbles are quartzite, schist,

limestone, quartz, chert and rarely crystalline rocks. This layer is only found in the Sansikwa

and Kimbungu massifs. They are considered to have a glacial or peri-glacial origin. But there

is some presence of pillow lava and varves, which indicates that the formation is at least

partially subaquatic. This till rests discordantly on the group of the Sansikwa.

5.4.4.1.6 group of the Sansikwa

This group can mainly be found in the Sansikwa massif and maybe also in the Kimbunga

massif. It is divided in:

• S2: mainly feldspar-like quartzite.

• S1: mainly psammites and violet phyllades.

• S0: conglomerate with a cement containing mainly schist or arkoses.


5.4.4.2 The Zadinien supergroup

This group is built up of metamorphic stones. It is only found in the Sansikwa massif.



Page 29

5.5 Geomorphology

5.5.1

In general three kinds of alternation exist: biological, physical and chemical alternation. The

biological alternation exists actually of chemical and/or physical alternation. Chemical

alternation largely depends upon water. If there is no water present, no chemical alternation

can take place. It is also known that most chemical alternation reactions are endothermic,

meaning that temperature has also a major influence. A third important factor is the presence

of humid acids in the water. As water and humid acids (due to the plant cover) are largely

present in the tropics and the temperature is high, the tropics are topic of a deep chemical

alternation. This is expressed in the Strakhov-diagram. The deep chemical tropical alternation

is not only quantitative but also qualitative as the alternation is going very far: the hardest

rocks are transformed to soft rocks. Saprolite is the term for the rotten rock that is formed in

this way.

Introduction: alternation

Figure 9: Strakhov diagram. Neerslag= precipitation; temperatuur= temperature; toendra= tundra;

woestijn en halfwoestijn= dessert and semi-dessert; savanne= savannah; tropisch woud= tropical forest;

saproliet=saprolite; onverweerd gesteente= un-alternatd rock; produktie plantenafval= production plant-

waste. Source: De Dapper 1994.

It should be noted that actually the often-used name tropical alternation is incorrect: the

alternation processes are mainly identical for the tropical regions as for the more temperate

regions. Actually it should be called alternation under tropical circumstances.


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(De Dapper 1994, De Dapper 1998, Driessen et al. 2001)

The process of pediplanation, first described by Penck (1924) and for Africa modified by

King (1942, 1949, 1953), is based on the theory that after a deep incision of the rivers, there is

a parallel regression of the valley faces. In this process a pediment is formed at the foot-slope.

The pediments are growing and the interfluvia are shrinking. At the end these interfluvia form

small islands of hard rocks in the middle of the pediplain: the inselbergs.

Another theory is the normal erosion cycle of Davis. This theory mainly applies to the

temperate regions. In this theory the original surface is eroded to a peneplain with

monadnocks as remnants of the original relief.

(De Dapper 1994)

Figure 10: pediplanation: with the formation of inselbergs. “Parallelle dalwandregressie” means parallel

slope regression. Source: De Dapper 1994.


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Figure 11: peneplanation: 1 is the young relief with the initial surface, 2 is the mature landscape and 3 is

the old landscape with monadnoks and peneplaines. Dashed line expresses the sealevel (“zeeniveau”).

Source: De Dapper 1994.

A third theory was developed by Wayland (1934) and others. This theory is assuming that the

original surface is alternated to a depth of several meters. After a tectonic uptilt this saprolite

is partially eroded and on the new surface deep alternation takes place again. These surfaces

that are uncovered by stripping are called etchplains. To get such a deep alternation profile,

protected regions are necessary. These are mostly cratons (Precambrian shields), the

tectonically stable zones in old crystalline cores.

The front of the chemical alternation is rather abrupt. This front forms an important surface:

between the rotten rock and the hard rock (it should be noted that this surface is not flat!). In

this way a kind of denudation surface is formed. As there is also a second denudation surface

is formed (the topographical surface), we have two denudation plains, and in this way the

concept of the Doppelte Einebnungsfläche is created (Büdel 1957). When the saprolite is

stripped, the hummocky relief of the alternation front is exposed. In this way the inselbergs

are created with between them still saprolite (preserved at a lower topography). Once such an

inselberg is exposed it has less influence of the chemical alternation (no humid acids, the rain

washes of it, …) and it will slowly fall into pieces due to physical alternation. Also the

climatic fluctuations of the Quaternary play a role in this theory: during the dry periods the

denudation process was more important.

The term inselberg, first used by Bornhardt (1900) is nowadays used for residual uplands

standing in isolation above the level of the surrounding plains in tropical regions. The original


Page 32

concept refers to dome-formed high monoliths with a clear bend at the food. These are now

called bornhardts. A bornhardt hat is just exposed is called a dwala or whaleback. After the

physical alternation it crumbles towards a castle koppie.

The exposure of the alternation front can also give some heaps of granite boulders, known as

tors.

(De Dapper 1994, Driessen et. al. 2001)

Figure 12: etchplanation. Two surfaces are recognised: the upper topographic surface (“bovenste

topografisch oppervlak”) and the lower alternation front with a hummocky relief (“onderste basal

verweringsfront met bultigeondergronds relief). A thick saprolitecover (“dik saprolietdek”) is present in 1

and is partially stripped in 2, leaving some bornhardts. Source: De Dapper 1994.

A very important product of the alternation are sesquioxides of Al and Fe, and they are

concentrated in the upper part of the erosion profile. This sesquioxides rich part is often called

laterite. When laterite exists only of Al sesquioxides it is called bauxite. Plinthite is “an iron-

rich, humus-poor mixture of kaolinitic clay with quartz and other constituents that changes

irreversibly to a hard pan or to irregular aggregates on exposure to repeated wetting and

drying” (Driessen et al. 2001 p. 149). The hard form is called petroplinthite. This hardening

involves two processes: crystallisation of amorphous iron compounds (mostly to goethite) and

the dehydratation of goethite to hematite and possibly also of gibbsite to boemite. This

hardening is often initiated when the removal of the vegetation (forest) triggers erosion which

exposes the plinthite to the open air. The soils with plinthite occur most under tropical forest


Page 33

while petroplinthite is more common in the transition zone between rain forest and savannah.

The petroplinthite can form a hard ironcap that protects from further erosion. As plinthite

forms often in depressions, the petroplinthite can form a relief inversion. The hard banks are

sometimes called duricrusts or cuirasses.

(De Dapper 1994, Driessen at al. 2001)

In Bas-Congo most soils are formed in alternation products of the geological substratum

(except when the yare formed in recent alluvium). The ideal profile is described in Figure 13.

In general three units can be recognized:

• Level A: the upper level, with a thickness of some decimetres to some meters. It

contains very alternated material and has in general a weak developed structure. The

material is fine (sand, loam and clay), with some rare larger fragments. Sometimes an

alignment can be recognized of the gravel. Sometimes the lower part of this level is

characterized by a concentration of coarser material, mostly iron-concretions or

quartz. This level is called the “recouvrement” or the superficial cover.

• Level B: this level is characterized by the presence of more or less abundant coarse

elements. These elements are rather resistant to chemical alternation. They are

composed of debris of local rocks, they and may be lateritisized or silicisized, angular

quart fragments and debris of hard laterite fragments. Sometimes it contains also

rounded fragments, for instance when it lies under fluviatile material or on a

conglomerate substratum. The size of the fragments varies between gravel and blocs.

The thickness of this layer varies between a simple line and several meters. Mostly

two sublayers can be recognized:

o The upper layer, B1: this layer is composed of rather hard material with a

dark colour. The dark colour is explained by a cuticule.

o The second layer, B2: this layer is rougher and has a lighter colour. This

sublayer has a more autochthonous character and contains quartz and silicated

material.

• Level G: the lower layer. This level can be split in two levels: an upper level with

locally completely weathered material, homogenized and almost without traces of an

original structure. The lower sublevel is constituted of a saprolite sensu strictu. The

geological structures of the underlying rocks are still visible. Most of the time there is

gradual transition between the two sublevels, proving a homogenisation by a very


Page 34

active pedoturbation. The thickness of level G is mostly several meters or even tens of

meters.

This subdivision in three levels is very typical for the intertropics and is often referred to as

stone-line.

(Baert 1991a)

Figure 13: ideal alternation profile for the tropics with the stone-line. Figure adapted to: De Dapper 1994.

One or more of the following processes form Stone lines:

• Alternation in situ: with the autochthonous deep chemical alternation material is lost

with a volume decrease (to 50%). Some harder material is accumulated. This layer of

hard material can be elongated by soil creep

• Erosion and sedimentation: this theory says that the fine material was removed by

sheetwash (superficial transport) and that the stone-line is a kind of lag deposit.

Afterwards this grind was again covered by fine material. Another possibility is that

local transport occurred and that in this way material was selected according to

texture! For this mechanism the Quaternary climate variations are important as these

processes would be situated in the dryer climates of the glacial maxima.


Page 35

• Internal transport: this supposes that coarse material sinks through the fine material to

some depth in the soil.

• Animal activity: burying animals can move large amounts of fine material. In the

tropics this is in particularly done by termites. The termite species which make their

nest above the surface brings fine material from the underground to the surface to

build their nest. Once the nest is left the rain erodes it and the fine material is spread

above the ground.

(Baert 1991a, De Dapper 1994, De Dapper 1998, Driessen et al. 2001 and Thomas 1994)

5.5.2

Table 3: applanation levels. Source: Baert 1991a, Baert 1991b, Baert 1991c, Baert 1991d.

General geomorphological structure

Applanation level Altitude (m)

Location on map… Kinshasa Inkisi Luozi Mbanza Ngungu

P1 Mid-Tertiary 700-900

Batekes plateau and some residual hills

between N’Sele and N’Djili

Batekes plateau and some residual hills at Kimvula and on

the Mbanza-Ngungu crest

Schist-sandstone massif on the

culminating point

Mbanza Ngungu crest (residual

hills), culminating point of the Bangu

massif

P2 Late Tertiary

incomplete cycle 600-700

Haute Lukunga basin and transition to Batekes plateau

Haute Lukunga basin and transition to Batekes plateau

Central part of the Schist-sandstone

massif

Bangu massif and culminating point

of Sansikwa massif, border of

the Mbanza-Ngungu crest

P2a intermediate

Pleistocene level 550-600

Transition between the schist-sandstone

and schist-limestone regions

Transition between the schist-sandstone region towards the

Inkisi plain

transitions

Transition between the schist-

sandstone region towards the Inkisi

plain

P2b Pleistocene 450-550

Inkisi plain, old terraces of N’Sele

and Lukunga

Inkisi plain, old terraces of N’Sele

and Lukunga

Part of the Schist-sandstone massif

close to the Congo River

Inkisi plain, Sansikwa massif

and Kasi-Kimbungu

P3 incomplete

Pleistocene cycle 330-450

SW part of the map on the Schist-

limestone massif

Kwilu plain, large synclinals and old

terraces of the rivers in the schist-limestone region

P4 Pleistocene and

Holocene incomplete cycle

200-300

Depressions and valleys in the

Schist-limestone massif

Depressions and valleys in the

Schist-limestone massif

The landforms, which are on the highest positions and the furthest away from the hydrological

axes, are formed from old applanation surfaces. Several levels can be recognized in the area

and they are schematised in Table 3. They are formed in the Tertiary and the beginning of the

Quaternary. They are rather flat with a slope of less than 2° to 3°. They have a very thick


Page 36

alternated layer, up to tens of meters thick! The upper part of the saprolite shows often a

plinthite or petroplinthite level. This iron enrichment is the highest at places where the

original rocks are already rich in iron. As the borders of these surfaces are clearly marked,

they have the aspect of a plateau. On the borders of these plateaus the plinthite is often

indurated and transformed in hard laterite, with a thickness up to several meters. This laterite

can retard the erosion on the borders of the plateau and preserve these plateaus. Due to this

regressive erosion along the borders the applanation surfaces are split in small pieces. These

pieces can form residual witness hills, which are progressively lowered. The alternation layer

on top of them is eroded and eventually the fresh uneroded rock can come to the surface.

Mostly the surface is affected leaving a landscape of witness hills. On these old surfaces the

stone-lines are deep and are composed of lateritic elements.

In some places there are residual hillocks occurring on the old applanation surfaces. They can

correspond with the remnants of an older applanation level that was eroded but mostly they

correspond with the basal alternation front. This is mostly the case for bornhardts made up

of gneiss or granite. They have a rather steep slope (up to 30°) near their summit where the

hard rock lies on the surface. At the lower slope the stone-line lies at the surface and is

discontinuous.

The region of the residual witness hills forms a transition towards the large depression that is

related with the rivers. This region was mainly formed in the Quaternary. In Bas-Congo the

quaternary was characterized by alternating dry and wet periods. In the drier period a dry

tropical or semi-arid climate occurred with an active pedimentation. The erosion mostly

happened on the surface (superficial streams and concentrated erosion in ravines). This lead to

scouring of the alternation profiles and the origination of pediments. These pediments form a

kind of glacis with very gradual smaller slopes (from 10° to less than 1°, thus a concave

profile). The contact between the pediment and the higher situated old relief (an older

pediment or the old applanation surface) has mostly a steeper slope and can be an escarpment.

On the other side the pediment gradually goes into the alluvial plain. The rivers had an

intermittent and irregular character during the dry periods (they were of the braided river

type). Sometimes pediments are also formed in the transitional hilly region at the food of the

hills.

The alternation profiles on these pediments are mostly less developed and profound. In the

upper part of the saprolite (level G1) there is often some plinthitisation. The stone-line is

mostly from autochthonous origin with a lot of ferrigenous elements. This stone-line is in


Page 37

general less thick than on the old applanation surfaces but can reach a thickness of several

meters. At the steeper slopes of the pediments the stone-line is less thick and contains

elements that prove the erosion of the hard ferrigenous layer. At the food of these slopes the

cover can locally be very thick and can have the character of alluvium. Towards the river

valleys, at the food of the pediments, the stone-line cover can gradually go into the alluvial

sediments.

The pedimentation process was slowed down and finally stopped by a transition towards a

wetter climate with shorter and less expressed dry season and with a more dense vegetation

cover. The rivers became of the meandering type and their thalweg occupied the valley-

bottoms in a more narrow way. The linear regressive erosion transformed parts of the

pediplain into small terraces. The rivers incised in the old thalwegs of the pedimentation

period and formed in this way river terraces. Because the dry and wet cycle repeated several

times, several levels of pediplains and terraces can be found.

The large alluvial plains, in which the rivers are situated nowadays, are formed in a more and

more humid climate. The extension of the alluvial plain is inherited from the last dry period at

the end of the Pleistocene. This plain is bordered by a low well-marked bank of maximal

some meters height. The alluvial deposits are mostly about 10 meters thick and is at the

bottom mostly coarse and sandy and more clayey and humiferous at the top. This proves the

transition from a dry climate to a wet climate with the development of a perennial river

system. In these meanders there are several oxbows with variable dimension, and which

sometimes have eroded the borders of the subrecent terrace.

The current rivers have a narrow valley incised in the alluvial plain of the subrecent rivers.

The alluvial sediments have mostly a fine texture.


Page 38

Figure 14: general geomorphological structure. Explanation in the text. Source: Baert 1991a.

Figure 15: general geomorphological structure (continued). Explanation in the text. Source: Baert 1991a.


Page 39

5.5.3

5.5.3.1 Plateau du Bangu

The geomorphological units

This plateau describes a large synclinal with a north-east orientation. It is build up of the

schisto-gréseux (schist-sandstone). The surface has an inclination towards the north and the

east with a very low but regular slope. The highest points are situated in the west at an altitude

of 800 to 850 meters, with as top the 870 meters high Mont Uia. Just to the east of these high

points the plateau culminates with the crest of Mbanza Ngungu and has an altitude of 700 to

725 meter. More in general the plateau has a height of 650 to 700 meters, decreasing

progressively towards the north and east until 550 to 600 meter. Towards the south and west

the plateau lowers in abrupt grades, with a height difference of up to 200 to 300 meters, to go

into the depression of the Schisto-calcaire. Often the abrupt height difference consists at the

top of a cliff, which exists of the top of the Schisto-calcaire and the base of the Mpioka

formation in the Schisto-gréseux.

The central part of the plateau has a slightly undulating relief. More towards the borders it

gets deeply incised by rivers. Over here the height difference can attain 550 meters over 8

kilometres!

(Baert 1991a, Baert 1991b, Baert 1991c and Baert 1991d)

5.5.3.2 Crête de Mbanza Ngungu

The crête de Mbanza Ngungu (or crest of Mbanza Ngungu) is SE-NW orientated. Its altitude

declines within the study area from 850 meters towards 725 meters in the northwest (this is a

slope of 2m/km). In the northwest it goes into the plateau du Bangu. This crest is formed in

the Schist-limestone and the Schist-sandstone formations with pieces of Cainozoic formations

(Grès Polymorphes and Ochre Sands) and their local in the Pliocene and Pleistocene

reworked sediments on it.

On the higher parts of the crest there are some very characteristic dolines, which are caused

by the dissolution of limestone, permitting the collapse of the sandy layers above this

limestone.

This crest is the divide between the basins of the Inkisi in the east and the Kwilu and Lukunga

in the west.



Page 40

5.5.3.3 Plateau des Batekes – high plateau du Kwango

This plateau is a structural surface dating from the Pliocene. It has a very monotonous

topography with large completely flat areas. The high declines from the Angolan border,

where the plateau reaches about 1000 meters, towards the north, where it has a height of about

700 meters close to the Congo River, with about 1 meter each kilometre! The underground is

build up from Neogene ochre sands (“Upper Kalahari”). This plateau is regularly incised by

river valleys with a south-north direction. The valleys have the form of a through, their slopes

have an medium steepness and they have a flat valley bottom. The height difference between

the valley bottom and the plateau is about 75 meter. This corresponds with the thickness of

the Ochre Sands and the valleys are incised up to the level of the underlying hard rocks. Some

river valleys are incised in the underlying hard rocks and form valleys up to 250 meters deep!

In the northern part the plateau has a lot of circular depression with a depth of 4 to 5 meter.

They are probably formed by the discharge of material in the underground. They have a wet

marshy vegetation in the wet season and at the borders Podzols have developed.

The base of the Ochre Sands dates from the Mid-Tertiary and is slightly inclined towards the

north (0,8 m/km). Under it are the grès polymorphs with also a slightly north-inclining base

(0,7 m/km inclining). This base is an Upper Cretaceous applanation surface.

In the west the plateau is suddenly interrupted by the deep N’Sele River valley. An abrupt

escarpment separates the plateau in the west from the more dissected regions that are situated

over here. This escarpment has a height difference of about 250 meter! From this escarpment

towards the Inkisi valley there are three applanation levels: the surface of the basin of the

Haute Lukunga, an intermediate surface and a lower surface in the bottom of the Inkisi valley.


5.5.3.4 Surfaces of the basin of the Haute Lukunga

This applanation level is dominated by some important relief items from which the most

important are the Mountains Kibila, Kisinsi, Mansimba, Londo, Luila and Mundakani. These

mountains are the remnants of what was once the Batekes plateau and prove how far this

plateau extended in the past. On top of them the residual Kalahari Sands are observed.

The applanation surface is slightly undulating and more or less highly dissected by a dense

hydrographical network with an incision depth of 50 tot 100 meter. The surface has a north-

south orientation and a width of about 20-30 km. The tops of the hills or somewhat aligned

and their height is inclining to the north, from about 750 meters in the south of the region


Page 41

towards 650 meters at 5°S. It is a late tertiary peneplain formed as a real erosion surface on

the Mesozoic formations and the Precambrian schisto-grèseux formations.

Deposits of sands, formed by local reworked and by a short distance transport of Kalahari and

“Grès Polymorphes” sands, are deposited at the foot of the plateau and at the foot of certain

hills.


5.5.3.5 The intermediate surface (of the Batekes-Inkisi surfaces)

This surface is situated about 75 meters below the Haute Lukunga plateau. It is incised in

Precambrian schist-limestone formations, the schist-sandstone formations and Mesozoic

formations. It has an inclination towards the north. The height in the more northern parts of

the surface is about 550-575 meters.


5.5.3.6 The lower surface (of the Batekes-Inkisi surfaces)

This level is coincidencing partially with the plain of the Inkisi River. The surface is also

partially situated along the Congo River plain. It lies between 75 (in the north) and 150 meters

(in the south) below the surface of the Haut Lukunga. Also in the N’Sele valley there are two

levels that can correspond with this surface and the intermediate surface.


5.5.3.7 Pool Malebo plain

This plain is 8 to 10 km broad and is situated on the left side of the Congo River between

Kinshasa until just north of the N’Sele mouth. The altitude varies between 280 and 350

meters and can be split in two well distinguished levels:

• A lower part constituted of recent alluvium. It has a marshy character.

• The upper level composed of thick sand deposits. It forms a flat surface and is largely

occupied by the city Kinshasa.


5.5.3.8 The Schist-Limestone depression

This depression has a weak developed relief, which is aligned along the layers. The region is

characterised by several karst forms: dolines, dry valleys, caves, and kegelkartst. The altitude

is between 250 and 300 meters, corresponding with an old applanation surface. Some higher


Page 42

parts, with an altitude between 330 and 350 meters, correspond with another surface. Close to

Luozi old river terraces of the Congo River can be observed at an altitude of 170 to 230 meter.


5.5.3.9 Schist-Sandstone massif

This massif is situated at the east of the Schist-Limestone depression and between them there

are abrupt height differences of 200 to 300 meter. The underground is entirely the Schisto-

Gréseux group. East of Luozi the massif is split in two by the Congo River, in the north the

Manianga massif, in the south the Bangu massif.

There are different applanation surfaces present:

• The mid- or late-Tertiary applanation level. Here some sandy deposits can be

observed, possible Ochre Sands. The altitude of this surface is 680 to 800 meter. It is

mostly observed in the Manianga massif.

• At 600 to 650 meters a surface is present corresponding with P2 or P2a. In the Bangu

massif sands often mask this surface.

• Between 400 and 520 meters a surface corresponding with P2b is situated.

• Close to the Congo River some surfaces at an altitude of 300 to 350 meters can be

found, corresponding with P3.



Page 43

5.6 Vegetation

5.6.1

The main source for the vegetation is the ‘carte des sols et de la végétation du Congo, du

Rwanda et du Burundi’. Applying to the study area is the map ‘25. Bas-Congo B Végétation’

and the attached booklet ’25. Bas-Congo B notice explicative de la carte de la végétation’

written by P. Compère (1970). Both are published by the ‘Institut National pour l’étude

agronomique du Congo’ with collaboration of the ‘ministère Belge de l’éducation nationale et

de la culture’ (Brussels). Beside this also a lot of information on the vegetation types can be

found in the different explanatory booklets of the soils maps made by Baert (1991).

Introduction

5.6.2

5.6.2.1 Aquatic and semi-aquatic vegetation

Vegetation types

5.6.2.1.1 Aquatic vegetation

The aquatic vegetation has the following subtypes:

• ‘Les mares à Nymphaea lotus et Utricularia inflexa’ (the pools with Nymphaea lotus

and Utricularia inflexa).They can be found in the most important pools and left river

arms.

• ‘Les mares acides oligotrophes à Eleocharis acutangula’ (the acid oligotrophic

pools with Eleocharis acutangul). They can be mainly found in the peatlands and low-

marshes with peat on the Kalahari sands.

• ‘L’association à Pista stratiotes et Lemna paucicostata’ (the association of Pista

stratiotes and Lemna paucicostata). This association colonises the marshes and small

watercourses in the forests.

• The group of Polygonum senegalense f. albotomentosum. This species colonises the

mostly the borders of lakes in slightly deep water. This group with only one species is

a kind of transition between pure aquatic groups and more marshy groups.

• The group of Eichhornia crassipes. This species is an introduced one. It forms large

floating areas around the Congo River and can block the river.

(Compère 1970, Baert 1991a)


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5.6.2.1.2 The vegetation of inundated rocks and rapids

These areas are mostly colonised by mosses and some very specialised species. (Compère

1970, Baert 1991a)

5.6.2.1.3 Semi-aquatic and marsh vegetation

The following types are described:

• The papyrus marshes, mainly appearing around the Stanley Pools.

• The aquatic pastures with Echinochloa pyramidalis. It covers large areas,

particularly in the depressed schist-limestone zone.

• The marshy pastures of Setaria anceps. This group is found in certain depressions in

the savannah.

• The peat-bogs and associated marshes. This type mainly appears in the east of the

Inkisi.

• The herbaceous vegetation of the sandbanks and sandy rivers. This vegetation type

is very instable and unstructured. Mostly it are very common plants.


5.6.2.2 The pioneering vegetation of the loose landslides.

This are plants colonising ravines, erosional relief-forms and recent talus in the whole Bas-

Congo. (Compère 1970, Baert 1991a)

5.6.2.3 Post-agricultural and nitrophilic vegetation

After abandoning the agricultural grounds several new vegetation types can appear:

• The newly generating forests (fallow lands) on sandy and sandy clay soils contain

other species than the newly generating forests on clay soils.

• In the alluvial plains of the Bas-Congo that are periodically inundated, often a Post-

Agricultural savannah regenerates.


5.6.2.4 The savannah vegetation

The savannah makes up the main-types of vegetation in the region. The type of savannah

appearing mainly depends on the soil type and the grade of soil degradation. There is high

variation in the amount of trees appearing in the savannah and when there are no fires the

savannah transforms into forest.


Page 45


5.6.2.4.1 Savannah in the valleys

This savannah is mainly dominated by Andropogon gabonensis and Nephrolepis cordifolia.

This savannah appears mainly on the clayey soils with a good water household. The

herbaceous layer is very dense and very high, sometimes up to 4 meters high. The tree layer

has a maximal coverage of 15-20%. (Compère 1970, Baert 1991a)

5.6.2.4.2 Savannah on heavy soils

On the not-eroded or slightly eroded parts there appears a savannah dominated by

Hyparrhenia diplandra. This savannah contains little trees and exists of a high and dense

grass cover.

On the soils with degradation by erosion there are gradually appearing more xerophillitic

species. In an early erosion stage an association with Andropogon schirensis is colonising.

This savannah is less dense and the grasses are less high. This association is an intermediate

stage between the mesophyllitic savannah and the xerophillitic savannah.

On more eroded soils an association dominated by Andropogon pseudapricus and Sopubia

angolensis appears. The tree-layer is reduced and contains only xerophyllitic trees.

The heavily eroded soils that are incised with ravines have a highly reduced herbaceous layer

with mainly Elyonurus hensii and Andropogon pseudapricus. The main tree species are

Crossopterix febrifuga, Hymenocardia acida, accompanied by Annona arnaria and Vitex

madiensis (Makanga). Between the herbaceous parts there appear large areas with bare soil.


5.6.2.4.3 Savannah on light soils

The savannahs on sandstone and arkose are constituted of Hyparrhenia diplandara,

Panicum fulgens, Andropogon pseudapricus. This type has a rather dense tree layer

dominated by Syzygium guineense, Hymeocardia acida and Crossoperyx febrifuga.

When these oils get more eroded they are covered by a steppitic savannah with Ctenium

newtonii and Trachypogon spicatus. Here is a slight tree layer and the herbaceous layer is low

and not dense. There can appear large areas with bare soil.


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The more sandy soils of the schist-sandstone region there appears a savannah with Aristida

dewildemanii and Helichrysum lechowianum. The tree layer is better developed than in the

xerophillitic savannahs on heavy soils.

On loamy sand soils and sandy clay loam soils of the Batekes plateau and the hilly region

there appears a savannah with dominance of Loudetia arundinacea and Landolphia

lanceolata. The herbaceous layer can be up to 2 meters high and has a groundcover of more

than 90%. The tree layer is mostly good developed. This savannah type can easily develop in

a Bateki-type forest.

On very sandy soils a steppitic savannah with Loudetia demeusei appears. This savannah has

a reduced tree layer with mainly Hymenocardia acida, Strychnos cocculoides, Securidaca

longipedunculata, Crosspterix febrifuga, Combretum laxiflorum, Annona areanaria and

Strychnos cocculoides.


5.6.2.4.4 Steppe

Steppe vegetation appears on the white sands of circular depressions and valleys on the upper

Bateke plateau. The dominating species are Loudetia simplex and Monocymbium

ceresiiforme. There appear only very few isolated trees.


5.6.2.5 Forests

In some parts the forests are rather rare and appear in valleys (galleryforests) and on some

hills that are protected against fires or that are preserved for religious reasons. In other regions

the forests are more common.

The forests are in generally reconstructed. They form a mosaic of forests, secondary forests

and follow lands and everything between it. Despite this, the forest is still shadow-rich and

clearly of the subequatorial type of guinea- or periguinea-forest.


5.6.2.5.1 Guinea forests on heavy soils

This forest meanly appears in galleries and on some hills of the schist-sandstone region. They

are mostly preserved for religious reasons or as orientation point. The main species are Celtis


Page 47

mildbroedii and Celtis zenkeri, associated with Staudtia stipitata, Xylopia aethiopica and

Uapaca nitida. The local population calls these forests SANGI.


5.6.2.5.2 Forests on light soils

On light soils there are two forest types:

• The Guinean association, which is dominated by Millettia laurentii and Xylopia

gilbertii. This association is mainly present on the Mesozoic formations, the sandy

hills and the Mbanz-Ngungu ridges. Under the trees there is a dense layer with lianas

and ferns.

• The peri-Guinean association is dominated by Marquesia acuminate and Pteleopis

diptera. This association appears in dryer biotopes. The lower layers are rich in lianas

and Rubiaceae, Sabiceae and Stipulariaceae.


5.6.2.5.3 The antropogenous forests

There appear two types:

• NKUNKU: a secondary forest with a limited area, which can be found around villages

where it appears as a protection against forest-fires. It is also used for food (fruits) and

as fire-wood.

• NVOKA: this vegetation can be found at abandoned villages. Over here there is a

forest regeneration.


5.6.3

The following vegetation types appear on the vegetation map:

Vegetation types appearing on the vegetation maps

• The forest vegetation:

o Evergreen or semi-deciduous dense humid forest.

o Semi-deciduous, dense and humid forest.

o Guinéennes and periguinénnes secondary forest and reconstructed forest.

o Fallow forests.

• Savannah vegetation

o Mesophyllitic savannah with high Andropogon grasses on heavy clay soils,

savannah and herbaceous postcultural groups on heavy soils.


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o Xerophillitic and meso-xerophillitic savannah on heavy soils.

o Degraded xerophyllitic savannah in Makanga on clayey and sandy-clay soils.

o Meso-xerophyllitic transitional savannah on sandy-clay soils.

o Mesophyllitic and xero-mesophyllitic savannah on sandy soils.

o Steppe and steppitic savannah on sandy soils.

o Mosaic of mesophyllitic transitional savannah, degraded xerophillitic savannah

and fallow forests on sandy and sandy-clay soils.

o Mosaic of mesophyllitic savannah with high Andropogon grasses and meso-

xerophyllitic grasses on heavy clay soils.


xerophyllitic grasses on heavy clay soils. Dominance of the mesophyllitic

savannah.


xerophyllitic grasses on heavy clay soils. Dominance of the meso-xerophyllitic

savannah.

o Mosaic of the degraded facies in Makanga in the transitional meso-

xerophillitic savannah on the sandy-clay soils.

o Mosaic of the degraded facies in Makanga in meso-xerophyllitic savannah on

the heavy clay soils.

• Aquatic and marshy vegetation

o Periodically inundated, riverside and marshy forest.

o Herbaceous or bushy vegetation of the aquatic, semi-aquatic and marshy

zones.

o Riverbank forest with Parinara congensis colonisation along the Congo River.

(Compère 1970)


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5.7 Soils

5.7.1

The basic information about the soils of Bas-Congo was obtained from the different

explanatory texts accompanying the soil maps, produced by Baert (1991). For these maps

some basic concepts are used:

Introduction

• A catena: a sequence of soils of approximately the same age and developed on the

same parent material, with different characteristics due to variation in relief and

vegetation.

• Complexes: the dominant soil units are indicated.

Catenas are described for each type of parent material. They just describe the major soil types.

The soil units are described by morphogenetical criteria, in such a way that they can be

identified by description and by their position in the landscape. A good observation of the

morphology is necessary for a regional classification. The advantages of such a morphological

classification are:

• The independence of the analytical data

• The units are corresponding with the situation on the terrain.

The classification is of the open type, meaning that new characteristics can always be

introduced. The units don’t correspond with the systems of any other system!

Within the system two hierarchical levels are distinguished: series subdivided in phases:

• Series (série): the series are recognised by the following criteria:

o The nature of the parent material

o The texture at a depth of 50 to 100 cm

o The natural drainage class

o The succession of morphogenetic horizons in the upper 2 meter

These criteria have a pedogenetic importance. They group soils with a high

resemblance in their profile development.

• Phase: the phases are recognised by the following criteria:

o The depth towards a physical limit that limits the growth of tree roots.

o The landscape type


Page 50

o The erosion type and grade

These criteria have an importance in the way the soil is used.


5.7.2

The criteria for the division in series are:

The criteria to which the soils are grouped in series and phases

• The parent material. As soils on some types of parent material can develop the same

profiles in the very high alternating circumstances of the tropics, some parent material

types are grouped. The following groups are recognised:

o Acid magmatic rocks and acid highly migmatised rocks.

o Average metamorphic rocks

o Sedimentary rocks and slightly metamorphic rocks

o Basic rocks

o Calcareous rocks

o Hard rocks with a quartz dominance

o Soft rocks with a quartz dominance

o Alluvial and colluvial sediments

• Texture: the texture between 50 and 100 cm depth is considered. This zone is

important for the water supply to plants in the dry season and for the ankering and

oxygen supply of tree roots. This zone is also less affected by allochtonous material

and by pedological surface processes. The classes are reorganised according to the

USDA classes. They are: sableux, loameux, limoneux, argileux leger, argileux,

argileux lourd.

• The natural drainage: an important of soil qualities is directly related to the height of

the water table. The used criteria to identify the classes are the depth the

oxidoreduction spots are occurring, in relation with the texture. There also some cases

in which these spots can not be used. In these cases the drainage class is estimated

according to observations over a whole year to according to the general knowledge of

the terrain.

• The horizon succession: these horizons indicate the stage in the evolution and

alternation of the soil. The horizons according to the FAO are used. In Bas-Congo the


Page 51

following development of a profile is observed: AC, ABwC, ABtC, ABwsC, ABhsC.

These horizons are:

o A: a humeferous surface horizon

o Bw: alternation horizon (recognised by its degree of alternation or structure)

o Bt: illuvial horizon recognised by its accumulation of clay accompanied by

sesquioxides.

o Bws: very alternated horizon, chemical very poor and with a residual

sesquioxides accumulation.

o Bhs: illuvial horizon with an accumulation of iron oxides and organic material.

Above this horizon often an E horizon occurs with a light colour.

o C: parent material

Each series is indicated by the first two letters of a geographic name. For instance: the

Mawinzi series is indicated with Ma, the Kwilu-Ngongo series with Kn.

The phases are recognised by the following criteria:

• The effective soil depth: expressed by the presence of a rock-waste layer at a depth of

more or less than 120 cm. Also the nature of this layer is considered (petroplinthite or

quartz and chert). A symbol is added to the series symbol. For instance Ma.L with L

standing for a petroplintothic layer before 120 cm depth.

• The landscape type and the erosion phase: the main criterion for the landscape type is

the maximal slope, the secondary criteria are the altitude difference between the

lowest and highest points and the height of the denivellation. The erosion is expressed

by the dominant erosion phenomena observed on an air photo and identified on the

terrain. In this way a number and letter can be added to the code of the phase, placed

between brackets: for instance So.(4E).


5.7.3

Due to the complexity of the geology and the scale of the maps not all the series could be

mapped. Therefore it was chosen to map the catenas or complexes composed of several series.

In the legend the cartographic units are followed by the symbols of the series of which they

are composed. Often a map unit represents soils on more than one type of parent material, as

the scale doesn’t permits to map all these types separately.

The maps


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5.7.4

5.7.4.1 Introduction

The different series

This chapter is not meant to give all the information about the different soil types as described

in the descriptive texts, but will give only a short survey of the existing series in the region.

5.7.4.2 Soil series on micaschist

As micaschist have a low resistance against erosion compared to other rocks appearing in the

region (like quartzite, tills, …) there are always situated in a depression position. The relief

exists of rounded hills with a height difference of maximal 50 meters and the steep slopes are

situated between 20 and 50%. The alternation of micaschist is very intense and deep. The

rivers have a very deep incision in the soft alternation products and this causes a renewal of

the landscape. Where the covering layer (level A) is absent, the soil is covered by a very stony

pavement that rests sometimes direct on the hard rock. (Baert 1991a, Baert 1991b, Baert

1991c, Baert 1991d)

Table 4: soil series on micaschist. Source: Baert 1991a, Baert 1991b, Baert 1991c, Baert 1991d.

Texture Profile development

AC ABtC

Light clay Kinza-Vuete

Loam Minkelo Singini

The following series are distinguished:

• Series Kinzau-Vuete (Kv): this series appears in regions that are only slightly

affected by erosion. The profile is developed in the covering layer and rests on a

stone-line, which is mainly composed of angular quartz fragments. On highly eroded

slopes the Minkelo series is observed.

• Series Singini (Si): this series appears mostly on hills and is developed in the saprolite

of micaschist and talcschist.

• Series Minkelo (Mi): these soils have a AC profile and can be found on very steep

slopes. The surface is always covered by a gravel pavement.



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5.7.4.3 Soil series on basic rocks

The basic rocks in Bas-Congo are mainly doloritic and basaltic intrusions in the Lower Tills.

They are rich in amhiboles and epidote. They have two clear landscape types:

• Small lateritic plateaus: they are a residue of an old applanation plain.

• Hills with a rounded summit: hills with a very steep slope, occurring at places where

the lateritic plateau has been dissected.

The vegetation is mainly a slightly afforested savannah with a very dense herbaceous layer.

Locally gallery forests can appear. The covering layer is mostly thick, the stone line exists

mainly of lateritic nodules or debris from the duripan.

After alternation of basic rocks the soil has in general a clayey texture.

Because these soils occupy sometimes small areas, they are often mapped as complexes

together with soils developed on tills. (Baert 1991a, Baert 1991b, Baert 1991c, Baert 1991d)

Table 5: soil series on basic rocks. Source: Baert 1991a, Baert 1991b, Baert 1991c, Baert 1991d.

Texture Development of the profile

ABtC ABwsC

Clay Isangila Gimbi

Light clay Nganda-Sundi

Loam Tschimpi

The different series can be ordered according to their physiographic position:

• Series of the lateritic plateaus of Kasi:

o Series Gimbi (Gi): on the tops and higher slopes, sometimes on the colluvium

o Series Isangila (Is): in the middle of the slopes where the covering layer is

present and rests on a stone-line with laterite fragments.

o Series Nganda-Sundi (Ns): situated on places that are highly rejuvenated.

These soils are chemically richer than the former series

o Tschimpi (T): soils developed on saprolite.

• Series of the rounded hills of Kasi and Sansikwa: these are almost entirely covered by

the Isangila series and locally by the Nganda-Sundi series and Tschimpi series. The

stone-line is rich in grind of the lateritic duripan.

Thus the following series are defined:

• Series Gimbi (Gi)

• Series Isangila (Is)


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• Series Nganda-Sundi (Ns): these soils occupy only very small areas on very steep

slopes. They are well drained and have an ABtC profile.

• Series Tschimpi (T): these soils appear in regions where the erosion ahs completely

removed the covering layer. The Bt horizon has a loamy clay texture. A thick

pavement covers the surface.


5.7.4.4 Soil series on not or slightly metamorphic rocks and till

Table 6: soil series on not or slightly metamorphic rocks. Source: Baert 1991a, Baert 1991b, Baert 1991c,

Baert 1991d.

Texture4 Profile development

ABwC ABtC ABwsC

Clay Kwilu-Ngongo Songololo

Light clay Ntadi

Mawanzi Zamba

silt Gombe-Sud

In this region the vegetation is often savannah, with pieces of forests on top of some hills and

along the valley flanks. Almost everywhere there is a stone line and saprolite present. The

dominant colour of the covering layer (= level A in the alternation pattern) is yellow or

sometimes red.

A high variation in landscape types can be observed:

• The synclinal plains of the schist-limestone region: an Appalachian landscape.

• The plain of Marchal: this landscape appears just east of the Mbanza-Ngungu ridge. It

is a large flat plain with valleys.

• The Mbanza-Ngungu ridge has a more abrupt and mountainous landscape.

• The massifs of Kasi-Kimbungu, Sanswika and Lingezi and the border of the ridge

have an abrupt relief with rounded hills and high height differences (more than 50

meter).

• The hills with a rounded summit form an abrupt landscape with quite high hills. The

slopes are steep (30-60 %) and have height differences of less than 50 meter. This

landscape occurs mainly on the Bangu massif.

4 According to the texture classes used for the soil maps of the Bas-Congo


Page 55

• The tills of Bas-Congo: here the landscape is abrupt with important height differences.

The following series are distinguished:

• Series Songololo (So): soils developed in the covering layer (level A) of the large

lateritic plains of the Schist-Limestone region.

• Series Kwilu-Ngongo (Kn): the most abundant series in the schist region. It is

developed in the covering layer. The series is present on the slopes and the tops of

(sub)rounded hills. The soils of this series have a high variation in their texture and

depth.

• Series Zamba (Za): this series represents the soil formed in the covering layer (level

A) with a sandstone-like schist as parent material. The series appears on the hills of

the Bangu massif. They are associated with the soils of the Ntadi series. Their texture

is intermediate between the soils on arkose (Vunda and Zongo) and schist (Kwilu-

Ngongo).

• Series Ntadi (Nt): soils developed in the covering layer (level A). They appear on the

slopes in the region with sandstone containing schist.

• Series Mawunzi (Ma): series developed in the saprolite of the schist. They cover the

very steep slopes in highly dissected regions. On the tops are mostly soils of the

Kwilu-Ngongo series situated. The soil develops in the stone-line, and if this stone

line is thicker than 50 cm and if there is no duripan, the soil is classified within the

Kwilu-Ngongo series. But mostly the stone-line is absent or very thin.

• Series Gombe-Sud (Gs): this series represent soils developed in the saprolite of the

schist. The soils appear on the steep slopes and sometimes on the tops. This series is

associated with the Mawunzi series.


5.7.4.5 Soils on calcareous rocks

The calcareous rocks of Bas-Congo vary between limestone and dolomite. The vegetation is

mostly composed of a slightly afforested savannah. The composition of the tree layer depends

on the soil degradation. Gallery forests occupy the flanks of the valleys, and they are often

reforested. The texture of the soil is mostly silty or clayey.

The typical landscapes on calcareous rocks are:

• Large peneplains: flat plains with valleys. Limestone outcrops are rare. Limestone is

often associated with schist. Dolines are present.


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• The Mbanza-Ngungu ridge has a landscape with rounded hills with very steep slopes.

There appear in deep dry valleys and grots.

• The debris-landscape of Lovo: limestone frequently appears at the surface and there

are a lot of grots. At the foot of these outcrops appear large plains with dolines.

• Little hills with rounded hills appear between the schist-limestone and schist-

sandstone regions. On the flanks there are limestone outcrops.

The thickness of the cover is very variable. The stone-line is often at the surface on the

steeper slopes. When there is heavy erosion also a part of the saprolite can be removed and

locally the hard rock can be present at the surface. The saprolite that is developed on

limestone is less thick than on schist.

There is more manganese in the soils on limestone than on schist. The highest manganese

accumulations can be found at the food of the slope because of lateral migration. In soils that

had a bad drainage in the past but a good current drainage the manganese is present in the

form of black nodules. Sometimes a real hard manganese layer is formed. (Baert 1991a, Baert

1991b, Baert 1991c, Baert 1991d)

Table 7: soil series on calcareous rocks. Source: Baert 1991a, Baert 1991b, Baert 1991c, Baert 1991d.


AC ABwC ABtC

Clayey Lovo Yanga

Kiazi-Col

Loam Sansikwa

The following series are recognised:

• Series Yanga (Ya): this series groups the well-drained soils with a good Bt horizon,

developed in the covering layer (level A). These soils are comparable with the Kwilo-

Ngongo series and are often associated with the Kwilo-Ngongo and/or Songololo

series. The series appears at the slopes and tops of rounded hills where the covering

layer is conserved. Mostly the stone-line appears at a shallow depth (less than 120 cm)

and exists of chert and/or lateritic nodules, surrounded by a fine earth that is identical

to the material of the Bt horizon in the covering layer.


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• Series Kiazi-Col (Kc): this series contains soil developed in saprolite and is often

associated with the soils of the Yanga series. On the slopes sometimes the limestone

comes to the surface.

• Series Sansikwa (Sa): this series contains soils developed in the saprolite. They are

often associated with the Kiazi-Col series and appear close to the outcropping

limestone.

• Series Lovo (Lo): this series contains the soils of closed depression (often

interconnected) in the calcareous region (dolines). The soils are bad drained and in the

wet season there are inundated, but in the dry season the water table lowers to a depth

of 1,5 to 2,5 meter. The depressions are formed by the dissolution of limestone in the

underground, causing a sink of the sediments above these limestones. The parent

material of these soils is highly alternated and contaminated with erosion products of

the environment. They have a AC horizon development and a clayey to heavy clayey

texture. This series is often associated with the Yanga series.


5.7.4.6 Series on hard rocks with a quartz dominance

Hard rocks with a quartz dominance are very common and widespread in Bas-Congo. They

vary between soft sandstone of Cretaceous origin to very hard quartzite of the Precambrian

socle. Arkoses and quartzite are often situated in a higher position than limestone and schist

because of their hard consistence.

The vegetation on this type of soils is often a poor savannah with a lot of bare soil. The soils

have mostly a hard crust originating from tillage. Forests appear mostly in the valley (gallery

forests) but also as sacred forests on hills (see chapter about vegetation).

The chemical properties of the soils on this parent material can largely vary due to variation

within the parent material. The texture is mostly loamy-sandy-clayey. There are three main

profile types:

• Soils with a Bws horizon. They have often a yellow colour, are developed in the

covering layer (level A) and are mostly situated on rather flat tops and some upper

slopes.

• Soils with a Bt horizon. Their colour is determined by the parent material: yellow to

ochre-red for soils in the covering layer, red for soils in saprolite in the heavy eroded

regions. These soils are mostly situated on slopes.


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• Soils with a Bw horizon. These soils have a ochre-red colour and developed in the

saprolite. These soils appear in highly eroded regions.

In the region these soils appear very heavy erosion can occur, causing very large ravines and

the landscape can be transformed towards bad land. Only an reforestation can help to protect

these soils. (Baert 1991a, Baert 1991b, Baert 1991c, Baert 1991d)

Table 8: series of the soils on hard rock with a quartz dominance. Source: Baert 1991a, Baert 1991b, Baert

1991c, Baert 1991d.

Texture Development of the profile

AC ABwC ABtC ABwsC

Light clay Vunda

Loam Lufu Ngidinga Zongo

Lunga-Vasa Teva


• Series Vunda (Vu): these soils, developed in the covering layer, are mostly found on

massif quartzite and sometimes also on mica-quartzite. They are mostly situated at the

tops or the upper parts of the slopes of hills and on flat crests. Soils on slopes have

mostly the same profile but their Bws horizon has a lighter texture (series Teva).

• Series Teva (Te): these soils differ from the former series by their lighter texture.

Possibly they are partially caused by a contamination of cover-sands.

• Series Zongo (Zo): the soils of this series are developed in the covering layer, which

can have a very variable thickness, from 50 cm to several meters. This series is

widespread and is mostly located on slopes, but can also appear on rounded hilltops.

In highly eroded zones this series is often associated with the series Lunga-Vasa,

which is developed in the saprolite.

• Series Lunga-Vasa (Lv): these soils, developed in the saprolite of mica-quartzite and

feldspar-quartzite, are covered by a pavement with a variable thickness and consisted

of quartz blocks and grind. The series appears in heavily eroded areas.

• Series Ngidinga (Ng): this series represents soils developed in the saprolite of

feldspar-quartzite and arkoses. They are typical for highly eroded zones and they are

often associated with the Lunga-Vasa series.

• Series Lufu (Lu): this series groups the soils with an AC profile and is mostly

associated with bare quartzite rocks.


Page 59


5.7.4.7 Series of soils developed in soft sediments with a quartz dominance

This type of parent material is very common in Bas-Congo and especially in the coastal zone

and the east. The soils have a light texture, sandy or loamy, and a ABwsC profile

development. The dominant vegetation is steppe or very open savannah. (Baert 1991a, Baert

1991b, Baert 1991c, Baert 1991d)

Table 9: series of soil developed in soft sediments with a quartz dominance. Source: Baert 1991a, Baert

1991b, Baert 1991c, Baert 1991d.


ABwsC AEBwsC AEBhsC

Loam Phonzo Impete

Sand Mpese

Buense

Lingezi


• Series Phonzo (Ph): containing well-drained soils. They are mainly situated on the

higher parts of the Bangu Massif and are associated with the Mpese series and

sometimes with the Buense series.

• Series Mpese (Mp): soils with a very sandy texture and a slightly excessive drainage.

They are always associated with the Phonzo series, and sometimes also with the

Buense series.

• Series Buenze (Bu): a series that is comparable with the Kai-Mpimbi series of the

Matadi region. Their profile has a loamy-sandy-clayey Bws horizon with above it

some sandy layers. The texture difference is very abrupt.

• Series Lingezi (Li): this series contains the typical podzols of the closed depression of

the NW Batekes plateau. This series has a small surface as it occupies only the lower

part of the depressions.

• Series Impete (Im): this series makes up the transition between the soils of the closed

depressions (Lingezi) and the plateau soils (Phonzo). They can sometimes occupy vast

areas in association with the Mpese series.



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5.7.4.8 Soils of alluvia and colluvia

The soils of the alluvia and colluvia show a large complexity and it is not possibly to map

this. Moreover, the valleys have a limited extend and it is even mostly not possible to map

them! Therefore the soilmap maps mostly the soils of the summits and slopes and not the soils

of the valleys. The alluvial soils can be split in two main categories:

• Soils of the old terraces. These soils have mostly an ABwsC or ABtC profile

development, or sometimes an AEBhsC (podzol) profile in sandy material. Their

texture is loamy sandy to heavy clayey. The clay fraction is dominated by kaolinite.

They have a good drainage, and the presence of some oxidoreduction spots can be

explained be a former bad drainage. The chemical characteristics are mostly not very

favourable because of the acid character and an unsaturated absorbing complex.

Colluvia on the footslopes have the same characteristics.

• Soils of the valley bottoms and lower terraces are influenced by a shallow water table.

The horizon succession depends on the conditions in which the soils are developed.

The chemical conditions are in general relatively good. These soils have a very limited

extend and are prone to long inundations. Three classes can be distinguished according

to the current drainage:

o Hydromorph organic soils: soils which are full of water and inundated during

the large parts of the year. Peat rests on material with a variable texture and a

gley character. These soils are mainly covered by papyrus forests or other

aquatic forests.

o Hydromorph mineral soils: these soils are completely or partially filled with

water during a part of the year. All horizon succession except ABwsC are

present. These soils are mainly covered by a wet steppe vegetation.

o Not hydromorph mineral soils with a typical profile development of ABwC or

ABtC. The groundwater level is situated under 120 cm during the whole year.

Sometimes an old higher water table leaves some remnant gley spots above

120 cm. The drier character is caused by a slightly higher landscape position

than the two former types. The vegetation exists of gallery forest or an

afforested savannah.

The hydromorph soils are also present outside the valleys, for instance in dolines.



Page 61

Table 10: series of mineral and organic soils with a bad drainage. Source: Baert 1991a, Baert 1991b, Baert

1991c, Baert 1991d.

Texture

Development of the profile

Organic soils Mineral soils

HC AC ABwC ABtC

Light clay Kitobola

Loam Shiloango Lemba Mvuazi

Sandy Mwana

Organic Mbola

Bu-Bateke

The following series are recognised within the mineral and organic soils with a bad drainage:

• Series Mbola (Mb): this series contains the organic soils of fresh water. The water

table is situated close to the surface. The underlying sediments are mainly clayey in

the dolines and more variable in the river valleys.

• The Bu-Bateke (Bb) series covers the organic soils of the closed depressions of the

Batekes plateau. They develop on a water table that is perched on the Bhs horizon,

which is cemented and slightly permeable. The soils are full of water during the wet

season and the beginning of the dry season. The water table doesn’t disappear

completely in the dry season. The same soils appear on the N’Sele plain and in some

large valleys of the Batekes plateau.

• Series Mvuazi (Mv): this series contains soils with an imperfect drainage and a Bt

horizon, mainly developed in the recent valleys of the schist-limestone region (and

sometimes also in the schist-sandstone region).

• Series Kitobola (Ki): this series groups the soils that have an imperfect drainage and a

Bw horizon with a light clay texture. They are mainly present in the valleys with a

high contamination of colluvium.

• Series Lemba (Le): this heterogeneous series contains bad or imperfect drained soils

with a ABwC horizon development. The texture is mostly loamy. These soils are

inundated or at least full of water during a large part of the year.

• Series Shiloango (Sh): this series contains very bad drained soils with a AC profile

and a loamy to clayey texture. In general these soils are not deep and stony. They are


Page 62

situated along the riverbanks and they are regularly inundated, and even in the dry

season the water table is often situated at less than 50 cm.

• Series Mwana (Mw): soils that are bad or imperfect drained with a sandy texture and

a AC profile development. They are associated with the Ndjili series but have a lower

situation and are full of water during the main part of the year.


Table 11: series of soils with an excessive, normal or moderate drainage. Source: Baert 1991a, Baert

1991b, Baert 1991c, Baert 1991d.


ABwsC ABtC AE (BhsC)

Light clay Lufu-Toto

Loam Kundi

sand Fuma Ndjili

The mineral soils with an excessive, normal or moderate drainage appear at old rover terraces,

colluvia on the borders of valleys and on the transition between the high terrace and the

pediment. On the terrain it is sometimes difficult to distinct the pediments from the terraces.


• Series Ndjili (Nd): this series contains very good drained sandy soils with an A(E)C

profile. The sands have a white colour.

• Series Lufu-Toto (Lt): this series of well-drained soils has a Bt horizon with a clay or

sandy clay texture. It appears on old terraces of the large alluvial valleys.

• Series Kundi (Ku): this series groups soils with a good drainage and a loamy texture.

They are developed in the colluvium in the regions with a dominance of sandstone and

quartzite. The soils are often stonish in depth and associated with the Zongo series.

• Series Fuma: this series groups a part of the soils of the high terraces of the Kinshasa-

plain and some soils on the borders of the Batekes plateau. These soils are probably

mostly colluvial and not really alluvial. The drainage is slightly excessive.



Page 63

6 Used data

6.1 ASTER images

6.1.1

6.1.1.1 The TERRA platform

Introduction to ASTER

Terra is the name of a satellite launched on 18 December 1998, and starting operations in

2000. This satellite carries a total of five remote sensors, with as aim to “state of Earth’s

environment and ongoing changes in its climate system” (terra website, consulted 28 may

2004). It studies interactions among the Earth's atmosphere, lands, oceans, life, and radiant

energy (heat and light). The orbit descends the equator around 10:30 a.m. local time.

The five instruments:

• ASTER: Advanced Spaceborne Thermal Emission and Reflection Radiometer

• CERES: Clouds and the Earth's Radiant Energy System

The two identical CERES instruments aboard TERRA measure the earths total

radiation budget and provide cloud properties estimates. One of the two works in a

cross-track scan mode, the other in a biaxial scan mode.

• MISR: Multi-angle Imaging Spectro-Radiometer

The MISR can view the earth in nine different angles: one looks straight down, and

the others look for- or backwards with angles of 26.1°, 45.6°, 60.0°, and 70.5°. It is

designed to study the amount of sunlight scattered at these different angles.

• MODIS: Moderate-resolution Imaging Spectroradiometer

This sensor sees each place of the earth every one or two days. It has a viewing swathe

of 2330 km wide. It has also a very high resolution and is actually an improvement of

the NOAA Advanced Very High Resolution Radiometer. It has 36 discrete spectral

bands. It is mainly used to determine the percentage of the earth covered by clouds. It

can also measure a lot of other things like the photosynthetic activity of land and

marine plants, the extent of snow and ice coverage, aerosols in the atmosphere, fires,

…

• MOPITT: Measurements of Pollution in the Troposphere

The life expectancy of TERRA is 6 years. (source: TERRA website, consulted May 28th

2004)


Page 64

6.1.1.2 ASTER

ASTER or Advanced Spaceborne Thermal Emission and Reflection Radiometer is a sensor on

the TERRA platform as described above. It receives height resolution images in 14 different

spectral bands. The main uses that are mentioned are the detailed mapping of land surface

temperature, emissivety, reflectance, and elevation. It can also be used as a kind of zoom lens

for the other Terra sensors. Important for this sensor is that it is not continuously collecting

data but it collects an average of 8 minutes of data per orbit. It was build in Japan for the

Ministry of Economy, Trade and Industry (METI) and the responsible science team is a joint

between Japan (METI) and the USA (NASA), and there are also French and Australian team

members. Aster has in fact three different subsystems:

• Visible and Near Infrared (VNIR)

• Short-wave Infrared (SWIR)

• Thermal Infrared (TIR).

(ASTER website, consulted 28 May 2004)

6.1.1.2.1 Visible and Near Infrared (VNIR)

The VNIR subsystem is designed to make observations in visible and near infrared light. It

has a resolution of 15 meter. The VNIR has two independent telescopes:

• A nadir looking telescope, which is a reflecting-refracting improved Schmidt design.

This telescope detects in three spectral bands (VNIR 1, 2 and 3).

• A backward looking telescope that is detecting in one spectral band (VNIR 3b).

The detectors of the VNIR telescopes consist of 5000 element silicon charge coupled

detectors (CCD's), of which 4000 are used. The entire telescope can be rotated +- 24 degrees

to provide extensive cross-track pointing capability. The VNIR subsystem produces 62 Mbps

data when using all four detectors.

(ASTER website, consulted May 28th 2004)


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Figure 16: VNIR band spectral wavelengths. Source: official Aster website, http://asterweb.jpl.nasa.gov,

consulted 30 May 2004.

Figure 17: SWIR band spectral wavelengths. Source: official Aster website, http://asterweb.jpl.nasa.gov,


http://asterweb.jpl.nasa.gov/�



Page 66

6.1.1.2.2 Shortwave Infrared (SWIR)

The SWIR telescope is produced to observe in near infrared light and has a resolution of 30

meter. It is able to observe in six different spectral ranges due to six optical bandpass filters.

Due to a movable mirror the telescope is able to point in a cross track direction up to 8,550

degrees from nadir. In this way they are able to map every point on the earth surface within 16

days. The data rate or all SWR bands together is 23 Mbp. The different detectors are widely

spaced and this causes a parallax error of 0,5 pixels per 900 meters height difference!

(ASTER website, consulted May 28th

2004)

6.1.1.2.3 Thermal Infrared (TIR)

The TIR subsystem detects in five bands in the thermal infrared region with a resolution of 90

meter. It uses a single nadir looking fixed telescope and it has a "whiskbroom" scanning

mirror. The mirror is both used for the scanning as for cross-track pointing (up to 8,55

degrees). On board there is back body that can be cooled or heated and that is used for

calibration. For this calibration the mirror has to move 90°. The accuracy is given for the

several temperature ranges: 200 - 240K, 3K; 240 - 270K, 2K; 270 - 340K, 1K; and 340 -

370K, 2K. This subsystem can produce up to 4,2 Mbps data. It has a duty cycle of 16%,

which is the double of the other subsystems (because this one can also observe at night).

(ASTER website, consulted May 28th 2004)

Figure 18: SWIR band spectral wavelengths. Source: official Aster website, http://asterweb.jpl.nasa.gov,




Page 67

6.1.1.2.4 Overview

Table 12: overview of the ASTER sensors. Source: official Aster website, http://asterweb.jpl.nasa.gov,


Characteristic VNIR SWIR TIR

Spectral Range Band 1: 0.52 - 0.60 µm

Nadir looking Band 4: 1.600 - 1.700 µm Band 10: 8.125 - 8.475 µm

Band 2: 0.63 - 0.69 µm


Band 3: 0.76 - 0.86 µm


Band 3: 0.76 - 0.86 µm

Backward looking Band 7: 2.235 - 2.285 µm Band 13: 10.25 - 10.95 µm

Band 8: 2.295 - 2.365 µm Band 14: 10.95 - 11.65 µm

Band 9: 2.360 - 2.430 µm

Ground Resolution 15 m 30m 90m

Data Rate (Mbits/sec) 62 23 4.2

Cross-track Pointing (deg.) ±24 ±8.55 ±8.55

Cross-track Pointing (km) ±318 ±116 ±116

Swath Width (km) 60 60 60

Detector Type Si PtSi-Si HgCdTe

Quantization (bits) 8 8 12

Figure 19: ASTER spectral bands. Source: official Aster website, http://asterweb.jpl.nasa.gov, consulted

30 May 2004.




Page 68

6.1.2

An important factor in the selection of the study area was the availability of data. Because it

seemed there were a lot of data about the Bas Congo, it was chosen to select a satellite image

between Matadi and Kinshasa. Beside this criterion it was also the intention to choose an

image in an interesting area: as much variation as possible in soils and geology. But this

seemed impossible as a large problem occurred during the image selection: the cloud cover. In

this selected region most available satellite images had a large loud cover (going to 100%)

making these images useless. Only one image had an acceptable cloud cover and thus this

image was selected.

Selection of an image

6.1.3

Granule ID: SC:AST_L1B.003:2016603609

Metadata of the chosen image

Scene center: 4.83S/14.89E

Acq Date: 18Sep2000

Rows/Columns: 4200x4980

Level: L1B

Images of the level L1B are images with a systematic correction including radiometric and geometric correction. The data are processed to unsigned 8-bit digital numbers5

6.1.4

.

The ASTER images are delivered in the HDF-EOS-dataformat. This data format can be

converted to TIF using the software PCI-GEOMATICA.

Preparation of the image

6.2 LANDSAT images In the beginning of this study it was the purpose to use also some LANDSAT images (and

therefore the term LANDSAT appears in the title). The study area was chosen according to

the available ASTER images (because these images are necessary for the production of a

DEM). After the selection of a study area LANDSAT images were searched on the internet,

but there appeared to be no free LANDSAT images of the study area with a satisfying quality.

All existing LANDSAT images were not satisfying because the cloud cover was too dense.

5 DN or digital number: “number assigned to a pixel which is related to the parameters being measured by a

remote sensing system”. (Gibson and Power 2000 p. 171)


Page 69

7 Introduction in teledetection and air photography

7.1 Introduction It is not the purpose to give a complete introduction of teledetection in this work, as this

would just mean a rewriting of some other works. Other thesisses recently produced at the

University of Ghent give a good introduction to teledetection or air photography: Verbeken

2003 (teledetection), Van Coillie 2003 (air photography) and Bossyns 2004 (teledetection and

air photography). There are also several good books available about these items.

Therefore only some items are discussed that are necessary to have an understanding of this

thesis.

7.2 Basic principles of stereoscopy The basic principle of stereoscopic vision is that the left and right eyes look to objects under a

different angle. By means of this angle-difference distances can be observed and the spectator

gets a three dimensional view. The parallax is the difference in angle under which both eyes

observe an object.

The formula is:

Ps= Bo dZ/Z²

With: Ps = stereoscopic parallax

Bo = eye basis (distance between two eyes)

Z= distance between the two eyes and the object

When looking through a stereoscope to two air photographs, the left eye will only see the left

photo and the right eye only the right photo. By the difference in parallax between two points,

the eye will observe a three dimensional view. With air photographs the photos overlap about

60% in the direction the plane flies.

Another possibility is to make a red print of the left photo and a green print of the right photo.

When using a pair of glasses with filters (left a green filter, right a red filter) the eye will be

mislead by the difference in refractive index and a three-dimensional view will be obtained.


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The eye is however limited in its ability to view a parallax difference, and this limit is specific

for each person. When a distance is more than 1316 meters no stereoscopic vision can be

obtained, and this applies also on the flying height of the plane (or satellite). To solve this

problem one can use the technique of hyperstereoscopy, this is basically taking oblique photos

and also making the flying basis larger in order to obtain a larger parallax. The flying basis B

is the distance between two recording points. The possibility that is used in this study (by the

ASTER satellite) is taking an oblique picture. By taking an oblique picture all points will be

shifted laterally and this will augment the parallax.

When looking to Figure 20 the parallax of point A is: p= xl-xr

(Bossyns 2004, Van Coillie 2003, Goossens 2002)

. When this point A is situated

on both aerial pictures, the parallax can be measured. From this parallax the height difference

can be measured.

Figure 20: explanation of a parallax. Source: Bossyns 2004, original source Bethel et al. 2001.

The following satellites have the ability to take stereoscopic pictures:

• The corona satellite was active between 1960 and 1972 and the pictures were released

in 1995. This satellite had two cameras: one looking forward and one looking

backwards.

• The first SPOT (satellite pour l’observation de la terre) satellite was lanced in 1986 by

a joint venture of France, Belgium and Sweden. The satellites can be programmed to


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take oblique pictures and by programming the satellites in this way that the same area

is observed from two different positions, stereoscopic images can be obtained. A

disadvantage is that the time between two observations can be large. The resolution is

10 meters for panchromatic images and 20 meters for multispectral images. The

maximal angle of the scanner is 27°. This method of turning the scanner results in

cross-track stereoscopic images.

From SPOT-5 the resolution became 10 meters for the multispectral images and 5

meters for the panchromatic images. It is even possible to get a resolution of 2,5

meters as two images are recorded shortly after each other so that the difference is 0,5

pixel in X and Y. These instruments still work with the cross-track method, but a new

instrument works along-track to get stereoscopic images. In this way the recording

time between two images is 1,5 minutes. The resolution of these images is 5 meters for

a panchromatic band, the angle of the sensors is 20° back- or forward.

• IKONOS was launched in 1999 and has a multispectral resolution of 4 meters and a

panchromatic resolution of 1 meter. This sensor can observe under a degree of 26° in

both along-track as in cross-track modus.

• Quickbird was launched in 2001 and records stereoscopic images in a comparable

way as IKONOS. The multispectral resolution is 2,4 meters and the panchromatic

resolution is 0,61 meter.

• The sensors ASTER and MISR are discussed further.

(Bossyns 2004, Van Coillie 2003, Goossens 2002)


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7.3 Photogrammetry Photogrammetry is “the art, science and technology of obtaining reliable information about

physical objects and the environment through processes of recording, measuring and

interpreting photographic images and patterns of electromagnetic radiant energy and other

phenomena” (Thompson and Gruner 1980, p. 1)

This science exists mainly of two disciplines: the terrestic photogrammetry in which the

survey is performed from the earth surface, and the air photogrammetry, performed from a

certain height (plane, satellite, …). (Thompson and Gruner 1980)

A complete overview of photogrammetry lies not in the objectives of this work. The

procedures and operations applying to the production of a DEM include large mathematical

explanations. More information about this can be obtained in the different publications that

were referred in the introduction of this chapter (Bossyns 2004, Van Coillie 2003) and also in

Goossens (2002), Lillesand and Kiefer (1994), Schenk (1996) and Wong (1980).

These concepts are however important within this work:

• Scale: the digital image (satellite image) seems to have no scale, as one can zoom in or

zoom out on these images. This is however not true, as the zooming only is a way of

representing the image.

• Relative orientation: this process includes the matching of homologous points on both

images. In Virtuozo this is a automatic process, that needs a manual control

afterwards.

• Absolute orientation: this is a manual process in Virtuozo. Points with known terrain

coordinates are indicated on the image. These ground control points have to be spread

over the whole image, and a large number of points increases the accuracy of the

results.

• Epipolar resampling: this is a process that resamples both images in such a way that

the Y-parallax for both images is zero, and only the X-parallax is unresolved.

• DEM and DTM: a Digital Elevation Model is represented as a regular grid of points (“

a regular grid array of numbers in which the numbers represent elevation” according

to Gibson and Power 2000 (p. 171)), a Digital Terrain Model uses an irregular grid. In

principle the DEM represents the naked surface, from which the buildings and


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vegetation are filtered. A Digital Surface Model (DSM) represents the visible surface,

it is to say with vegetation and buildings. Within a DEM there is only one possible Z

value for each point.

• A TIN (triangulated irregular network) uses irregular spread points that are

interconnected to represent the surface. The surface is actually represented by

triangles.

• An orthophoto are pictures which are made from vertical or almost vertical air

photographs and the following effects are eliminated: deformations that are caused by

a conical projection, deformations by the relief of the surface and deformations by the

tilt of the camera.

(Bossyns 2004, Van Coillie 2003, Goossens 2002, Wong 1980, Lillesand and Kiefer 1994,

Jacobson 2003, Falkner and Morgan 2002)


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7.4 Principles of teledetection

7.4.1

Teledetection or remote sensing is “the science and art of obtaining information about an

object, area, or phenomenon through the analysis of data acquired by a device that is not in

contact with the object, area, or phenomenon under investigation” (Lillesand and Kiefer 1994

p. 1). In the object of this study the observations take place from space by satellites, and are

using the electromagnetic radiation.

Introduction

Figure 21 represents the different steps that are performed

during the teledetection.

The first steps includes the obtaining of the data. In this study this happens by the ASTER

platform. The radiation that is observed is in fact reflected by the earth surface (or by parts of

the atmosphere) and before reaching the registering platform it can be changed (for instance

by certain atmospheric conditions). The ASTER platform saves this data digitally. The data-

analysis includes the research of this data using a computer. This data-analysis is actually the

subject of this study. The result can be represented as a map, a table or a digital file.

(Lillesand and Kiefer 1994)

It is not the purpose of this work to give a complete survey of teledetection and teledetection

methods, but only the most important methods are discussed.

Figure 21: different steps of teledetection. Source: Lillesand and Kiefer 1994.


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7.4.2

Before explaining the analysis of remotely sensed data, it is important to know what these

data represent. Electromagnetic radiation is energy that is moving according to the wave

theory. This means the energy travels as sinusoidal harmonic waves with the speed of light

(c). Each wave has a wavelength λ representing the distance between two wave peaks, and a

frequency ν, this is the number of waves passing a point within a certain time unit. The light

speed, wave length and frequency follow the formula: c=νλ. In this formula c is constant,

meaning that λ en ν are related inversely. In remote sensing the electromagnetic waves are

categorised according to their wavelength within the electromagnetic spectrum (see

Electromagnetic radiation

Figure

22).


Figure 22: the electromagnetic spectrum. Source: Lillesand and Kiefer 1994.

Not all the electromagnetic radiation that is sent out by the sun reaches the earth surface, as

the substances present in the atmosphere block a part of the radiation. In Figure 23 can be

seen which wavelengths are mainly influenced (blocked) by the atmosphere. There is also an

effect called scattering: “the unpredictable diffusion of radiation by particles in the

atmosphere” (Lillesand and Kiefer 1994 p.9). There are three types of scattering:

• Rayleigh scatter: occurs when radiation interacts with small (much smaller than the

wavelength of the radiation) atmospheric molecules and other particles. Short

wavelengths are much more influenced by the Rayleigh scattering than other

wavelengths.

• Mie scatter: when the diameters of atmospheric particles equal the wavelengths of the

radiation. This type of scatter influences longer wavelengths than the Rayleigh scatter.

The main particle types causing this type of scatter are water vapour and dust, and

therefore this type of scatter is most important when the sky is overcast.


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• Nonselective scatter is caused by particles much larger than the wavelength of the

radiation. This type scatters all wavelengths almost equally. This type is for instance

caused by water droplets.


Figure 23: spectral characteristics of energy sources, atmospheric effects and common remote sensing

systems. Source: Lillesand and Kiefer 1994.

When the incoming radiation reaches the earth surface there are mainly three possibilities

(EI): it can be reflected (ER), absorbed (EA) or transmitted (ET

Also the properties of the surface can play a role in the way the radiation is reflected: this

ranges from the ideal specular reflection to diffuse reflection (Lambertian surface). See also

). The proportions between

these possibilities depend on the different earth surface types. There is also a wavelength

dependency, meaning that even within the same earth surface the proportion between

transmitted, absorbed and reflected radiation varies with the wavelength. These differences

result within the visible wavelengths to what we call colour: when an object highly reflects

the blue wavelengths, we see a blue colour.

Figure 24.


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Figure 24: different reflectance types. Source: Lillesand and Kiefer 1994.

Most remote sensing sensors detect the reflected portion of the radiation. Spectral reflection is

defined as the ratio between the reflected and incoming energy:

ρλ=ER(λ) / EI

With: ρ

(λ)

λ

E

=spectral reflection

R

E

(λ)= reflected radiation

I

Figure 25

(λ) = incoming radiation

represents the spectral reflectance of different surfaces for different spectral

wavelengths. This curve is very important to come to an interpretation of a satellite image.


Figure 25: reflectances of different surfaces for a variety of wavelengths. Source: Tso and Mather 2001.


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7.4.3

The ASTER platform was used in this study. More information about this platform and the

produced images can be obtained in chapter

Images

6. In general an image has the following kind of

resolutions:

• A temporal resolution or the time between two observations of the same point

• Spectral resolution: this indicates the different spectral bands which are present

• Spatial resolution: expresses the pixel size

• Radiometric resolution: this expresses the number of grey values that can be assigned

to a pixel

(Goossens 2002)

7.4.4

In this chapter some image processing tools are discussed that are important for this study and

were applied in Ilwis. Besides the processing methods discussed here there are several other,

and more information about this methods can be found in specialised literature (like Lillesand

and Kiefer 1994) and the thesisses mentioned above (Verbeken 2003, Bossyns 2004).

Image processing

7.4.4.1 Rectification and restoration

Because there are slight variations in altitude, attitude and velocity of the sensor platform, the

images can show some geometric distortions. There are also some systematic distortions, for

instance caused by the movement of the earth. The geometric correction corrects for these

distortions. Resampling is the process in which the pixel values for a geometrical correct

raster are calculated. This happens in two steps:

• First the coordinates of each pixel in the output (= geometrical correct) matrix are

transformed to determine the corresponding location in the original matrix

• Then digital number (DN) values are assigned to the pixels of this output matrix. In

general the pixels of the output matrix will not totally overlap the input pixels.

Therefore some mathematical function is used to calculate the output DN value.

The mathematical resampling functions available in the used software (Ilwis) are:

• Nearest neighbour resampling: for each output pixel the pixel value of the closest

input pixel is assigned.


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Figure 26: resampling using nearest neighbour method. Source: Ilwis Help.

• Bilinear resampling : the values of the four closest input pixels to the output pixel are

used to calculate an interpolated value for the output pixel. This is done by first

calculating two interpolated values in the Y direction (between 15 and 17 and

between 16 and 19 on Figure 27) and then performing an interpolation between those

two values in the X direction.

Figure 27: resampling using bilinear resampling. Source: Ilwis Help.

• Bicubic resampling: with this method the values of 16 surrounding pixels are used to

calculate the output pixel value. This is calculated by first calculating 4 interpolations

in the Y direction and then calculating 1 interpolation in the x direction.

Figure 28: resampling using Bicubic resampling. Source: Ilwis Help.

Besides the resampling also radiometric and noise corrections can be performed.

7.4.4.2 Image enhancement

Often the images only use a part of the available data range (0-250). The stretch operation is

used to spread these values over the whole possible range of 0-255. This process is very

usefull to get a better view and interpretation of an image. In Ilwis basically the following

stretch algorithms are available:


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• Linear stretch: the highest and lowest chosen values are assigned a value of 0 and 255

respectively, and the values in between them are accordingly stretched. The result is

that all input values are stretched to the same extent.

Figure 29: linear stretch. Source: Ilwis Help.

• Histogram equalization : this type of stretch includes the number of pixels assigned to

a certain class in this calculations. The parts of a histogram with more pixels are

stretched more than the parts with few pixels. Figure 30 gives a representation of this

stretching type.

Figure 30: histogram equalization stretch. Source: Ilwis Help.

(Lillesand and Kiefer 1994, Gibson and Power 2000, Ilwis Help)


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Figure 31: stretching methods. Source: Lillesand and Kiefer 1994.

Filtering is a process in which the pixels of the output map are assigned a new value that is

calculated via a certain function on the input pixel and its neighbours. For this a matrix is

composed around the input pixel, and this matrix has always an odd number as columns and

rows. Filters can be used for several reasons:

• Enhancing the sharpness of an image

• To reduce noise in an image

• To detect line features and edges

• To do all sorts of calculations on DEM’s: steepness, aspect, shape, …

• To perform some calculations on classifications like assign values to pixels which did

not get a value during the classification

• …

In Ilwis a large number of filter operations are available, and it also possible to make some

user defined filters.

(Ilwis Help, Gibson and Power 2000, Lillesand and Kiefer 1994)


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7.4.4.3 Feature space and soil line

A feature space is “a graph that shows the distribution of digital numbers in a dataset in which

the axes are formed by different spectral bands.” (Gibson and Power 2000 P. 172)

When plotting the red and near infrared bands of bare soils are plotted on a scatter diagram,

they will form a more or less straight line. This is because when the reflectance is high in the

NIR, it will also be high in the red spectral range (this arises from the spectral properties of

soils with a more or less comparable reflection in NIR and red spectral ranges). As the

reflectance of vegetation is always the same or higher in the NIR spectral range than for bare

soil, and the reflectance of vegetation is always the same or lower in the visible red spectral

range than for bare soil, vegetated areas will give points right above this line in the scatter

diagram. (Gibson and Power 2000)

7.4.4.4 Vegetation indices

Vegetation indices are ‘empirical formulae designed to emphasis the spectral contrast between

the red and near-infrared regions of the electromagnetic spectrum’ (Gibson and Power 2000,

p. 115). They are an attempt to measure vegetation and vegetation health. They are also a way

to combine and compress image band data. For all vegetation indices there are two basic

assumptions:

• That useful information about vegetation can be obtained by an algebraic combination

of remotely sensed spectral bands.

• That all bare soil in the feature space will form a soil line.

There are mainly two kinds of indices:

• Ratio-based indices. These indices assume that all isovegetation lines converge in one

single point when plotting the red and near infrared (NIR) bands. Examples are the

Normalized Difference Vegetation Index (NDVI), the Soil Adjusted Vegetation Index

(SAVI) and the Ratio Vegetation Index (RVI).

• A second school believes that in such a graphic the isovegetation lines are parallel to

the soil line. These indices measure the perpendicular distance between the soil line

and the red and NIR point of the pixel. Examples are the Perpendicular Vegetation

Index (PVI) and the Difference Vegetation Index (DVI).

(Gibson and Power 2000)


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Richardson and Wiegmand (1977) defined the Perpendicular Vegetation Index (PVI). The

PVI is a measure for the distance between the pixel ad the soil brightness line. The formula is:

PVI= 1 / √[(Sr-Vr)² - (Sir – Vir)²]

With S= soil reflection, V= vegetation reflection, r= red wavelength, ir= infrared wavelength.

The values range between –1 and +1. (Gibson and Power 2000)

The Different Vegetation Index (DVI) is calculated with the formula:

DVI=(near infrared-visible red).

This formula assumes that the isovegetation lines are parallel to the soil line. (Gibson and

Power 2000)

The normalized difference vegetation index (NDVI) is a measure for the presence and

condition of green vegetation. The NDVI is calculated with the formula:

(near infrared band - visible band)/ (near infrared band + visible band)

The NDVI values can range between –1 and +1:

• Vegetated areas have a relatively high near-infrared reflectance and a relatively low

visible reflectance and will yield high values for the NDVI.

• Clouds, water and snow have larger visible reflectance than near-infrared reflectance

and will yield negative values.

• Bare soil and rock have similar reflectance in both near-infrared and visible and the

NDVI will be around zero.

This indices is widely used and was first proposed by Kriegler et al. (1969). An advantage of

this indices is that it compensates for differences in illumination conditions and slope and

aspect variations. (ILWIS 3.0 help, Gibson and Power 2000, Lillesand and Kiefer 1994)

The Weight Difference Vegetation Index (WDVI) is a mathematical simpler version of the

PVI. The formula is:

WDVI= near infrared – g x red

With g= gradient of the soil brightness line. (Gibson and Power 2000)

In all indices listed above, the soil brightness line in the red/near infrared featurespace is

assumed to be a single line. But different soils can in practice have different line slopes. This

is mainly a problem with a low vegetation cover. Therefore a series of other indices has been

developed. (Gibson and Power 2000)


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The Soil Adjusted Vegetation Index (SAVI) is a hybrid between a perpendicular and a ratio

based index. The index values range between –1 and +1. An adjustment factor L is used, and

this factor was found by trial and error. The formula is:

SAVI= (near infrared – red) / [(near infrared + red +L) x (1+L)]

The value for L ranges from 0 (for a very high vegetation cover) over 0,5 (for an intermediate

vegetation cover) tot 1 (for a very low cover). (Gibson and Power 2000)

The ratio vegetation index (RVI) measures the proportion between the red and near infrared

reflectance. The values are always positive but can be endless end this is a main disadvantage

of this indices. It is calculated with:

RVI= near infrared/ red

(Rees 2001)

7.4.4.5 Classification

“Classification is the process by which pixels which have similar spectral characteristics and

which are consequently assumed to belong to the same class are identified and assigned a

unique colour” (Gibson and Power 2000 p. 72). In practice however they are assigned the

same class name. Classification can be applied on several bands, which increases the number

of classes that can be recognised, as some features can have the same spectral reflectance in

one band, but a different reflectance in another band. It is also true that the larger the number

of bands used, the larger the accuracy of the classification will be, but also the longer the

calculation time will be!

There are mainly two ways of classification:

• Supervised classification

• Unsupervised classification

(Gibson and Power 2000)


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Figure 32: principles of classification. Source: Gibson and Power 2000.

Unsupervised classification groups the pixels into clusters based on the distribution of the

DN in the image. Mostly it is required that the operator specifies the number of clusters the

computer should recognise. The unsupervised classification works with an iterative process.

In Ilwis this operation starts with assigning all pixels to the same cluster. This cluster is split

in two along its longest axis in the feature space. This process is repeated until the desired

number of clusters is reached. This method is applied when no terrain data are available.

(Gibson and Power 2000, Ilwis Help)

When there are terrain data available (like a map or even better field observations) supervised

classification can be performed. Within supervised classification the operator selects some

pixels and assigns a certain class to it (training pixels). This pixels should provide the typical

examples of each kind of land cover. From these training areas the computer generates

statistical parameters and then compares the DN values of each pixels with this parameters of


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the classes. If the DN values for a pixel fall within the training area in the feature space, this

pixel is supposed to belong to the same class as this training area. With this method a

sufficient number or training pixels have to be selected for each class, in order to ensure that

these training pixels are representative for the whole class. The training pixels of one class

should also not be selected in the same area of the map but they should be spread over the

map. The training areas should also be as separated and uniquely representative as possible to

avoid overlapping of classes. Sometimes the classes may have very similar reflective

properties and it may be necessary to merge classes for this reason.

(Gibson and Power 2000, Ilwis Help)

Figure 33: different classification methods. Source: Gibson and Power 2000.

Supervised classification in ILWIS needs two steps: first training pixels are selected (a

sample set is made) and in the second stage the classification is performed.

There are some requirements to perform a sample set operation:

• You need an image that can be easily interpreted, like a colour composite.

• You also need a map list. This map list contains the set of images that should be

classified.


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• A class domain is also needed. This is the set of class names that will be assigned to

the training pixels.

The maplist that was chosen to classify on is the maplist all+NDVI, containing all ASTER-

bands in their stretched form and georeferenced according to the georeference VNIR and also

the NDVI-map stretched to values between 0 and 255 (see before). For the easily interpretable

map there can be chosen several possibilities: one of the two false colour composites that have

a low correlation (as described before) or the NDVI map.

A new class domain was created with new domain. In this domain only two classes were

made: water and cloud, because all the other classes can be added during the sample set

operation. This was regarded as more easy because it was not clear beforehand which classes

are recognisable on the picture!

In the sample set operation one or more pixels can be selected and they can be assigned to a

class. These so called training pixels have to be chosen carefully and should be representative

for the class. During the sampling both feature space and sample statistics can be displayed.

The sample statistics window displays two things: in the upper half a class can be selected and

several statistical values (mean, standard deviation, number of training pixels with the

predominant value, predominant value and total number of training pixels in this class) for the

training pixels of this class are displayed. In the lower half of the window the same statistics

are displayed for the selected pixels.

During sample also one or more feature spaces can be shown. In these feature spaces the

different classes are displayed. The two bands used for the two axes can be chosen from all

bands present in the maplist.

The classify operation in Ilwis performs the classification on the sample set. In classification

the decision is taken to assign a pixel to a class, depending on the classes that were defined in

the sample set. The decision to assign a class to a pixel depends on a comparison of the

spectral values of the pixel with the spectral values of the training pixels. During classification

the mean values per band are calculated (called a class mean). There are different possible

classification methods in Ilwis, each with their own settings.

The box classifier draws multidimensional boxes around the class means, depending on the

standard deviation. A multiplication factor can be chosen to define the multiplication of the

standard deviation to draw the box. When a pixel falls in the box of two classes, the class with

the shortest distance from the class mean is assigned. The standard multiplication factor is the

root of the number of bands.


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The Minimum Distance to Mean classifier calculates the distance towards the class means.

The pixel gets the class with the shortest (Euclidean) distance, except when this distance is

larger than the threshold.

The Minimum Mahalanobis Distance classifier calculates the distance towards the class

means as Mahalanobis distances. The Mahalanobis distance depends on the distances towards

class means and the variance-covariance matrix of each class (citing Ilwis Help). If the

distance is smaller than the user-defined threshold the class name with the shortest distance is

assigned.

The Maximum Likelihood classifier assumes that there is a distribution according to a

'multi-variate normal probability density function' of the spectral values of the training pixels.

In this way a kind of distance is calculated, also using the Mahalanobis distance and another

factor. The class with the shortest distance is assigned to the pixel.

(source: Ilwis Help)


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8 Production of a DEM

8.1 Introduction

8.1.1

The definition of a DEM is given in paragraph

Concept

7.3.

8.1.2

The used software package is VirtuoZoNT (from here on I use the term Virtuozo), from

Supersoft Inc. This program is able to create DEM’s, contourmaps and orthophotos.

Used software

8.2 Available data The DEM was based on some available data. In first place this are the geodetic points that

were found in the ‘Canevas du Bas Congo’ of 1955. These points can be divided in three

groups:

• Points for which coordinates are given in Fuseau 16 (X,Y) and with an Z coordinate.

• Points for which coordinates are given in Fuseau 14 (X,Y) and with an Z coordinate.

• Points for which the X and Y are given in both Fuseau 14 and Fuseau 16.

Both Fuseau 14 and Fuseau 16 are based on the ellipsoid of Clarke 1880, but the Fuseau 14

has as central meridian the 14° meridian while the Fuseau 16 has the 16° meridian in the

centre. The canevas makes no notice of any way to convert data between these coordinate

systems. Using the points for which the coordinates are given in both systems, no

mathematical formula could be easily found for the conversion between these systems. In this

way only one coordinate system can be used later on!

These points with known coordinates had to be indicated on the satellite images to make the

DEM. For the allocation of the points, two sources were used:

• Where available the topographic maps of Congo (scale 1/25000) were used. In fact

these maps only exist for the southern part of the satellite image and only one geodetic

point could be found on it (close to Inkisi).

• For the other part of the satellite image a controlled aerial photograph mosaic was

used. On this mosaic the geodetic points are indicated as triangles.


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The geodetic points occurring in the canevas have coordinates for X and Y given in meter,

with two numbers after the comma. For Z the coordinates are also in meter, with one decimal.

The error for the X and Y coordinates is not given in the canevas, for the Z coordinates it is

between 0,15 and 0,43 meter, with a mean around 0,30 meter.

8.3 Preparing the images in Ilwis

8.3.1

Each pixel of the images can have a value between 0 and 255, but this range is not entirely

used by the original images. To make it easier to identify pixels and recognise patterns, the

original images were stretched. In this procedure, the grey-values are spread over the

available range of 0-255. A linear stretched was used in this case, which means that the

original values are linear spread over the new range.

Stretching

8.3.2

A first attempt to create a DEM as described further, failed and only a tilting plain was

produced. This problem was solved by georeferencing the two used images (the nadir and

backward images) to each other, using the master-slave method. This procedure was used

after personal advise by Lic. D. Devriendt who is familiar with the software package.

Georeferencing and making submaps

First of all a set of submaps were produced. These submaps are produced in such a way that

they don’t contain the black borders of the satellite image (which contain no data). This was

done because these black zones can possibly influence the processes in Virtuozo.

After these submaps were created, a local georeference system was created for the nadir

image. For this the corners of the image have got the following coordinates:

• Lower left corner: 1,1

• Lower right corner: # of columns, 1

• Upper left corner: 1, # of rows

• Upper right corner: # of columns, # of rows

This was done by using the georeference tiepoints method in Ilwis.

After this local system was created, the backward image was georeferenced towards this

system. This was also done by using the georeference tiepoints method in Ilwis.


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8.4 Production of the DEM in Virtuozo

8.4.1

8.4.1.1 Creating a block

Preparative steps

File>open block

In this way a block is opened. A block exists of a number of strips each containing on or more

stereopairs. A stereopair exists of two overlapping images. Opening a block is always the first

step when working in Virtuozo, even if we have just one stereopair (like in this case).

In the dialog box we find following items:

• Home directory: this is the directory in which all files are stored we use in Virtuozo.

• Control Points file: this is referring to the file containing the control points. As we

don’t have any control points we can leave this blank or we can choose the pass points

file over here.

• Pass points file: this is the file with the pass points that are used during the external

and absolute orientation.

• Camera calibration: this should be left blank because we use non metric images.

• Basic Parameters: Photo scale, total strips and sensor type. The photo scale can be left

like it is (we have no data about it), the total strips number is 1 and the sensor type is

non metric.

• Block defaults:

o DEM spacing: this defines the interval of the grid of the produced DEM. The

unity is in groundunits. Because the scale of the picture is 15 meters (see

ASTER properties) a DEM spacing of 15 meters was chosen).

o Rotation angle: here the rotation angle of the DEM according to the

coordinates can be set.

o Contour interval: here the contour interval (vertical distance interval) can be

set.

o Orthoimage GSD: Ground sampling Distance or Ground Pixel Size. This is

related to the scale and resolution.

(VirtuoZo Help, Van Coillie 2003)


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Figure 34: setup of a block in Virtuozo. Source: own research in Virtuozo.

8.4.1.2 Importing images

File>Import>Images

VirtuoZo NT doesn’t work with the standard TIF or BMP image formats but has an own

system. Therefore the images have to be imported again. When a file vnir3.tiff is imported

two files are created: vnir3.vz and vnir3.vz.spt. The .vz file contains the pixel information, the

.vz.pt file contains additional information like:

• Dimensions of the image in number of rows and columns

• Scale of a pixel in micrometer

• Kind of camera (metric/non metric)

• Colour / grey scale images

(Virtuozo Help, Van Coillie 2003)

8.4.1.3 Turning the images

Virtuozo is programmed in such a way that the flying direction would be from left to right,

while the ASTER images are made with a North-South flying path, with the nadir image

taken first and the backward image taken last (as suggested by its name). This means that the

original two images have to be turned over 270° to use them proper in Virtuozo. Also the pass

points have to be adapted to this turn of 270°. The turning of the images happens with tools >

image processing and there we can select turn 270 degrees.



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Figure 35: turning of images in Virtuozo. In red is indicated how to turn the images over 270°. Source:

own research in Virtuozo.

8.4.1.4 Creation of the Pass points file

The Pass Point File is a file containing the coordinates of the pass points. This is an ASCII

file with a given format and is saved as a .txt file. The first rule contains the number of points,

the other rules contain following data with each time a space between them:

• Point number

• X coordinate

• Y coordinate

• Z coordinate

The X and Y coordinates should be in the same unit (for instance in meters in this case).

Virtuozo calculates with a flying direction of left to right while the ASTER images are made

in a flying direction from north to south. Therefore the X and Y coordinates should be

swapped and the new X coordinate becomes negative. This procedure actually follows from

the procedure explained in the former paragraph.

For instance, if a point has the coordinates X= a Y= b then the new coordinates which have to

be put in this control point file are X= -b Y= a. In total 38 points of the canevas were found to


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be situated on the satellite image but only ten of them could be used (this is explained

further).


Figure 36: setup window for the ground control points in Virtuozo. Source: own research in Virtuozo.

8.4.1.5 Creating a model

File > open model

Setup > model

In this way a stereomodel is created, consisting of two images, in this case the VNIR1 nadir

and backward images. The different items in the dialog box are:

• Model directory: the directory in which the model data will be saved

• Left image: the left image of the stereo pair

• Right image: the right image of the stereo pair

• Temporary data: the folder in which temporary data will be saved

• Product data: the folder in which the ‘products’ will be saved

• Patch width/height: the number of pixels in each row/column of the epipolar images

on which the epipolar resampling will take place.

• Column space and Row space: they determine the distance between match points.

Match points are the centre of a match window to which the value for the x-parallax is

attributed. These match windows are a kind of calculating units.


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• Image overlap: the overlapping of both images. This was estimated at 90%.


Figure 37: setup of a model in Virtuozo. Source: own research in Virtuozo.

8.4.2

8.4.2.1 Relative orientation

Production of the stereopair

Process > model orientation > relative

In the relative orientation the two pictures are put in their original recording position. In the

relative orientation it is possible to let the program automatically orientate the images. The

program is doing this by searching the same point on both images (homologous points), by

looking to the pixel-patterns (colour and tonality recognition). If this process is repeated some

times the program finds more points. In this way the program found 434 points on the selected

satellite images, with a RMS6

6 RMS: Root Mean Square error

of 0.0050.


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All points were manually checked because these points are not always correct (for instance

when a pattern is repetitively appearing). 24 points were deleted and after this the RMS was

0.0049. The errors occurring are of two categories:

• Points situated on or near a cloud. These clouds can have moved, but in some cases it

is also possible that the cloud shows low variation in the pixel values and that the

program can’t recognise the pixels.

• Points situated close to the border of the image.

Besides the automatic procedure it is also possible in this interface to place points manually.

This was performed for the pass points, in combination with the use of the absolute

orientation interface, and after the absolute orientation was performed.

In the relative orientation view the area for which a DEM will be produced can be selected.

This can be done via right mouse click>define area or via right mouse click>select maximal

area.

(Van Coillie 2003, Virtuozo Help)

Figure 38: relative orientation window in Virtuozo. Source: own research in Virtuozo.


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8.4.2.2 Absolute orientation

epipolar images normal epipolar

right mouse click stereo

In this interface we can see the image already in a three dimensional view, by using a special

pair of glasses. To make a real absolute orientation in which the images are put in a world

coordinate system, the pass points have to be inserted. In this view a number of pass points

were inserted. These points are the points from the canevas and were searched on the satellite

image. When putting such a point the program asks for a number that it should assign to the

point, and this number has to be the same as the number that was used in the pass points file.

For the process of inserting the pass points, both the relative and absolute orientation views

were used.

The pass points which are inserted occur as a yellow cross in this relative orientation view,

while the automatically generated points occur as red crosses. As soon as three pass points are

indicated blue circles appear at the places the program expects other pass points to be situated.

This can be a help in finding the location of pass points. For slight changes of the position of

the pass points, the relative orientation window appeared to be easier working. In this

orientation the errors in X, Y and Z are visible for each points and the error in X and Y

(combined) and Z for all points together. The absolute orientation view was used to see the

relief and thus allocate the highest points.



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Figure 39: absolute orientation window in Virtuozo. Source: own research in Virtuozo.

8.4.2.3 Absolute orientation: finding the pass points and occurring problems

The used pass points are the geodetic points occurring in the canevas. These points had to be

allocated on the satellite images as precise as possible. Knowing that one pixel on the satellite

image is 15 meters, a precision of 7,5 meters should be obtained. This means that the position

is situated within half a pixel of its real position.

Such a precise orientation is in theory possible from the pass point coordinates, as these

coordinates are given in centimetres. But a mean problem was determining the exact

allocation of the different points. First off all the points are not all given in the same

coordinate system (see paragraph 8.2). A choice had to be made between the points given in

the Fuseau 14 ad Fuseau 16 coordinates. The points given in Fuseau 14 were chosen because:

• On the satellite images there are more points allocated given in Fuseau 14 than in

Fuseau 16

• The points given in Fuseau 14 were situated more in the centre of the image than these

given in Fuseau 16


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A second problem concerning the control points was the localisation of these points on the

satellite images. Some points were not indicated on the photomosaics that were used, other

points were covered by a cloud on the satellite images. An exact location (on pixel-scale) was

very difficult because of the quality and age of the photomosaics. For instance at places were

the forest pattern was changed over time or at places were no clear pattern was encountered

for the localisation of the point, there were much difficulties in the localisation of the points.

After all, 13 points could be located with a precision of some pixels.

Using these 13 points, the combined x/y error in the absolute orientation was very high (more

than 300 meter). After some attempts to lower the error on each individual control point by

slightly moving the point (a few pixels), it appeared that for three of these points the error

could not be lowered enough (under 100 meter) unless the point was placed in a position

which was markedly different from the position indicated on the mosaics! From these

observations it was concluded that not all points were exactly indicated on the photomosaics

and it can be questioned if it is desirable to use such photomosaics in further studies!

However, after deleting the 3 points with an uncorrectable error, the error was much smaller

(about 40 meter) and by moving the points a little (the points can be moved over a distance of

1/5 pixel or more) the error could be lowered to 3,0036 meter.

8.4.3

8.4.3.1 Image matching & editing the match

Production of the DEM

Process Epipolar resampling

In this way two epipolar images are produced.

Process Prepare for match

In the prepare for match interface lines or polygons can be created that have to get the same

height value. This interface didn’t seem useful for this work and was not used.

ProcessImage matching

Process match edit

In this way the image match is done and this match can be edited. This match edit is only

performed for the selected area. In the match edit interface the contourlines can be displayed

(and the contour interval can be set). You get a three dimensional view of the selected area.


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The height model can be edited in several ways. First a number of pixels have to be selected

and then a smoothing can be performed. There is a choice between soft, intermediate and hard

smoothing. Besides the smoothing there is also a possibility to give the selected pixels a

constant (height) value or the mean also for these pixels. For each match point (peg) a certain

value of correctness can be displayed by means of the colour it gets (green - yellow – red).

Each of these match points can be edited individually.

The image match can be used to solve errors the program makes. The following problems are

mentioned by Van Coillie (2003) and were also encountered in this study:

• Clouds: the clouds form the major problem. Not only do they cover a part of the

surface and they have a height on their own, but their displayed height is also

influenced by the displacement of the cloud in the time between the recording of the

nadir and backward images. This displacement of the clouds means also a

displacement of their shadows and also these shadows have wrong values.

• Rivers: the rivers are very dark on the images. The program can’t recognise

homologous points here because there is almost no colour difference between the

different pixels on these surfaces. This has as a result that the rivers are displayed as

big pits. This problem only occurs where the rivers are broad, and this applies only to

the Congo rivers and some places on the Inkisi river.

• Other parts of the image with very dark colours have the same problem as the rivers.

Probably most of these very dark places are burned areas. Maybe some of the errors

on the cloud shadows are also due to this and not only to the displacement of the

clouds.



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Figure 40: match edit window in Virtuozo. Source: own research in Virtuozo.

8.4.3.2 Creating a DEM

Product create DEM

Setup DEMs

For the DEM a spacing in X and Y can be chosen in he set-up. A value of 15 was chosen, as

this was also the original pixelscale of the image. First a DTM is created and from this DTM a

DEM with a regular grid is created.

(Van Coillie 2003, Virtuozo Help)


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Figure 41: DEM setup window in Virtuozo. Source: own research in Virtuozo.

8.4.4

Table 13: overview of the exported filetypes from Virtuozo. Adapted from Van Coillie 2003.

Overview of the exported filetypes from Virtuozo

Product VirtuozoNT 3.5.1 format Exported format

DEM .dem • .dxf

• ASCII textfile

Orthophoto .orl: orthophoto left

.orr: orthophoto right

Image file (TGA, BMP, Sun Raster, SGI RGB, JPEG, TIFF,

GeoTIFF)

Contours map .cnt • .dxf

• image format(TGA, BMP, Sun Raster, SGI RGB,

JPEG, TIFF, GeoTIFF)


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8.5 Problems encountered in Virtuozo

8.5.1

To the Y-coordinates mentioned in the tables of the canevas 9 000 000 has to be added to get

the real coordinates, as described in the canevas. But when working with these large

coordinates the software package Virtuozo frequently crashes and was not able to produce a

DEM. The solution to this problem was easily found:

Problems with the coordinates

• In virtuozo the ‘short Y-coordinates’ were used, thus without he 9 000 000 added to it.

• The exported DEM exists of a table (see further) and in Arcview a new column was

created in this column. This column got the value of the Y calculated in Virtuozo and

to this added 9 000 000. The new column was afterward used as the Y coordinate.

8.5.2

Serious problems were encountered during the match edit. The main problem was

encountered when closing the match edit interface after saving the edits. When the match edit

window was opened again the areas that were previously edited seemed to have changed (in

other words: their height values are changed) during the saving or closing of the match edit

window. After some try outs it appeared that this problem is related to the use of the parallax

by the program and not the height when working in this match edit interface.

Problems during match edit

Before editing all the pegs have a parallax with an accurateness of two decimals. But once a

region is edited and afterwards the image is saved, the parallax of the edited pegs is saved

without decimals. During the saving the program appears to round the parallax value of, and

this rounding off always happens downwards. This means that if the peg has a parallax of

16,01 it is rounded off towards 16,00, but also a peg with a value of 16,99 is rounded off to

16,00.

As the parallaxes exist of rather low values (mainly values between 10 and 20) a rounding off

at this scale has large influences and all edited regions appear as plains with the same

parallax. In this way the match editing process was not possible. As no solution was found for

this problem, the match editing process was not used, and this means that the resulting DEM

will still contain large errors, mainly around rivers and clouds.


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8.5.3

In the match edit interface it appeared that the large cloud covering the southern part of the

satellite image has main influences on the DEM, not alone at the place of this cloud but also

around this cloud. In order to avoid this problem two solutions were tested:

Problem with the large cloud

• First two separate DEM’s were produced. One DEM is situated north of the cloud the

other north and east of the cloud. Both DEM’s overlap for a major extent. To make a

final evaluation of the two DEM’s, the difference between them in the overlapping

part can be calculated. This was done in Arcview. Therefore first the two DEM’s were

imported in Arcview (this procedure is explained further) and there they were

compared via analysis>map calculator. The resulting map shows some regions with a

big difference between the two DEM’s, coincidence with the clouds. The main part of

the overlapping areas shows a low difference between the two DEM’s, mostly

between +15 and -15 meters or smaller. If these two DEM’s would be merged

together, there would arise problems at the borders of the overlapping zone if for

instance contour lines would be produced. Therefore it was concluded that producing

two DEM’s was not preferable.

• Eventually the DEM was created of an area that doesn’t contain the large cloud. This

area was kept as large as possible.


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8.6 Converting the DEM to a grid The DEM’s produced in Virtuozo are not directly usable in other software packages. In order

to use them some steps have to be performed:

• First the DEM has to be exported out of Virtuozo. The exported file is a .txt file made

up of the X, Y and Z values as obtained in Virtuozo. These values are separated by a

non-constant number of spaces. Because this number of spaces is not constant this text

file can not be used in other programs and has to be converted.

• The spaces were converted to two times the “ symbol with the software utility

pointextractor.exe. This utility creates a .csv file (congo.csv) thus existing of a number

of lines containing e.g. following information: “-9436350””594030””618.7”,

representing the X, Y and Z values as calculated in virtuozo.

• This .csv file was imported in Microsoft Access. In the importing process the delimited

method was used, using “ as the delimiter. In this way Access recognizes 6 fields: 3

empty ones in-between the “ symbols and the X,Y and Z fields. It was chosen in the

importing wizard not to import the empty fields and to give the other fields the names

old_X, old_Y and Z. After having imported this file, it was again exported, this time

as a .txt file in which the columns are separated by tabs.

• This .txt file was imported in Arcview GIS 3.2 as a table. After this it was added to a

view with the command view>add event theme in which old_x and old_y were chosen

as the X and Y columns. This event theme was converted to a shapefile

congo_imported.shp (theme>convert to shapefile).

• After this the attribute table of this shapefile was opened in Arcview. To edit this table

the command table>start editing was chosen. In this table two new fields were added:

X and Y_temp. These fields are of the type number with a length of 7 because the

coordinates can have 7 digits. Via the calculator these fields got their values:

o The X field got the value of the old Y field (with the formula [X]=[old_Y]).

o The Y_temp field got the positive value of the old X field with the formula

[Y]=0-[old_X].

o This Y_temp field was again used to calculate the real Y value by:

[Y]=9000000+[Y_temp]

After this the old_X, old_Y and Y_temp fields were deleted to save disk space

(knowing that each column contains over 5 million records!).


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• To avoid problems with the recognition of the X and Y coordinates, the attributes table

was again exported as a .txt database (congo_newcoord.txt) and afterwards as an event

theme imported in a view and then converted to a grid (congo.grd). As properties were

chosen:

o A cell grid size of 15 meter

o An output grid extent ‘same as congo_newcoord.txt’

o The number of rows and columns was kept as the computer automatically

calculated after doing the previous two steps!

In the next dialog box ‘Z’ was chosen as the field for cell values.

• The txt database was also converted to a dbf database.

To import the DEM in Ilwis, the software package that was used for classification (see

further) the following steps were performed:

• The dbf database containing the points was imported in virtuozo as a table (via import

table).

• This table was converted to a point file, using the command table to point. As X and Y

column the X and Y column have to be selected. For the domain the option use

column of table has to be chosen and the column Z selected.

• This point file can be converted to a raster in Ilwis, using the command point to raster.

When the point size is set to 15 meters (the DEM spacing chosen in Virtuozo) has to

be selected.

The DEM can be easily adapted in Ilwis with map calculator:

• The clouds and their shadows were previously digitised. With the map containing

these clouds a map calculator operation was performed that turns the value of the

DEM to ? for the areas covered by the clouds

• The river was classified (see further) and from this classification map a new map can

be made containing the value 1 for the pixels representing the river and 0 for the other

pixels. From this map the DEM can be adapted: all pixels which are classified as river

get a ? and the other pixels keep their value.

The resultant map is displayed as Map 1 in the annex.


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8.7 Production of an orthophoto

8.7.1

ProductsOrthorectify

Production of the orthophoto

In this process a picture with an orthogonal projection is created. All errors occurring due to

the height are also corrected. As this orthophoto is metrical correct it can be used as a kind of

map, and it will be used further as a georeferencing base.

Within the setup window for the orthophoto several parameters have to be filled in:

• Orthoimage GSD/output scale

• Used image (left or right)

• Resampling method

(Van Coillie 2003 and Virtuozo Help)

Figure 42: setup window of the orthoimage. Source: own research in Virtuozo.


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8.7.2

The main problem occurring before the orthophoto can used is that it is till turned 270° (see

before). This turning can easily be done in any image-viewing software like in windows the

utility image viewer. But also the coordinates have to be turned. These coordinates are saved

in a .TFW file, which contains the coordinates for the upper left corner pixel (the centre of

this pixel) and the scale of one pixel. In this case the X and Y coordinate are -

9486690.000000, 627705.000000.

Preparing the orthophoto for use

When turning the image -270° (or 90°) the coordinates also have to be changed, in the

opposite way of the change that was performed. The old upper left corner now becomes the

new upper right corner with coordinates 627705.000000, 486690.000000. The new upper left

corner, this is the corner for which the coordinates have to be saved in the .TFW file, has the

same Y coordinate as this new upper right corner, thus 486690.000000. The X coordinate can

be calculated: it is the X of the new upper right corner minus [(the number of pixels in the X

direction minus 1 ) times the size of one pixel]. We take the number of pixels in the X

direction minus 1 because it are the coordinates of the centres of the pixels that are used. In

this case we get : 627705.000000 – [(2872-1)x15]= 584640

Figure 43: turning the orthophoto and changing its coordinates. Source: own research.


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The orthophoto has still the old Y-coordinates, without he 9 000 000 added (see before). This

can easily be corrected in the .TFW file by adding 9 000 000 to the Y coordinate.

The orthophoto can be found as Map 2 in the annex.

8.7.3

Products > create contours

Contourmap

Setup > contours

View > contour image

The contourmap shows lines that represent an equal height. This map is based on the DEM.

Several parameters can be chosen in the setup interface:

• Contour interval

• Index interval

• Line width

• Label height

• Label interval

The contourmap is displayed as Map 3 in annex. This map is not valuable as it still is based

on the uncorrected DEM. An overlay of the contourlines over the orthophoto can be found in

Map 4 in the annex.


8.7.4

View > drape (mono)

Drape

View > drape (stereo)

With these commands a visualisation can be made of the orthophoto draped on the DEM. By

choosing the function save capture two separate images are saved as .TIF images: a left image

and a right image. If a colour composite would be produced using the left image for the green

band and the right image for the red band (and an image containing only the value 0 for the

blue band), this colour composite can be seen as a three-dimensional view (“anaglyph-

image”) using a special pare of glasses.



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Figure 44: anaglyph image of the study area. To see this image in 3D a special pair of glasses has to be

used (with one red and one green eye). Source: own research in Ilwis and Virtuozo.

Figure 45: anaglyph image of the study area. To see this image in 3D a special pair of glasses has to be

used (with one red and one green eye). Source: own research in Ilwis and Virtuozo.


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8.8 Quality report The complete quality report is added as an appendix on a CD-ROM.

Absolute orientation information:

Left SPOT-Image parameters:

-0.06727032 0.01028816 -0.30917737

-0.00999409 -0.06659808 -0.36070636

-0.00000012 -0.00000026 0.00021807

Right SPOT-Image parameters:

-0.06766521 0.01038791 -0.26381150

-0.00986016 -0.06696796 -0.38084295

-0.00000015 -0.00000027 0.00020972

Residual: point NO. dX dY dZ

5234 1.367 -2.724 10.198

5214 -2.577 -0.240 -0.017

5107 2.335 2.170 -0.306

5226 -1.351 0.012 -2.864

5225 -2.199 -0.644 -1.554

5215 3.767 -3.244 3.543

5376 -1.495 -0.764 0.064

5223 -1.289 1.889 4.415

5245 0.329 3.839 -11.090

5213 -1.926 2.367 -2.769

RMS: mx= 2.062122 my= 2.182133

mxy= 3.002341 mz= 5.267048


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8.9 Discussion During the production of this DEM many difficulties were encountered making this process

very labour and time intensive. Despite this problems the produced DEM seems of rather

good quality, especially when it is corrected in Ilwis for the rivers and clouds. The calculated

errors are 2.062122 meters in X, 2.182133 meters in Y (resulting in a combined error of

3.002341 in X and Y) and 5.267048 meters in Z. These errors are however calculated on the

pass points. The question is if the DEM is in reality as good as the quality report suggests. On

this item the following questions can raise:

• How is it possible that some points seemed to have a bad location on the photomosaic

and were not used for the production of the DEM? If these points have such a large

error, is the photomosaic a reliably source for selecting these points?

• All points are situated at the top of the hills. Has this any influence on the DEM and

more in particular on the lower situated regions (valleys)?

• The height of the pass points varies between 513 and 602 meters, but most points are

even in a smaller range (513 to 539 meter). Has this any influence on the quality of the

DEM and the calculated error on the DEM? Is the error which can be expected for

points that are situated much lower (like along the Congo River) not much larger?

Actually most of these questions could be answered if a good evaluation of the DEM was

possible. In order to make a good evaluation of these problems a set of control point should be

made. By checking the height of these points on the DEM with their real height an idea could

be formed about the accuracy of this DEM.

One of the difficulties in creating a DEM was also to find the exact allocation of the geodetic

points on the satellite image. To solve this problem several possibilities can be presented:

• After obtaining a satellite image, the exact location of the geodetic points is searched

in the field and indicated on the satellite image (also in the field). This implies that the

stone indicating the geodetic point is still present! It also implies that finding the point

in the field would make it easier to find the point on the satellite image, which is not

entirely true.

• After obtaining the satellite image, some easy recognisable points are selected and

their coordinates are measured in the field (for instance with a GPS). With these points

the image can be georeferenced. After this the exact locations of the geodetic points

can be found on the georeferenced image.


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• An ideal situation would exist in selecting (easily recognisable) points on the satellite

image and measuring the coordinates and height of these points in the field. This

however implies a topographic mission as with a GPS no accurate height measurement

can be made.

• Another possibility, for instance applied by Van Coillie (2003) and Bossyns (2004) is

making a relative height model. The height differences are calculated using a

stereoscope and a relative local height model is made in this way. This implies that the

resulting model has no absolute height values, but this model would remain very

usable to calculate things like aspect and slope! This method implies also a good

stereoscopic vision, as my personal experience learns that working with a stereoscope

is very difficult for some people.

All the proposed solutions require a field study, and this of course requires the availability of

necessary funds and the accessibility of the terrain.

Besides this considerations about the quality of the DEM and about the quality check of the

DEM, there are also some considerations to be made about the possibilities to create a DEM.

One of the considerations is the presence of forests inside the valleys and the absence of

forests outside the valleys. The obtained DEM is in fact not a DEM in the strict meaning, as it

displays the height of the surface, thus including the vegetation. It is actually a DSM (digital

surface model). In this case with the forest covering the valleys, the valleys will be less deep

on the model than in reality. There are no data available of the vegetation height in the

valleys, or the vegetation height in general, but the book included with the vegetation map

mentions a height of the tree layer of 40 meters for some forest types (the evergreen dense

forest). But even a height of the tree layer of 10 meters or more would have a large influence

on the results. Were some valleys have a depth of 30 meters in the model, their real depth can

be twice as much! If good information would be available about the height of the tree layer,

some corrections to the DEM could be made using the vegetation classification produced in

this study.

The clouds and the rivers give also big problems for the creation of the DEM. The rivers

because the program has problems in recognising points in these zones. With the clouds and

the shadows the same problems appear but also some other problems: the clouds themselves

have some height above the surface, but they can also move and this can influence the

parallax. Therefore it can be advised for further studies to select images without any clouds.


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Of course in a region like Bas-Congo it is already difficult to find an image with a low cloud

cover like the images used. Selecting an image without the river is of course not possible if a

DEM around this river is needed.


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9 Digital soil map of the region

9.1 Introduction The digital soil map of the region was produced at the University of Kinshasa. It was set to

my deposal by Prof. Dr. Geert Baert. This soil map is based on the previously mentioned soil

map (see paragraph 5.7). The soil map was delivered in the Arcview shapefile (.shp)

dataformat. For each sheet one file was produced. For the study area the following sheets

apply: Kinshasa (NE), Inkisi (SE), Mbanza Ngungu (SW) and Luozi (NW).

9.2 Correcting the map As the digitised soil map contained a lot of errors these errors were corrected in Arcview. For

this correction the original maps (in black and white) available at the geology and soil science

department of the University of Ghent was used.

The errors are mainly of two kinds:

• The polygons are not at their correct position. In this way polygons don’t interconnect

between the maps at the borders of the maps. Therefore the polygons were corrected in

such a way that they interconnect at the borders.

• The attribute or the class assigned to the polygons are often incorrect. Therefore this

class was for each polygon checked and if necessary corrected.

In order to have one map the four maps were merged using the geoprocessing wizard.


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9.3 Units of the soil map The units present on the soil map are not the series which are treated in paragraph 5.7 but as

mentioned before catenas and complexes are mapped. The following units appear on the map

(with in bold the units which apply for the study area)7

Cartographic units on micaschist

:

• M: KvMi.Q’(4E

Cartographic units on basic rocks:

) : complex of soils of the series Kinza Vuete and Minkelo, mostly

covered with a quartz pavement. Locally soils of the series Bata Siala.

• B3: Ts.l(4E): dominance of soils of the series Isangila in the more dissected regions

with rounded cols. Locally soils of the Tshimpi series.

• B4-1: TsYe.Q’(2-3E): complex of soils of the series Tschimpi and Yelala and alluvial

soils of the series Shiloango and Lemba. Rather much outcrops.

Cartographic units on a complex of basic rocks and till and schist:

• BS1: GiSo.(0-1): complex of soils derived of basic rocks (series Gimbi, locally

Isangila) and soils derived from schist containing till and schist (series Songololo,

locally Kwilu Ngongo). Deep soils of laterite plateaus.

• BS2: IsKn.q(4-4’E) complex of soils derived from basic rocks (series Isangila, locally

Ngandia Sundi and Tschimpi) and soils derived from schist containing till and schist

(dominance of the series Kwilu Ngongo, locally series Mawinza and Ntadi).

Cartographic units on hard rocks with quartz dominance:

• Q2-1: ZoVu.q(4’’E): dominance of soils of the series Zongo and Vunda, loally soils of

the series Lunga Vasa. Quartzite outcrops.

• Q2-3: ZoVu.l(3-4*): association of the series Zongo on slopes and the series Vunda on

flattened summits. Lateritic stone-line.

• Q3-1: ZoTe.q(4’’E): dominance of soils of the series Zongo and Teva, in highly

dissected landscapes (crests). On highly eroded slopes soils of the series Lunga vasa

(5%). Rather much outcrops.

7 First the symbol of the map unit is given. After this a symbol occurs which expresses both the series as the

phase properties. More information about this code can be found in paragraph 5.7 and in the explanatory texts of

the soil maps.


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• Q3-3: ZoTe.l(3-4*E): association of soils of the series Zongo on slopes and Teva on

flat tops. Lateritic stone-line. At some places a very sandy soil of the series Mpese.

• Q3-4: Zo.l(4E): dominance of the series Zongo in abrupt landscapes. In highly eroded

zones (ravines) soils of the series Ngidinga and Lunga Vasa are present.

• Q4-1: LvNgLu.Q’Zo.q(4E): mainly soils of the series Lunga Vasa and Ngiding with a

quartz pavement at the surface. Soils of the series Zongo in the little eroded places.

Rather much outcrops (series Lufu).

• Q4-2: LvNgLu.Q’Zo.Q(2-4E): like the proceeding unit (Q4-1) but a vary variable in

general less dissected landscape. A lot of grind at the surface.

Cartographic units on schist:

• S1: So.(0-2*): dominantly deep soils from the series Songololo on tarerite plateaus.

Locally soils of the series Kwilu Ngongo on the slopes.

• S2 : KnMaGs.l(4’E) : association of soils of the series Kwilu Ngongo on eroded

slopes, and of the series Mawinzi and Gombe Sud in highly degraded parts.

• S2-1: MaGsKn.l(4’-5E): like the precedent unit (S2) but a more dissected landscape

(crests and mountains).

• S2-2: MaGs.l(5E): dominance of the soils of the series Mawinzi and Gombe Sud,

very locally the series Kwilu Ngingo. Soils of cliffs (Bangu).

• S3-1: KnMa.l(3-4E): dominance of the soils of the series Kwilu Ngongo, at places

associated with soils of the series Mawinza. Locally soils derived from limestone

(series Yanga and Kiazi Col).

• S3-2: KnMa.L(3-4E): the same as the former unit but the lateritic stone-line mostly at

less than 120 cm depth.

• S4-1: KnSo.l(2-3*E): association of soils of the series Kwilu Ngongo on the sloped

and the series Songolo on the flattened summits.

• S4-2: KnSo.L(2-3*E): like the precedent unit (S4-1) but with a lateritic stone-line

within a depth of 120 cm.

• S5-1: KnNt.l(4’E): complex of the series Kwilu Ngongo and Ntadi, the latter one on

slightly sandy schist.

• S5-2: KnNt.L(4’E): same as the former unit but locally soils of the series Mawinzo on

highly eroded slopes.

Cartographic units on limestone :


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• C1: Ya.q(4’E): dominance of soils of the series Yanga, locally soils of the series Kiazi

Col on eroded slopes. Sometimes soils of the series Songololo on flattened summits.

Stone-line of chert. Some limestone outcrops.

• C2: YaKc.Q(4’E): association of soils of the series Yanga and Kiazi Col, the last one

at more eroded places. Stone-line of chert at low depths and often at the surface.

Rather much limestone outcrops.

• C2-1 : YaKc.Q(4’-5E

• C3: KcSa.Q’(4E)YaLo.q(0-2e): association of soils of the series Kiazi Col and

Sansikwa, in a highly eroded landscape with a lot of limestone outcrops. Stone-line

with chert mostly at the surface. Between the outcrops a dominance of the series

Yanga and Lovo (dolines) with a chert containing stone-line at a variable depth.

) : association of the soils of the series Yanga and Kiazi Col, the

last ones at more eroded places. Stone-line of chert on shallow depths and often at the

surface. Rather much outcrops of limestone.

Cartographic units on a complex of schist and limestone:

• SC1-1: SoKnYa.l(2-3*E): complex of soils derived from calcareous rocks and schist

(series Songololo, Kwilu Ngongo and Yanga). Stone-line with a lateritic dominance

but highly mixed with fragments of chert on a limestone underground.

• SC1-2: SoKnYa.L(2-3*E): same complex as the preceding unit but with a shallow

stone-line, often at the surface.

• SC2-1: SoKnYa.l(0-2e): like SC1-1 but with less steep slopes. Locally hydromorph

soils of the series Lovo (dolines). Several alluvial valleys with very variable soil, with

a dominance of the series Mvuazi and Shiloago. Sometimes organic soils (series

Mbola).

• SC2-2: SoKnYa.L(0-2*e): like unit SC2-1 but with a shallow stone-line that is even

often situated at the surface.

• SC3: KnMaYaKc.l(3-4E): complex of soils derived from schist (series Kwilu

Ngongo, lacally Mawinza) and soils on limestone (series Yanga and Kiazi Col).

Stone-line with a dominance of laterite, highly mixed with chert, on limestone.

Cartographic units on a complex of hard rocks with a dominance of quartz and schist:

• QS2: NtZo.l(4): soils complex with a dominance of soils derived of sandy schist

(series Ntadi and Zamba) and soils derived from arkose (series Zongo). Lateritic

stone-line.


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• QS3: KnNtZo.L(4): complex of soils with a dominance of soils derived from schist

(series Kwilu Ngongo, locally Mawinzi) and from sandy schist (series Ntadi). Less

important are soils derived from arkose (series Zongo). Lateritic stone-line.

• QS3-1: KnMaZoLv.L&Q(4E’): like the former unit but with a heavy erosion. A lot of

ravines.

• QS4: NtZo.l(3-4*E): complex of soil of the series Ntadi and Zongo, in a broken to

abrupt landscape with flattened summits. On these summits soils of the series Zanba

and Vunda.

Cartographic units on soft rocks with quartz dominance:

• K1-1: Mp.(0-1): almost only deep soils of the series Mpese, locally also the series

Phonzo. Plateau soils.

• K1-2: Mp.(4-4’E): like the previous unit (K1-1) but a highly dissected landscape. On

the slopes a lot of erosion cirques.

• K1-3: Mp.(4*): almost only soils of the Mpese series on high cols with flat summits

and often very large.

• K2-1: MpPh.(0-2e): dominance of the Mpese series, associated with the Phonzo series.

Soils of plateaus.

• K2-2: MpIm.(0-1): dominance of soil of the series Mpese ad soils of the series Impete,

rarely soils of the series Lingesi. Plateau soils.

• K3-1: PhMp.(0-1): mainly soils of the Phonzo series and less important the Mpese

series. Soils of plateaus.

• K3-2: PhMpLiIm.(0-1): dominance of the soils of the Phonzo series, locally of the

Mpese series (10%). Soils of the Lingesi and Impete series in closed depressions with

an organic bottom (series Bateke).

• K3-3: PhMpBu.(4’*): complex of soils of the series Phonzo and Mpese. Locally soils

of the series Buense on reworked coversand. Hills with a large flat surface.

• K4-1: Kw.(2-3E): almost uniquely red soils of the series Kwango, locally the series

Mpese (mostly on the flattest positions).

• K4-2: Kw(3-4E): like the former series (K4-1) but with a more broken relief.

• K5-2: BuMp.(3-4*): complex of soils of the series Buense and Mpese in an broken

and abrupt landscape. Locally soils of the series Phonzo.

Cartographic units on a complex of hard rocks and soft sediments with a quartz

dominance:


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• QK1: NgLv.Q’MpBu.(3-4E): complex of soils derived from soft sandstone and soils

developed in coversand.

• QK2: MpPh+NgLv.Q’(3-4E): complex of the soils of the series Mpese and Phonzo in

the upper parts of the landscape and of the Ngidinga and Lunga Vasa series in the

lower parts. The latter ones are often masked or reworked by sands.

Cartographic units on alluvia and colluvia:

• A2: MwMb.(0): complex of organic soils of the series Mbolo and hydromorph mineral

soils of the series Mwana. Sometimes soils of the series Bu Bateke.

• A3: NdMwFu.(0-1): complex of the soils of the series Ndjili, Mwana and Fuma. Very

locally soils of the series Mbola and Ba Bateke in the lower parts.

• A6: KiLtMv.q(0): complex of the soils of the series Kitobola, Lufu Toto and Mvuazi

with a stone-line existing of quartz at a variable depth. Locally soils of the series

Shiloango and the valley bottoms.

• A6-1: LtKiMv.q(0-2): dominance of soils of old terraces of the series Lufu Toto. Less

important are soils of the series Kitobola and Mvuazi. Locally soils of the series

Shiloango in the valley bottoms.

• A7: KuLe.q(0-2): complex of colluvial soils of the series Kundi on the lower slopes

and soils of the series Shiloango and Lemba in the hydromorph valley bottoms. Often

a stone-line from quartz at a shallow depth.

Cartographic units on very complex and reworked soft rocks

• Z: BuPhMp+Zo.qKn.l(3-4*E): complex of soils in the transitional zone between

arkoses and schist and strongly reworked sandy cover deposits. Soils of the series

Buense, Phonzo and Mpese (reworked sands), Zongo (arkose) and Kwilu Ngongo

(schist).

• Z1: BuPhMp+Zo.qKn.l(2-3*E): like the former unit but a less broken landscape.

Cartographic units on very complex and highly reworked soft rocks

• Y: LtKiYa.qSoKn.l(0-2*e): complex of soils derived from alluviaum and schist or

limestone (pediments) : dominance of soils of the series Lufu Toto, Kitobola,

Songololo, Kwilu Ngongo and Yanga. Flat landscape with valleys. Stone-line from

quartz or lateritic on variable depth.


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10 Classification

10.1 Introduction The main objectives of this chapter is to check which soil related parameters can be classified

using the given dataset. As the satellite images show the reflectance of the surface, it can be

expected that they mostly show the reflectance of the vegetation. Therefore it was decided to

attempt first to classify the vegetation. As the texture and/or parent material can possibly have

some influence on the thermal properties of the soil and thus some influence on the spectral

reflectance in the TIR bands, these items were tried to classify next.

10.2 Importing the data in Ilwis

10.2.1

10.2.1.1 Importing the soilmap

Importing the soilmap and DEM

The soilmap existing of an Arcview shapefile, was imported with the command import via

geogateway. The resulting map however has a legend with the different polygon-numbers and

not with the soil type or parent material. This can be solved by transforming the map towards

a new map via vector operations > attribute map. As attribute table ‘parent material’ has to

be chosen.

10.2.1.2 Importing the DEM

The importation of the DEM was described in paragraph 8.6.

10.2.2

10.2.2.1 The orthophoto

Preparing the satellite images in ILWIS

To be able to use all imported bands it was necessary to georeference them. The best source

available was the orthophoto produced with Virtuozo. This orthophoto has coordinates based

on the Fuseau 14 local system.

First a coordinate system was created in Ilwis with the name Fuseau 14. For the several

parameters the parameters mentioned in the canevas were used. These are:

• Projection: Transverse Mercator


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• Ellipsoid: clarck 1880

• Central meredian: 14° E

• Central Parallel: 0° N

• False Easting: 500000

• False northing: 10 000 000

• Min X,Y: 400 000, 9 000 000

• Max X,Y : 800 000, 10 000 000

After this the orthophoto congo.tif was imported as a map using geogateway. In this way the

georeference was kept during the import process. Within the created ‘object collection’ a

‘Georeference corners’ file can be found. Within the properties of this file the coordinate

system should be set to Fuseau 14.

10.2.2.2 The VNIR bands

As the VNIR bands are already in the 1 byte format they were just imported in Ilwis via

map>import. The bands were grouped in maplists: a maplist for the VNIR bands, one for the

TIR bands and one for the SWIR bands.

After this the VNIR maplist was georeferenced using the tiepoints method. As the coordinate

system Fuseau 14 was selected. The corresponding tiepoints were indicated on the orthophoto

to find their coordinates. A total of 24 tiepoints were used. Applying the affine transformation

method a sigma of 1,072 pixels could be achieved.

To get a better visual representation of the several bands a stretching was performed. To

perform the stretching the histogram of the different bands was studied first. From this

histogram it was decided which range was stretched over the new range of 0 to 255. For the

different bands the following original range was stretched over 0 to 255:

• VNIR1: 73 -107

• VNIR2: 42-107

• VNIR3: 37-111

As the stretching method always linear stretching was used, because the stretching is only to

enhance the display of the image.


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10.2.2.3 The SWIR bands

Because the different SWIR detectors are widely spaced (see paragraph 6.1.1.2.2) it was

decided to georeference them separately, despite the labour this costs. The problems which

would be caused by georeferencing them together can be easily understood when displaying

three SWIR bands as a colour composite: at the borders it can be seen clearly that they are not

overlapping 100%! The different SWIR bands were georeferenced with the georeference

tiepoints method and an affine transformation and these georeferences had the following

properties:

Band Number of tiepoints Sigma (in pixels)

SWIR1 14 1,057

SWIR2 13 0,799

SWIR3 15 1,064

SWIR4 14 0,738

SWIR5 13 0,801

SWIR6 13 0,535

The SWIR bands were also stretched. Like with the VNIR bands a linear stretch was used and

the following ranges were stretched to 0-255

• SWIR1: 40-105

• SWIR2: 23-96

• SWIR3: 18-100

• SWIR4: 20-95

• SWIR5: 16-91

• SWIR6: 21-97

10.2.2.4 The TIR bands

In Ilwis the TIR bands were imported via Import map. The resulting maps are of a type with 4

bytes per pixel. To do a proper classification they should use 1 byte per pixel. To do this the

TIR maps are stretched. A linear stretch was used converting the original range of pixels

towards a range from 0 to 255. This was done by image processing stretch. The minimal

and maximal values were taken in such a way that the range of values present in any pixel

was not used. In this way the contrast of the image was intensified and redundant information

was avoided.


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The different bands were stretched in this way:

Band name Original range Stretched to… Resulting image

TIR 1 742-2477 0-255 TIR1_stretched





The TIR images were grouped in a map list before georeferencing. This map list was again

georeferenced using the tiepoints method and with an affine transformation. For a total of 15

tiepoints a sigma of 0,437 pixel was achieved.

10.2.2.5 Making the map lists

To perform raster operations in Ilwis, like classifying, all images should have the same

georeference. But this is not the case here as several georeferences were created: one for the

VNIR bands, one for the TIR bands and a total of 6 for the SWIR bands. To handle this

problem the different maps should be resampled. This can be done by right clicking a map in

Ilwis and choosing Image Processing>Resample. In the next dialog box several parameters

have to be filled in:

• Raster map: the map that has to be resampled.

• Resampling method:

o Nearest neighbour: with this type the value of output pixel becomes the value

of the closest input pixel.

o Bilinear: the value of the output pixel becomes an interpolation of the closest 4

pixels.

o Bicubic: in this method the values of 16 input pixels are interpolated to get the

value of the output pixel.

As resampling method bicubic was chosen. This method is the slowest of the three but

there are fewer discontinuities.

• Output raster map: the name of the rastermap that will be created.

• Georeference: this is the ‘target georeference’.

For this parameter always the VNIR georeference was chosen.

First off all the SWIR bands were resampled. The output raster map was each time named

SWIRx_stretched&resampled (e.g. SWIR1_stretched&resampled). After this a maplist

SWIR_stretched&resampled was created.


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A next step was resampling the TIR_stretched maps. This was done for each map individually

in the same way as the SWIR bands were resampled. After this a maplist TIR_resampled was

created.

After this an overall map list was created (maplist all), containing all resampled VNIR and

TIR bands and also the VNIR bands.

10.2.3

As the ASTER satellite has no blue band it is impossible to make a true colour composite.

Therefore a false colour composite was created. In order to find the band that have the least

correlation a correlation matrix can be calculated in ILWIS.

Making a colour composite

Figure 46 displays the correlation

matrix. In this matrix the values range between –1 and +1. A value of +1 means a high

correlation while –1 means a low correlation. From such a correlation matrix Optimum Index

Factors (OIF) can be calculated. These gives the three maps with the smallest correlation.

This OIF can be visualized by selecting the map list properties and there choosing the tab

additional info. In this map list the maps with the least correlation are vnir2_stretched,

vnir3_stretched and tir5_resampled. With these three bands a false colour composite was

produced (named fcc1), with the bands vnir2_stretched, vnir3_stretched and tir5_resampled

respectively displayed as the red, green and blue colours on the map.

Figure 46: correlation matrix in Ilwis. Source : own research in Ilwis.

A second false colour composite, fcc2, was created with the bands VNIR1, VNIR2 and

VNIR3 (respectively displayed as the red, green and blue colours on the map). This colour

composite was produced because all three these bands have the highest resolution.


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A visual evaluation of both colour composites learns that the second colour composite fcc2

gives the best image and most contrasts. Possibly the bad results of the first composite fcc1,

despite the low calculated correlation, is the very low resolution of the used TIR band. It is

also possible that the low calculated correlation mainly applies on the clouded zone or the

river.

10.3 Unsupervised Vegetation classification The unsupervised classification can be performed in Ilwis with the command cluster. In the

dialog box the following properties can be selected:

• Number of input maps, ranging between 1 and 4

• Number of clusters: this is in fact the number of classes the computer has to try to

make.

There were several clustering attempts performed. First of all a cluster analysis was

performed using the three visual bands (VNIR1, VNIR2 and VNIR3). The number of

vegetation types occurring in the study area found on the vegetation map is 12. Because there

should also be classes for the clouds and the river, a total of 14 clusters were made. A part of

the resulting map cluster1 is shown in Figure 47. A comparison with the existing vegetation

map shows that the result is not satisfactory. Forests appear to be classified in several

categories while the Savannah areas are made up of a mosaic of classes.

A second cluster-operation was performed using the vnir2_stretched, vnir3_stretched and

tir5_resampled as these bands have the lowest correlation (see before). The resulting map

(cluster2) is displayed in Figure 48. In this view the forest and savannah regions are also

displayed as mosaic. Therefore a third cluster operation was performed, this time only using 7

classes. The result is shown in Figure 49. Also this third map is not satisfactory. Although the

overall image is better, it is still too much build up of mosaics.

A last cluster operation was performed using the bands ndvi_stretched, vnir1_stretched and

vnir2_stretched as these bands appear to have the lowest correlation of the maplist all+vnir

(see before). This time 7 clusters were produced. The resulting map cluster4 is displayed in

Figure 50. This map also gives an unsatisfactory image with to many mosaics, although it is

clearly a better map than these of the former cluster operations.


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Figure 47: part of the map produced with the first cluster operation. More information in the text. Source:

own research in Ilwis.

Figure 48: part of the map produced with the second cluster operation. More information in the text.

Source: own research in Ilwis.


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Figure 49: part of the map produced with the third cluster operation. More information in the text.


Figure 50: part of the map produced with the fourth cluster operation. More information in the text.



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10.4 Supervised vegetation classification

10.4.1

In this study also some vegetation indices were calculated in order to get a better

classification. More information about vegetation indices is given in paragraph

Vegetation indices

7.4.4.3. The

chosen vegetation indices should be easy to calculate (their calculation should be possible).

This excludes for instance the WDVI as no information for g is available. The chosen indices

are: NDVI and DVI. It was also tried to use RVI but this was not possible (see further).

In ILWIS the NDVI was calculated using the operation map calculation. The following

formula was used:

(vnir3_stretched-vnir2_stretched) / (vnir2_stretched+vnir3_stretched)

The resulting map (named NDVI) contains values between -0,59 and 0,96. A first examination

learns that:

• A clear forest pattern with high values can be easily recognised

• The Congo river and Inkisi River can be easily recognised by their very low values

• On first sight the big cloud contains values of 0.0 or lower.

• The areas between the forests that are not covered by clouds have values that are

slightly positive or slightly negative.

In order to be able to use the NDVI, the values were stretched over the range 0-255. A linear

stretch was performed.

The DVI was calculated with the simple expression:

(vnir3_stretched- vnir2_stretched)

After calculating the map it was stretched towards values within the range 0-255 with the

linear stretch operation. The map shows comparable patterns as the NDVI map.

The RVI was calculated with:

(vnir3_stretched / vnir2_stretched)

In principle the values can be endless large. To deal with this, the range for the calculated

values was set to 1012 (a larger is not possible in ILWIS). In this way no endless large values

would be possible. After calculating this map it was also stretched towards values between the

range of 0-255. Because the values are not equally distributed but mainly around 0,5 a


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histogram equalisation stretching was used. However, it appears that some zones have a value

of 0 after stretching because their calculated RVI was higher than the range (after calculation

these cells got a value of “?” and after stretching this became 0). Because of the problems this

possibly can cause during classification it was decided not to use the RVI.

The calculated indices maps were added to a new map list, which contains also all the maps

present in the maplist all that was made before and contains all the bands in their stretched

form. On this maplist a correlation matrix was calculated and from this the OIF, in the same

way as this was done before for the maplist all (see paragraph 10.2.3). After this a new

correlation matrix and OIF was performed on this maplist. It appears that now the bands with

the least correlation are vnir1_stretched, vnir2_stretched and NDVI_stretched.

10.4.2

When considering the different vegetation types occurring in the region, one should also

consider whether these types could also be detected with teledetection. To distinguish

vegetation types the reflection in one or more bands should be different. Considering the

different types of vegetation that are distinguished in the area (see paragraph

Vegetation types and selection of training pixels

5.6.2) the

following vegetation types can possibly be detected on the map:

• Forests with different grades of density.

• Savannah with different grades of tree-density and with different grades of naked soil.

• Aquatic vegetation.

Actually a gradual transition between dense forests, savannah with trees and treeless savannah

with bare soil can be expected.

Beside these main vegetation types a classification should also be taken into account with the

following land-use (or land-cover) types:

• Clouds appearing on the image. These clouds can totally mask the reflectance of the

earth surface or only partially mask it (at the edges of the clouds).

• Water (Congo River and smaller rivers).

• Bare soil. This category can however be part of a savannah type with a large grade of

bare soil.

• Possible antropogeneous land-use. It is not clear for which extent there is

antropogeneous land use in the region, mainly because there are no recent maps. The


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antropogeneous land use types that can possibly be detected can vary from villages to

agricultural grounds to roads.

The only available source for the ground truth was the vegetation map of the region. This

source was however quite limited, because of the age of the map (the vegetation could have

changed). Also the scale of the map (1/250 000) causes limitations, because this scale doesn’t

allows a large precision. Furthermore the different classes that appear on the map are difficult

to recognise on the satellite images.

Because of the limited value of the sources for the vegetation, a visual interpretation of the

different classes was necessary. This visual interpretation is based on the recognition of

patterns (for instance gallery forests appearing in the valleys) in combination with the feature

space of the different bands. It was tried to select an equal number of training pixels for each

class.

10.4.2.1 Forests

As can be seen on the vegetation map the main vegetation types occurring in the study area

are forests and savannahs. In order to perform a good classification it was tried to hold the

standard deviation for the different sampling sets as low as possible. Therefore the different

forest and savannah types were arbitrary split in several subtypes and each of these subtypes

was sampled separately.

When sampling the forests a trend can be discovered from forests with a very high NDVI

towards forests with a lower NDVI. The forests with the highest NDVI can be assumed to be

the densest forests. They have the lowest DN values for the red and green bands. The forests

with the lower NDVI values have gradually higher DN values for the red band. For the green

band the DN value is higher than for the less dense forests but this DN value doesn’t

gradually increases with an increasing NDVI. When sampling the forests along the valleys

(gallery forests), on the top of hills (the forests preserved for religious reasons) and the more

dense afforested area in the northeast of the study area, they all seem to have the same DN

values for the different bands.

On the borders of the southern part of the Inkisi River in the study area appears another forest

type according to the vegetation map: marshy forest and periodically inundated forest.

When sampling these forests they have another feature space than the other forests: in the


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NDVI/vnir2 feature space they have a slightly higher NDVI value for the same red value than

the other forests (or with other words: they have a slightly higher red value for the same

NDVI). But the main difference lies in the vnir1/vnir3 feature space: this forest type has

clearly higher values for the green band than the other forests. Inside the aquatic forest types

two subtypes could be distinguished by their difference in NDVI value.

Figure 51: screenshot showing a part of the study area around the Inkisi river. The aquatic forests have a

higher DN value for the vnir1 (green) band than the other forests. Within the aquatic forests two types are

recognised: one with a high density and one with a lower. Source: own research in ILWIS.


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Figure 52: screenshot showing a part of the study area. In the colour composite the red colour represents

the vnir2 band, the green colour the vnir1 band and the blue colour the calculated NDVI values. The

forests in the valleys have the same feature space as the forests in the valleys. Within these forests there is

however still some variation and therefore the forests were split in different classes. By visual

interpretation the afforested savannah can be recognised from the forests. Source: own research in

ILWIS.

A main problem is defining a limit between forests and savannahs (with a lot of trees).

When sampling pixels, most of the time a difference could be made between (afforested)

savannah and forest, with between these two classes some pixels some pixels for which the

class is doubtful (arising from the mixed values from forests and savannahs). But on some

cases there arose some doubt about the class of vast areas on the map, and these areas also

represent a clearly defined area in the different feature spaces. A closer study of these areas

pointed out that they represent a kind of class in between savannah and forest. This class was

called open forest, but could as good be called dense afforested savannah.


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the vnir2 band, the green colour the vnir1 band and the blue colour the calculated NDVI values. This

figure illustrates the appearance of the class “open forest”. In this case the selected pixels represent a less

dense part in the middle of the forest. Source: own research in ILWIS.

In Figure 52 is showed that the forests on top of the hills have the same feature space as the

forests in the valleys. Beside these forests some afforested savannah could be recognised with

a clearly different feature space than the forests. The more yellow-greenish regions on the


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map represent other savannah types, which are discussed further.


the vnir2 band, the green colour the vnir1 band and the blue colour the calculated NDVI values. This

figure illustrates the appearance of the class “open forest”. In this case the selected pixels represent a

denser part in the middle of the afforested savannah. Source: own research in ILWIS.

Figure 53 and Figure 54 show the appearance of some pixels classified as “open forest”. Their

DN values lie in between the values for forests and afforested savannahs in the different

feature spaces. These two figures represent two opposite cases: in Figure 53 there is a spot

with less dense forest in the middle of the forest qualifying for the class “open forest” while in

Figure 54 there is a spot with denser savannah/forest in the middle of the savannah qualifying

for this class. In Figure 54 most pixels in the dark blue spot qualify for one of the classes of

afforested savannah, but some pixels classify for the “open forest” class.

10.4.2.2 Savannah

As discussed in the previous paragraph, it is not easy to set the difference between forest and

afforested savannah, and therefore an intermediate class “open forest” was created. But within

the areas which could be classified as savannah a high variability could be detected within the

feature space. Therefore savannah was classified in different classes. The feature space of the

training pixels for savannah in the VNIR bands and calculated NDVI are represented in

Figure 55, Figure 56 and Figure 57. On Figure 55, representing the red/near infrared relation,

the soil line can be recognised.


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A part of the pixels lie clearly at some distance of this soil line. These pixels are represented

in reddish to yellowish colours in the different figures. These different classes represent the

afforested savannah. They have the highest NDVI values. Apparently there is still quite

some chlorophyll present in these areas, even at the end of the dry season (when the satellite

image was recorded). Therefore it was concluded that these areas probably represent the

savannah with a tree cover. Some of the types of afforested savannah are darker in the visible

bands (vnir1 and vnir2). They are situated besides areas that were burned (see further) and are

in this way interpreted as afforested savannah that were partially burned.

Figure 55: feature space for the vnir2 (red) and vnir3 (near infrared) band. The blue dots represent the

different classes that are recognised in forests, the other dots represent the different savannah classes.

Source: own research in ILWIS.


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Figure 56: feature space for the vnir2 (red) band and calculated NDVI . The blue dots represent the



Figure 57: feature space for the vnir1 (green) and vnir3 (near infrared) band. The blue dots represent the




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Most of the pixels however are situated on the so-called soil line (or soil brightness line). This

soil line represents the variance in brightness of the soil, with the darkest soils in the down left

corner (low values in both red and near infrared spectral range) and the lightest in the upper

right colour (high red and near infrared values). As wet soils tend to be darker than dry soils it

is supposed that the line represents the variation in wetness of the soil. But the colour can also

depend on the mineral and organic material that builds up the soil (Gibson and Power 2000).

Within the study area however, the darkest part of the soil line represent burned areas. These

areas can be easily detected on the satellite image. Within these burned areas still two

subclasses could be recognised: one with a very dark soil and extremely low NDVI and one

with a less dark soil and higher NDVI. These differences can be explained by the degree the

savannah was burned or by the presents of some trees that were possibly not affected by the

fire.


the vnir2 band, the green colour the vnir1 band and the blue colour the calculated NDVI values. The

burned area can be split in two classes: one with a very low reflectance in the visible and near infrared

wavelengths (dark grey on this figure) and one that is less dark (light grey on the figure). Source: own

research in Ilwis.


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On the colour composite there is also a road network easily recognisable. At most places

these roads are small (possibly even smaller than one pixel and thus the DN of the pixel

represents a mixed value of the road and the adjacent area), but at some places these roads

become broader. There is no recent information about the present status (extent and exact

position) of the villages and cities in the area but from the older maps it can be concluded that

these broader parts of the roads are actually villages. When sampling the pixels of the roads

they appear to have the same occurrence in the feature space as the pixels of these villages.

Only where the road is very small (smaller than one pixel?) the position in the feature space

seems to be somewhat different. As it can be assumed that the roads exist of bare soil, they

may be taken as representative training pixels for a class bare soil. These training pixels

appear at the right upper part of the soil line (see also Figure 59)!

Because of their different appearance in the feature space the pixels of the places where the

roads are small are assigned to a different class. Some parts of the savannah also classify for

this class as their DN values appear at the same place in the feature space (see also Figure 59).

From this it was concluded that this class corresponds with savannah with a lot of bare soil.


the vnir2 band, the green colour the vnir1 band and the blue colour the calculated NDVI values. A road

network can easily be recognised on the map. Explanation in the text. Source: own research in Ilwis.


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In between the classes with bare soil and the classes with burned savannah there lie some

other classes. These classes were arbitrary split in some smaller classes of savannah to avoid

very large standard deviations. These areas were interpretated as savannahs with a limited

amount of plant cover (according to Gibson and Power (2000) only a vegetation cover of 30%

can be detected) or with only dead plants (dead grasses because of the end of the dry season).

The colour is less bright than the colour of the bare soil and darker than the colour of the

burned savannah. This can be explained by the presence of some plants, dead or alive. These

plants have a lower spectral reflectance than bare soil.

10.4.2.3 Agricultural land

When examining the colour composite the region around the village Inkisi and the Inkisi

valley north of it show a different pattern than the rest of the area. They are not constituted of

the typical pattern with forests in valleys and savannah in between it. Actually two subregions

can be recognised: in the south a region with a low NDVI and a very varying reflectance in

the green band, and in the north a region with large uniform areas around the Inkisi river, also

with a low NDVI. There were no recent maps available to evaluate with which vegetation

type this corresponds. But the old maps show a concentration in population around Inkisi with

also some plantations. It was also affirmed by Prof. Dr. Geert Baert that this region has still a

rather high population density with a lot of agriculture.

To test the variability in the vnir1 (green) band some filter operations were performed. First

the average filter for a 3 x 3 kernel was calculated on this band. Then these values were

subtracted from the pixels original DNs. These calculated values were again stretched. The

same operation was also performed using a 11 x 11 kernel in the average filter. Both methods

gave no satisfying results: when using them in the classification the agricultural lands had no

specific distribution within the feature space of the new created bands.

Because one of the agriculture areas has a specific pattern a pattern filter was applied on the

vnir1 band. This filter gives a value of 255 when there are small absolute differences between

the centre pixels and their neighbours and 0 when these differences are large. Values between

0 and 255 represent the direction in which differences occur. Because this pattern filter gives

a very irregular image an average filter was applied on the new map, giving for each pixel he

average of the surrounding 7 x 7 kernel. In this way the agriculture region has a specific

appearance: the values are very high around here. However, when applying this in the


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classification, there appears another problem: also the roads (which are inside the class bare

soil) have such high value in this new created map. These roads also have the same

appearance in the feature space for the other bands ad therefore they will be classified in the

same class as this agriculture land.

From all observations mentioned above it was concluded that it is impossible to classify

agricultural land based on the given satellite images. The agricultural land appears to have the

same appearance in the feature space as savannah. There is some variation inside the

agricultural land: some have the same DN values as savannah with a lot of bare soil or even in

the class bare soil (high reflection in the visible and near infrared wavelengths), others have a

reflection in somewhat darker coloured savannah (lower reflection in the visible and near

infrared wavelengths).

As the different agricultural lands all seem to have a low NDVI and they appear in the feature

space very close to the soil line, it can be concluded that they have a very low plant cover

(less than 30%) or that they are left fallow or just ploughed at the time of the observation. As

the observations were done at the end of the dry season it is understandable that there are no

green plants in the plantations or that they have been ploughed to prepare for the coming wet

season.

10.4.2.4 Clouds

The DN values of the clouds themselves form clearly defined regions in the feature space. A

problem that can possibly occur is that the DN values for some bands is constant, and thus the

standard deviation is zero, making some classification methods impossible. A second problem

is that the large cloud en the small clouds form different areas in the feature space. This can

be solved by splitting the cloud class in two classes.

Other problems appear in the zone that is shadowed by the clouds. In these zones the spectral

values for all bands are affected. This could possibly be solved by defining new classes (for

instance called forest-shadowed) but here arises another problem: defining to which class the

pixels belong and finding enough pixels for each class (because the area for each vegetation

type that lies in the shadow of a cloud is sometimes limited). Moreover, the dark colour of

shadowed savannah may lead to some confusion with the burned savannah.

A last set of problems appears at the borders of clouds. In these areas the clouds are less thick

or form a kind of haze. Figure 60 illustrates the problems that arise around the border of a

cloud.


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Because off all the problems the clouds bring with them it was decided not to classify them.

In stead they were digitised (polygons were created) and in this way later on included into the

classification map (see further).


the vnir2 band, the green colour the vnir1 band and the blue colour the calculated NDVI values. It can be

noticed that the thickness of the cloud has an important influence on the DN values of the selected pixels.

All selected pixels are representative for forests. Source: own research in Ilwis.

10.4.2.5 Water

On first sight water can be easily divided in two classes:

• A dark class: low DN values for the vnir1 and vnir2 bands, very low for the vnir3

band. For the SWIR bands the values are also very low. The values of the TIR bands

are somewhat higher.

• A light class: higher values for the vnir1 and vnir2 bands, low values for the vnir3

band. Higher values for the SWIR bands than the dark water class. The values for the

TIR bands are comparable with those of the dark water class.

Both types appear next to each other in the Congo River. Besides he Congo River also the

Inkisi river is easy detectable on the images. Training pixels were selected in both rivers.

A problem when sampling the rivers is that their standard deviation for some bands (like the

vnir3 or near infrared band) is zero. This makes the use of some classification methods

impossible. This was solved by selecting one training pixel with slightly deviating values


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from the real class (for instance along the borders of the rivers). When enough pixels are

selected this one pixel will only very slightly affect the classification.

Because the Inkisi river is not very broad it can be expected that the values in the TIR and

possible also in the SWIR bands will have mixed values of the river and the surrounding area.

To avoid problems with this the water of the Inkisi got two separate classes: one for light and

one for dark water.

The difference between the water types can be easily explained by a difference in sediment

load. One could think that the light water colour actually is a dried out river bank, but this is

not the case: the DN values for the bare soil class are very different from these from the light

water class, most in particular for the TIR bands.

10.4.3

10.4.3.1 Choosing the maps that will be used further

Discussion of the results

The maximum likelihood classification is often considered to be the best method (Gibson and

Power 2000) but in this case several classification methods were tried out.

Box classifications were performed using multiplication factors of 1,732 (as this is standard

filled in in Ilwis) and 4,000 (as this is the root of 16, 16 being the umber of bands used).

When evaluating the classifications it appears that with a factor of 1,732 only a small number

of pixels were classified. With 4,000 as factor more pixels were classified but still an

important number was left blank.

There were Minimum Distance classifications performed using a threshold of 50, 100, 120

and without threshold. With a threshold of 50 important areas on the map remained

unclassified. With the threshold set to 100 or 120 the results are almost the same: most pixels

are classified. Only in the neighbourhood of clouds and their shadows, and around the borders

of rivers, major areas remain unclassified. Besides these areas also some smaller spots are not

classified. Without a threshold the completely map was classified.

The classification according to the Minimum Mahalanobis Distance was performed with a

threshold of 50, 100 and without threshold. All these maps gave an satisfying result with

almost each pixel classified (except for some pixels of the large cloud using the threshold of

50).

Classification using the maximal likelihood method and with a threshold of 100 or no

threshold used gave the same results: all pixels were classified!


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From these observations it was decided to use following maps in order to evaluate the

methods:

• No map made by the box classifier method

• From the maps made with the minimum distance method the map without threshold.

• From the Minimum Mahalanobis Distance classifier the map with a threshold of 50.

• From the maximal likelihood method the one with a threshold of 100.

10.4.3.2 Preparing the maps

A correction for the clouds was performed. This was done in the same way as for the

correction of the borders: first a polygon map was created containing the clouds and their

shadows (see also before) and then this polygon map was transformed to a raster. After this

the following map calculation was performed:

IFF (cloudpol = "cloud", "cloud", vegMD)

Meaning that the output map gets the value “cloud” when the rastermap cloudpol has the

value “cloud” (thus when a cloud or cloud shadow was digitised) and otherwise it gets the

value of the map vegMD.

The resultant maps still have some errors in them: also the borders of the black map will be

classified in some cases (when no threshold was used). Also along these borders some pixels

can be classified wrong as they fall for some bands in an area without pixel information and

for others not (as the bands are not fully overlapping). Therefore a polygon was created

around the map borders making that for each band all pixels inside the polygon have a value

and are not a part of the black borders. After creating this polygon it was transformed into a

raster with for the cells inside the raster the value “in” (and for the others no value). With this

raster a map calculation was performed for each classification result. For example:

IFF (studyarea="in", vegMD_1, ?)

This formula makes that for each pixels inside the polygon (thus the map studyarea has a

value “in”) the new map gets the value of the map vegMD_1 (in other words the result of the

former calculation) and otherwise the map gets no value (“?”). This operation was performed

for all three maps that were hold in the former paragraph.

A third adjustment that has to be made to the maps is adjusting their classes! A lot of classes

were arbitrary chosen (to hold the standard deviation as low as possible) and therefore new

classes are created:


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• The class cloud

• The class water, containing all water classes defined before

• The class forest – dense containing the most dense forest type

• The class forest containing the former classes forest – 2 and forest – 3

• The class open forest being the same as the former class open forest

• The class burned containing the former darkest burned class

• The class savannah – burned containing the two former less dark burned classes

• The class savannah – afforested containing all the former afforested savannah classes

• The class savannah containing the former classes savannah – 4, savannah – 5,

savannah – 6

• The class savannah – light containing the former classes savannah – 7 and bare soil

savannah

• The class bare soil

• The class aquatic forest containing all former aquatic forest classes

This procedure was performed using the map calculation function.

10.4.3.3 Comparison of the methods: confusion matrix

In order to check the accuracy of a classification a confusion matrix operation can be

performed in ILWIS. This confusion matrix identifies both the nature of the errors as the

quantity. The disadvantage of this method is that there is a ground truth needed, while this is

not available. Therefore this operation cannot be performed properly. A possibility would be

to make a new sample set and to interpret the pixels in the same way. This seems however

rather impossible as the class borders were chosen randomly in some cases (e.g. to split the

savannah classes in smaller subclasses).

Another possibility however is to compare the different resultant maps with each other in this

way. To perform this operation first a cross operation has to be performed with as first set the

test set and as second set the classified image. Then in the table view of the resulting table

confusion matrix operation can be performed via the menu view. As first column the test set

has to be chosen, as second the classification. This operation was performed for the several

combinations:

• The minimum distance classification map and the Maximal likelihood classification

map.


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• The minimum distance classification map and the Minimum Mahalanobis Distance

classification map.

• The Minimum Mahalanobis Distance classification map and the Maximal Likelihood

classification map.

The resulting confusion matrix displays the test set in the columns and the classification in the

rows (in this case the test set is also a classification). The accuracy is calculated for each row.

This is the fraction of correctly classified pixels, or the fraction of pixels of a classification

class that have the same class test set. The reliability is calculated for each column. This is the

fraction of correctly classified ground truth pixels, or the fraction of pixels in a test set class

that have the same class in the classification. From this an average accuracy and average

reliability are calculated. The overall accuracy is the sum of correctly classified pixels

divided over the total number of pixels.

For the confusion matrix of the minimum distance classification map (used as the

classification) and the Maximal likelihood classification map (used as the test set) the

Average Accuracy is 67,75 %, the Average Reliability is 62,00 % and the Overall Accuracy

is 70,30 %. A low accuracy was attained by the classes forest – open (0,42), bare soil (0,23)

and aquatic forest (0,12). A low reliability was attained for the classes forest – dense (0,14),

aquatic forest (0,41), burned (0,15) and savannah – light (0,20).

For the confusion matrix of the minimum distance classification map (used as the

classification) and the Minimum Mahalanobis Distance classification map (used as the test

set) the Average Accuracy is 67,31 %, the Average Reliability is 61,47 % and the Overall

Accuracy is 69,21 %. A low accuracy was attained by the classes forest – open (0,40), bare

soil (0,22) and aquatic forest (0,12). A low reliability was attained for the classes forest –

dense (0,13), savannah – afforested (0,48), aquatic forest (0,44), burned (0,13) and savannah

– light (0,17).

For the confusion matrix of the Minimum Mahalanobis Distance classification map (used as

the classification) and the Maximal Likelihood classification map (used as the test set) the

Average Accuracy is 95,89 %, Average Reliability is 97,51 % and the Overall Accuracy is

97,83 %. These high values are also reflected in the individual accuracies and reliabilities for

each map, as none of these values is below 0,87.


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When evaluating the errors from these matrices is appears that most errors between classes

are between classes that are very alike: between bare soil and savannah – light; between

forest-open and savannah - afforested and forest; between aquatic forest, forest- dense and

forest.

It is also important to notice that there are some main differences in the classification of

water. For the Minimum Mahalanobis Distance classification map vs. Maximal Likelihood

classification map an accuracy of 1,00 and a reliability of 0,99 was attained. This means that

all pixels classified as water using the Maximal Likelihood classification were also classified

as water in the Minimum Mahalanobis Distance classification, but vice versa some pixels

were classified in another class. But when comparing the minimum distance classification

map with the Minimum Mahalanobis Distance classification map and the Maximal Likelihood

classification map the accuracy is respectively 0,55 and 0,60 while the reliability for each is

0,99. This means that a lot of pixels classified as water in the Minimum Mahalanobis Distance

classification or Maximal Likelihood classification are classified as something else in the

minimum distance classification.

10.4.3.4 Comparison of the methods: visual comparison

As no additional methods are available to test the different methods, a visual comparison of

the classifications seemed necessary. In order to perform this the differences between all three

maps were visually evaluated. Via the map calculator a map was created displaying the zones

with different classifications. This map contains the value 1 when there is a different class

assigned to a pixel and 0 when it is the same. This procedure was followed for all possible

combinations of the three classifications. From these maps it appears that the differences are

rather randomly distributed. However, using these maps and a visual control of the different

classifications, some zones could be found with important differences. These zones are

discussed in this paragraph. The legend for the several classification maps displayed in this

chapter is displayed in Figure 61.


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Figure 61: legend of the several classification maps displayed in this paragraph. Source: own research in

Ilwis.

Figure 62: zone 1: Minimum Distance classification. Source: own research in Ilwis.


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Figure 63: zone 1: Maximal Likelihood classification. Source: own research in Ilwis.

Figure 64: zone 1: Minimum Mahalanobis Distance classification. Source: own research in Ilwis.


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Figure 65: zone 1: calculated NDVI. Red colours for positive values, green for values around 0 and blue

for negative values. Source: own research in Ilwis.

Figure 66: false colour composite of zone 1. The vnir2 band is represented in red, the vnir1 band in green

and the calculated NDVI as blue. Source: own research in Ilwis.


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A first zone (zone1) was selected for evaluation of the classification. On the colour composite

some roads are easily recognisable but they cannot be detected in any classification. The

villages that are visible are classified as bare soil areas.

In the centre there is a forest with a very high NDVI. This forest is classified as a dense forest

with the Minimum Distance method but as forest with the other methods. It appears that for

this case the Minimum Distance method is the best. A lot of the forests are classified as

aquatic forests, especially on the Minimum Distance Classification map. On the soil map

there is no aquatic forest indicated for this region, but as a major river is situated along the

zone that is as such classified, it is possible that there appears indeed an aquatic forest.

A major difference between the classification types is the forest type that dominates the

gallery forests. With the Minimum Mahalanobis Distance classification and the Maximal

Likelihood classification open forest dominates while for the Minimum Distance

classification forest dominates. When comparing with the false colour composite and the

NDVI map it seems that the Minimum Distance Classification gives a better image

concerning these forests.

Both the Minimum Mahalanobis Distance classification and the Maximal Likelihood

classification contain another error: some pixels in the middle of the savannah are classified

as water.

When examining other regions on the map it appears that several regions are erroneous

classified as water by the Minimum Mahalanobis Distance classification and the Maximal

Likelihood classification, for instance along the Inkisi river (zone 2). The opposite problem

occurs less frequently for the Minimum Distance classification: some parts of the river are not

classified as river.

In the same region around the Inkisi river in the south of the study area all classification

methods classify the agriculture area as a combination of bare soil, light coloured savannah,

savannah and afforested savannah, but the Minimum Distance classification classifies (a lot)

more pixels as afforested savannah than the two others! The Minimum Mahalanobis Distance

classification and the Maximal Likelihood classification represent better the patterns that

occur at a part of this agriculture region.


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Figure 67: zone 2: Minimum Distance classification. Source : own research in Ilwis.

Figure 68: zone 2: Maximal Likelihood classification. Source : own research in Ilwis.

More in the north of the study area a vast forest appears, and this forest is not situated in the

valleys but on the plateau. In this forest there are also a lot of pixels classified as aquatic


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forest, and this in al three classifications. From this it was concluded that aquatic forest cannot

be separated from the other forest types. Therefore the aquatic forest class was added to the

forest class using the map calculator.

From all the observations mentioned above, it was decided that the minimal distance

classification map gives the best results. This is probably due to the rather large standard

deviation some classes (like water) have for some bands, which highly influences the

classification methods like Maximal Likelihood and Minimum Mahalanobis Distance.

The different maps created can be found in the annex: Map 5, Map 6 and Map 7 (this is

actually the map after smoothing, see next paragraph).

10.4.3.5 Smoothing

Post classification smoothing can be applied to a classified map to lower the salt and pepper

appearance. To smooth the image a majority filter can be applied. This filter assigns the

majority class (=predominant class) to the central pixel for each group of pixels considered.

Applying the majority filter has the advantage that the resulting image is smoother. The

disadvantage is that features like roads or small rivers may disappear after applying the filter.


The smoothing operation was performed on the minimum distance classification. Due to this

smoothing the roads, classified as bare soil, were partially lost, but as these roads were hardly

present in the classification (often they ware classified as the light coloured savannah type)

this loss was considered as unimportant. The results is displayed as Map 7.

10.5 Supervised texture classification The only source about the soils and their texture are the different soil maps. The mapped units

on this map are catenas and complexes (see before) and these catenas and complexes exist of

several series. In this way most map units don’t represent one texture type as they contain

series with a different texture. To solve this problem some map units were selected with each

of them containing series with only one dominating texture type:

• The K 5-2 map unit existing of sandy series (Buense and Mpese series) and present in

rather large areas of the study area

• The K 1-2 map unit, representing mainly sandy series (Mpese series) and also locally

a loamy series (Phonzo series)


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• The QS 3 map unit, dominantly existing of clayey series (Kwilu-Ngongo, Ntadi and

Mawinzi series) and some less important loamy series (Zongo series)

• The map units S 3-1 and S 4 – 1 representing clayey series (Kwilu-Ngongo, Mawinzi,

Yanga, Kiazi Col and Songolo series)

As the vegetation is possibly influenced by the texture of the soil, the danger exists that when

classifying the texture in reality again the vegetation is classified. This danger is particularly

large because the vegetation makes up the land surface and it can be expected that the

vegetation has in this way the largest influence on the spectral reflectance. To avoid that in

reality the vegetation is mapped, the training can be selected in such a way that they are

always selected in the same vegetation type. The chosen vegetation type was afforested

savannah. This type was chosen because:

• Enough pixels of this vegetation type occur for each map unit that was selected. For

instance other types of savannah didn’t occur within the K 1-2 map unit.

• This type of vegetation is also mainly present outside the valleys for each selected

map unit type. The valleys are not suitable for use, as the valley soil series are not

always mapped on the soil map (actually they are almost never mapped) because of

their small superficies. Forests are for instance in some parts of the map only present

in the valleys.

• It can be supposed that under savannah, even if it is afforested, some spectral

reflectance of the soil appears.

• Bare soil or savannah with lots of it could also be very usable, but this vegetation type

doesn’t occur frequently in the more sandy regions.

If the classification of texture for this vegetation type would appear possible, the method can

also be applied for other vegetation types. Once a classification for one vegetation type is

successful, the classification can be used by only selecting the classified pixels under the

selected vegetation type and exporting this towards a new map. Such an operation can for

instance be performed in the map calculator via:

IFF (vegetationmap = “afforested savannah”, textureclass, ?)

This operation returns the value of the map textureclass for each pixel that has the value

afforested savannah in the map vegetationmap, and “?” for all other pixels.

After this it was tried to create an accurate sample set for both heavy soils (clay) and light

soils (sand) under afforested savannah, and to check whether these sample sets are separated.


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After several attempts to select representative training pixels, it could be concluded that the

chosen classes could not be classified: their appearance in the feature space was for each band

highly overlapping. Thus it is with the given data not possible to classify the texture of the

afforested savannah.

A second attempt was performed on the vegetation class savannah. As this class doesn’t cover

large enough areas in all map units a selection of the map units had to be made. For the sandy

samples the K 5-2 map unit was hold, for the clay map units the QS3 and S 3-1 map units

were hold. The sample sets were again formed and also in this case, it could be concluded that

a classification was impossible. The appearances in the feature space of the different bands

was also in this case highly overlapping for the classes.

10.6 Supervised parent material classification For the parent material classification the same reasoning was followed as for the texture

classification: the selection of the training pixels should be performed within one vegetation

type, as this vegetation type is the actual land-cover and can be expected to have the main

influence on the spectral reflectance.

The parent material classification was performed on two different vegetation types: savannah

and forest. These types were chosen because they occur on all the different parent material

types in large enough amounts to selected a representative sample set.

For both the vegetation types the results were the same: for all bands the different classes

were highly overlapping in the feature space. It seemed impossible to select pixels in such a

way that separate clusters in the multi-dimensional feature space could be obtained. From this

it was concluded that classification of parent material is impossible with the given dataset.


Page 156

10.7 Discussion The three classification attempts that were performed the vegetation classification was the

most successful. This vegetation classification has however also rather limited results. The

main disadvantage of the classification is that no difference could be made by savannah (or

afforested savannah) and agriculture. Possibly this is due to the period in which the satellite

images were taken: at the end of the dry season. It can be expected that at this moment most

agricultural lands with annual crops would be without crops (they are ploughed or left fallow

after the harvest for instance). Agricultural lands with more permanent but deciduous plants

can also be expected to have a low chlorophyll level as the deciduous plants will be mainly

leafless at this moments (and thus the plants will almost have the same reflectance as dead

plants).

A solution to the classification problems for agricultural grounds would be to classify on an

image that is taken within the growing period of the crops. This seems however impossible

for this study area as the area is for the main part of the year covered with clouds and the

moments without a cloud cover occur mainly (or exclusive?) within the dry season.

Smaller disadvantages of the vegetation classification are the difficulties to map roads and

villages, but this is probably more a problem of the pixel scale of the used data. When a larger

pixel scale (for instance pixels of 1 meter) would be used the roads would exit for a lesser

extent of mixed pixels (meaning that the pixels represent no a mix of the spectral reflectance

of the roads and the surrounding terrains). A larger pixel scale would also include the

mapping of individual houses in the villages and possibly also of parcels within them.

The methodology of the vegetation mapping itself can also be questioned. The classification

and selection of training pixels is based on old vegetation maps with a very low scale and low

level of detail, and mainly on the interpretation of the spectral reflectances. In this way the

quality of the vegetation map highly depends on this interpretation. Where forests are indeed

easy to interpret and classify as they are easy to recognise on a colour composite and have

very specific spectral properties and NDVI values, the savannahs are more difficult to

classify. The high variation within the savannah spectral reflectances was in this study

interpreted, but better would be to have real terrain data of the occurring types. This terrain

data could also be used to find some vegetation types with a smaller occurrence. The possible

occurrence of these vegetation types could not be researched in this study, as no data were

available. And at last a ground truth set would also be very handsome to check the quality of


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the classification. In this case half of the ground truth points can be used as training pixels,

and the other half can be used to check the accuracy of the classification. In this way the

vegetation classification can be evaluated in a scientific way.

Therefore it can be advised for future studies that if the resources (financial!) are available a

field trip has to be organised to sample some vegetation types. In the ideal situation the

sampling moment would coincidence with the moment the satellite image is taken (because

the vegetation type can change over time, for instance by the burning of the savannah). But

even when the satellite images dates from another moment than the field sampling, this field

sampling would largely increase the reliability of the classification.

The classification of the texture of the soil yielded no results. It appeared that with the given

dataset a classification was not possible. It is however mentioned in literature (like Lillesand

and Kiefer 1994) that texture has influence on the spectral reflection of bare soil. Also the

classification of the different parent material types did not yield any results. A different

chemical composition can cause some spectral differences, for instance for the presence of

iron (and the state of the iron (Fe3+ or Fe2+

But this wetness of the soils cannot be noted: all soils appearing outside the valleys (so in the

savannah) have according to the soil maps a good to excessive drainage, and thus they can be

expected to be dry at the end of the dry season. Moreover, the tone differences between the

different soil units do not have a relation with the relief: the higher parts (expected to by the

driest and thus the lightest colours) have not always the lightest colours. It is of course

possible that these tone differences do indeed represent a humidity difference (for instance the

driest soils are indeed not situated on the tops but on the more eroded flanks in some

regions!?), but in that case it is not possible to classify this from the given dataset, mainly

because no data are available about which soils are dry and which soils are wet. As seen in the

)), carbonates, …, mostly in the spectral ranges of

the SWIR bands. But the same authors who mention this difference (Gibson and Power 2000)

mention also that it is unusual that a specific spectral signature can be assigned to a particular

rock type. In general it appears that the spectral reflectance of bare soil is influenced by many

factors: texture, moisture, surface roughness, chemical composition, …. Maybe more

important for this study and the study area is that the presence of some vegetation will in all

cases have a higher influence on the reflectance properties of an area than the soil. In general

the soil parameter that appears to have the most influence on the spectral reflectance is the

humidity, with the dry soils with mostly a coarse texture light coloured and the light soils with

mostly a fine texture darker colours. (Lillesand and Kiefer 1994)


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courses of soil physics it would be wrong to assign a texture class on the basis of soil

humidity alone, as texture is just one of the factors influencing the soil humidity.

Also important to note is that in this study actually it was not sure if the sampled areas that

were classified under savannah have or have not a bare soil. This class is actually situated on

the soil line in the feature space and it is possibly that they have no plant cover. But when

taking into account the data from the vegetation map and their extent it is more probable that

they do have a plant cover but that the plants have such a low reflectance in the near infrared

because they are withered (dead). From this it can be concluded that possibly not the bare soil

makes up the reflectance but the withered plants. It could still be expected that the soil under

this withered vegetation has some influence on some of the band, for instance on the TIR

bands. But as seen from the classification this influence was not detected.

One could also wonder if the lack of difference in feature space would not be due to the

nature of the tropical soils, which are mostly very thick and their upper layers exist of very

altered material with only the most resistant fragments still present. An argument against this

vision for the study area is that several series on the soil map actually don’t have such a thick

saprolite layer and that for some series even the parent material outcrops at places. But as

these soils are actually not very common in the studied area, this argument may be partially

true.

A last, but important, remark can be made about the selected study area. Selecting an area

with more and bigger differences in parent material, like the more western part of Bas-Congo

could possibly yield other results. Actually there is a quite low difference in parent material

and texture types within the studied area. But as written before it was not possible to select a

more western study area as the satellite images from this area are to cloudy!

At last it should be also noted that a visual check of the different SWIR and TIR bands with

an overlay of the soilmap boundaries show no pattern that is related with these boundaries!

In general it can be concluded that a vegetation classification was possible but that this

classification would probably yield better results if the image was taken in another season

(which is practically very difficult) and if some (recent) field data would be available.

The classification of other soil parameters, and more in particular of texture and parent

material, was not possible with the given dataset. Probably the main problem for these

classifications is the presence of a (withered) vegetation cover. Maybe there are also other


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factors influencing these classifications like the nature of the deep highly alternated tropical

soils. For the parent material classification it can also be questioned if a study area with more

difference in parent material wouldn’t yield better results. Field data recorded at the moment

the satellite image was recorded would give the possibility to research whether the soil

humidity has some influence on the spectral values.


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11 Practical application: erosion map

11.1 Methods and results In order to show the possible practical applications of the produced DEM and classification,

an erosion map was produced of the region. As the explanatory text of the soil maps already

mention, soil erosion is a main problem in the region, in particular on steep slopes.

In order to calculate the slopes with Ilwis the following steps have to be performed on the

DEM raster:

• First the height differences in X have to be calculated via the filter operation dfdx. The

result is saved as congo1bDX.

• Second the height differences in Y have to be calculated via the filter operation dfdy.

The result is saved as congo1bDY.

• To calculate a slope map from this the following formula can be used:

100 * HYP(congo1bDX,congo1bDY)/ PIXSIZE(congo1b)

with congo1b = the original DEM; HYP = Ilwis internal mapcalc function; PIXSIZE

returns the pixel size of the selected DEM

The created slopemap was sliced (using the slicing operator) in classes:

• <2%: flat

• 2-6%: medium to flat

• 6-25%: medium

• 25-50%: steep

• >50%: very steep

These class boundaries were chosen because they are also used in the land evaluation of the

region (Baert 1991a). The different classes need some practical agriculture practices:

• 2-6%: agriculture in bands following the contours

• 6-25%: anti-erosion hedges have to be used

• >25%: agriculture in terraces

• >50%: these terrains need afforestation

The map which was created in this way can form a first output map, showing the regions

which can be used for agriculture and which can’t be used for it.


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In order to determine the regions with a high erodability, the vegetation was also taken into

account. It was supposed that the following vegetation types have a high erosion risk:

savannah, bare soil, burned savannah and light coloured savannah (for the meaning of these

classes: see before). Thus these classes were selected from the vegetation classification using

the map calculator. The new created map has a value of 1 for the erosion sensitive classes and

0 for the other classes.

The last step was combining the slope class map and erodable vegetation map. This was done

via the map calculator. The classes for the erosion map are:

• low: for slopes of less than 2% and savannah or bare soil as vegetation class

• medium: for slopes of 2-6% and savannah or bare soil as vegetation class

• high: for slopes of 6-25% and savannah or bare soil as vegetation class

• very high: for slopes of 25-50% and savannah or bare soil as vegetation class

• tremendously high: for slopes of more than 50% and savannah or bare soil as

vegetation class

The resultant map is displayed as Map 8.

11.2 Discussion The resulting map shows the erosion risk at the moment the satellite image was recorded. This

map doesn’t take into account that burned savannah has a tremendously erosion risk in the

season the image was taken (Baert 1991a). This is more a disadvantage than an advantage, as

this means that the map is not only valid at the moment the satellite image was taken but also

later, as the next year other regions will be covered by burned savannah. Of course the map

can’t take into account that some forests or afforested savannah will in time be turned into

savannah (or vice versa).

The quality of the resulting map mainly depends on the quality of the used DEM and

vegetation classification. Possible errors are due to a mis-classification of savannah and

afforested savannah. All errors in the Dem will also produce main errors in the erosion map.

Only the fact that the DEM is actually a DSM (see paragraph 0) won’t have much effect

(maybe except for the borders of the forests) on the erosion classification, as this classification

wasn’t performed on the forests.

The pixel scale should also be taken into account! The slope will always be an average value

of the slope within the pixel. When we consider for instance the slope in the x-direction. Over


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the extent of the 15 meters of one pixel, it is for instance possible that the first 1,5 meter

shows a height difference of 1 meter (or 66%) and that the next 13,5 meters shows no height

difference (or 0%). The total height difference is 1 meter over 15 meters, and this value will

also be mapped, meaning that a slope of 6,6% is mapped. Thus the mapped slope is just an

average of the pixels and the values in reality can be locally higher.

A possible alternative could also be in mapping the erosion in a more graduate way, and not

with classification. Some weight factors could be given to both the slope and the vegetation

classes (or alternatively the NDVI could be used in stead of the vegetation classification) and

possibly also to other factors (like aspect). From this the erosion risk could be calculated and

in this way quantified. Further research has however to be performed to determine which

weight factors have to be used.

As a last conclusion it can be stated that the erosion classification map shows how the

produced maps can be used for practical appliance in agriculture.


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12 Conclusions and advise for further research

This work exists of four main parts: an introductory literature study, the production of a DEM,

an attempt to classified some soil related topics and a practical application of the produced

data. The second and the third part are actually the most important as they give an answer to

the objectives of the study.

The introductory literature study was mainly performed to get a good image of the study area.

The literature study about the soils and vegetation were very important for the later performed

classifications, while the other items (geology, geomorphology) treated in this literature study

were mainly important to get a good idea about the soils.

The existing vegetation exists within the study area mainly of following classes: different

types of forests, post-agriculture land, aquatic vegetation and different types of (afforested)

savannahs and steppe. The main disadvantage of this map was its age.

The existing soil map describes units that are made up of catenas and complexes of series.

These series are based on morphological and topographical criteria and they can be

recognised in the field without the need of analytical data. Te used system is strictly local and

the used classification is not based on another international classification.

One of the main purposes of this study was the creation of a DEM. This was done by using

the software package VirtuoZo NT. A lot of problems were encountered during this process

and the resulting DEM still contained a lot of errors, because no editing was possible for the

clouds and rivers in the area (due to software problems). The software program indicated an

error on the resulting DEM of 2.062122 meters in X, 2.182133 meters in Y (resulting in a

combined error of 3.002341 meters in X and Y) and 5.267048 meters in Z. The error is

calculated on the used pass points and one can wonder if for instance the fact that all pass

points are situated on hills has no influence on the DEM. There was however no external

possibility to check the error and questions can raise about the correctness of this error.

One of the main difficulties for the creation of the DEM was also the allocation of the pass

points of the satellite image. Several solutions were proposed, but most of them imply a

terrain campaign and would make the process much more labour intensive. Maybe the most

suitable solution would be to create a relative DEM using also a stereoscope.


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At last some considerations were made about the validity of a DEM created in this way.

Actually the name DEM (digital elevation model) is wrong and it should be called DSM

(digital surface model). This is because the DEM actually represents the surface that gives the

spectral reflectance. In the landscape of the area the valleys are filled with forests and the tops

are covered by savannah. This implies that the DEM will underestimate the height differences

(the valleys will actually lay much lower than they are indicated in the model). These errors

could possibly be corrected using the vegetation classification map if some data about the

height of the tree layer were available!

The third part of this study some classification attempts were performed. The classification of

the vegetation seemed to be possible but the classification of parent material and texture were

impossible. In the vegetation classification implied the classification of forests (with several

types according to the density), afforested savannah, savannah (with several types), burned

savannah, bare soil and water. The main disadvantage to the classification was that agriculture

grounds could not be classified (they are classified as savannah and afforested savannah).

This is probably due to the season in which the image was taken (end of the dry season).

A main criticism to the vegetation classification is that no recent terrain data were used. The

classification is mainly based on the spectral reflectances of the several classes and on the

general information about the soils of Bas-Congo that was found in the literature study. To a

lesser extent the vegetation map was used but this map was not very useful for it low scale

and for its age. The reliability of the vegetation classification would be much higher if some

recent field observations would be available. In this way the classification could also be

evaluated in a scientific way!

Both the classification of texture and parent material failed. It s not clear if this failure is due

to the fact that classifying these things with ASTER data in tropical regions is not possible, or

whether it is just not possible within the study area. In particular for the parent material

classification, it is advised to future researchers to do some research in an area with more

variation in the parent material. A good region for this would be the more western parts of

Bas-Congo but a problem is that these regions have very often a thick cloud cover (and no

ASTER images are available).

A last part of this study was the application of the obtained data to come to a erosion

classification. The resultant map shows the regions that have an erosion risk and gives some

quantification of this erosion risk. This erosion risk is partially based on the DEM and the


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derived slope. It should be noted that the slope is always a function of the pixel scale, as this

slope is actually the average slope over the pixel. It is also based on the vegetation

classification. Errors in both the DEM and vegetation classification will also appear in the

erosion classification. In this study the erosion classification was performed in a rather easy

way (actually the erosion risk is expressed in classes) because this erosion classification is not

a part of the objectives. It is however also possible with the given dataset to make a more

gradual map (in stead of defining discrete classes) expressing the erosion by a mathematical

calculated value. For this it is necessary to do some research on the assignment of weight

values to the different variables.


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13 Bibliography

13.1 Written publications

13.1.1

• Ameryckx, J.B., Verheye, W. and Vermeire, R., 1995. Bodemkunde. Gent.

Books

• Bethel, J. S., McGlone, J. Ch. and Mikhail, E. M., 2001. Introduction to modern

photogrammetry. Taylor ad Francis.

• Bornhardt, W., 1900. Zur Oberflächengestaltung und geologie Deutsch-Ostafrikas.

Reimer, Berlin.

• Compère, P., 1970. Carte des sols et de la végétation du Congo, du Rwanda et du

Burundi. 25. Bas-Congo. B. Notice explicative de la carte de la végétation. I.N.É.A.C.,

DRC.

• Driessen, P., Deckers, J., Spaargaren, O., Nachtergaele, F. (eds.), 2001.Lexture notes

on the major soils of the world. Food and Agriculture Organization of the United

Nations, Rome.

• Falkner, E. and Morgan, D., 2002. Aerial mapping: methods and applications. CRC

press LLC, Boca Raton, Florida.

• Gibson, P. J. and Power, C. H., 2000. Introductory Remote Sensing. Routledge,

London.

• King, L.C., 1942. South African Scenery. Oliver & Boyd, Einburgh.

• Lillesand, T. M. and Kiefer R. W., 1994. Remote sensing and image interpretation.

Third edition. John Wiley and sons Inc., New York.

• Schenk, A. F., 1996. Automatic Generation of DEM’s. In: Greve, C. (ed.). Digital

Photogrammetry, an addendum to the manual of photogrammetry. American society

for photogrammetry and remote sensing, Bethesda, Maryland.

• Thomas, Michael F., 1994.Geomorphology in the tropics, a study of weathering and

denudation in low latitudes. John Wiley & sons, Chichester.

• Thompson, M. M. and Gruner, H., 1980. Foundations of Photogrammetry. In: Slama,

Ch. C., Hendriksen, S. W. and Theurer, C. (eds), 1980. Manual of photogrammetry,

Fourth edition. American society of Photogrammetry, Falls Church, pp. 1-36.


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• Tso, B. and Mather, P. M., 2001. Classification methods for remotely sensed data.

Taylor and Francis, London.

• Wong, K. W., 1980. Basic Mathematics of photogrammetry. In: Slama, Ch. C.,

Hendriksen, S. W. and Theurer, C. (eds.). Manual of Photogrammetry, Fourth edition.

American society of photogrammetry, Falls Church.

13.1.2

• Büdel, J., 1957. Die Doppelten Einebnungsflächen in feuchten Tropen. In: Z.

Geomorph. N. F., 1: 201-228.

Articles

• Davis, W.M., 1899. The geographical cycle. In: The Geographical Journal, 14: 481-

504.

• De Dapper, M., 1994. Diepe verwering en tropische geomorfologie. In: Bull. Déanc.

Acad. R. Sci. Outre-Mer Meded. Zitt. K. Acad. Overzeese Wet. 39 (1993-4): 507-539

(1994).

• De Dapper, M., 1998. Stone Lines. In: Natuurwet. Tijdschr. Vol.78 (1998) p. 60-71, 5

fig., 5 foto’s.

• King, L.C., 1949. A theory of bornhardts. In: The Geographical Journal, 112: 83-87.

• King, L.C., 1953. Canons of landscape evolution. Bull. Geological Society of

America, 64: 721-752.

• Penck, W., 1924. Das Problem afrikanischer Inselberglandschaften. In: Pet. Mitt. 70 :

117-120.

• Tack, L., Wingate, M.T.D., Liégeois, J.-P., Fernandez-Alonso, M. and Deblond, A.,

2001. Early Neoproterozoic magmatism (1000-910 Ma) of the Zadinian and

Mayumbian Groups (Bas-Congo): onset of Rodinia rifting at the western edge of the

Congo craton. In: Precambrium research 110 (2001): 277-306, Elsevier.

• Wayland, E.J., 1934. Peneplains and some other erosional platforms. In: Bull. Geol.

Surv. Ugunda, Annual Rept., Notes 1, 74: 366 pp.

13.1.3

• Baert, Geert, 1991a. Cartographie des sols, évaluation des terres - version

préliminaire. Feuille de Mbanza-Ngungu. University of Ghent.

Non-published works

• Baert, Geert, 1991b. Cartographie des sols, évaluation des terres - version

préliminaire. Feuille de Kinshasa. University of Ghent.


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• Baert, Geert, 1991c. Cartographie des sols, évaluation des terres - version

préliminaire. Feuille d’Inkisi. University of Ghent.

• Baert, Geert, 1991d. Cartographie des sols, évaluation des terres - version

préliminaire. Feuille de Luozi. University of Ghent.

• Bossyns, Bert, 2004. Aanmaken van een DEM en een vegetatieclassificatie van een

deel van het Salonga notionale park in de “République Démocratique du Congo” aan

de hand van satellietbeelden. Thesis, University of Ghent.

• FAO, 1990. Guidelines for soil description, 3rd

• Goossens, R., 2002, Teledetectie en beeldverwerking. Unpublished course notes.

edition. FAO, Rome.

• Jacobsen, K., 2003. DEM generation from satellite data. 23rd

• Van Coillie, Stijn, 2003. Onderzoek naar de mogelijkheden van de Terra-

satellietsensoren voor de aanmaak va relatieve en absolute DEM’s voor de

Republique Democratique du Congo. Thesis, University of Ghent.

EARSeL Symposium :

remote sensing in transition, 2-5 June 2003, Gent. Presentation, cited in Van Coillie

2003.

• Verbeken, Joris, 2003. Integratie van vroegere zendingsrapporten voor de kartering

van de vegetatie in het Virunga park (DRC) met behulp van Landsat en ASTER.

Thesis, University of Ghent.

13.1.4

• Institut Géographique du Congo Belge. 2

Cartographic material ème direction Géodesie et topographie, 1955.

Canevas du Bas Congo 1955 4ème

• Soil maps of the region, sheets Kinshasa, Inkisi, Luozi, and Mbanza Ngungu. See

Baert 1991a, Baert 1991b, Baert 1991c and Baert 1991d. Scale: 1/50000.

partie.

• Compère, P., 1970. Carte des sols et de la végétation du Congo, du Rwanda et du

Burundi. 25. Bas-Congo. B. végétation. 1/250 000. I.N.É.A.C., DRC.

13.1.5

• U. S. Geological Survey, 2003. ASTER product readme: HDF-EOS.

Digital documents

13.1.6

• Official Aster website,

Websites

http://asterweb.jpl.nasa.gov, consulted 30 May 2004.



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14 Appendix

14.1 Digital files There is a CD-ROM attached to this work. This CD-Rom contains some digital files:

Congo1.txt: this file contains the height model that was produced in this thesis.

• Author: Bastiaan Notebaert

• Produced in VirtuoZo NT using ASTER

• The file contains columns expressing X, Y and Z coordinates based on the canevas du

Bas-Congo (see bibliography).

o X and Y coordinates in meters according to the Fuseau 14 local coordinate

system.

o Z in meters above see level.

o The quality report and the errors on this model can be found in this thesis.

• The procedure to use this file in Arcview is described in this thesis. When using this

file some corrections have to be made.

Congo1.dbf: this file contains the height model that was produced in this thesis.


• Produced in VirtuoZo NT using ASTER, it was converted in Arcview

• The file contains columns expressing X, Y and Z coordinates based on the canevas du

Bas-Congo (see bibliography).

o X and Y coordinates in meters according to the Fuseau 14 local coordinate

system.

o Z in meters above see level.

o The quality report and the errors on this model can be found in this thesis.

• The procedure to use this file in Ilwis is described in this thesis. When using this file

some corrections have to be made.

Map height_ilwis: this map contains all files necessary in Ilwis to open the file

congo1b_riverandclouds. This file is the corrected version congo1.dbf saved as a Ilwis raster

map. The following modifications were made:


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• The clouds and cloud shadow got the value “?” in the height model. Therefore the

clouds and their shadows were manually digitised.

• The river also got the value “?”. The rivers were digitised via a vegetation

classification.

This map also contains the files:

• Congo1.grf: the georeference of the rastermap. Author: Bastiaan Notebaert.

• Fuseau14: the coordinate system used. Author: Bastiaan Notebaert

Disolv10.shp: Arcview shapefile: this is the soilmap of the region.

• Author: unknown (university of Kinshasa?)

• Corrected and adapted by Bastiaan Notebaert

• Coordinates in degrees latitude and longitude

• Attribute table:

o Unit: unit on the soil map. Explanation in this work.

o Par_mat: code for the parent material.

• Source: soil maps produced by Baert (1991)

The individual corrected soil maps are also included: Kinshasa.shp, luozi.shp, Inkisi.shp and

mbanza.shp.

The map veg_ilwis contains all files necessary to open the raster map vegMDFINmaj. This

file represents the vegetation classification of the area, using the minimum distance method

and after applying a majority filter.


• Produced in Ilwis using ASTER

• Coordinate system: Fuseau 14

• More information on the procedure and possible errors can be found in this work.

QA report DEM 1.doc: the quality report of the produced DEM (see paragraph 8.8).

14.2 Maps The following pages contain some maps that were produced in this thesis. Because they were

too large to insert in the plain text they are added to this work as an appendix. These maps

are:


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• Map 1: Bas-Congo: Height model.

This map represents the produced DEM (see chapter 8) that was corrected in Ilwis (see

paragraph 10.2.1.2).

• Map 2: orthophoto of the study area.

This map was produced with Virtuozo (see paragraph 8.7)

• Map 3: contourlinemap of the study area

This map was produced in Virtuozo (see paragraph 8.7.3).

• Map 4: overlay of contourmap on orthophoto.

This map was produced in Virtuozo (see paragraph 8.7.3).

• Map 5: vegetation classification using maximal likelihood.

This map represents the vegetation classification using the maximal likelihood method. This map

was not withheld. See chapter 10.4.3.

• Map 6: vegetation classification using the mimimal mahalanobis distance.

This map represents the vegetation classification using the mimimal mahalanobis distance

method. This map was not withheld. See chapter 10.4.3.

• Map 7: Bas-Congo: vegetation classification.

This map is the final result of the vegetation classification. See chapter 10.

• Map 8: Bas-Congo: erosion risk classification.

This map was produced in chapter 11.

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