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Digital terrain analysis and image processing for assessing erosion prone areas
A Case Study of Nam Chun Watershed,
Phetchabun, Thailand
Monton Suriyaprasit
March, 2008
Digital terrain analysis and image processing for assessing erosion prone areas
A Case Study of Nam Chun Watershed, Phetchabun,
Thailand
by
Monton Suriyaprasit
Thesis submitted to the International Institute for Geo-information Science and Earth Observation in
partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science
and Earth Observation, Specialisation: (fill in the name of the specialisation)
Thesis Assessment Board
Prof.Dr. V.G. Jetten (Chair)
Dr. T.W.J. van Asch (External Examiner)
Dr. D.P. Shrestha (First Supervisor)
Dr. D.G. Rossiter (Second Supervisor)
Observer
Drs. T.M. Loran (Course Director AES)
INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION
ENSCHEDE, THE NETHERLANDS
Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.
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Abstract
Soil erosion is one of the severe land degradation problems in many parts of the world. This requires
the crucial data to predict critical areas for erosion but they are not easily to acquire in mountainous
areas because of inaccessibility. To assess soil loss in such mountainous area image processing
approach were applied. Suitable image processing approaches were undertaken. The RMMF erosion
model was used considering crucial input parameter such as cover management known as C-factor.
The model then was run by using script in ILWIS 3.3 after all the input parameters were generated.
The results of soil loss 2007 in different land use/cover types were used as standard pattern to study
the effect of land use/cover change in periods 1988, 2000 and 2007 on overall amount of soil loss in
this area. Terrain parameters i.e. slope, flow accumulation were used to map gully formation and
compared with the erosion prone areas that were classified from the results of RMMF erosion model.
The result from land use/cover classification showed the trend of the study area was transformed from
forest to agriculture areas. For C-factor generation, the regression equation (curve estimation) based
on field assessment of C-factor using training values and NDVI gave the satisfy results; adjust R2
(0.78), C.E. (0.77), M.E. (-0.04) and RMSE (0.03). The results from the erosion model illustrated the
highest soil loss occurred in the agriculture areas meanwhile the lowest was found in forest areas.
After applied the rate of soil loss in 2007 as the standard for periods 1988 to 2007, the results
illustrated that the overall amount of soil loss in the study area was raised followed the increasing of
agriculture areas. The critical zones defined most of the gully formation occurred in the same
locations as the RMMF model prediction. These results showed that remote sensing data can use for
assessing erosion prone areas in the areas where accessibility is limited.
Keywords: C-factor, NDVI, RMMF erosion model, critical zones
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Acknowledgements
I would like to express my gratefulness to the Netherlands government and people for granting me the
scholarship and Land Development Department, Thailand for giving me the chance to study in this
nice country. Special thanks to Mrs. Parida Kuneepong and Mr. Boonrak Patanakanog who did all
effort bringing me here.
I am greatly indebted to my supervisors Dr. D.P. Shrestha and Dr. D.G. Rossitor for their valuable
support, supervision advices and constructive comments. They teach me the real scientific research
and enormous encourage me to improve the quality of my work.
Especial thanks go to all Earth System Analysis staff members. Special gratitude to Prof. V.G. Jetten
who gave the valuable comments during Midterm presentation and Dr. A. Fashad for the parental care
me during whole period of my study.
I would like to thank the staff members of the Land Development Department, Thailand for their
hospitality during the fieldwork.
Many grateful to all my classmates who gave me friendship and cheerfulness during this period:
Sanjaya Devkota who suggested me the professional knowledge in soil science. Kanya Souksakul who
was encourages me and passed very hard time together. Thanks for supporting me and having good
time together.
The deepest appreciation goes to my fiancé Adchara Thongyou for everything she have done for me:
Her love, care, support and patience.
Finally, I would like to express the greatly thanks to my parents and brother for their love and every
good things that make me success. I dedicated this work to them.
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Table of contents
1. Introduction .......................................................................................................................1
1.1. Background.............................................................................................................................1
1.2. Statement of the problem........................................................................................................1
1.3. Objectives ...............................................................................................................................2
1.4. Research Hypothesis...............................................................................................................2
1.5. Research Questions.................................................................................................................2
2. Literature review...............................................................................................................4
2.1. soil erosion..............................................................................................................................4 2.1.1. Soil erosion by water ....................................................................................................................... 4 2.1.2. Factors controlling rate of soil erosion by water.............................................................................. 5
2.2. Land use changes effect soil physical properties....................................................................6
2.3. Terrain parameters ..................................................................................................................7
2.4. Digital terrain analysis............................................................................................................8
2.5. Erosion model .........................................................................................................................9
2.6. Image processing for removing topographic effect ..............................................................13 2.6.1. Topographic normalization............................................................................................................ 14 2.6.2. Sum normalization ......................................................................................................................... 14
2.7. Normalized Difference Vegetation Index (NDVI) ...............................................................14
3. Study area ........................................................................................................................16
3.1. Location ................................................................................................................................16
3.2. Climate..................................................................................................................................17
3.3. Geology.................................................................................................................................18
3.4. Soils ......................................................................................................................................18
3.5. Vegetation and land use........................................................................................................18
4. Material and methods .....................................................................................................19
4.1. Materials ...............................................................................................................................19
4.2. Research methods .................................................................................................................20 4.2.1. Land use/cover classification in mountainous areas....................................................................... 24 4.2.2. C-factor mapping ........................................................................................................................... 27 4.2.3. Erosion assessment ........................................................................................................................ 33 4.2.4. Assessing create critical zones for ephemeral gully incision ......................................................... 38
4.3. Data analysis .........................................................................................................................40 4.3.1. Laboratory analysis........................................................................................................................ 40
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4.3.2. Soil properties analysis ..................................................................................................................42 4.3.3. Statistical analysis ..........................................................................................................................43
5. Results and discussions ...................................................................................................45
5.1. Land use classification......................................................................................................... 45 5.1.1. Land use/cover classification result ...............................................................................................45 5.1.2. Accuracy assessment......................................................................................................................47 5.1.3. Trend of land use/cover change from 1988 to 2007.......................................................................49
5.2. C-factor mapping for erosion assessment ............................................................................ 50 5.2.1. Validation of C-factor map ............................................................................................................51
5.3. Soil erosion assessment ....................................................................................................... 52
5.4. Distribution of soil properties in different land use/cover types ......................................... 54 5.4.1. Distribution of soil organic matter in different land use/cover types .............................................54 5.4.2. Distribution of bulk density in different land use/cover types........................................................56
5.5. Assessment of land use/cover change effect on soil erosion............................................... 57
5.6. Assessing critical zones for ephemeral gully formation...................................................... 58
6. Conclusions and recommendations................................................................................62
6.1. Conclusions.......................................................................................................................... 62
6.2. Recommendations................................................................................................................ 62
6.3. Limitations of the study ....................................................................................................... 63
Reference..................................................................................................................................64
Appendices...............................................................................................................................68
Appendix 1: Field data ...................................................................................................................... 68
Appendix 2: Laboratory analysis ...................................................................................................... 75
Appendix 3: Organic matter 2006..................................................................................................... 78
Appendix 4: Regression analysis result summaries between C-factor values and NDVI................. 79
Appendix 6: Histogram of organic matter (a) and bulk density (b) ................................................. 82
Appendix 7: Geopedologic map (Solomon, 2005) used in RMMF model and legend.................... 82
Appendix 8: ILWIS script to run the RMMF model for annual soil loss prediction .......................84
Appendix 9: The photographs of gully erosion in the study area ..................................................... 85
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List of Figures
Figure 2-1: Flow chart of Revised Morgan –Morgan –Finney model ...................................................12 Figure 2-2: Effect of topography on reflectance ....................................................................................13 Figure 2-3: Effect of topography on the amount of sun illumination ....................................................14 Figure 3-1: The study area in the Phetchabun Province of Thailand ; (a) map of Thailand, (b) map of
Phetcabun province and (c) 3D view of Namchun watershed .......................................................16 Figure 3-2: Average annual rainfall and temperature of the study area.................................................17 Figure 3-3: Land use/cover changed from forest to orchard and cropland............................................18 Figure 4-1: Flow chart of overall methodology .....................................................................................23 Figure 4-2: Lansat TM March 3, 2007 color composite in 453 BGR ;..................................................25 Figure 4-3: Feature space plot of training samples ................................................................................26 Figure 4-4: Estimation Fc from field......................................................................................................27 Figure 4-5: Estimation Sp from field; (a) Sp around 75% and (b) Sp around 35%...............................28 Figure 4-6: The relationship between annual rainfall and elevation......................................................33 Figure 4-7: Slope map (a) and Aspect map (b) ......................................................................................38 Figure 4-8: Drainage network (a) and catchment (b) extraction............................................................39 Figure 4-9: Critical zones creation.........................................................................................................39 Figure 4-10: Laboratory analysis in ITC; (a) preparing soil samples, (b) walkley - black method, .....40 Figure 4-11: USDA soil texture triangle................................................................................................42 Figure 4-12: SPAW model .....................................................................................................................43 Figure 5-1: Land use/cover classification maps; (a) 1988, (b) 2000 and (c) 2007 ................................46 Figure 5-2: Area in percent in different land use/cover 1988 – 2007....................................................46 Figure 5-3: Area of land use/cover change in periods 1988, 2000 and 2007.........................................50 Figure 5-4: C-factor map 2007 derived from NDVI; .............................................................................51 Figure 5-5: The relationship between C-factor prediction and validation from curve estimation.........52 Figure 5-6: Soil loss map 2007 ..............................................................................................................53 Figure 5-7: Erosion prone areas classified from soil loss map 2007 .....................................................54 Figure 5-8: Distribution of soil organic matter (%) in different land use/cover....................................55 Figure 5-9: Distribution of bulk density (g/cm3) in different land use/cover ........................................56 Figure 5-10: Sensitive areas (a) and critical zones (b)...........................................................................58 Figure 5-11: Gully erosion formation prediction with validated gully erosion points ..........................59 Figure 5-12: The areas of erosion level in percent.................................................................................60 Figure 5-13: The areas of gully formation in percent ............................................................................60 Figure 5-14: The comparison between gully erosion prediction and erosion prone areas ....................61
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List of Tables
Table 3-1: Average monthly rainfall, rainy day and temperature period 36 years (1970 – 2006)....... 17 Table 4-1: Parameters input to the model and their sources ................................................................. 19 Table 4-2: Validation data and sources................................................................................................. 20 Table 4-3: Canopy cover sub factor in different land use/cover........................................................... 29 Table 4-4: Surface cover sub factor in different land use/cover ........................................................... 29 Table 4-5: Surface roughness sub factor in different land use/cover.................................................... 30 Table 4-6: Annual rainfall at various elevations obtained from ITC.................................................... 33 Table 5-1: Comparison the areas of land use/cover in 1988, 2000 and 2007 ....................................... 47 Table 5-2: Comparison of the accuracy between topographic normalization and sum normalization . 47 Table 5-3: Accuracy assessment of land use/cover classification 2007 ............................................... 48 Table 5-4: Crop calendar for cultivation of Phetchabun Province in 2007........................................... 48 Table 5-5: Land use/cover change in the periods 1988 - 2007 ............................................................. 49 Table 5-6: Comparison between three of C-factor prediction techniques ............................................ 52 Table 5-7: Soil loss prediction in different land use/cover 2007 .......................................................... 53 Table 5-8: Erosion prone areas in different land use/cover .................................................................. 54 Table 5-9: Average soil organic matter content in different land use/cover......................................... 55 Table 5-10: Average bulk density in different land use/cover .............................................................. 56 Table 5-11: Amount of soil loss (t/y) in periods of 1988, 2000 and 2007............................................ 58 Table 5-12: Contingency matrix between Critical zone and Erosion data from fieldwork ..................59 Table 5-13: Area of erosion prone areas prediction.............................................................................. 59 Table 5-14: Area of gully erosion prediction from critical zones......................................................... 60
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List of Abbreviations
AGNPS Agricultural Non-point source pollution model
DEM Digital elevation model
EUROSEM European Soil Erosion Model
FAO Food and Agriculture Organization
LDD Land Development Department, Ministry of agriculture and cooperatives, Thailand
MMF Morgan, Morgan and Finney Model
NDVI Normalized Difference Vegetation Index
SLEMSA Soil Loss Estimation Method for Southern Africa
RMMF Revised Morgan, Morgan and Finney Model
RUSLE Revised Universal Soil Loss equation
USDA United States Department of Agriculture
USLE Universal Soil Loss equation
WEPP Water Erosion Prediction Project
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
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1. Introduction
1.1. Background
Population has been increasing everywhere especially in developing countries, which leads to more
demand for food production. This situation can cause major changes in land use in the coming
decades and demand on higher agriculture production (Stein and Goudriaan, 2000). To support
increased food demand, expansion of agriculture lands is necessary increasing. This leads to
encroachment of sloping and marginal lands in the mountainous areas. Clearing land for agriculture
areas, cutting down forests together with intensive use of land for more products in the sloping land
have led to land degradation.
Land degradation is a process that lowers the capacity of land. According to FAO (1994), there are six
types of land degradation: water erosion, wind erosion, soil fertility decline, salinitation, water
logging, and lowering of the water Table. The unbalance between land resource regeneration rate and
population growth rate leads to lack of suitable land for agriculture (Vargas Rojas, 2004). Unless soil
conservation and management practices are implemented properly, soil erosion can cause loss of plant
nutrient, weak soil aggregation and finally low agriculture production. Improper land use practices in
sloping areas accelerate soil erosion.
Soil erosion not only reduces soil depth, but also reduces the capacities of soil such as water holding
and decrease plant nutrient. In the long term, soil productivity will be decreased. Furthermore it can
cause offsite effects including pollution in water, downstream sediment in river bank and reservoirs. It
is necessary to understand erosion and sedimentation process for soil conservation planning.
1.2. Statement of the problem
Namchun watershed is in severe problems of deforestation for the long times. The original
characteristic of this mountainous areas are dense forest that have been occupied by local farmers
since long. The areas are changed to cropland where maize is the main crop in. Due to the cultivation
on steep slopes together with removal of vegetation cover, has caused negative effect on soil
properties and its structure. Moreover, improper land use practices in agricultural areas triggering soil
erosion process in the watershed. Although several studies were carried out in past, none of them are
able to predict soil loss and land degradation; leading to look alternatives way analysis.
In order to assess soil erosion, erosion model such as RMMF is one of the alternative ways to
investigate the soil loss rate. By the reason of simplicity of the model and also it was developed for
hill slope, is the reason behind the selection of the model. This model involves with a number of input
parameters. One of the crucial parameters is cover management factor known as C-factor which
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
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depends on land use/cover types. To obtain C-factor values and model the soil loss, the classification
of land use/cover is needed. Unfortunately, there are a lot of inaccessible areas causing the major
problem. Remote sensing data seem to be the appropriate solution to derive these essential parameters.
Terrain parameters such as slope that extracted from elevation model (DEM) are also required
parameter in this erosion model. For example, slope gradient is not only using to calculate soil
detachment by runoff but also transport capacity by runoff as well. Due to the lack of erosion data, the
model result could not be validated at this stage, however, the result can be compared by deriving
critical zones threshold. Critical zones threshold is calculated using hydrological parameters such as
flow direction, flow accumulation, slope and catchment area together with flow width.
1.3. Objectives
The main objective of this research is to assess erosion prone areas in inaccessible mountain areas.
The specific objectives of the research are the following.
� To investigate the potential of image processing techniques such as correction of topography
induced constraints for classifying land use/cover in the mountainous area.
� To generate cover factor (C-Factor) from remote sensing techniques to be used in erosion
modelling.
� To study the distribution pattern of soil properties in different land use/cover types and the
effect of land use/cover change in soil erosion.
� To use terrain parameters to map critical zones for gully formation.
1.4. Research Hypothesis
� Land use/cover pattern change from forest to agriculture areas increases overall amount of
soil loss in the study area.
� C-factor estimation from NDVI can be improved using field data.
� Critical zones for gully formation can be mapped using terrain parameters.
1.5. Research Questions
� What are the image processing techniques that help to remove topographic effect?
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
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� What is the change pattern of land use/cover to the soil properties? And what role it has in
overall soil erosion in the watershed?
� What will be the best technique of generating C-factor from remote sensing data?
� Which terrain parameters indicate gully formation?
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
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2. Literature review
2.1. soil erosion
Among the several land degradation processes, such as soil compactness, soil salinitation, soil acidity,
etc. Soil erosion is a one, that affect environmental and food production. Kruthkul et al. (2001) define
soil erosion as the processes of detachment and transportation of soil. It is caused by erosion agents
including water and wind. Soil erosion can be divided in two groups, natural erosion and accelerated
erosion. Both natural and accelerated erosion appear in the nature, it may slow continue process or
sudden occurs with severe loss of topsoil. However, accelerated erosion also can occur where natural
rate of erosion is increased by human activities. Soil erosion is widespread in mountainous areas due
to steepness of slope, this in combination with improper land use practices including overgrazing
without proper conservation plan. Erosion degrades soil by removing topsoil, decreasing plant
nutrients and rooting depth (Petter, 1992)
2.1.1. Soil erosion by water
Soil erosion by water can be described in two stages: detachment and transportation of sediments. The
detachment of soil particles is due to raindrop impact, caused by its kinetic energy. Soil particle
detachment is also caused by the scouring effect of overland runoff. The other is transportation of soil
particles by water that can be caused by buoyancy of particles and turbulence of water (Kruthkul et
al., 2001). Water erosion starts in the form of shallow flow which is called sheet erosion. There is no
channel form in this step. It is the removal of topsoil as thin layer. Rill erosion consists of numerous
small channels caused by concentrated overland flow. Rills occur mostly on bare surfaces on sloping
areas, they can be eliminated by tillage operations. The severe permanent erosion form is gully where
huge channels can remove large quantities of topsoil. Unlike rill, gully erosion cannot be annihilated
by normal tillage (Gebrekirstos, 2003).
Accelerated erosion by water can be divided as following (Shrestha, 2007):
1. Sheet erosion is the uniform removal of soil particles where the areas are influenced by effect
of rain splash. The result of this action is detachment and transportation of the detached
sediment by runoff. On slope areas, sheet wash can takes place to remove shallow layer of
soil.
2. Rill erosion is the removal of soil where small linear channels are formed due to concentrated
runoff. This type of water erosion can occur during or suddenly after rainfall. Sometime rills
can be discontinuous and do not thus take part of drainage network. Rills not only act as
erosion process, but also act as transporting agent for the detached particles. Since 30 cm. is
the maximum size for defining rills, they can easily be obliterated by normal tillage operation.
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
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3. Gully erosion is developed by scouring effect of concentrated overland flow causing deeply
incised channels. Sometime they are initiated after surface collapse from piping. Definition of
gullies is limited by 30 cm. minimum depth. They can take place during or suddenly after
heavy rainfall. Gullies may have also continuous flow at gullies bottom due to seepage water.
Collapsing of the steep wall can widen gullies and the depth can go deeper, up to a limiting
depth of 30 meters above which they are no longer consider gullies. The direction of gullies
enlargement is upstream during concentrated runoff and head ward erosion due to formation
of small waterfalls. Because of depth themselves, they cannot easily eradicated by normal
tillage operation and can damage infrastructure such as road or building.
2.1.2. Factors controlling rate of soil erosion by water
The rate of soil erosion by water can be control by following factors (Wall et al., 1987):
1. Rainfall intensity and runoff
Rainfall intensity is considered as an important initial factor in erosion process. The kinetic energy of
rain is highly correlated with soil detachment and eventually to erosion. Hence, erosion and runoff
can be predicted by rainfall intensity (Hammad et al., 2006). The raindrops on soil surface can
collapse soil aggregates and scatter those materials. Heavier materials for instance larger sand and
gravel need more raindrop energy to detach as well as more amount of runoff to remove. Conversely
lighter materials can be easily removed. Surface runoff is formed when rainfall intensity of storm
exceeds the infiltration capacity of soil. Moreover, runoff can occur whenever there is excess water on
a slope that cannot be absorbed into the soil or trapped on the surface. The amount of runoff can be
increased if infiltration is reduced due to soil compaction, crusting or freezing. Runoff from the
agricultural land may be greatest during spring months when the soils are usually saturated, snow is
melting and vegetative cover is minimal (Wall et al., 1987).
2. Soil erodibility
Soil erodibility is used as indicator of soil erosion because it is a measure of soil susceptibility to
detachment and transport by the agents of erosion (Tejada and Gonzalez, 2006). Soil erodibility is
integrated effect of processes that regulate rainfall acceptance and the resistance of the soil to
detachment and subsequent transport of the detached particles. Soil properties including particle size
distribution, structural stability, organic matter content, clay mineralogy and water transmission
characteristics influence these processes (Lal, 1994). Usually, soils have more resistance to erosion if
they have high infiltration rates, high organic matter content and have developed structure. Soil
particle size classes such as very fine sand and silt tend to be more erodible than sand. Clay is also
resistant to erosion due to its consistency. Poor soil structure can occur because of tillage and
cropping practice with low level of organic matter. This may result in compacted soil which increases
soil erosion.. Increasing in runoff and decreasing infiltration rate can be the result from compacted
subsurface layer of soil. Further more, formation of soil crusting can also decrease infiltration rate in
the mean of surface sealing (Wall et al., 1987). Although sometime soil crusting can decrease erosion
such as sheet or rain splash, however, more rill erosion problems can occur because of increasing
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runoff. Low organic matter content in soil may lead to low nutrient content and poor vegetation cover
that means less protection of soil.
3. Slope gradient and slope length
A factor that is crucial in erosion assessment is slope. Not only slope gradient but also slope aspect,
length and shape of slope are equally important. Generally, steep slope has higher soil loss because
runoff water becomes more erosive and can detach sediment due to increasing flow velocity and high
runoff down the slope. Apart from slope gradient, slope length and aspect also become important
factors. On longer slope, an increased accumulation of overland flow tends to increase rill erosion.
Even steep slope may become less erosive if slope length is short, on the other hand gentle slope can
have more erosion because of longer slope length (Shrestha, 2007).
4. Vegetation
Vegetation cover plays an importance role by protecting soil, thereby minimizing the impact of
raindrop. Moreover, runoff rate also slows down and it provides enough time for surface water to
infiltrate. In completely dense cover situation it can be assumed that there is no erosion because the
impact of all the raindrops will be intercepted. Additionally, dead leaves also provide litters that
maintain organic matter of the soil. Reducing in organic matter with cultivation as soil disturbance
lead to deplete the stability of soil aggregates (Six et al., 2000).
2.2. Land use changes effect soil physical properties
Human activities impacts on the topography such as changes in land use are often observed with
synchronous changes in erodibility of soils, erosion patterns and suspended sediment concentration
and characteristics in river (Gerald, 2006). Land use changes influence the soil properties such as
bulk density, soil structure, and organic carbon content that affect soil hydraulic properties, including
soil hydraulic conductivity function and water retention characteristics (Zhou et al.). Increase of
vegetation cover increases infiltration capacity of soil by increasing soil organic matter and decreasing
bulk density. Giertz (2005) reported that macro porosity and permeability can be decreased because of
cultivation practices. This can be related to vegetation cover and land use changes.
Land use changes from forest to agriculture, can result in major changes in soil physical properties.
Vegetation cover protects the soil not only against the impact of falling rain but also protects the soil
by its root system. The plant roots help increase soil pore space thus helping in infiltration and better
soil structure. In addition, the dead leaves also increase organic matter content in soil. Modifications
of land use have an important influence on the soil organic matter content (Yadav and Malanson,
2007). Decreasing in organic matter due to cultivation is related to obliteration of macro aggregate.
Soil physical properties such as porosity and bulk density are important factors that can effect
hydraulic conductivity of soil and eventually soil erosion. As negative changes of these factors,
decreasing soil aggregate, porosity, increasing bulk density and reducing infiltration rate can lead to
soil erosion.
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2.3. Terrain parameters
Digital terrain parameters or topographic attributes known as or morphometric variable (Shary et al.,
2002) explains about features on the terrain surface that can be described in terms of morphographic
and morphometric attributes (Sharif and Zinck, 1996). Morphographic concept explains about
geometry of geoforms such as shape and profile of the topography, aspect and drainage pattern.
Conversely, morphometry is described about dimensions of the geoforms, including relative elevation,
valley density and slope steepness. They can be derived using digital terrain analysis techniques. The
digital terrain parameters can be divided in to three categories (Hengl et al., 2003):
1. Morphometric parameters are derived directly from the DEM by using filter operation. They can
be grouped as:
· Elevation change gradients: e.g. slope
· Orientation gradients: e.g. aspect, steepest downhill slope, viewshed;
· Curvature gradients: e.g. horizontal or tangent curvature, vertical or profile curvature, mean
curvature
Slope shows the rate of change in elevation in x and y direction. For aspect, it gives azimuth angle of
the sloping surface (orientation of central pixel). Plan curvature is curvature of corresponding normal
section, which is tangential to a contour, positive values give the divergence of flow, conversely
negative indicate concentration of flow. Vertical or profile curvature is curvature of corresponding
normal section, which is tangential to a flow-line. If the normal section concavity is directed up, it
gives negative values. For the positive values that means opposite case. An average of normal section
curvature is called mean curvature. Negative values describe mean-concave landforms, while positive
values refer to mean-convex ones (Hengl et al., 2003).
2. Hydrological or flow-accumulation based terrain parameters are normally used to explain flow of
material over a grid surface (Hengl et al., 2003), for example quantify flow intensity, accumulation
potential or erosion potential. They are Compound topographic Index (CTI), Stream Power Index
(SPI) Sediment Transport Index (STI). In general, CTI reflects the accumulation processes. STI and
SPI reflect erosive power of terrain and overland flow respectively.
Terrain parameters can be very much useful as supporting variable to facilitate spatial prediction of
erosion processes. Martinez-Casasnovas (2003) used digital elevation model as spatial information for
mapping gully erosion. Mati et al. (2000) reported slope steepness and slope length as input
parameters for assessment of erosion hazard in north basin of Kenya. Siepel et al. (2002) developed a
simple water erosion simulation model based on stream power, handles vegetation in terms of contact
cover, and considers the settling characteristics of the eroding sediment. Moreover Menendez-Duarte
et al.(2007) derived slope, flow direction and flow accumulation from digital elevation model to
quantify erosion forms and drainage areas in northern Iberian peninsular.
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2.4. Digital terrain analysis
Digital terrain analysis is a process to describe the terrain quantitatively. This process is used to derive
terrain parameters from DEM (Hengl et al., 2003). Some terrain parameters that necessary for this
research including slope aspect, flow direction, flow accumulate were generated from DEM. These
parameters together with drainage network and upper stream area were used to calculate critical zones
for gully formation as following:
Slope aspect The slope aspect describes the direction of maximum rate of change in the elevations between each
cell and its eight neighbours. It can essentially be thought of as the slope direction. It is measured in
positive integer degrees from 0 to 360, measured clockwise from north (Paron and Vargas, 2007).
Aspects of cells of zero slopes (flat areas) are assigned values of -1. Flow direction The flow direction defines the direction of flow from each cell in the DEM to its steepest down-slope
neighbour. The method designated D8 (eight flow directions) for defining flow directions was
introduced by O’Callaghan and Mark (1984). It uses for assigning flow from each pixel to one of its
eight neighbors, either adjacent or diagonal, in the direction with steepest downward slope (David,
1997).
Flow accumulation Flow accumulation is the total number of cells that would contribute water to a given cell (ESRI,
2007); it defines the amount of upstream area drainage based on the accumulated weights for all cells
that flow into each down slope cell. It is essentially for measuring the upstream catchment area. The
flow direction layer is used to define which cells flow into the target cell. The results of flow
accumulation can be used to create a stream network by applying a threshold value to select cells with
a high accumulated flow (ESRI, 2007).
Drainage network extraction The Drainage Network Extraction operation extracts a basic drainage network (boolean raster map).
The output raster map showed the basic drainage as pixels with value true, while other pixels have
value false. Depending on the flow accumulation value for a pixel and the threshold value for this
pixel, it is decided whether true or false should be assigned to the output pixel. If the flow
accumulation value of a pixel exceeds the threshold value, the output pixel value will be true; else,
false is assigned (ITC and RSG/RSD, 2005).
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
9
Critical zone The critical zones or sensitive areas are the areas that prone to ephemeral gully erosion. Generally, the
gully incision is expected to appear when contributing area together with local slope exceed a given
threshold (Jetten et al., 2006). There are many algorithms for determine threshold based on slope
(m/m), upstream area (m2) and flow width (m). A strong correlation was found between the rill cross-
sections and a power function of slope gradient and contributing area reference.
2.5. Erosion model
Every soil erosion model try to simplify and represent the complexity of natural processes (Shrestha,
2007). Model building is based on defining the essential factors relating to erosion and soil loss
through obtaining from methodology of field observation, measurement, experiment and statistical
analysis. With increasing computation power of computers, many models have been developed.
However, only one model can not cater and solves various problems (Gebrekirstos, 2003). This is the
reason why many models are available. Users need to understand the concepts behind the models
before applying them. Some models are developed for particular conditions that can not be directly
applied to other locations. Usually, erosion models can be categorized into three groups: empirical,
conceptual and physical based. The distinction between models is not obvious and therefore can be
somewhat subjective. They are likely to contain a mix of modules from each of these categories
(Merritt et al., 2003). The frequently used models are described as follows:
1. The Universal Soil Loss Equation (USLE)
USLE was developed in the 1970s by United States Department of agriculture (USDA). This soil
erosion model used widely within the United States and worldwide (Merritt et al., 2003). The
equations in this model have been developed using statistical analysis of data from 10,000 plots years
from natural run off plots together with 2,000 plot years of artificial rainfall simulators in USA
(Wischmeier and Smith, 1978). Sheet and rill erosion are predicted by using values for indices that
represent the four major factors affecting erosion: R-climatic erosivity, K-Soil erodibility, L- and S-
topography, and C and P-landuse. The model has undergone a number of modifications. The model
has also been upgraded to take into account additional information that has become available since the
development of the USLE (Renard, 1997). There are some limitations in this model and can not
identify events as long term erosion. The model can only predicts inter rill erosion, but not gully,
channel or stream bank erosion. It can estimate soil particles movement but ignore deposition. The
accuracy of the equations is bias when using only short-term rainfall records (Merritt et al., 2003).
2. Revised Universal Soil Loss Equation (RUSLE)
This model has the same factors as USLE. It updates the USLE model and incorporates new material
that has been available informally or from scattered research reports and professional journals. It has
been developed to replace the USLE, but it has the same limitations (Gebrekirstos, 2003;
Saengthongpinit, 2004).
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3. SLEMSA (Soil Loss Estimation Method for Southern Africa)
SLEMSA was developed in Zimbabwe, base on USLE. It was developed from the data of the
Zimbabwe highveld to evaluate the result of erosion from various farming systems (Morgan, 1995).
The technique has since been adopted throughout the countries of Southern Africa.
4. Water Erosion Prediction Project (WEPP)
WEPP is a physical based hydrological and erosion model designed to replace the USLE (Laflen et
al., 1991). This model contains two sub-models with hill slope version and watershed version. Hill
slope version can estimate soil detachment and deposition along a hill slope profile and the net total
soil loss is estimated from the end of the slope without considering erosion, transportation and
deposition processes in permanent channels. For watershed version that allows estimation of net soil
loss and deposition over small catchments, it uses for applying to field areas that include ephemeral
gullies which can be farmed over and links these surface erosion processes to the channel network. It
can run for a single storm and on a continuous simulation.
5. Agricultural Non-point source pollution model (AGNPS)
Objective of AGNPS is to compute soil erosion within a watershed. This model is grid cell based and
was developed to estimate runoff quality, with primary emphasis on sediment and nutrient transport
(Young et al., 1989). Since it can be linked to a geographic information system (GIS), its application
in a watershed environment may be more interesting for data integration. Input data for the AGNPS
model include parameters describing catchment morphology, and land use variables and precipitation
data (Merritt et al., 2003). The model extracts topographic variables and land surface characteristics
from basic GIS data layers such as contour, drainage lines and watershed boundaries. The large data
requirements and computational complexity of AGNPS are the limitations of this model.
6. European Soil Erosion Model (EUROSEM)
This model is an event-base model that designs for computing erosion, sediment transport and
deposition over the land surface throughout a storm. It can simulate both rill and inter-rill erosion
including the transport of water and sediment from inter-rill areas to rills. Moreover, it takes into
account effect of leaf drainage and rainfall intercept by vegetation cover. This model can be applied to
individual fields or small catchments (Shrestha, 2007).
7. The Morgan Morgan Finney Model (MMF)
This model was developed to predict annual soil loss from field sized areas on hill slopes. The model
has the simplicity of the universal Soil Loss Equation and yet it covers the advances in understanding
of erosion process (Morgan et al., 1984). This model is a physically based empirical model (Mix
model) and needs less data than most of the other erosion predictive models. This model divides soil
erosion process in two phases including a water phase and a sediment phase. The MMF model can be
easily applied in a raster-based geographic information system (Shrestha, 2007).
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
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8. Revised Morgan – Morgan – Finney (RMMF)
The RMMF model separates the soil erosion process into two phases: the water phase and the
sediment phase. The water phase determines the energy of the rainfall available to detach soil
particles from the soil mass and the volume of runoff. In the erosion phase, rates of soil particle
detachment by rainfall and runoff are determined along with the transporting capacity of runoff
(Morgan, 2001). The difference from MMF model are the stimulate of soil particle detachment by rain
drop that takes account of plant canopy height and leaf drainage, and a component has been added for
soil particle detachment by flow (Morgan, 2001). The detail of RMMF model can describe as
following (Morgan, 1995):
A. Water phase
Estimation of rainfall energy
Rainfall energy was calculated by using the partitioned rainfall after interception together with energy
of the leaf drainage. First the model computed the proportion of rainfall amount that reach the ground
surface after allowing for rainfall interception to derive effective rainfall. After derived, effective
rainfall then was distributed in two parts. First, rainfall that reached the ground surface after being
intercept by plant canopy as leaf drainage, conversely, second part that rainfall reached the ground
surface without interception. Then kinetic energy was calculated by distributed for effective rainfall
of leaf drainage and direct through fall. Kinetic energy of leaf drainage was a function of plant height
meanwhile kinetic energy of direct through fall was a function of rainfall intensity.
Estimation of runoff
The annual runoff was calculated by using three factors including soil moisture storage capacity,
annual rainfall and mean rainy days. For soil moisture capacity, the calculation was in turn a function
of bulk density, effective hydrological depth, ratio of actual to potential evapotranspiration and soil
moisture content at field capacity.
B. Sediment phase
Estimation of soil particle detachment by raindrop and runoff
The calculation of soil particle detachment was divided in two parts. First, the model considered soil
particle detachment by raindrop impact. Second, soil particle detachment by runoff was taken to
account. Soil particle detachment by raindrop was a function of kinetic energy of effective rainfall and
soil erodibility. The calculation of detachment by runoff was a function of runoff, soil resistance in
turn of surface cohesion, ground cover and slope steepness. In this model assumed that soil
detachment by runoff can only appear where soil was not protected by ground cover. Total particle
detachment (D ; kg/m2) was finally calculated as a sum of both soil particle detachment by raindrop
and runoff.
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Transport capacity of runoff
The transport capacity was estimated by using runoff, surface cover factor and slope.
Prediction soil loss
The last calculation step of RMMF model was prediction annual soil loss. Total detachment and
transport capacity were compared and erosion rate was the minimum of the result from the
comparison.
The flowchart of RMMF model is shown in Figure 2-1 as following:
Figure 2-1: Flow chart of Revised Morgan –Morgan –Finney model
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13
There many studies used erosion model for predict soil loss by modifying various factors. Evaluates
soil erodibility (K) and identifies factors affecting K for calcareous soils was done in Hashtrood City,
northwestern Iran (Vaezi et al.). In the study of Assessment of USLE cover-management C-factors for
40 crop rotation systems on arable farms (Gabriels et al., 2003), The distribution of the rainfall
erosivity over the year was calculated and crop rotation systems were examined, in order to assess the
C-values according to the USLE methodology.
2.6. Image processing for removing topographic effect
Image processing involves the manipulation of images for the following purpose (Cracknell and
Hayes, 1991):
1. To extract information;
2. To emphasize or de-emphasize certain aspect of the information contained in image ; or
3. To perform statistical or other analyses to extract non-image information
Satellite remote sensing produces very large quantities of digital data that including the mountainous
areas. The image processing is the sensible way to handling vast quantities of information available in
remote sensing data (Harris, 1987). The images of mountainous areas regularly distorted in
radiometric known as topographic effect. As the result from variations of illumination since the solar
and the terrain’s angle, formulates a brightness variation in the images. The topographic effect consist
of the following factors (Hodgson, 1994).
� incident illumination —the orientation of the surface with respect to the rays of the sun
� existence angle—the amount of reflected energy as a function of the slope angle
� surface cover characteristics—rugged terrain with high mountains or steep slopes
Figure 2-2: Effect of topography on reflectance
Source: (Riano et al., 2003)
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2.6.1. Topographic normalization
This technique is used to correct the illumination of the sun that influence of topography induced
constraints in the mountainous areas. Topographic normalization is based on Non-Lambertian
Reflectance model, which is operated by using solar azimuth and solar elevation of satellite images.
The model assumed that the observed surface does not reflect incident solar energy uniformly in all
directions (Minnaert and Szeicz, 1961). Instead it needs to take into account variations of terrain.
Minnaert Constant (k) The Minnaert constant (k) may be found by regression a set of observed brightness values from the
remotely sensed imagery with known slope and aspect values, provided that all the observations in
this set are the same type of land cover. The k value is the slope of the regression line (Hodgson,
1994):
Figure 2-3: Effect of topography on the amount of sun illumination
Source: (Shrestha and Zinck, 2001)
2.6.2. Sum normalization
Another technique to remove topography effect from satellite images was sum normalization.
According to Shrestha and Zinck (2001), this technique used to minimize the effect of illumination
differences on the surface reflectance. The intensity of each satellite band was normalized by using
sum of illumination of every band then multiplies with constant value.
2.7. Normalized Difference Vegetation Index (NDVI)
Normalized Difference Vegetative Index (NDVI) is calculated as a ratio between measured
reflectivity in the red and near infrared portions of the electromagnetic spectrum. These two spectral
bands are very affected by the absorption of chlorophyll in leafy green vegetation and by the density
of green vegetation on the surface. Further more, in these two bands, the different between soil and
vegetation is at a maximum. (CGIS, 2004). In general, vegetated areas have high reflectance in the
Multi – spectral Sensor
Shadow
High Illumination
Low Illumination
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near infrared and low reflectance in the visible red. In this index green vegetation are high values ,
water has negative values and bare soil has value around 0. For the middle values, they are indicator
for differences in coverage with green vegetation. The NDVI, as a normalized index, is compensating
changes in illumination conditions, surface slopes and aspect (Lillesand and Kiefer, 1999). NDVI can
be calculated from the following equation.
Equation 1:( )
( )
NIR RNDVI
NIR R
−=+
Where : NIR = a reflectance in the near infrared band
R = a reflectance in red band
For Landsat TM , the formula can change into following equation.
Equation 2: ( 4 3)
( 4 3)
TM TMNDVI
TM TM
−=+
Where : 4TM = Landsat TM band4 (0.76 - 0.90 mµ )
3TM = Landsat TM band3 (0.63 - 0.69 mµ )
Relation between NDVI and C-factor
There are many researches involve with the relationship between NDVI and C-Factor, De Jong (1994)
reported in his PhD thesis on Remote Sensing Applications in Mediterranean areas. By using field
data of 33 plots for statistical analysis, He described that there was a linear relation between NDVI
and USLE C-Factor with a correlation factor of -0.64. In the report of Soil Erosion Risk Assessment
in Italy, Van der Knijff et al. (1999) found the relationship between them in exponential equation.
This seems to be can give more realistic C-factor than linear equation.
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3. Study area
3.1. Location
Study area is in Nam Chun watershed in Thailand (Figure 3.1). It falls in two districts namely Lom
Sak and Khoa Khor districts of the Petchabun province in Thailand. It is 400 kilometers north of
Bangkok and 40 kilometers far from the provincial capital. It lies between the latitudes 16� 40’ and 16�
50’ North and between the longitudes 101� 02’ and 101�15’ East. The study area covers surficial areas
of 67 km2.
(a) Map of Thailand (b) Map of Phetchabun province
(c) 3D view of Namchun watershed
Figure 3-1: The study area in the Phetchabun Province of Thailand ; (a) map of Thailand, (b)
map of Phetcabun province and (c) 3D view of Namchun watershed
Source: (a) http://www.lib.utexas.edu/maps/middle_east_and_asia/thailand_pol88.jpg
(b) http://www.tat.or.th/travelmap.asp?prov_id=67
N
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17
0.0
50.0
100.0
150.0
200.0
250.0
Jan
Feb Mar Apr
May Ju
n Jul
Aug Sep OctNov Dec
Month
Rai
nfal
l (m
m)
0.05.010.015.020.025.030.035.0
Tem
pera
ture
(C)
Rainfall (mm)
Mean Temp. (C)
3.2. Climate
The climate in the study area is tropical which is characterized by having high humidity, moderate to
high temperature and a distinct climatic variation between dry and wet seasons. This climate is
influenced by the northeast and southwest monsoon. According to Table 3-1, there are three seasons
in this area, dry and hot, wet and hot and relative dry cool periods. The dry and hot period began from
March until April, hot rainy period illustrated from May until October and relative cool period started
on November to February. Average annual rainfall in this area is 1075 mm with 120 rainy days that
are estimated from climatic data in the period of 1970 to 2006. Average annual temperature is 28 oC.
Highest temperature is of 37.5oC in April and lowest temperature is 17.4 oC during winter in
December. Detail climatic data is given in Table 3-1. Figure 3-2 shows average monthly rain and
temperature, derived from Lom Sak Meteorological station’s records that cover 36 years period from
1970 – 2006.
Table 3-1: Average monthly rainfall, rainy day and temperature period 36 years (1970 – 2006)
Month Rainfall (mm) Rainy day Max Temp. (oC) Min Temp. (oC) Mean Temp. (oC)
Jan 4.5 1 32.8 17.5 25.2
Feb 22.2 2 34.8 19.5 27.2
Mar 46.5 5 36.6 22.0 29.3
Apr 58.9 8 37.5 24.3 30.9
May 159.1 16 35.8 25.0 30.4
Jun 148.1 17 34.0 25.1 29.6
Jul 141.4 18 33.2 24.8 29.0
Aug 197.4 21 32.5 24.7 28.6
Sep 198.3 19 32.9 24.5 28.7
Oct 78.1 10 33.2 23.3 28.3
Nov 15.3 2 32.5 20.4 26.5
Dec 4.8 1 31.6 17.4 24.5
Total 1074.6 120 34.0 22.4 28.2
Source: Lom Sak Meteorological station
Figure 3-2: Average annual rainfall and temperature of the study area.
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3.3. Geology
According to Mineral Resources Department, Thailand (2006), geology in this area is composed of
uplifted sedimentary rocks of the Korat group in the upper catchments. Various formations were
formed in different periods of time. In Triassic period, the oldest Huai Hin Lat formation consists of
conglomerate, sand stone and shale. The Nam Phong formation contains reddish-brown cross-bedded
sand stone and conglomerate. In Jurassic period, Phu Kradung formation was formed along the scarp
in study area which consists of silt stone, shale and sandstone. The youngest formation is Pha Wihan
which consists of white and pink, cross-bedded sandstone with pebbly layers in the upper beds with
some intercalations of the reddish-brown and grey shale. Quaternary colluvial and alluvial terrace
deposits occur in the lower areas
3.4. Soils
The main landscapes of the area are the high plateaus, the mountainous areas and the low-lying
narrow valley. The soils are characterized by high clay content categorized mainly in the silt loam to
silty clay loam textures. Soils are mainly of different groups of Inceptisols, Alfisols, Ultisols, and
Entisols (Auanon et al., 2004). The availability of high clay content in the soils may indicate that the
erodibility of the soil tends to be less as far as its physical characteristic is concerned (Morgan, 1995).
3.5. Vegetation and land use
In the study area, mainly five land use types can be classified namely forest, degraded forest,
cropland, grassland and orchard. The area has undergone heavy deforestation in the recent past. The
forest areas were turned into orchard and cropland (Figure 3-3). Recently reforestation program have
been implemented, some forest tree species such as teak, eucalyptus, gliricidia and leucaena were
planted. Tamarind trees are cultivated in the hill slope areas which are intercroped with maize,
soybeans and mungbeans. For grasslands, the upper catchment is dominated by grass species Impecata cylindica (Saengthongpinit, 2004; Solomon, 2005).
Figure 3-3: Land use/cover changed from forest to orchard and cropland
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4. Material and methods
4.1. Materials
Materials used in this study are:
� Digital elevation model (DEM) resolution 5 meters from Land Development Department
(LDD) Thailand.
� Ortho-photo mosaic of the study area from Land Development Department (LDD) Thailand
with scale 1: 4,000 resolution 0.5 meters.
� Topographic maps of study area were obtained by with scale 1:50,000 from Land
Development Department (LDD) Thailand.
� Satellite images: Landsat TM obtained on March 3, 2007 from Land Development
Department (LDD) Thailand, Landsat TM data obtained on November 2, 2000 from website
of Global Land Cover utility (http://glcf.umiacs.umd.edu/data/landsat/) and Landsat TM data
obtained on November 9, 1988 from ITC.
� Geo-Pedological map from ITC (Solomon, 2005).
Table 4-1: Parameters input to the model and their sources
Input data in RMMF Sources of data
Rainfall data :
Soil data: � Bulk density, BD
� Cohesion of the surface soil, COH
� Effective Hydrological Depth
of the soil, EHD
� Soil moisture content at field capacity, MS
� Soil detachability index, K
Landforms: Slope steepness, Critical zone
Land use/land cover: � Rainfall intercepted by the crop cover, A
� Et/Eo
� Cover management, C-factor
� Canopy Cover, CC (0 -1)
� Ground Cover, GC ( 0 -1)
� Plant Height, PH
� meteorological stations, previously
researches
� calculate from soil texture and OM
� from LDD
� from LDD
� from LDD
� from LDD
� extract from DEM
� from LDD
� from LDD
� Derived from NDVI
� estimated from field
� estimated from field
� estimated from field
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Table 4-2: Validation data and sources
Validation data Sources of data
Validation data : � Ground truth
� Erosion data
� from field
� from field
Softwares applied included:
� Microsoft office 2003; Word, Excel, Visio and PowerPoint
� SPSS v.15.0 for windows
� ILWIS 3.3 Academic
� ERDAS IMAGINE 9.1
� Arc Pad 7.1
� Arc GIS 9.2
� SPAW (Soil Plant Atmosphere Water) model v.6.02.75
4.2. Research methods
To achieve the objective of the study, the methods included image processing, digital terrain analysis,
erosion modeling and statistical analysis is applied. Since the study area is mountainous, the
topographic effects on satellite imagery need to be removed for improving land use/cover
classification. The various techniques were used and the result was compared for accuracy.
In order to model soil erosion, one of the crucial parameter required is C-factor. Remote sensing data
could be used to establish C-factor by correlating with NDVI. Comparison between various
techniques were made and the result was validated with C-factor obtained from field method using
statistical techniques such as adjust R2, coefficient of effectiveness (C.E.), mean error (M.E.) and root
mean square error (RMSE).
Soil properties including soil organic matter content and bulk density were also the crucial factor for
erosion. Laboratory analysis was carried out of the soil samples collected during fieldwork.
Assessment of soil properties in different land use/cover was investigated by using statistical analysis
such as one-way ANOVA.
Result of the erosion model was used to classify erosion prone areas in the watershed. In addition
critical zones derived from terrain parameters computed from digital elevation data were also used
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
21
for finding out whether they could be used to predict erosion prone areas or not. The research process
was divided into three steps as follows:
1. Pre fieldwork In this stage the main work was focused on reviewed the relevant literature and looking for the
approach applied in data collection method. These included land use/cover assessment, C-factor
estimation, soil data collection and gully erosion investigation. Then material required for data
collection was acquired.
In order to obtain the approximate land use/cover, pre classification was done by using Landsat
satellite image 2000 together with High resolution Ortho-photo map. Then defining sampling area and
sampling design was done by using ArcGIS 9.2.
2. Fieldwork The fieldwork was done during 3rd – 27th September 2007; reconnaissance survey was performed to
get the general overview of the study area. Identification of the location for sample points were done
and saved coordinates in Compaq iPAQ pocket pc by using Arcpad 7.1 software. Necessary data such
as ground truth as training samples and validation data for supervised classification were collected.
Soil samples were also collected. Simple field estimation followed by laboratory test was done.
Climatic and other secondary data like soil map, digital elevation model, soil parameters were also
collected from Land Development Department and other government offices such as Meteorological
station.
The reconnaissance survey showed that random sampling method could not be accomplished because
of the mountainous and rugged terrain together with heavy rainfall in that period. So, data was
collected only from areas which could be access by car or by foot. Based on representative land
use/cover and landscape, ground truth data and soil samples were collected respectively. During data
collection, some parameters for running the RMMF model were also estimated (Section 4.2.2.1).
Secondary data collecting were included in this fieldwork such as climatic data from meteorological
station. Other data were collected from Land Development Department (LDD), Bangkok. The data
collected from the fieldwork can be described as follows:
� Ground truth data
In order to access the accuracy of land use/cover classification and estimated C-factor values,
stratified random sampling were used by selecting representative of each land use/cover type (strata)
that could access by car or by foot and then random points were defined into those strata. At each
observation points, ground truth information of land use/cover, fraction of land surface covered by
canopy (Fc), percentage of land area covered by surface cover (Sp) and plant height were collected
and recorded coordinates by using GPS. A total 263 samples were derived from five land use/cover
class. Each class contained 50 samples exclude forest came with 63 samples. Then the samples in
each class were divided in equally two groups including training and validating by using random
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
22
technique. Each group in each class contained 25 samples except forest had 38 in training and 25 in
validating.
� Soil samples
Soil samples were collected together with other members of the ITC fieldwork team (the author, Mr.
Kanya Souksakul, Mr. Raju Sapkota and Mr. Yergalem Naga) at different slope positions (summit,
shoulder, foot slope) and different land use/cover types for characterizing soil properties such as
organic matter and for analyzing soil particle size distribution. A total 177 samples were collected in
plastic bags. Samples were air dried during the fieldwork periods; 100 g of each sample were packed
and labeled. These samples were brought to ITC soil and water laboratory for analysis. Finally only
126 samples within the study area were selected.
� Erosion data
Erosion data was collected in locations having gully erosion by visual observation and taking their
coordinates using GPS receiver. Total number of erosion data was from 20 points.
� Organic matter content values in Namchun watershed 2006
Organic matter content values in Namchun watershed 2006 were obtained from literature (Neguse,
2007). The total number that covered the study area was 63 values.
� Input parameters in RMMF model
Climatic data and soil properties that used as input parameters in the RMMF model from
meteorological station and LDD respectively (Table 4-1).
3. Post fieldwork This phase consisted of data processing and soil laboratory work followed by accuracy assessment of
classification of land use/cover, C-factor mapping, erosion model and critical zones calculation.
Beside these activities, statistical analysis was done to examine the difference of soil physical
properties between land use/cover classes, investigate soil loss quantities in different periods and
compare the critical zones with observation points of gully erosion.
The overall methodology is illustrated in Figure 4-1. Detail description of the methods is presented in
the following flow chart.
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23
Pre fieldwork
Preparation for fieldwork
Image classification (Unsupervised)
Land use/cover clustering map
Identification of target location for sampling
Gathering of available data Material and methods for
data collection
Fieldwork
Data collection
Secondary dataField data
Gully erosion
points Ground truth data
Land use class, Fc , Sp
Soil samples (shared)
Training Validating
Climatic dataDEM , Satellite images
Erosion model parameters
Post fieldwork
Data analysis
Laboratory analysisTopographic removal
Image classification (Supervised)
Land use/cover
maps
Erosion model
Organic
matter, Bulk density
DEM
Critical
zones
Slope
Rainfall Model parameters
ValidationTraining
only 2007
Erosion prone areas
Gully erosion points
Validation
Compare
Soil loss
Analysis Analysis
only 2007
Conclusion and recommenndation
NDVI
C-factor
Figure 4-1: Flow chart of overall methodology
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4.2.1. Land use/cover classification in mountainous areas
Land use/cover maps were created for the years of 1988, 2000 and 2007. These maps were used for
investigating the difference of land use/cover areas in those periods. Based on fieldwork data that
collected in 2007, only land use/cover map 2007 was used as input map for erosion model.
Furthermore, it was very useful for analyzing soil properties such as compactness (bulk density) and
soil organic matters in different land use/cover.
4.2.1.1. Pre classification
Pre classification was done by applying unsupervised classification before getting to the field. The
reason was no prior knowledge about the study area. It could give the idea of approximately land
use/cover and used for sampling planning. This technique is used for classifying an image in a feature
space then analyses and groups the feature space vector into clusters. In this state can only find out the
different appearance in the image for separating in different classes. The user has to define the
maximum the maximum number of clusters, maximum cluster size and minimum distance. Then
computer use these information locates arbitrary vectors as the center points of the clusters. Next step
each pixel is assigned to a cluster by using minimum distance to cluster centroid decision rule. Once
all pixels have been labeled, recalculation of the cluster will takes place is repeated until the proper
clustered center are found and the pixels are labeled accordingly. The iteration stops when the cluster
centers do not change any more. Usually, computer designs to split the clusters when it larger than
maximum size, conversely, clusters are merged in to one if they are closer more than minimum
distance (Janssen et al., 2001). The result from this process is a raster map that each pixel has a class
belongs to the cluster.
4.2.1.2. Topography effect removal
Most of the study area was covered by the mountain therefore It was affected by the terrain such as
illumination variations. This terrain effect caused the severe consequents for land use/cover
classification. The first objective of this study was investigate the potential of image processing
techniques such as correction of topography induced constraints for classifying land use/cover in the
study area. The topography removal techniques were applied and compared. First, all Landsat TM
satellite images had original format in TIFF. They were exported to img format in ERDAS 9.1
software by using function layer stack. Then they were georeferenced and geocoded into the same
map projection of WGS84 datum. Landsat TM image November 9, 1988 had not map projection. On
the other hand, Landsat TM image November 2, 2000 and March 3, 2007 had UTM map projection in
WGS84 datum. A georeferencing technique was applied to Landsat TM image November 9, 1988 by
using Landsat TM March 3, 2007 as referenced image. After that geocoding step was taken place by
using nearest neighbor interpolation. All satellite images were sub mapped for covering only study
area. These operations were accomplished by ERDAS 9.1 software.
Next step, all satellites images were removed topography effect by using both topographic
normalization and sum normalization techniques. For topographic normalization technique, satellites
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
25
images were corrected illumination by using solar azimuth and solar elevation (derived from the
satellite header file) together with DEM by using ERDAS 9.1 software followed the Equation 1
(Colby, 1991) and 2 (Hodgson, 1994) below. Conversely, sum normalization was done in ILWIS 3.3
academic software. All satellite images were exported to mpr format in ILWIS. In this technique, each
band was normalized according to Equation 3 (Shrestha and Zinck, 2001) in map calculation function.
After topographic effect had been removed, color composite was taken place in 453 BGR for each
technique (Figure 4-2 (b), (c)). Then, satellite images in mpr format were exported to img format in
ERDAS 9.1 again for preparing final classification. The equations used in this state are showed as
following.
(a) (b) (c)
Figure 4-2: Lansat TM March 3, 2007 color composite in 453 BGR ; (a) Original image ; (b) Topographic normalization; (c) Sum normalization
Equation 3: ( cos( ))
(cos( ) cos( ))Observed
Normal
Bv eBv
ki ke
λλ =×
Where: NormalBv λ = normalized brightness values
ObservedBv λ = observed brightness values
cos( )e = cosine of the incidence angle
cos( )i = cosine of the existence angle, or slope angle
k = the empirically derived Minnaert constant
Equation 4: log( cos( )) log log(cos( )cos( ))Observed NormalBv e Bv k i eλ λ= +
Equation 5:
1
( ) 255ii n
ii
BB Normal
B=
= ×∑
Where: iB Normal = Normalized individual band of any sensor
iB = Individual band of any sensor
i = number of bands (from 1 to n bands)
255 = Compensation factor
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
26
4.2.1.3. Classification
For classification of land use/cover, supervised classification was applied in ERDAS 9.1 software. A
number of five land use/cover classes were considered including forest, degraded forest, agriculture,
grassland and orchard. The classification was divided in two phases. First, 138 training samples
(collected from fieldwork) were defined as training areas in images by assigning a limited number of
pixels of each class. In ERDAS 9.1, signature editor was created for defining the classes. The training
samples in feature space were shown in Figure 4-3. Then the satellite images that had removed
topography effect were added. The relationship between image spectral characteristics and training
samples information were considered. By using AOI (area of interest) tools, the boundaries and
number of pixels for each class were added into signature editor. After that the decision making phase
was taken place, maximum likelihood algorithm was selected because of the advantages of
considering the centre of the clusters together with shape, size and orientation. Finally land use/cover
maps in periods 1988, 2000 and 2007 were classified. Each period was consisted of two maps from
both topographic removal techniques.
Figure 4-3: Feature space plot of training samples
4.2.1.4. Accuracy assessment
Accuracy assessment was applied when classification was finished. The 125 validated data from
fieldwork were used to validate the results of classification through confusion matrix (error matrix).
Moreover, it was used to compare the potential from both topographic effect removal techniques. In
ERDAS 9.1, accuracy assessment function was selected. Validated points with coordinates and land
use classes in text format were imported as true classes. Overall accuracy was computed from
correctly classified pixel divided by total number of pixels checked. The topographic effect removal
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
27
technique that obtained higher accuracy of land use/cover classification was justified to be the
suitable technique. Land use/cover maps from that technique were used for further analysis.
4.2.2. C-factor mapping
Another objective of this study is to find out the possibility of estimating C-factor from NDVI. For
this purpose C-factor map was derived only for the year 2007 because of validation data. For C-factor
mapping using NDVI, three techniques were applied: De Jong’s equation, Van der Knijff’s equation
and regression method using field assessment of C factor and NDVI (Section 4.2.2.2). The generated
C-factor maps were validated using field data. Statistical analysis was applied such as adjusted R2,
coefficient of effectiveness, mean error and Root mean square error (RMSE).
4.2.2.1. Field method for deriving C factor
The cover-management factor (C-factor) represents the effects of vegetation, management, and
erosion-control practices on soil loss (Toy and Foster, 1998). The C-factor value was estimated using
sub factor from the field together with using some sub factor values from literature, as explained
below. In total 138 samples was collected as training samples and 125 samples were collected for
validation. The C factor estimation is based on sub factor including canopy cover, surface cover
(ground cover), prior land use (PLU) and surface roughness by using RUSLE method (Renard, 1997)
as described below:
Canopy cover and surface cover
Canopy cover and surface cover were estimated from field in term of fraction of land surface covered
by canopy (Fc) and percentage of land area covered by surface cover (Sp) respectively. Measuring
tape was used to measure the distance between trees. Fc was estimated by using a tape laid on the
ground at the base of the plants and radius measurement of the upper canopy (Figure 4-4).
Combination of one quarter area of each four trees is equal one circle area that gave area of canopy.
Then calculate whole area by using distance between trees. Finally, Fc can was calculated by area of
canopy divide by whole area. For short crops, the radius could be directly measured
Figure 4-4: Estimation Fc from field
Distance between trees
Dis
tan
ce b
etw
ee
n t
ree
s
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
28
In the case of Sp, visual interpretation was used to estimate (Figure 4-5) Sp within the Fc estimation
area. For open areas, Sp was estimated by using tape measured exact area of surface cover such as 2 x
2 m2 then visual interpret within that small area.
(a) (b) Figure 4-5: Estimation Sp from field; (a) Sp around 75% and (b) Sp around 35%
Plant height was estimated using a measuring tape and clinometer. For short crop, plant height was
measured directly using a measuring tape. But taller crop like tamarind, a clinometer was used to
measure the height. The angles at the top (A1) and bottom (A2) of the plant were measured at a
known distance in this study 5 m was used. These equations were used to derive two heights as
follows:
Equation 6: 1 1tan( )H A d= ×
Equation 7: 2 2tan( )H A d= ×
Where: 1H , 2H = height of the top angle and bottom angle
1A , 2A = top angle and bottom angle
d = estimation distance
The summation of 1H and 2H gave the plant height values.
� Canopy cover sub factor
Canopy cover was obtained from estimation based on vegetation in land use classes. The dominant
crops in study area were maize, mungbean and soya and orchard based on Tamarind. For forest and
degraded forest, canopy cover was major estimated from teak and bamboo respectively. Natural grass
was considered from grassland. In the field, fraction of land surface covered by canopy (Fc) were
collected and used for calculation. The equation below was employed to derive canopy cover
(Wischmeier and Smith, 1978).
Equation 8: ( 0.1 )HCC Fc e H− ×= ×
Where: CC = canopy cover sub factor (0-1)
Fc = fraction of land surface covered by canopy
H = distance that raindrops fall after striking the canopy (mm.)
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
29
Canopy values that derived from equation above were averaged by different land use/cover as showed
in the Table 4-3 below:
Table 4-3: Canopy cover sub factor in different land use/cover
Land use class Height (m) Canopy cover (CC)
Forest
Degraded Forest
Agriculture
Grassland
Orchard
19.91
7.52
0.85
0.77
4.28
0.79
0.59
0.51
0.37
0.63
� Surface cover sub factor
For second sub factor, surface cover was derived from field data on percentage of land area covered
by surface cover (Sp). The other variables such as random roughness (RU) and an empirical
coefficient (b) were obtained from literature (Renard, 1997). These variables were based on different
types of vegetation and erosional condition. For calculation, surface cover sub factor values were
obtained by equation as follow:
Equation 9: 0.080.24
( ( ) )b SpRuSC e
− × ×=
Where: SC = surface-cover sub factor
b = empirical coefficient
Sp = percentage of land area covered by surface cover
Ru = random surface roughness
Derived surface cover sub factor values were shown in Table 4-4 separated by different land
use/cover.
Table 4-4: Surface cover sub factor in different land use/cover
Land use class Ru b Surface cover (SC)
Forest
Degraded Forest
Agriculture
Grassland
Orchard
0.40
0.25
0.44
0.25
0.34
0.050
0.025
0.035
0.045
0.025
0.26
0.17
0.34
0.12
0.15
� Surface roughness sub factor
Soil surface roughness describes the micro variation in the surface elevation across a field resulting
mainly from tillage practices and soil texture (Moreno et al., 2008). This factor controlled most of the
hydraulic and erosion processes and rapid changed caused by the tillage operations, followed by a
slow evolution of the soil structures due to rainfall (Taconet and Ciarletti, 2007). Surface roughness
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
30
sub factor was shown in Table 4-5 and estimated in turn of surface random roughness (RU) as
follows:
Equation 10: ( 0.66 ( 0.24))RuSR e − × −=
Where: SR = surface roughness sub factor
Ru = random surface roughness
Table 4-5: Surface roughness sub factor in different land use/cover
Land use class Ru Surface roughness (SR)
Forest
Degraded Forest
Agriculture
Grassland
Orchard
0.40
0.25
0.44
0.25
0.34
0.90
0.99
0.87
0.99
0.94
� Deriving C-factor value
The calculation of C-factor value was done by multiply sub factors together. One more sub factor was
prior land use (PLU). This factor was adapted from the RUSLE guideline (Toy and Foster, 1998).
This factor involved with tillage operation or other soil disturbance that makes the soil more erodible
because of less consolidate and unstable aggregate. For forest and degraded forest, value 0.5 was
given because they were sometime disturbed by human activity such as machinery tillage operation in
degraded forest or forest fire (Toy and Foster, 1998). Conversely, agriculture areas, grassland and
orchard were assigned value 1(Shi et al., 2004). The crop residue in agriculture areas was removed or
burn. Forest fire in dry season frequently occurred in grassland. For orchard, farmer used fertilized or
removed grass. The equation below was applied to derived C-factor values.
Equation 11: C PLU CC SC SR= × × ×
4.2.2.2. Deriving C factor using satellite data (NDVI)
Normalized Difference Vegetation Index (NDVI)
NDVI map was generated from landsat March 3, 2007 in ERDAS 9.1 by spectral enhancement with
indice function. Then this NDVI map was exported to ILWIS 3.3 academic software. The NDVI map
was masked with the boundary of the study area by using map calculation function. Three techniques
were applied to generate C factor using NDVI as follows:
Generating C-factor using De Jong’s (1994) technique
In his PhD thesis, De Jong (1994) reported the used of vegetation indices to extract vegetation
parameters for erosion model. By using field data 33 plots, statistical analysis was done and the result
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
31
showed linear relationship between NDVI and USLE C-factor. The correlation showed negative value
-0.64. In ILWIS 3.3 academic software, map calculation was implied by entering his formula and
generate C-factor map with NDVI map as follows.
Equation 12: 0.431 (0.805* )C NDVI= −
Where: C = C-factor
NDVI = Normalized Difference Vegetation Index
Generating C-factor using Van der Knijff (1999) equation
Van der Knijff (1999) described in his report about soil erosion risk assessment in Italy that estimated
C-factor value derived from linear regression with NDVI quite low. As the same method above, C-
factor map in this state was done by map calculation function in ILWIS 3.3 academic software as
follows:
Equation 13: ( )
( )
NDVI
NDVIC eα
β−
−=
Where: C = C-factor
NDVI = Normalized Difference Vegetation Index
,α β = Parameters that determine the shape of the NDVI-C curve
The value 2, 1 were given to ,α β respectively (Van der Knijff et al., 1999).
Generating C-factor from regression equation based on field assessment of C factor using 138 training values and NDVI
In this technique NDVI map of 2007 was crossed with 138 C-factor training values assessed using
field technique. These values were exported to SPSS version 15 for windows format. By using
regression function and curve estimation tool, the relationship was formed and equation was obtained
(Appendix 4(a)). Then, the equation was applied in map calculation function in ILWIS 3.3 to create
C-factor map as follows:
Equation 14: ( 7.337 )0.227 NDVIC e − ×= ×
Where: C = C-factor
NDVI = Normalized Difference Vegetation Index
4.2.2.3. Validating C-factor mapping based on NDVI
The reliability of generated C-factor map was evaluated by comparing predicted C-factor values from
NDVI with 125 validation data. In ILWIS 3.3 academic software, predicted C-factor value from each
generated techniques (Section 4.2.2.2 above) were crossed with 125 validated C-factor data. Then
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
32
these values were exported to SPSS version 15 for windows software. The regression analysis with
linear estimation was applied to obtain adjusted R2 between predicted values from each generated
technique and validated values. Moreover, coefficient of effectiveness (Morgan, 2005), mean error
(M.E.) and root mean square error (RMSE) were also calculated. The C-factor map that obtained
higher reliability was used as input parameter for calculation transport capacity in the erosion model.
The statistical techniques that used in this state were shown as bellowed:
Equation 15:
2 2
1 1
2
1
( ) ( ). .
( )
n n
i ii i
n
ii
Xval Xval Xp XvalC E
Xval Xval
= =
=
− − −=
−
∑ ∑
∑
Where: . .C E = coefficient of effectiveness
iXval = validated C-factor values
Xval = mean of validated C-factor values
iXp = Predicted C-factor values
If C.E. values closer to 1 indicate better prediction.
Equation 16: 1
( ). .
n
i ii
Xp XvalM E
n=
−=∑
Where: . .M E = mean error
iXp = predicted C-factor values
iXval = validated C-factor values
n = total numbers of validated C-factor values
If M.E. values closer to 0 indicate better prediction.
Equation 17:
2
1
( )
1
n
i ii
Xp XvalRMSE
n=
−=
−
∑
Where: RMSE = root mean square error
iXp = predicted C-factor values
iXval = validated C-factor values
n = total numbers of validated C-factor values
Smaller RMSE values indicate better prediction more than higher values.
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
33
4.2.3. Erosion assessment
To estimate soil erosion in this study, RMMF model was selected. From the original equations
(Morgan, 1995), script was created in ILWIS 3.3 academic software. Soil parameters were added in
the attribute table of geo pedological map. On the other hand, plant parameters were added in the land
use/cover attribute table. These parameters were used to create attribute maps subsequently run the
model by using the script. Input parameters of RMMF model are shown in Table 4-1.
4.2.3.1. Estimation of rainfall kinetic energy
Due to the availability of data from only one meteorological station located at Lom Sak, it was not
possible to create rainfall map of the study area. Because of the reason the average annual rainfall data
was used in the erosion model. The data was made available by ITC. The data came from 11
meteorological stations in Phetchabun province. These rainfall data and elevation at meteorological
station were correlated to investigate the variations of rainfall due to elevation. After that regression
technique (Figure 4-6) was applied to obtain the equation, which help to predict rainfall map
(Appendix 4(b)). Table 4-6: Annual rainfall at various elevations obtained from ITC
Station Annual rainfall Elevation X-coordinate Y-coordinate
Lom Sak
Lom Khao
Khao Kao
Na Sum
Hin Hao
Nam Ko
Lao Ya
Dong Khwang
Khao Kho
Om Kong
Na Ngua
1089.6
1050.5
1556.1
972.0
837.0
1108.0
1742.0
843.0
1595.0
1045.0
946.0
140
160
720
180
170
170
720
150
920
140
140
740000
738000
715000
737200
736300
732400
716700
732500
713500
730000
729000
1857000
1868000
1854000
1880900
1873700
1857700
1854600
1848400
1840400
1837000
1827700
Figure 4-6: The relationship between annual rainfall and elevation
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
34
The equation for prediction amount of rainfall in study area was showed as follow:
Equation 18: 0.9803 ( ) 840.48R elevation= × +
Where: R = Amount of rainfall
The above formula was applied in map calculation function in ILWIS 3.3 software. By using DEM,
rainfall amount whole area of Namchun watershed was predicted. Then rainfall map was classified to
15 classes every 100 meters.
After obtained rainfall map, then rainfall energy was calculated by using the partitioned rainfall after
interception together with energy of the leaf drainage. First the model computed the proportion of
rainfall amount that reach the ground surface after allowing for rainfall interception to derive effective
rainfall. Effective rainfall was calculated by multiply rainfall intercept value range 0 to 1 that got from
literature (Morgan, 1995). Effective rainfall was calculated as following equation:
Equation 19: ER R A= ×
Where: ER = effective rainfall (mm.)
R = annual rainfall (mm.)
A = rainfall interception (0-1)
Then effective rainfall was divided to two parts, First, rainfall that reached the ground surface after
being intercepted by plant canopy as leaf drainage, conversely, second part that rainfall reached the
ground surface without interception. Plant canopy was added in attribute table of land use/cover for
calculation leaf drainage effective rainfall; the values came from estimation from the fieldwork and
average them per land use/cover classes. The rest of effective rainfall was direct through fall.
Leaf drainage was calculated by using equation below:
Equation 20: LD ER CC= ×
Where: LD = leaf drainage (mm.)
CC = plant canopy (%)
Direct through fall then was calculated by removing leaf drainage from effective rainfall as equation
below:
Equation 21: DT ER LD= −
Where: DT = direct through fall (mm.)
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
35
Then kinetic energy was calculated by distributed for effective rainfall of leaf drainage and direct
through fall. Kinetic energy of leaf drainage was a function of plant height meanwhile kinetic energy
of direct through fall was a function of rainfall intensity.
Plant height value estimated from the fieldwork, the average values for each land use/cover class were
added into the land use/cover table. For intensity, the value 25 mm/hr was the reasonable for tropical
countries supported by (Morgan, 2001). Finally two kinetic energy maps were combined together to
produce total kinetic energy map.
Kinetic energy of leaf drainage was calculated by using equation as follow:
Equation 22: 0.5( ) (1.58 ) 5.87KE LD LD PH= × × −
Where: ( )KE LD = kinetic energy of leaf drainage (j/ m2)
PH = plant height (m.)
Kinetic energy of direct through fall was computed as follow:
Equation 23: 10( ) (11.9 8.7 log )KE DT DT I= × +
Where: ( )KE DT = kinetic energy of direct through fall (j/ m2)
I = rainfall intensity
Finally, the total kinetic energy ( TotalKE ;j/ m2) was obtained from:
Equation 24: ( ) ( )TotalKE KE DT KE LD= +
4.2.3.2. Estimation of runoff
The annual runoff was calculated by using three factors including soil moisture storage capacity,
annual rainfall and mean rainy days. For soil moisture capacity, the calculation was in turn a function
of bulk density, effective hydrological depth, ratio of actual to potential evapotranspiration and soil
moisture content at field capacity.
The parameters to estimate soil moisture storage capacity including soil moisture content at field
capacity (MS), effective hydrological depth (EHD) and ratio of actual to potential evapotranspiration
(Et / Eo) were obtained from literature (Morgan, 1995; Morgan, 2001). Conversely, bulk density was
calculated from soil texture and organic matter that analyzed in ITC soil and water laboratory by using
SPAW software (Section 4.3.2.2), then the average values for each pedological class were added into
the pedological attribute table. The rainfall map that obtained from above and rainy days was used to
calculated mean rainy days. By following script that created in ILWIS 3.3 academic software, these
parameters were automatically generated and calculated annual runoff.
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
36
Following equation was used to calculate soil moisture storage capacity:
Equation 25: 1000 ( / )c t oR MS BD EHD E E= × × × ×
Where: cR = soil moisture storage capacity (mm.)
MS = soil moisture content at field capacity (%ww.)
BD = bulk density (g/ cm3)
EHD = effective hydrological depth (m.)
/t oE E = ratio of actual to potential evapotranspiration
For mean rainy days, it was calculated by using equation below:
Equation 26: on
RR
R=
Where: oR = mean rainy days
R = annual rainfall (mm.)
nR = number of rainy days in a year
To estimate runoff, Annual runoff was computed by using equation below:
Equation 27: ( )c
o
R
RQ R e−
= ×
Where: Q = annual runoff (mm.)
4.2.3.3. Estimation soil particle detachment by raindrop and runoff
The calculation of soil particle detachment was divided in two parts. First, the model considered soil
particle detachment by raindrop impact. Second, soil particle detachment by runoff was taken to
account.
For soil particle detachment by raindrop, total kinetic energy and soil erodibility parameter were
applied following Equation 28 below. Soil erodibility values were obtained from literature (Morgan,
2001). Combination of both parameters, soil particle detachment by raindrop map was generated
following the script. The other one, soil particle detachment by runoff, was calculated by various
parameters such as annual runoff, slope steepness extracted from DEM, percentage of ground cover
estimated and calculated from field and soil resistance in turn of cohesion derived from literature
(Morgan, 2001). In the end, total soil particle detachment was computed by summation of both soil
particle detachment maps.
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
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Soil particle detachment by raindrop was a function of kinetic energy of effective rainfall and soil
erodibility. The calculation was done as follow:
Equation 28: 310TotalF K KE −= × ×
Where: F = soil particle detachment by raindrop impact (kg/ m2)
K = soil erodibility (g/j)
In this model assumed that soil detachment by runoff can only appear where soil was not protected by
ground cover.
The calculation of soil resistance was showed as follow:
Equation 29: 1
(0.5 )Z
COH=
×
Where: Z = soil resistance
COH = surface cohesion (kpa)
Soil detachment by runoff was calculated by applied equation below:
Equation 30: 1.5 3sin (1 ) 10H Z Q S GC −= × × × − ×
Where: H = soil particle detachment by runoff (kg/ m2)
S = slope steepness (degree)
GC = ground cover (%)
Total particle detachment (D ; kg/m2) was finally calculated as a sum of both soil particle detachment
by raindrop and runoff. The equation was shown as below:
Equation 31: D F H= +
4.2.3.4. Estimation of transport capacity of runoff
The transport capacity was estimated by using runoff, surface cover factor and slope. Parameters to
estimate transport capacity including surface cover management (C-factor) derived from regression
equation with NDVI, slope steepness extracted from DEM and annual runoff were computed follow
the script. The transport capacity map finally was generated. The equation was showed as below:
Equation 32: 2 3sin 10TC C Q S −= × × ×
Where: TC = transport capacity (kg/ m2)
C = surface cover factor (C-factor)
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
38
4.2.3.5. Estimation of erosion in turn of soil loss rate
The last calculation step of RMMF model was prediction annual soil loss. Soil loss rate was estimated
by comparing between total soil particle detachment and transport capacity of runoff. The minimum
function was applied following the last step in the script. For the minimum between total soil particle
detachment map and transport capacity map was created to soil loss map. Soil loss estimation was
computed by using equation as follow:
Equation 33: min( , )Sl D TC=
Where: Sl = annual soil loss rate (kg/ m2)
4.2.3.6. Analysis of model result
The model results were analyzed by evaluating descriptive statistic such as mean and standard
deviation was used. Aggregation function in ILWIS 3.3 academic software was applied to average the
soil loss in to different land use/cover.
4.2.4. Assessing create critical zones for ephemeral gully incision
DEM of the study area available in 225 map sheets were get mosaic in ERDAS 9.1 from which subset
of the study area was created. The DEM was later exported to ILWIS 3.3 format for further analysis.
Terrain parameters; slope gradient and slope aspect (Figure 4-7 (a) and (b)) were extracted by using
filter function and hydro processing function in ILWIS. Finally slope, flow direction, flow
accumulation and catchment area together with flow width were used to define ephemeral gully
erosion by using critical zones concept. The objective of creating critical zones was to compare them
with the erosion prone areas that classified from the results of the erosion model.
(a) (b)
Figure 4-7: Slope map (a) and Aspect map (b)
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
39
Hydrological parameters were used to derive critical zones for ephemeral gulley incision as suggested
by Jetten et al. (2006). By using DEM hydro processing function in ILWIS 3.3 academic software,
flow determination such as fill sink, flow direction and flow accumulation were obtained. Then flow
accumulation was used to calculate drainage network and catchment area (Figure 4-8 (a) and (b) by
using network and catchment extraction function. Sensitive areas after that were derived from these
parameters. The algorithm that use in this study showed as following equation (Desmet and Govers,
1997).
Equation 34: 0.4( ) 0.72c
AF S
w= × >
Where: cF = critical threshold
S = slope (m/m)
A = upstream area (m2)
w = flow width (m)
(a) (b)
Figure 4-8: Drainage network (a) and catchment (b) extraction
Critical zones were classified in two classes; gully erosion and no gully erosion by using threshold
0.72. The overall method for critical zones creation is shown in Figure 4-9 below.
Figure 4-9: Critical zones creation
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
40
4.3. Data analysis
4.3.1. Laboratory analysis
Laboratory analysis of the soil samples was accomplished in the ITC soil and water laboratory (Figure
4-10). The objectives for this analysis were determination of soil organic matter and particle sizes
distribution. Preparing soil samples was done by sieving soil samples 100 g pass 2 mm sieve. Weight
approximate 20 g into 1 l beaker to preparing for particle size distribution analysis (Pipette method).
Meanwhile, weight approximate 5g and sieve them pass 0.25 mm sieve, then weight 1 g (with
accuracy 0.01 g) into a 500 ml flask for analyzing % organic matter (Walkley-black method).
(a) (b) (c) Figure 4-10: Laboratory analysis in ITC; (a) preparing soil samples, (b) walkley - black method,
(c) FAO pipette method
4.3.1.1. Walkley-black method
In this method the known volume mixture of potassium dichromate and sulfuric acid at 125 oC was
used to oxidize soil organic matter. After oxidation, titration of residue dichromate was taken place by
using Barium diphenylamine sulphonate as indicator against ferrous sulphate. The soil organic matter
was calculated by using different between the total volume of dichromate and residue volume after
titration. For calculation, the equation used in this step was shown as follow (Van Reeuwijk, 2002):
Equation 35: ( 1 2)
% 0.39V V
C M mcfS
−= × × ×
Where: M = molarity of ferrous sulphate solution (from blank titration)
1V = ml ferrous sulphate solution required for blank
2V = ml ferrous silphate solution required for sample
S = weight of air-dry sample in gram
0.39 = 33 10 100% 1.3−× × × ( 3 = equivalent weight of carbon)
mcf = moisture correction factor
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
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Conversion of the % carbon to % organic matter was done by multiply empirical factor 2 with %C
Equation 36: % 2 %OM C= ×
4.3.1.2. FAO pipette method
For particle size distribution analysis, pipette method was done. Important part in this analysis was the
pretreatment of the samples aimed at complete dispersion of the primary particles. Consequently,
organic matter needs to be removed. Oxidation of organic matter was done by using
Hidrogenperoxide and water then let them cool down in cold water bath for one night. Next day,
beakers were place on hot water bath (80 oC) and adding 5 to 10 ml of Hidrogenperoxide until
decomposition was complete. Then to remove Hidrogenperoxide, water was added (300 ml) and boil
for 1 hour and allowed them to cool down and settled in beaker. Then siphon off and transferred
sediment to 1 l polythene bottle and added 20 ml of dispersing agent, made the volume to 400 ml
with water and caped the bottle. Next, the bottles were shaken for 16 hrs on an end-over-end shaker at
30 rpm. For determination of sand fractions, sieve suspension through 50 um sieve to the cylinder was
done then added water until reached 1 l, washed sand fraction remained on the sieve into porcelain
dishes. In the method of determination the fractions of silt and clay, shook cylinders and immediately
pipette 20 ml and transfer aliquot the tarred moisture tins for separating silt fractions less than 50mµ .
After that shook cylinders again and wait for 5 minutes pipette 20 ml again for silt fractions less than
20 mµ and transfer aliquot in the same way as above step. The last pipette was done after shook
cylinders waiting for 5 and a half hrs. Pipette last 20 ml for clay fractions and then transfer aliquot.
After finished pipette method, sand in porcelain dishes and aliquot of silt and clay were dried in oven
overnight. Next day, sand, silt and clay fractions were weighted with 0.001 g accuracy (Van
Reeuwijk, 2002).
The basis of calculations was the oven-dry sample weight after all treatments. It was obtained by these
formulas (Van Reeuwijk, 2002):
Equation 37: ( 2 ) ( 50) ( 50)Clay m H Zµ< = × − × ( . )wt K
Equation 38: (2 20 ) ( 50) ( 50)Silt m G Z Kµ− = × − × − ( . )wt L
Equation 39: (20 50 ) ( 50) ( 50)Silt m F Z K Lµ− = × − × − − ( . )wt M
Equation 40: ( 50 )Sand m Aµ> = ( . )wt M
Equation 41: Sample weight K L M N= + + + (all weight in gram)
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
42
Where: A = weight of sand fractions
F = weight 20 ml pipette aliquot of fraction < 50 mµ
G = weight 20 ml pipette aliquot of fraction < 20 mµ
H = weight 20 ml pipette aliquot of fraction < 2 mµ
Z = weight 20 ml pipette aliquot of blank
The proportion amounts of the fractions were calculated by using equation below (Van Reeuwijk,
2002):
Equation 42: % ( 2 ) 100K
clay mSampleweight
µ< = ×
Equation 43: % (2 20 ) 100L
silt mSampleweight
µ− = ×
Equation 44: % (20 50 ) 100M
silt mSampleweight
µ− = ×
Equation 45: % ( 50 ) 100A
sand mSampleweight
µ> = ×
4.3.2. Soil properties analysis
Before analysis, 166 soil organic matter values from laboratory analysis were combined with 63
organic matter values from the dataset 2006 (Neguse, 2007).
4.3.2.1. Soil texture by using USDA soil texture triangle
Following USDA system, soil is separated in three major particle size groups including sand silt and
clay. Clay particles are the smallest size less than 2 mµ meanwhile silt is a medium size in between 2
and 50 mµ . The largest particle is sand, that their sizes in between 50 and 2,000mµ . Soil texture
refers to proportion of sand, silt and clay relatively found in the same soil sample (Kruthkul et al.,
2001). One of the results from laboratory analysis was percentage of soil fractions. These fractions
were used to calculated soil texture by using USDA soil texture triangle (Figure 4-11).
Figure 4-11: USDA soil texture triangle
Source: http://wps.prenhall.com/wps/media/objects/1411/1445480/FG12_15_wo_arrows.JPG
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
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On each corner of triangle showed the major texture, the top was clay, lower right was silt and lower
left was sand. In each side of triangle are scaled for the percentage of sand, silt and clay. For clay,
percentage increasing from bottom to top on the left side of triangle and read from left to right across
the triangle. Sand showed the increasing from right to left along the base and read the percentage from
lower right to upper left of the triangle. The last one, silt was increasing from top to bottom and read
the percentage from upper right to lower left of the triangle.
4.3.2.2. Bulk density from soil texture and organic matter by using SPAW software
Bulk density was obtained from SPAW (Soil Plant Atmosphere Water) model v.6.02.75 (Saxton and
Willey, 2007). The parameters that used for calculation were soil organic matter contents and soil
texture. Soil organic matter contents and soil texture were derived from laboratory analysis (Appendix
2). The SPAW software was showed in Figure 4-12.
Figure 4-12: SPAW model
4.3.3. Statistical analysis
To assess the objective that land use/cover change effect on soil erosion, the soil physical properties
such as bulk density and soil organic matter were investigated in different land use/cover type.
Descriptive statistical approach was employed including arithmetic mean of soil properties per land
use/cover and also standard deviation as well. From table calculation operation in ILWIS, aggregate
function was applied to derive average values of those soil properties in different land use/cover.
After that significant test was applied by using One - way ANOVA function to determine whether
there was the difference between land use/cover or not. If the significant less than 0.05 then a post hoc
approach was done by using Turkey’s HSD technique to examine the significance difference among
the land use/cover type (Appendix 5(a) and (b)). The results of statistical analysis were present in
Chapter 5.4.
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For comparison between the results from soil erosion model and critical zones, a descriptive statistic
such as percentage was applied to compare the areas of erosion prone areas with the areas of critical
zones for gully erosion formation. The results are presented in Chapter 5.6.
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5. Results and discussions
The main objective of this study is to assess erosion prone areas in inaccessible mountain areas by
applying image processing and digital terrain analysis techniques. The topographic removal
techniques were implemented to improve spectral reflectance of Landsat satellite images obtained in
1988, 2000 and 2007. Image classification was applied to generate land use/cover map. Trend of land
use/cover change was also investigated to know the conversion between land use/cover classes in
those periods. For assessing soil erosion, the erosion model played important role. An assessment of
the relationship between NDVI and C-factor was done to see whether NDVI could be used as
estimator of C-factor in the study area or not. Land use/cover map 2007 was used to predict soil loss
rate and classified erosion prone areas. It was also used to analysis bulk density and soil organic
matter distribution pattern in different land use/cover classes. The results of analysis together with
trend of land use/cover change are reflected in soil erosion. Digital terrain analysis was carried out to
define critical zones for gully formation which was then compared with the erosion prone areas for
confirmation. In this chapter, results are summarized and discussed as follows:
5.1. Land use classification
Two techniques, topographic normalization and sum normalization, were applied to Landsat satellite
images obtained in 1988, 2000 and 2007. The topographic normalization technique uses solar azimuth
and solar elevation, available in the header file of the image data. In addition to this, digital elevation
data (DEM) is also used. In sum normalization technique, each satellite band is normalized by
summation of all of the bands. The land use/cover classification result after removal of topographic
effect using topographic normalization technique gave higher classification accuracy as compared to
the classification results after performing sum normalization (Section 5.1.2).
However, affect from seasonal influence such as cloud remains in the image. This affects the accuracy
of land use/cover classification. It was not easy to classify orchard from degraded forest because in
some areas the canopy of orchard and degraded forest reflected the same spectral characteristic. In
some cases, some areas of orchard and agriculture areas were mixed because the farmers planted
crops in between the fruit trees. Furthermore, the newly plantation forest in earlier state was easily
classified as degraded forest.
5.1.1. Land use/cover classification result
Land use/cover classification of 1988, 2000 and 2007 were done by using supervised classification
with the maximum likelihood algorithm in ERDAS 9.1 software. Training samples were separated
from validated data (Section 4.2). The land use/cover maps were classified in five classes including
forest, degraded forest, agriculture, grassland and orchard with total areas 6,658 hectares. In Figure 5-
1 (a), (b) and (c) showed land use/cover classification maps in periods of 1988, 2000 and 2007
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
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Area in percent in different land use 1988 - 2007
0
5
10
15
20
25
30
35
40
45
F DF A G O
Land use
Area in percent
Area1988
Area2000
Area2007
respectively. The comparison of the different land use/cover between those periods is shown in Figure
5-2 as follows.
(a) (b)
(c)
Figure 5-1: Land use/cover classification maps; (a) 1988, (b) 2000 and (c) 2007
Figure 5-2: Area in percent in different land use/cover 1988 – 2007
Legend
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Table 5-1: Comparison the areas of land use/cover in 1988, 2000 and 2007
Area in 1988 Area in 2000 Area in 2007 Land use/cover class hectares percent hectares percent hectares percent
Forest
Degraded Forest
Agriculture
Grassland
Orchard
2699
2663
740
496
60
40.54
40.00
11.11
7.45
0.90
1411
2551
1107
1081
508
21.20
38.33
16.62
16.23
7.62
857
2267
1686
778
1070
12.87
34.04
25.32
11.70
16.07
Total 6658 100 6658 100 6658 100
According to Table 5-1, land use classification map 1988 showed the study area was covered by
natural forest (forest and degraded forest) more than 80% followed by agriculture (11.11%), grassland
(7.45%) and orchard (0.90%) respectively. In 2000, the area of forest decreasing to 21.20% and
degraded forest 38.33% meanwhile agriculture areas increased to 16.62% followed by grassland
7.45% and orchard 7.62%. The land use/cover 2007 showed forest area continue decreasing to
12.87%, degraded forest to 34.04%. Agriculture areas and orchard areas still increased to 25.32% and
16.07% respectively.
5.1.2. Accuracy assessment
Based on ground truth data was collected in 2007, accuracy assessment was applied to the results of
land use/cover classification 2007 after removing topographic effect. With 125 validated data
collected from fieldwork (Chapter 4.2), the accuracy was calculated for the land use/cover
classification maps from two different techniques for correcting topographic effect. Then the accuracy
was compared for selecting the representative of land use/cover map. The Table 5-2 showed the
comparison of accuracy between both techniques. Because of higher accuracy, the topographic
normalization was the appropriate technique for remove effect from topography. Accuracy of land
use/cover classification map after applying topographic normalization was shown in error matrix in
Table 5-3 as following. Table 5-2: Comparison of the accuracy between topographic normalization and sum normalization
Classification accuracy with removal of topography effect (%) Period
Topographic normalization Sum normalization
Classification accuracy without correcting
topographic effects (%)
2007 74.4 69.1 67.3
The land use/cover classification without correcting topography effects gave the lowest accuracy
(67.3%). The accuracy could be improved by applying topography effect removal techniques that gave
69.1% and 74.4% respectively. The similar finding was reported by Shrestha and Zinck (2001) that
reducing the topographic effect could improve the classification accuracy. Topographic normalization
technique gave the highest results because it used solar azimuth and solar elevation together with
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DEM to correct the illumination variants meanwhile sum normalization technique only normalized
each satellite image band with sum of illumination and multiply with the constant.
Table 5-3: Accuracy assessment of land use/cover classification 2007
Table 5-4: Crop calendar for cultivation of Phetchabun Province in 2007
Where: = the whole period of vegetations and crops growing
= starting the planting period (young plants)
= the crops and vegetations grow up
= the harvesting period of crops and vegetations Source: Provincial agriculture department of Phetchabun Province, Thailand.
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
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The overall accuracy for land use/cover map 2007 after applying topographic normalization was
74.4%. The accuracy of classification was acceptable however it could not get higher accuracy
because of some limitations.
The main limitation of the classification was the seasonal influence. Land use/cover map 2007 was
classified from Landsat March 3, 2007 that appeared some clouds in the image. Classification was
done by using spectral reflectance that cloud effect caused misclassified. Furthermore, it was different
season from fieldwork period. Some areas, crops like maize were harvested already in the end of
March and appeared like grassland in September as showed in crop calendar (Table 5-4). Moreover
canopy cover of orchard areas gave the same reflectance like forest areas. Plantation forest areas in
earlier stages also gave the same reflectance as degraded forest.
5.1.3. Trend of land use/cover change from 1988 to 2007
From the results of classification (Table 5-5) in the periods 1988 to 2007, forest and degrades forest
areas were decreasing especially forest areas loss around 1,842 hectares from 2,699 hectares within 19
years. Degraded forest areas also decreased from 2,663 hectares to 2,267 hectares. Conversely, in
agriculture areas and orchard that increased 946 and 1,010 hectares respectively. These were an
evident that deforestation had occurred in this area. Most of the area has rugged terrains that mean the
deforestation also occurred on hill slope areas, which was replaced by agriculture. This led to soil
erosion problem and severe disaster that caused heavy landslide and flooding in 2001. During field
work it was observed that, orchard and agriculture areas were on the mountain slopes. Although the
government policy is a forestation on sloping land but it lacked of realistic implementation and
responsibility of the villagers and officers. Also the forest areas from plantation project were not
permanent. Teak plantation has the objective for commercial reason; they will be cut down again
when they grow up. The change of land use/cover areas in periods 1988, 2000 and 2007 is shown in
Figure 5-3 as follows:
Table 5-5: Land use/cover change in the periods 1988 - 2007
Land use class 1988 Area in hectares
2000 Area in hectares
2007 Area in hectares
Change area from
1988 to 2007 in hectares
Change from the total area
(%)
Forest
Degraded Forest
Agriculture
Grassland
Orchard
2699
2663
740
496
60
1411
2551
1107
1081
507
857
2267
1686
778
1070
-1842
-396
946
282
1010
-27.67
-5.95
14.21
4.24
15.17
Total 6658 6658 6658
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Area change 1988 - 2007
-2000
-1500
-1000
-500
0
500
1000
1500
F DF A G O
Land use
Area in hectares
Area
Figure 5-3: Area of land use/cover change in periods 1988, 2000 and 2007
5.2. C-factor mapping for erosion assessment
The C-factor map was generated using Landsat TM data obtained on March 3, 2007. The NDVI was
produced and subset to the study area. Three equations were used to calculate C-factor maps from
NDVI. The results were compared with validation data, generated during fieldwork. The C-factor map
derived using De Jong’s equation (De Jong, 1994) (Equation 12) was showed in Figure 5-4 (a). The
range was between 0.020 – 0.431. Meanwhile, Van de Knijff’s equation (Van der Knijff et al., 1999)
(Equation 13) gave more C-factor values with the range between 0.135 – 1.000 (Figure 5-4 (b)). The
last C-factor map was derived from the relationship between training samples of C-factor values from
fieldwork and NDVI (curve estimation, Equation 14). This gave the lowest range between 0.010 –
0.240 (Figure 5-4 (c)). The relationship between training samples of C-factor values from fieldwork
and NDVI was showed in Figure 5-4 (d).
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(a) (b)
(c) (d)
Figure 5-4: C-factor map 2007 derived from NDVI;
(a) De Jong’s equation (De Jong, 1994), (b) Van der Knijff’s equation (Van der Knijff et al., 1999), (c) Regression equation (curve estimation) derived from the relationship between training samples of
C-values and NDVI and (d) the relationship between training samples of C-values and NDVI
5.2.1. Validation of C-factor map
C factor maps were generated using Landsat TM data of March 2007. Validation of C-factor maps
was done by crossing C-factor maps with 125 validated C-factor values (Section 4.2.2.3). The
statistical techniques (Equation 15 - 17) and adjusted R2 were applied to access the C-factor values
estimation. The result shows that the C-factor map with the highest correlation was the one using the
function derived from the relationship between C-factor training samples and NDVI (Curve
estimation). It gave highest value in adjusted R2 (0.78) (Appendix 4 (c)) and C.E. (0.77) and also
lower values in mean error (-0.04) and root mean square error (0.03) (Table 5-6). The relationship
between C-factor prediction and C-factor validation is shown in Figure 5-5.
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C-val i dat e
0. 300. 250. 200. 150. 100. 050. 00
C-predict
0. 25
0. 20
0. 15
0. 10
0. 05
0. 00
Li near
Obser ved
Figure 5-5: The relationship between C-factor prediction and validation from curve estimation
Table 5-6: Comparison between three of C-factor prediction techniques
Statistical techniques C-factor Prediction techniques Adjust R2 C.E. M.E. RMSE
De Jong’s equation
Van de Knijff’s equation
Curve estimation
0.37
0.25
0.78
0.11
0.06
0.77
0.22
0.61
-0.04
0.23
0.62
0.03
As the results from Table 5-6, De Jong and Van de Knijff’s equations gave low correlation and
seemed to give over estimated values for prediction (C.E. = 0.37 and 0.25 respectively). The reasons
could be that De Jong’s equation provided linear relationship between C-factor values and NDVI that
not the realistic characteristic between them. For Van der Knijff’s equation, although the relationship
illustrated in exponential function but the data used for developing were different from the study area.
Furthermore, both of them were developed in semi arid areas zones. Conversely, regression equation
(curve estimation) used C-factor values estimated from the study area as the training data showed high
correlation with NDVI (adjusted R2 = 0.701, Appendix 4(a)). Therefore it gave satisfactory results.
5.3. Soil erosion assessment
The annual soil loss predictions using data from 2007 (Figure 5-6) ranges between 0 and 61
tons/hectare. Average soil loss was highest (26 tons/hectares/year) in agriculture area and lowest soil
loss rate was found in forest area (0.99 tons/hectare/year). For degraded forest, grassland and orchard,
the soil loss rates were 1.47, 5.39 and 8.76 tons/hectare/year respectively as showed in Table 5-7.
These results proved that vegetation cover strongly influenced erosion process. Due to high vegetation
cover such as in forest and degraded forest area, annual soil loss rate seems to be low, conversely in
agriculture area more erosion because of less vegetation cover. Although orchard area had more
canopy cover as compared to degrade forest or forest area but ground cover was low because farmers
frequently remove grasses. This reason caused more erosion in orchard than degraded forest. For
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
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grassland, it depended on prior land use/cover types before. It gave less soil loss rates because some
areas the farmer was not removed the residues after harvesting and let the grass growth in the areas.
Table 5-7: Soil loss prediction in different land use/cover 2007
Land use class Area in percent Soil loss rate(t/h/y) SD.
Forest
Degraded Forest
Agriculture
Grassland
Orchard
12.87
34.04
25.32
11.70
16.07
0.99
1.47
26.06
5.39
8.76
0.27
0.55
13.52
3.63
0.51
Total 100
Figure 5-6: Soil loss map 2007
The soil loss map then was classified into erosion prone areas using threshold values adapted from
literature (Morgan, 1995; Singh and Phadke, 2006). Five classes were made: very slight (1-4.99
tons/hectares/year), slight (5-9. tons/hectares/year), moderate (10-24. tons/hectares/year), severe (25-
44.99 tons/hectares/year) and very severe (> 45 tons/hectares/year). The tolerance of soil loss rate that
agriculturist should be concerned was more than 10 ton/hectares/year (Morgan, 1995). This threshold
was applied to Namchun watershed for the difference between slight class and moderate class. The
erosion prone areas map of Namchun watershed 2007 is shown in Figure 5-7.
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Figure 5-7: Erosion prone areas classified from soil loss map 2007
Table 5-8: Erosion prone areas in different land use/cover
Land use class Soil loss rate(t/h/y) Erosion prone areas
Forest
Degraded Forest
Agriculture
Grassland
Orchard
0.99
1.47
26.06
5.39
8.76
Very slight
Very slight
Severe
Slight
Slight
According to Table 5-18, agriculture areas were high erosion level. They fell into severe class
meanwhile orchard and grassland were slight class. Conversely, forest and degraded forest were found
lowest erosion level (very slight). Overall areas had average 20.22 (tons/hectares/year) of annual soil
loss rate. It illustrated more than tolerance soil loss value 10 (tons/hectares/year) and fell in moderate
class.
5.4. Distribution of soil properties in different land use/cover types
5.4.1. Distribution of soil organic matter in different land use/cover types
From the Table 5-9 and Figure 5-8, the average of soil organic matter was separated in different land
use/cover types. The lowest average value occurred in agriculture areas (2.24%) and the highest was
in forest area (4.28%). The order from highest to lowest values was forest, degraded forest, orchard,
grassland and agriculture areas respectively.
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Table 5-9: Average soil organic matter content in different land use/cover
Land use class Average %OM S.D.
Forest
Degraded Forest
Agriculture
Grassland
Orchard
4.28
3.45
2.24
2.36
3.04
0.720
0.877
0.737
1.057
0.596
Figure 5-8: Distribution of soil organic matter (%) in different land use/cover
The statistical analysis showed that there was the difference of soil organic matter contents between
these land use/cover types. This analysis was done by using one-way ANOVA with showed high
significant at 95% confidence level (p < 0.05, F =20.593). Moreover, the further analysis with
Turkey’s HSD test showed multiple comparisons between each pair of land use/cover. There were
significant difference between almost every pair of land use/cover types except forest and degraded
forest, degraded forest and orchard as well as agriculture and grassland (Appendix 5).
Agricultural areas have the lowest soil organic matter content. The main cause of declining in soil
organic matter is due to tillage operation. Beside that tillage also accelerated aeration that caused
rapid and strong oxidation and break down of soil organic matter. Tillage management has a negative
effect on soil organic matter due to human influence. Different tillage systems cause different levels
of soil carbon losses depending on the intensity of the tillage (Iowa State, 2005). For grassland, the
organic matter content was also low. It is possible that these areas had been agriculture area before
and they were changed to grassland recently. Moreover, less soil organic matter content in grassland
came from very little leaf litter. Conversely in forest, degraded forest and orchard, higher organic
matter contents are due to leaf litter. In forest and degraded forest area, high litter coverage regulates
the microbial activity. Litter process helped to restore nutrient cycle including humus formation,
carbon sequestration and soil fertility buildup (Descheemaeker et al., 2006). In Orchard, soil organic
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
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matter content was relative high as compared to grassland and agriculture areas. This could be the
results from leaf litter together with direct use of fertilizers.
5.4.2. Distribution of bulk density in different land use/cover types
According to Table 5-10 and Figure 5-9, bulk density was averaged per land use/cover type. Highest
bulk density is in agriculture areas (1.38 g/cm3) and the lowest value occurred in forest and grassland
(1.27 g/cm3). Similar finding were reported by (Celik, 2005) that soils under cultivation had higher
bulk density than soils under forests. The order of highest to lowest values is in agriculture, orchard,
degraded forest, grassland and forest respectively.
Table 5-10: Average bulk density in different land use/cover
Land use class Average BD (g/cm3) S.D.
Forest
Degraded Forest
Agriculture
Grassland
Orchard
1.27
1.33
1.38
1.27
1.37
0.035
0.031
0.040
0.038
0.045
Figure 5-9: Distribution of bulk density (g/cm3) in different land use/cover
One-way ANOVA showed significant different of bulk density values between land use/cover at 95%
confidence level (p < 0.05, F = 19.066). The further analysis with Turkey’s HSD test illustrated the
different between each pair of land use/cover except forest and grassland as well as degraded forest,
agriculture and orchard (Appendix 6).
High bulk density values were found in agriculture, orchard and degraded forest areas. This indicator
showed that soils in these areas were more compacted from grassland and forest areas. The
compaction in agriculture and orchard not only came from decreasing of organic matter content but
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also the chemical fertilizers that farmers added in the soil for acceleration the production. There was
a negative relationship between bulk density and hydraulic conductivity. Then hydraulic conductivity
in these areas was decreasing opposite of infiltration rate. The rainwater could not easily penetrate
into deep soil and led to surface runoff. This led to soil erosion in these areas.
Soil properties play an important role for determination of soil erosion. These properties were directly
affects hydraulic properties of soil. Soil organic matter directly effects macro aggregate. Less organic
matter caused decreasing in soil aggregate stability that soil can not hold together in large unit. This
situation gains more chance of soil to easily erode. For bulk density, it directly effect to compactness
of soil. Increasing in bulk density caused soil less hydraulic conductivity that affect the rate of water
can infiltrate into the soil and begin to runoff. Land use change especially forest to agriculture areas
involved in vegetation cover that influenced to these soil properties. Less vegetation cover means less
soil organic matter and increasing bulk density.
As the results from the trend of land use/cover change in periods 1988 to 2007 in Section 5.1.3 above,
the deforestation and expansion of agriculture areas were found. Together with the distribution of soil
properties such as organic matter content and bulk density in different land use/cover types (Section
5.4.1 and 5.4.2), These results could be assumed that if the overall of study areas change from the
forest area into agriculture areas, soil organic matter content will decrease meanwhile bulk density
will increasing. By assuming that, soil organic matter and bulk density distribution pattern in different
land use/cover types were the same in the years 1988 and 2000 (same as in 2007). Then agriculture
areas seem to be prone to erosion. Conversely, forest areas definitely are less prone to erosion. These
could be concluded the erosion increasing in this watershed.
5.5. Assessment of land use/cover change effect on soil erosion
One of the objectives was to study the effect of land use/cover change on soil erosion. According to
Table 5-5 and 5-7, Trend of land use/cover change in periods 1988 to 2007 and the soil loss rates in
different land use/cover in 2007 were analyzed.
Assuming the estimated soil loss rates of 2007 as being standard erosion rates for different land
use/cover types the amount of soil losses (tons/hectare y) was calculated for 1988 and 2000. Total soil
loss estimated in the Namchun watershed was 29,070.06 (tons/hectare) in 1988 and 44,263.19
(tons/hectare) in 2000 (Table 5-11). Maximum total amount of soil loss occurred in 2007 with
61,684.70 (tons/hectare). Increasing erosion rates are caused by the transformation of natural forest
areas (forest and degraded forest) turned into cultivation areas (agriculture and orchard).
As mention in Chapter 5.4, the expansion of agriculture areas took place on forest areas will increase
the bulk density and decrease organic matter content. These evident could support the assumption of
increasing amount of soil loss above. Both the results from land use/cover change on amount of soil
loss per year and the distribution pattern of soil properties (bulk density and organic matter content)
together with trend of land use/cover change were the important evident that lead to soil erosion.
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
58
Table 5-11: Amount of soil loss (t/y) in periods of 1988, 2000 and 2007
1988 2000 2007 Land
use/cover Soil loss
rate (t/h/y) Area (Ha) Soil
loss(t/y) Area (Ha)
Soil loss(t/y)
Area (Ha) Soil
loss(t/y)
F
DF
A
G
O
0.99
1.47
26.06
5.39
8.76
2699
2663
740
496
60
2672.01
3914.61
19284.40
2673.44
525.60
1411
2551
1107
1081
507
1396.89
3749.97
28848.42
5826.59
4441.32
857
2267
1686
778
1070
848.43
3332.49
43937.16
4193.42
9373.20
Total 6658 29070.06 6658 44263.19 6658 61684.70
5.6. Assessing critical zones for ephemeral gully formation
Sensitive areas for ephemeral gully formation were calculated by using slope, catchment area and
flow width. The range of sensitive areas was between -0.0550 to 2.4457 as shown in Figure 5-10 (a).
These sensitive areas then were classified by using threshold 0.72 into two classes; gully erosion and
no gully erosion (Figure 5-10 (b)). According to land use/cover classification map of 2007, most of
the critical zones areas were found in agriculture areas which are located in the upper part, central part
and bottom right part of study area (Figure 5-10 (b)). Field studies also revealed that agriculture areas
really have a lot of gully erosion. However in forest areas especially plantation forest were also found
gully erosion because of less vegetation cover and distance between the trees quite far.
(a) (b)
Figure 5-10: Sensitive areas (a) and critical zones (b)
For validation, critical zone maps were crossed with 20 gully erosion validated points as illustrated in
Figure 5-11. Contingency matrix (Table 5-12) was done and showed overall accuracy of 65% for
predicting gully erosion. This result explained that critical zones with the threshold 0.72 seemed to be
acceptable index for prediction the gully erosion formation. However, because of inaccessible areas,
the lack of sufficient validation points was a limitation.
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
59
Table 5-12: Contingency matrix between Critical zone and Erosion data from fieldwork
Reference from fieldwork
Gully Erosion Total
Gully Critical zone No Gully
13
7
13
7
Total 20 20
Overall accuracy = (13/20)x100 = 65%
Figure 5-11: Gully erosion formation prediction with validated gully erosion points
Due to lack of quantitative data for soil loss validation in the study area it was not possible to validate
erosion level. Simple statistic such as percentage then was used for comparing the areas in percent of
erosion prone areas with the areas in percent of gully erosion formation from critical zones. In terms
of erosion prone areas, the percentage of each erosion level area were calculated as showed in Table
5-13 and Figure 5-12. Most of the study area fell on very slight (83.02%). The other areas was
classified into slight (6.12%), moderate (7.60%), severe (2.79%) and very severe (0.47%) level. On
the other hand, the area of gully erosion formation from critical zones was showed in Table 5-14 and
Figure 5-13. The gully erosion formation area was 5.53% and without gully erosion formation 94.47%
of study area.
Table 5-13: Area of erosion prone areas prediction
Erosion level Area in percent Area in hectares
Very slight
Slight
Moderate
Severe
Very severe
83.02
6.12
7.60
2.79
0.47
52831
3893
4833
1777
298
Total 100 63632
Gully points
Drainage
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
60
Gully Erosion Formation
0102030405060708090
100
No gully Gully
Erosion
Are
a in
per
cen
t
Percent
Erosion Prone Areas
0
1020
30
40
50
6070
8090
Veryslight
Slight Moderate Severe Verysevere
Erosion Level
Are
a in
per
cen
t
Percent
Figure 5-12: The areas of erosion level in percent
Table 5-14: Area of gully erosion prediction from critical zones
Critical zones Area in percent Area in hectares
No gully erosion
Gully erosion
94.47
5.53
60113
3519
Total 100 63632
Figure 5-13: The areas of gully formation in percent
The Figure 5-14 showed the comparison between the areas of gully erosion predicted by critical zones
and erosion prone areas classified from soil erosion model. The areas of moderate, severe and very
severe class appeared in the same locations as predicted by critical zones. On the other hand in the
central part of the study area, the erosion class of moderate to very severe contains more areas as
compare to gully formation. The reasons could be soil erosion model considered many factors more
than critical zones, which is primary based on DEM.
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
61
Figure 5-14: The comparison between gully erosion prediction and erosion prone areas
However, without the parameters that used in the erosion model, critical zones for gully formation
seemed to be the alternative way to approximately predict the erosion prone areas.
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
62
6. Conclusions and recommendations
6.1. Conclusions
According to the results discussed in previous chapter, following conclusions can be made:
The topographic effect removal technique was very useful to improve the accuracy of land use/cover
classification. Topographic normalization technique using solar azimuth, solar elevation and DEM
gave the better accuracy than sum normalization technique. It could reduce the illumination variant
which can cause misclassification of land use/cover types in mountainous areas.
Deriving C factor using satellite data (NDVI) approach could be improved with the field data. The
best results came from regression equation based on field assessment of C factor using 138 training
values as compared to the approaches described by De Jong and Van de knijff’s.
The results from soil erosion modeling showed that the overall average annual soil loss rate was
highest in the agriculture areas. The amount of soil loss per year during the period of 1988 to 2007
indicated that change in land use/cover from natural forest to agricultural areas caused more erosion.
The increase of the volume of soil loss followed the expansion of agriculture areas in the study area.
The study shows that agriculture areas have the lowest organic matter content and highest bulk
density of due to land use practices as compared to others. This could support the results from soil
erosion model that showed the highest soil loss rate occurred in agriculture areas.
The critical zones extracted from terrain parameters revealed that most of gully erosion formation
occurred in agriculture areas. The results illustrated the same distribution as the results from soil
erosion model. The critical zones could be mapped using terrain parameters in order to approximate
prediction for the erosion prone areas.
6.2. Recommendations
Based on the analysis and the result obtained following recommendation has been made:
The accuracy of land use classification in this study obtained was less then 75% , one of the reason is
could be cloud affects on the image, so the high resolution with cloud free images are suggested to
obtain better accuracy consequently to erosion prediction.
Most of the input parameters were acquired from literature. Therefore, more field measurement is
recommended for the other parameters in order to achieve the realistic model results.
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
63
6.3. Limitations of the study
The available Landsat satellite images used in this study had cloud cover in some areas. This affects
the accuracy of classification and consequently on C-factor prediction. The fieldwork was carried out
in 2007 and the satellite images had acquisition date in 1988, 2000 and 2007. These caused the
reduction of classification and prediction accuracy.
Gathering all the data required in erosion model were not possible due to limitation of time. Therefore
most of the parameters values that used in the erosion model were acquired from literature which may
cause uncertainty on the results. Some data such as rainfall was not local data that could not be
realistic representative of the areas.
The quantitative validation of the annual soil loss prediction was not possible due to lack of the
control erosion plots. The qualitative criteria could not give the values for validating the result from
the erosion model. They explained only which areas were prone to soil erosion
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
64
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Appendices
Appendix 1: Field data
Sample X Y Land use/cover Crop type Sc Fc Plant Height
1 722984 1856596 Agriculture Banana 50 55 2.0
2 722984 1856578 Agriculture Banana 50 55 2.0
3 722996 1856605 Agriculture Banana 50 55 2.0
4 723005 1856588 Agriculture Banana 50 55 2.0
5 723004 1856572 Agriculture Banana 50 55 2.0
6 728026 1853681 Agriculture Banana 60 65 2.5
7 728075 1853677 Agriculture Banana 60 65 2.5
8 728102 1853655 Agriculture Banana 60 65 2.5
9 728060 1853621 Agriculture Banana 60 65 2.5
10 728038 1853708 Agriculture Banana 60 65 2.5
11 725176 1855825 Agriculture Maize 80 75 2.0
12 725202 1855785 Agriculture Maize 80 75 2.0
13 725250 1855773 Agriculture Maize 80 75 2.0
14 725257 1855728 Agriculture Maize 80 75 2.0
15 725294 1855806 Agriculture Maize 80 75 2.0
16 724747 1855855 Agriculture Maize 40 70 1.5
17 724757 1855831 Agriculture Maize 40 70 1.5
18 724753 1855814 Agriculture Maize 40 70 1.5
19 724738 1855800 Agriculture Maize 40 70 1.5
20 724743 1855769 Agriculture Maize 40 70 1.5
21 721826 1853286 Agriculture Maize 60 70 1.0
22 721825 1853264 Agriculture Maize 60 70 1.0
23 721802 1853291 Agriculture Maize 60 70 1.0
24 721845 1853283 Agriculture Maize 60 70 1.0
25 721825 1853307 Agriculture Maize 60 70 1.0
26 725136 1855982 Agriculture Mungbean 60 95 0.2
27 725161 1855978 Agriculture Mungbean 60 95 0.2
28 725170 1855964 Agriculture Mungbean 60 95 0.2
29 725150 1855959 Agriculture Mungbean 60 95 0.2
30 725135 1855966 Agriculture Mungbean 60 95 0.2
31 720196 1853932 Agriculture Taro roots 30 40 0.7
32 720184 1853923 Agriculture Taro roots 30 40 0.7
33 720193 1853944 Agriculture Taro roots 30 40 0.7
34 720185 1853932 Agriculture Taro roots 30 40 0.7
35 720192 1853938 Agriculture Taro roots 30 40 0.7
36 727963 1853390 Agriculture Chili 40 30 0.4
37 727672 1853214 Agriculture Chili 40 30 0.4
38 727409 1853470 Agriculture Chili 40 30 0.4
39 727509 1853540 Agriculture Chili 40 30 0.4
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
69
Sample X Y Land use/cover Crop type Sc Fc Plant Height
40 727405 1853503 Agriculture Maize , Chili , Egg plant 45 70 0.3
41 727960 1853426 Agriculture Maize , Chili , Egg plant 45 70 0.3
42 725427 1856115 Agriculture Maize (Havested) 90 0 0.0
43 725414 1856093 Agriculture Maize (Havested) 90 0 0.0
44 725528 1856054 Agriculture Maize (Havested) 90 0 0.0
45 725503 1856082 Agriculture Maize (Havested) 90 0 0.0
46 725452 1856096 Agriculture Maize (Havested) 90 0 0.0
47 725373 1856085 Agriculture Maize (Havested) 85 0 0.0
48 725369 1856133 Agriculture Maize (Havested) 85 0 0.0
49 725342 1856122 Agriculture Maize (Havested) 85 0 0.0
50 725332 1856086 Agriculture Maize (Havested) 85 0 0.0
51 725340 1856055 Agriculture Maize (Havested) 85 0 0.0
52 725768 1856049 Degrade Forest Bamboo 80 75 6.0
53 725788 1856057 Degrade Forest Bamboo 80 75 6.0
54 725813 1856058 Degrade Forest Bamboo 80 75 6.0
55 725846 1856036 Degrade Forest Bamboo 80 75 6.0
56 725817 1856074 Degrade Forest Bamboo 80 75 6.0
57 725497 1855754 Degrade Forest Bamboo 65 70 7.0
58 725488 1855718 Degrade Forest Bamboo 65 70 7.0
59 725504 1855706 Degrade Forest Bamboo 65 70 7.0
60 725493 1855718 Degrade Forest Bamboo 65 70 7.0
61 725495 1855746 Degrade Forest Bamboo 65 70 7.0
62 725338 1856401 Degrade Forest Bamboo 75 89 8.0
63 725378 1856374 Degrade Forest Bamboo 75 89 8.0
64 725381 1856393 Degrade Forest Bamboo 75 89 8.0
65 725372 1856451 Degrade Forest Bamboo 75 89 8.0
66 725346 1856421 Degrade Forest Bamboo 75 89 8.0
67 726417 1855581 Degrade Forest Bamboo 80 78 7.5
68 726435 1855494 Degrade Forest Bamboo 80 78 7.5
69 726394 1855538 Degrade Forest Bamboo 80 78 7.5
70 726411 1855602 Degrade Forest Bamboo 80 78 7.5
71 726374 1855565 Degrade Forest Bamboo 80 78 7.5
72 720084 1856091 Degrade Forest Bamboo 60 64 7.0
73 720127 1856111 Degrade Forest Bamboo 60 64 7.0
74 720127 1856077 Degrade Forest Bamboo 60 64 7.0
75 720229 1856060 Degrade Forest Bamboo 60 64 7.0
76 720260 1856074 Degrade Forest Bamboo 60 64 7.0
77 718789 1856477 Degrade Forest Bamboo 70 75 8.0
78 718829 1856490 Degrade Forest Bamboo 70 75 8.0
79 718819 1856471 Degrade Forest Bamboo 70 75 8.0
80 718836 1856509 Degrade Forest Bamboo 70 75 8.0
81 718853 1856492 Degrade Forest Bamboo 70 75 8.0
82 722880 1856637 Degrade Forest Bamboo 15 70 6.0
83 722826 1856660 Degrade Forest Bamboo 15 70 6.0
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
70
Sample X Y Land use/cover Crop type Sc Fc Plant Height
84 722910 1856610 Degrade Forest Bamboo 15 70 6.0
85 722881 1856665 Degrade Forest Bamboo 15 70 6.0
86 726730 1855424 Degrade Forest Bamboo 80 75 7.3
87 726692 1855363 Degrade Forest Bamboo 80 75 7.3
88 726657 1855345 Degrade Forest Bamboo 80 75 7.3
89 726583 1855449 Degrade Forest Bamboo 80 75 7.3
90 726412 1855704 Degrade Forest Bamboo 80 75 7.3
91 726266 1855476 Degrade Forest Bamboo 77 66 6.7
92 726294 1855407 Degrade Forest Bamboo 77 66 6.7
93 726379 1855369 Degrade Forest Bamboo 77 66 6.7
94 726406 1855416 Degrade Forest Bamboo 77 66 6.7
95 726324 1855490 Degrade Forest Bamboo 77 66 6.7
96 726356 1855488 Degrade Forest Bamboo 68 72 7.0
97 726379 1855499 Degrade Forest Bamboo 68 72 7.0
98 726460 1855536 Degrade Forest Bamboo 68 72 7.0
99 726353 1855645 Degrade Forest Bamboo 68 72 7.0
100 722778 1856685 Degrade Forest Bamboo 40 70 6.5
101 722787 1856634 Degrade Forest Bamboo 40 70 6.5
102 723252 1856644 Forest TEAK 95 80 12.0
103 723207 1856532 Forest TEAK 95 80 12.0
104 723228 1856554 Forest TEAK 95 80 12.0
105 723211 1856579 Forest TEAK 95 80 12.0
106 723237 1856599 Forest TEAK 95 80 12.0
107 724211 1856526 Forest TEAK 20 70 8.0
108 724181 1856531 Forest TEAK 20 70 8.0
109 724170 1856550 Forest TEAK 20 70 8.0
110 724192 1856510 Forest TEAK 20 70 8.0
111 724205 1856480 Forest TEAK 20 70 8.0
112 722870 1855686 Forest TEAK 15 70 8.0
113 725104 1855972 Forest TEAK 65 85 9.0
114 725445 1855697 Forest TEAK 65 85 9.0
115 725430 1855693 Forest TEAK 65 85 9.0
116 725405 1855652 Forest TEAK 65 85 9.0
117 725431 1855642 Forest TEAK 65 85 9.0
118 726472 1855680 Forest TEAK 80 90 7.0
119 726189 1855477 Forest TEAK 80 90 7.0
120 726159 1855460 Forest TEAK 80 90 7.0
121 726203 1855444 Forest TEAK 80 90 7.0
122 726192 1855424 Forest TEAK 80 90 7.0
123 726259 1855502 Forest TEAK 65 85 9.0
124 726166 1855932 Forest TEAK 65 85 9.0
125 726123 1855888 Forest TEAK 65 85 9.0
126 726073 1855901 Forest TEAK 65 85 9.0
127 726211 1855988 Forest TEAK 65 85 9.0
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
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Sample X Y Land use/cover Crop type Sc Fc Plant Height
128 726139 1855953 Forest TEAK 70 80 8.7
129 726080 1855987 Forest TEAK 70 80 8.7
130 726090 1856029 Forest TEAK 70 80 8.7
131 726030 1856127 Forest TEAK 70 80 8.7
132 725962 1856010 Forest TEAK 70 80 8.7
133 725999 1856055 Forest TEAK 70 80 8.7
134 723954 1856474 Forest Eucalyptus 95 80 10.0
135 723983 1856468 Forest Eucalyptus 95 80 10.0
136 724043 1856437 Forest Eucalyptus 95 80 10.0
137 724080 1856325 Forest Eucalyptus 95 80 10.0
138 723978 1856375 Forest Eucalyptus 60 70 8.0
139 724062 1856304 Forest TEAK 10 55 20.0
140 724062 1856288 Forest TEAK 10 55 20.0
141 724001 1856217 Forest TEAK 10 55 20.0
142 723775 1856149 Forest TEAK 10 55 20.0
143 724081 1856325 Forest TEAK 10 55 20.0
144 724090 1856681 Forest TEAK 15 45 18.0
145 724071 1856720 Forest TEAK 15 45 18.0
146 724124 1856701 Forest TEAK 15 45 18.0
147 724078 1856672 Forest TEAK 15 45 18.0
148 724170 1856652 Forest TEAK 15 45 18.0
149 724462 1856730 Forest TEAK 20 50 19.0
150 724450 1856772 Forest TEAK 20 50 19.0
151 724418 1856723 Forest TEAK 20 50 19.0
152 719795 1853992 Forest Teak (plantation) 20 45 10.0
153 719779 1853981 Forest Teak (plantation) 20 45 10.0
154 719817 1854002 Forest Teak (plantation) 20 45 10.0
155 719768 1853986 Forest Teak (plantation) 20 45 10.0
156 719775 1853990 Forest Teak (plantation) 20 45 10.0
157 720892 1855840 Forest Teak (plantation) 25 55 16.0
158 720875 1855823 Forest Teak (plantation) 25 55 16.0
159 720870 1855843 Forest Teak (plantation) 25 55 16.0
160 720899 1855817 Forest Teak (plantation) 25 55 16.0
161 720897 1855803 Forest Teak (plantation) 25 55 16.0
162 724490 1856705 Forest TEAK 20 50 19.0
163 721707 1853092 Grassland Grass 60 85 1.0
164 721722 1853103 Grassland Grass 60 85 1.0
165 721707 1853107 Grassland Grass 60 85 1.0
166 721708 1853069 Grassland Grass 60 85 1.0
167 721724 1853088 Grassland Grass 60 85 1.0
168 721454 1853252 Grassland Grass 80 90 1.5
169 721469 1853232 Grassland Grass 80 90 1.5
170 721476 1853242 Grassland Grass 80 90 1.5
171 721442 1853228 Grassland Grass 80 90 1.5
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
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Sample X Y Land use/cover Crop type Sc Fc Plant Height
172 721445 1853241 Grassland Grass 80 90 1.5
173 721001 1855707 Grassland Grass 70 85 1.0
174 720970 1855706 Grassland Grass 70 85 1.0
175 720942 1855681 Grassland Grass 70 85 1.0
176 720952 1855671 Grassland Grass 70 85 1.0
177 720968 1855677 Grassland Grass 70 85 1.0
178 721751 1856736 Grassland Grass 70 90 1.0
179 721785 1856734 Grassland Grass 70 90 1.0
180 721773 1856765 Grassland Grass 70 90 1.0
181 721811 1856726 Grassland Grass 70 90 1.0
182 721790 1856704 Grassland Grass 70 90 1.0
183 724309 1856697 Grassland Grass 70 90 1.0
184 726078 1855755 Grassland Grass 80 95 1.0
185 726055 1855772 Grassland Grass 80 95 1.0
186 726099 1855964 Grassland Grass 80 95 1.0
187 727252 1856197 Grassland Grass 80 95 1.0
188 726112 1855724 Grassland Grass 80 95 1.0
189 725042 1857340 Grassland Grass 70 90 1.3
190 725039 1857445 Grassland Grass 70 90 1.3
191 724676 1857680 Grassland Grass 70 90 1.3
192 725099 1857358 Grassland Grass 70 90 1.3
193 724906 1857298 Grassland Grass 70 90 1.3
194 721580 1853320 Grassland Grass 60 85 1.4
195 721618 1853298 Grassland Grass 60 85 1.4
196 721593 1853281 Grassland Grass 60 85 1.4
197 721619 1853242 Grassland Grass 60 85 1.4
198 721574 1853254 Grassland Grass 60 85 1.4
199 718770 1856349 Grassland Grass 100 100 2.0
200 718800 1856323 Grassland Grass 100 100 2.0
201 718796 1856301 Grassland Grass 100 100 2.0
202 718773 1856300 Grassland Grass 100 100 2.0
203 718751 1856282 Grassland Grass 100 100 2.0
204 727554 1853631 Grassland Grass 80 95 1.0
205 727544 1853663 Grassland Grass 80 95 1.0
206 727558 1853649 Grassland Grass 80 95 1.0
207 727591 1853652 Grassland Grass 80 95 1.0
208 727577 1853626 Grassland Grass 80 95 1.0
209 722100 1853953 Grassland Grass 65 85 1.2
210 722095 1853941 Grassland Grass 65 85 1.2
211 722106 1853928 Grassland Grass 65 85 1.2
212 722102 1853967 Grassland Grass 65 85 1.2
213 722085 1853945 Grassland Grass 65 85 1.2
214 725590 1855987 Orchard Longan 80 16 3.5
215 725583 1855980 Orchard Longan 80 16 3.5
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
73
Sample X Y Land use/cover Crop type Sc Fc Plant Height
216 725595 1855978 Orchard Longan 80 16 3.5
217 725594 1855968 Orchard Longan 80 16 3.5
218 725588 1855963 Orchard Longan 80 16 3.5
219 725110 1855988 Orchard Tamarind , Maize 10 50 7.0
220 725111 1856002 Orchard Tamarind , Maize 10 50 7.0
221 725096 1855998 Orchard Tamarind , Maize 10 50 7.0
222 725071 1855957 Orchard Tamarind , Maize 10 50 7.0
223 725097 1855977 Orchard Tamarind , Maize 10 50 7.0
224 724673 1856045 Orchard Tamarind , Mango 40 70 8.0
225 724686 1856019 Orchard Tamarind , Mango 40 70 8.0
226 724667 1855994 Orchard Tamarind , Mango 40 70 8.0
227 724665 1855970 Orchard Tamarind , Mango 40 70 8.0
228 724636 1855959 Orchard Tamarind , Mango 40 70 8.0
229 725585 1855926 Orchard Tamarind 20 50 7.0
230 725602 1855910 Orchard Tamarind 20 50 7.0
231 725611 1855861 Orchard Tamarind 20 50 7.0
232 725601 1855842 Orchard Tamarind 20 50 7.0
233 725621 1855826 Orchard Tamarind 20 50 7.0
234 725450 1856009 Orchard Tamarind 5 28 4.5
235 725471 1856050 Orchard Tamarind 5 28 4.5
236 725425 1856029 Orchard Tamarind 5 28 4.5
237 725356 1856035 Orchard Tamarind 5 28 4.5
238 725393 1856003 Orchard Tamarind 5 28 4.5
239 724578 1856347 Orchard Tamarind 50 10 3.0
240 724542 1856325 Orchard Tamarind 50 10 3.0
241 724508 1856345 Orchard Tamarind 50 10 3.0
242 724586 1856368 Orchard Tamarind 50 10 3.0
243 724560 1856360 Orchard Tamarind 50 10 3.0
244 724629 1855844 Orchard Tamarind 40 30 5.5
245 724602 1855837 Orchard Tamarind 40 30 5.5
246 724581 1855843 Orchard Tamarind 40 30 5.5
247 724548 1855816 Orchard Tamarind 40 30 5.5
248 724524 1855827 Orchard Tamarind 40 30 5.5
249 721269 1853403 Orchard Tamarind 90 50 7.0
250 721283 1853413 Orchard Tamarind 90 50 7.0
251 721284 1853388 Orchard Tamarind 90 50 7.0
252 721303 1853403 Orchard Tamarind 90 50 7.0
253 721308 1853429 Orchard Tamarind 90 50 7.0
254 721515 1853724 Orchard Tamarind 70 45 7.0
255 721469 1853713 Orchard Tamarind 70 45 7.0
256 721479 1853685 Orchard Tamarind 70 45 7.0
257 721555 1853737 Orchard Tamarind 70 45 7.0
258 721543 1853741 Orchard Tamarind 70 45 7.0
259 724883 1856140 Orchard Tamarind 60 25 4.0
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
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Sample X Y Land use/cover Crop type Sc Fc Plant Height
260 724871 1856167 Orchard Tamarind 60 25 4.0
261 724876 1856196 Orchard Tamarind 60 25 4.0
262 724909 1856167 Orchard Tamarind 60 25 4.0
263 724909 1856187 Orchard Tamarind 60 25 4.0
The coordinate systems: WGS84 UTM zone 47
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
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Appendix 2: Laboratory analysis
Sample X Y clay% silt<20 % silt<50 % sand% Texture BD OM
1 721704 1854384 21.3 32.3 32.1 14.4 Silt loam 1.35 0.79
2 718457 1858568 23.4 34.8 37.3 4.5 Silt loam 1.44 0.93
3 719273 1858839 22.7 35.9 34.2 7.2 Silt loam 1.33 0.99
4 718572 1858070 18.5 33.6 41.3 6.6 Silt loam 1.35 1.04
5 719685 1853870 20.6 32.3 36.6 10.5 Silt loam 1.35 0.98
6 719397 1854158 26.2 33.7 33.2 7.0 Silt loam 1.30 1.00
7 720672 1855632 25.3 34.4 36.4 4.0 Silt loam 1.40 1.28
8 720767 1855741 27.0 32.9 34.7 5.4 Silt loam 1.30 1.43
9 727294 1855868 22.1 33.3 37.5 7.1 Silt loam 1.33 1.64
10 720656 1855915 29.3 33.8 32.3 4.6 Silty clay loam 1.36 1.49
11 724812 1856076 14.4 33.7 38.6 13.3 Silt loam 1.40 2.36
12 726844 1856081 23.0 34.0 37.0 6.0 Silt loam 1.32 2.15
13 725583 1856224 23.6 34.2 37.1 5.2 Silt loam 1.32 2.45
14 720292 1856341 24.7 34.2 36.9 4.3 Silt loam 1.46 0.54
15 724453 1856377 17.6 30.3 33.9 18.2 Silt loam 1.38 0.79
16 723210 1856582 14.4 31.6 39.8 14.2 Silt loam 1.41 1.73
17 718863 1856647 15.0 26.0 31.1 27.8 Silt loam 1.43 0.69
18 725908 1856812 21.3 34.8 39.2 4.7 Silt loam 1.37 1.94
19 726233 1857002 20.3 28.5 31.3 20.0 Silt loam 1.37 2.39
20 724567 1857236 19.7 31.9 36.6 11.8 Silt loam 1.36 3.22
21 725117 1857340 21.8 33.5 37.7 7.0 Silt loam 1.33 2.19
22 724573 1857350 25.6 34.2 36.6 3.6 Silt loam 1.39 1.33
23 722564 1857702 21.4 33.1 36.9 8.7 Silt loam 1.34 2.43
24 724887 1857850 21.1 33.7 38.6 6.6 Silt loam 1.34 2.19
25 717953 1858230 23.2 36.0 35.1 5.6 Silt loam 1.32 1.15
26 717231 1858512 21.0 32.6 37.5 8.9 Silt loam 1.34 1.17
27 725907 1856428 21.6 33.8 36.3 8.2 Silt loam 1.33 2.78
28 726107 1856428 21.2 36.1 37.8 4.9 Silt loam 1.24 3.37
29 726307 1856428 23.1 36.8 38.1 2.0 Silt loam 1.38 1.62
30 725707 1856228 24.0 33.2 33.3 9.5 Silt loam 1.32 1.85
31 725907 1856228 24.4 30.6 40.6 4.4 Silt loam 1.25 3.16
32 726107 1856228 24.2 33.7 36.8 5.3 Silt loam 1.30 2.03
33 726307 1856228 23.7 36.4 37.7 2.2 Silt loam 1.34 2.07
34 723507 1856028 23.2 33.6 37.5 5.7 Silt loam 1.32 1.91
35 723707 1856028 20.1 31.4 34.9 13.5 Silt loam 1.36 1.01
36 723907 1856028 18.9 30.4 37.0 13.7 Silt loam 1.36 1.21
37 724107 1856028 24.3 35.3 35.8 4.5 Silt loam 1.31 2.55
38 724307 1856028 18.6 34.6 38.9 7.8 Silt loam 1.36 2.32
39 724507 1856028 18.4 33.9 37.9 9.7 Silt loam 1.36 1.63
40 724707 1856028 21.4 33.4 33.7 11.6 Silt loam 1.34 2.18
41 724907 1856028 23.0 34.3 38.1 4.6 Silt loam 1.46 0.72
42 725107 1856028 24.1 35.4 35.0 5.6 Silt loam 1.31 2.71
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Sample X Y clay% silt<20 % silt<50 % sand% Texture BD OM
43 725307 1856028 22.6 33.4 37.8 6.3 Silt loam 1.32 1.13
44 725507 1856028 19.8 30.6 33.1 16.5 Silt loam 1.36 1.28
45 725707 1856028 22.4 32.3 35.1 10.2 Silt loam 1.33 2.20
46 725907 1856028 22.4 32.9 35.0 9.7 Silt loam 1.33 1.68
47 722707 1855828 14.4 28.8 31.6 25.3 Silt loam 1.43 1.87
48 722907 1855828 23.6 35.4 35.6 5.5 Silt loam 1.32 1.25
49 723107 1855828 26.3 37.5 34.0 2.2 Silt loam 1.35 1.79
50 723307 1855828 21.1 32.2 34.3 12.3 Silt loam 1.35 2.14
51 723507 1855828 11.9 31.5 33.7 22.9 Silt loam 1.45 0.94
52 723707 1855828 20.5 32.7 35.6 11.2 Silt loam 1.35 2.40
53 723907 1855828 21.9 33.2 34.1 10.8 Silt loam 1.34 3.84
54 724107 1855828 22.7 34.4 35.7 7.3 Silt loam 1.33 2.05
55 724307 1855828 25.4 32.0 36.0 6.6 Silt loam 1.31 2.48
56 724507 1855828 23.6 34.3 36.1 5.9 Silt loam 1.32 2.32
57 724707 1855828 26.9 35.0 33.1 5.0 Silt loam 1.30 1.80
58 724907 1855828 25.4 34.2 34.6 5.8 Silt loam 1.31 0.84
59 725107 1855828 20.5 32.8 35.8 10.9 Silt loam 1.35 1.08
60 725307 1855828 25.2 34.4 35.0 5.4 Silt loam 1.31 1.59
61 725507 1855828 22.9 31.0 33.2 13.0 Silt loam 1.34 1.52
62 725707 1855828 20.3 32.2 33.6 14.0 Silt loam 1.35 2.22
63 722707 1855628 16.1 29.7 30.8 23.4 Silt loam 1.41 0.98
64 722907 1855628 22.5 32.9 35.7 9.0 Silt loam 1.33 2.35
65 723107 1855628 16.1 36.9 35.3 11.7 Silt loam 1.38 3.24
66 723307 1855628 23.4 35.3 38.5 2.9 Silt loam 1.37 1.70
67 723507 1855628 16.7 34.2 34.9 14.2 Silt loam 1.38 4.31
68 723707 1855628 18.9 34.4 38.7 8.0 Silt loam 1.35 2.86
69 723907 1855628 24.9 35.8 35.8 3.5 Silt loam 1.31 2.36
70 724107 1855628 14.2 31.8 36.4 17.5 Silt loam 1.41 2.90
71 724307 1855628 23.2 33.7 34.9 8.2 Silt loam 1.32 3.15
72 724507 1855628 25.2 35.6 34.2 5.0 Silt loam 1.31 2.47
73 724707 1855628 24.5 34.5 37.2 3.8 Silt loam 1.31 2.38
74 724907 1855628 24.7 34.3 36.3 4.8 Silt loam 1.32 2.30
75 725107 1855628 20.3 34.5 36.1 9.1 Silt loam 1.35 1.05
76 725307 1855628 21.9 36.1 35.0 6.9 Silt loam 1.33 1.76
77 723107 1855428 19.1 35.6 36.0 9.3 Silt loam 1.35 2.15
78 723307 1855428 26.2 32.7 37.8 3.4 Silt loam 1.33 2.09
79 723507 1855428 22.7 35.2 38.7 3.4 Silt loam 1.37 1.77
80 723707 1855428 27.7 35.3 34.7 2.3 Silty clay loam 1.42 0.76
81 723907 1855428 22.3 35.6 38.1 4.1 Silt loam 1.35 2.12
82 724907 1855428 20.6 33.0 32.5 13.9 Silt loam 1.35 2.48
83 725107 1855428 23.0 35.7 36.2 5.1 Silt loam 1.32 1.57
84 725307 1855428 25.1 34.7 37.1 3.0 Silt loam 1.40 1.29
85 720977 1853525 18.6 31.7 37.3 12.3 Silt loam 1.36 1.06
86 718182 1857435 20.4 30.8 35.4 13.5 Silt loam 1.35 1.20
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Sample X Y clay% silt<20 % silt<50 % sand% Texture BD OM
87 718161 1856805 23.5 34.7 36.7 5.0 Silt loam 1.32 2.17
88 717254 1854755 18.1 34.7 31.2 16.1 Silt loam 1.38 0.57
89 719767 1852180 30.1 34.0 35.1 0.8 Silty clay loam 1.41 0.62
90 722749 1856685 17.8 32.0 36.9 13.2 Silt loam 1.37 1.52
91 725055 1855342 13.4 29.7 33.9 23.1 Silt loam 1.43 0.88
92 723697 1856135 11.6 31.1 36.3 21.1 Silt loam 1.45 0.92
93 726981 1853595 22.1 34.3 38.8 4.8 Silt loam 1.34 2.20
94 726287 1853355 21.1 33.1 38.2 7.7 Silt loam 1.34 1.76
95 729868 1854855 21.6 35.0 38.7 4.7 Silt loam 1.39 1.70
96 729407 1855045 20.8 33.2 33.5 12.5 Silt loam 1.35 2.45
97 718309 1854365 22.5 34.6 31.0 11.9 Silt loam 1.34 2.84
98 720202 1852595 27.1 35.3 37.0 0.6 Silty clay loam 1.40 1.03
99 721687 1854095 26.7 35.0 37.5 0.8 Silt loam 1.44 0.69
100 719103 1856625 19.2 32.5 38.3 9.9 Silt loam 1.36 0.33
101 720320 1856045 16.9 30.8 36.0 16.2 Silt loam 1.39 0.32
102 724030 1856835 19.4 27.8 31.4 21.4 Silt loam 1.38 0.88
103 723180 1857145 22.5 35.2 37.1 5.2 Silt loam 1.32 0.68
104 729831 1855885 18.8 29.7 33.0 18.4 Silt loam 1.37 1.13
105 726132 1855475 23.7 33.4 32.2 10.6 Silt loam 1.33 0.57
106 728131 1853646 18.6 32.6 36.1 12.6 Silt loam 1.37 2.69
107 728787 1855815 14.6 30.2 36.2 19.1 Silt loam 1.41 2.10
108 728949 1856065 19.8 34.0 35.7 10.5 Silt loam 1.35 4.05
109 724392 1857755 26.0 35.8 35.2 3.0 Silt loam 1.41 1.14
110 727655 1854298 21.1 30.4 29.9 18.6 Silt loam 1.36 2.35
111 726814 1854094 18.2 33.2 38.6 10.0 Silt loam 1.36 1.62
112 724148 1855466 22.0 32.7 36.9 8.3 Silt loam 1.33 1.61
113 727425 1853063 18.8 31.2 37.2 12.8 Silt loam 1.36 1.50
114 724697 1856525 24.3 33.8 35.4 6.4 Silt loam 1.31 1.19
115 724030 1856835 22.0 33.6 35.7 8.8 Silt loam 1.33 1.97
116 725716 1856665 25.6 34.9 35.8 3.7 Silt loam 1.42 0.95
117 719145 1852825 21.2 33.9 36.5 8.4 Silt loam 1.34 1.14
118 716860 1857949 25.4 35.1 36.7 2.8 Silt loam 1.41 1.05
119 716972 1858409 23.7 33.7 34.1 8.5 Silt loam 1.32 1.25
120 724922 1851675 24.8 35.3 33.9 6.0 Silt loam 1.31 2.19
121 725355 1853035 17.6 34.8 39.7 7.9 Silt loam 1.36 2.62
122 717309 1857535 22.1 34.3 37.8 5.8 Silt loam 1.33 2.38
123 717563 1857872 12.0 32.4 39.3 16.4 Silt loam 1.44 1.39
124 724805 1855868 10.3 28.8 32.6 28.3 Silt loam 1.48 2.54
125 729367 1854605 15.8 29.5 28.7 26.0 Silt loam 1.41 1.37
126 719385 1853715 17.8 32.3 37.0 12.9 Silt loam 1.37 1.95
The coordinate systems: WGS84 UTM zone 47
DIGITAL TERRAIN ANALYSIS AND IMAGE PROCESSING FOR ASSESSING EROSION PRONE AREAS
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Appendix 3: Organic matter 2006
Sample X Y OM Sample X Y OM
1 727967 1854046 2.18 33 727049 1855790 3.52
2 720676 1856122 2.16 34 727562 1856627 5.49
3 717773 1857973 2.01 35 726909 1854863 4.51
4 729180 1855194 3.36 36 726790 1854212 2.52
5 729158 1854570 2.48 37 727140 1855678 3.63
6 729062 1854497 3.56 38 727892 1853768 2.33
7 718772 1858082 0.4 39 719589 1855523 1.61
8 724780 1855717 3.71 40 721906 1853148 3.6
9 724793 1855722 2.22 41 721745 1852981 5.01
10 724778 1855825 1.11 42 721883 1852835 6.42
11 724719 1855844 1.41 43 721908 1852948 3.29
12 728208 1853907 2.5 44 721184 1853470 5.35
13 722790 1857516 2.87 45 721651 1853090 3.02
14 723113 1857471 2.37 46 725994 1855948 5.12
15 718171 1857716 0.74 47 717714 1858018 2.91
16 718156 1857772 2.46 48 722401 1853647 4.66
17 718233 1857700 1.38 49 722493 1853643 2.32
18 724582 1856787 2.09 50 720369 1854488 1.78
19 724903 1856659 3.15 51 720189 1854198 4.25
20 724627 1856523 4.25 52 720438 1855915 2.32
21 724584 1856368 3.17 53 720455 1855884 2.37
22 728112 1853935 4.28 54 720917 1855665 4.74
23 720906 1855807 2.75 55 720920 1855820 2.92
24 729116 1854508 3.52 56 727060 1855705 3.21
25 725324 1856109 2.82 57 725500 1855805 2.18
26 725288 1856054 1.86 58 725493 1855735 2.57
27 725295 1855924 2.96 59 724716 1856668 2.88
28 726071 1856766 3.9 60 725538 1855743 4.21
29 726057 1856626 3.36 61 725509 1855657 5.41
30 726435 1855476 3.71 62 721916 1855636 1.74
31 728139 1854025 3.59 63 721826 1855639 2.91
32 726422 1855589 3.67
The coordinate systems: WGS84 UTM zone 47
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Appendix 4: Regression analysis result summaries between C-factor values and NDVI
a) Regression between C-factor and NDVI Model Summary
R R Square Adjusted R Square
Std. Error of the Estimate
.839 .703 .701 .422 The independent variable is NDVI. ANOVA
Sum of
Squares df Mean Square F Sig.
Regression 57.316 1 57.316 322.056 .000 Residual 24.204 136 .178 Total 81.520 137
The independent variable is NDVI. Coefficients
Unstandardized Coefficients
Standardized Coefficients t Sig.
B Std. Error Beta B Std. Error NDVI -7.337 .409 -.839 -17.946 .000 (Constant) .227 .015 15.158 .000
The dependent variable is ln(C_factor). b) Regression between annual rainfall and elevation Model Summary
R R Square Adjusted R Square
Std. Error of the Estimate
.926 .858 .842 126.057 The independent variable is Elevation. ANOVA
Sum of
Squares df Mean Square F Sig. Regression 860612.200 1 860612.200 54.160 .000 Residual 143012.180 9 15890.242 Total 1003624.380 10
The independent variable is Elevation. Coefficients
Unstandardized Coefficients
Standardized Coefficients t Sig.
B Std. Error Beta B Std. Error Elevation .980 .133 .926 7.359 .000 (Constant) 840.486 57.927 14.509 .000
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c) Regression between C-factor predictions from curve estimation and C-factor validation Model Summary
R R Square Adjusted
R Square
Std. Error of
the Estimate
.884 .781 .779 .027 The independent variable is Cvalidate. Appendix 5: One-way ANOVA analysis summaries a) One-way ANOVA analysis of Organic matterin different land use/cover
ANOVA %OM
Sum of
Squares df Mean Square F Sig.
Between Groups 56.825 4 14.206 20.593 .000 Within Groups 108.999 158 .690 Total 165.825 162
Post Hoc Tests Multiple Comparisons Dependent Variable: %OM Tukey HSD
95% Confidence Interval (I) Land use
(J) Land use
Mean Difference (I-
J) Std. Error Sig.
Upper Bound
Lower Bound
Forest Degrade Forest .83306 .30954 .060 -.0211 1.6872 Agriculture 2.03689(*) .30643 .000 1.1913 2.8825 Grassland 1.91939(*) .30643 .000 1.0738 2.7650 Orchard 1.24389(*) .30791 .001 .3942 2.0936 Degrade Forest Forest -.83306 .30954 .060 -1.6872 .0211 Agriculture 1.20383(*) .19081 .000 .6773 1.7304 Grassland 1.08633(*) .19081 .000 .5598 1.6129 Orchard .41083 .19318 .214 -.1222 .9439 Agriculture Forest -2.03689(*) .30643 .000 -2.8825 -1.1913 Degrade Forest -1.20383(*) .19081 .000 -1.7304 -.6773 Grassland -.11750 .18572 .970 -.6300 .3950 Orchard -.79300(*) .18815 .000 -1.3122 -.2738 Grassland Forest -1.91939(*) .30643 .000 -2.7650 -1.0738 Degrade Forest -1.08633(*) .19081 .000 -1.6129 -.5598 Agriculture .11750 .18572 .970 -.3950 .6300 Orchard -.67550(*) .18815 .004 -1.1947 -.1563 Orchard Forest -1.24389(*) .30791 .001 -2.0936 -.3942 Degrade Forest -.41083 .19318 .214 -.9439 .1222 Agriculture .79300(*) .18815 .000 .2738 1.3122 Grassland .67550(*) .18815 .004 .1563 1.1947
* The mean difference is significant at the .05 level.
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b) One-way ANOVA analysis of Bulk density in different land use/cover ANOVA BD
Sum of
Squares df Mean Square F Sig.
Between Groups .114 4 .028 19.066 .000 Within Groups .076 51 .001 Total .190 55
Post Hoc Tests Multiple Comparisons Dependent Variable: BD Tukey HSD
95% Confidence Interval (I) Land use
(J) Land use
Mean Difference (I-
J) Std. Error Sig.
Upper Bound
Lower Bound
Forest Degrade Forest -.06083(*) .02055 .036 -.1189 -.0027 Agriculture -.10923(*) .02032 .000 -.1667 -.0518 Grassland -.00462 .02032 .999 -.0621 .0528 Orchard -.10385(*) .02032 .000 -.1613 -.0464 Degrade Forest Forest .06083(*) .02055 .036 .0027 .1189 Agriculture -.04840(*) .01546 .023 -.0921 -.0047 Grassland .05622(*) .01546 .006 .0125 .0999 Orchard -.04301 .01546 .056 -.0867 .0007 Agriculture Forest .10923(*) .02032 .000 .0518 .1667 Degrade Forest .04840(*) .01546 .023 .0047 .0921 Grassland .10462(*) .01514 .000 .0618 .1474 Orchard .00538 .01514 .996 -.0374 .0482 Grassland Forest .00462 .02032 .999 -.0528 .0621 Degrade Forest -.05622(*) .01546 .006 -.0999 -.0125 Agriculture -.10462(*) .01514 .000 -.1474 -.0618 Orchard -.09923(*) .01514 .000 -.1421 -.0564 Orchard Forest .10385(*) .02032 .000 .0464 .1613 Degrade Forest .04301 .01546 .056 -.0007 .0867 Agriculture -.00538 .01514 .996 -.0482 .0374 Grassland .09923(*) .01514 .000 .0564 .1421
* The mean difference is significant at the .05 level.
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BD
1. 501. 451. 401. 351. 301. 251. 20
Frequency
8
6
4
2
0
Normal Di st r i but i on
Mean =1. 33
St d. Dev. =0. 059
N =56
%OM
6. 004. 002. 000. 00
Frequency
25
20
15
10
5
0
Normal Di st r i but i on
Mean =2. 83
St d. Dev. =1. 012
N =163
Appendix 6: Histogram of organic matter (a) and bulk density (b)
(a) (b)
Appendix 7: Geopedologic map (Solomon, 2005) used in RMMF model and legend
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Source: (Solomon, 2005)
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Appendix 8: ILWIS script to run the RMMF model for annual soil loss prediction
// ILWIS script to run RMMF model for annual soil loss prediction
// Create attribute maps of A, Et/Eo, CC, GC, PH, EHD
A.mpr{dom=value;vr=-9999999.91:9999999.91:0.01} = MapAttribute(Landuse2007area,Landuse.tbt.A)
Et_Eo.mpr {dom=value;vr=-9999999.91:9999999.91:0.01} = MapAttribute(Landuse 2007area,Landuse.tbt.ET_EO)
CC.mpr{dom=value;vr=-9999999.91:9999999.91:0.01} = MapAttribute(Landuse2007area,Landuse.tbt.CC)
GC.mpr{dom=value;vr=-9999999.91:9999999.91:0.01} = MapAttribute(Landuse 2007area,Landuse.tbt.GC)
PH.mpr{dom=value;vr=-9999999.91:9999999.91:0.01} = MapAttribute(Landuse2007area,Landuse.tbt.PH)
EHD.mpr{dom=value;vr=-9999999.91:9999999.91:0.01} = MapAttribute(Landuse2007area,Landuse.tbt.EHD)
// Calculate Kinetic Energy
ER = Rainfall*A
LD = ER*CC
DT = ER - LD
KE_DT = DT*(11.9 + (8.7*(log(25))))
KE_LD = LD*(15.8*(PH^0.5))-5.87
KE_Total = KE_DT + KE_LD
// Calculate mean rainy days
Ro = Rainfall/120
// Create attribute maps of MS , BD , K , COH
MS.mpr{dom=value;vr=-9999999.91:9999999.91:0.01} = MapAttribute(Soilmap,Geopedologicalunits.tbt.MS)
BD.mpr{dom=value;vr=-9999999.91:9999999.91:0.01} = MapAttribute(Soilmap,Geopedologicalunits.tbt.BD)
K.mpr{dom=value;vr=-9999999.91:9999999.91:0.01} = MapAttribute(Soilmap,Geopedologicalunits.tbt.K)
COH.mpr{dom=value;vr=-9999999.91:9999999.91:0.01} = MapAttribute(Soilmap,Geopedologicalunits.tbt.COH)
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// Calculate soil particle detachment
RC = 1000*MS*BD*EHD*Et_Eo
Q = Rainfall*(exp(-RC/Ro))
F = K*KE_Total*0.001
Z = 1/(0.5*COH)
H = Z*(Q^1.5)*(SIN(DEGRAD(Slope))) *(1-GC)*0.001
D = (F+ H)
// Calculate transport capacity
TC = (Cmap2007*(Q^2)*(SIN(DEGRAD(Slope)))*0.001)
// Calculate soil loss rate
Soilloss2007 = min(D,TC1)
Appendix 9: The photographs of gully erosion in the study area