LAND USE/COVER CHANGE ANALYSES BASED ON REMOTELY-SENSED IMAGERY … · REMOTELY-SENSED IMAGERY AND...

LAND USECOVER CHANGE ANALYSES BASED ON REMOTELY-SENSED IMAGERY AND OLD MAPS AS MEANS

TO DOCUMENT FRAGMENTATION AND DISTURBANCE FOR EAST-AFRICAN RAINFORESTS

OVER THE LAST CA 100 YEARS

Schaab Gertrud Tobias Lung amp Nicholas Mitchell

Karlsruhe University of Applied Sciences Faculty of Geoinformation Moltkestr 30 D-76133 Karlsruhe Germany gertrudschaabfh-karlsruhede

Within the BIOTA East Africa project the influence of fragmentation and human use on the biodiversity of East-African rainforests is investigated following an interdisciplinary and integrated research approach In order to provide information on land usecover change to the biologists ecologists and socio-economists involved subproject E02 is assessing numerous remotely-sensed imageries and old maps related to three forest areas in Western Kenya (Kakamega Forest and associated forest areas) and Uganda (Mabira Forest Budongo Forest) that together form a disturbance gradient The anticipated joint analysis of the various spatial data in combination with descriptive information in a geographic information system will allow for a comparable in-depth investigation of forest cover change and forest use history This paper aims at providing an overview on the spatial data processing involved including somepreliminary comparative forest area numbers and graphic presentations for visual judgements INTRODUCTION AND BACKGROUND Highest species numbers and concentrations are found in tropical rainforests with 60-90 of the estimated total biological diversity encountered on just 7 of the land surface (UNEP-CBD GBO 2001) Among the human-induced inter-related environmental changes (summarized under the term global change) land usecover change (LUCC) as well as climate change have the strongest effect on biodiversity in the tropics (Sala et al 2000) Clear felling of rainforest but also selective logging result in lower biodiversity regarding both flora and fauna In Africa rainforest areas are mainly lost in favour of subsistence agriculture (UNEP-DEWA GEO3 2002) this being true in particular for Kenya and Uganda with there is high population pressure (UNEP-DEWA WRCF 2001) Conversion of forest areas to agricultural land is not sustainable in the longterm because forests provide not only goods (eg timber firewood game medical plants) to the local population but also services Here regulation of the water cycle and soil stabilization can be identified as the most important services (UNEP-CBD GBO 2001) The fragmentation of forests into smaller forest islands disturbs animal migration routes and therefore prevents genetic exchange between populations Forest fragmentation also leads to an improved accessibility by man-kind and invasive species (UNEP-DEWA GEO3 2002) This again increases the direct impact of deforestation and degradation on biodiversity In addition it is clear that forests are influencing climate and vice versa

Within BIOTA East Africa (a project funded by BMBF the German Ministry of Education and Research since 2001 see wwwbiota-africade or Koumlhler 2004) 15 subprojects are investigating the influence of fragmentation and human use on the biodiversity of East-African rainforests following an interdisciplinary and integrated research approach The project encompasses a strong scientific capacity building component for the East-African counterparts aiming at a sound handing-over towards the end of the funding period Research is related to the vegetation structure including factors responsible for regeneration to certain animal groups (emphasizing invertebrates) to ecological interactions (such as seed dispersal or pollination) and since the start of the second project phase also to socio-economic issues In the first project phase it has become evident that it is not just abiotic factors such as soil or climate that effect biological diversity but also forest fragmentation and in particular forest disturbance as a consequence of intensive human forest use Because ultimately recommendations for a sustainable forest and biodiversity management at the landscape level are aimed at considering global change aspects biodiversity research is in need of geo-spatial data In subproject E02 (see Schaab et al 2004) old maps and remotely-sensed image data are being used in combination as an invaluable means to creating the needed time series of LUCC They form the basis for extrapolation or scaling-up of field-based interdisciplinary findings in space and time The focus area of research within BIOTA East Africa is the Kakamega Forest in Western Kenya Budongo Forest and Mabira Forest in Uganda have been selected in the second project to enable comparison across disturbance gradients both within and across separate forests (see figure 1)

While climate change is difficult to describe for the African continent over the last decades due to deficiencies of the African observation sytem (Defra 2004) the creation of LUCC time series is expected to be less problematic due to the availability of satellite-based remote sensing since the early 1970s which can continue the series from earlier colonial maps In order to go back in time as far as possible a globally increasing number of case studies on LUCC (eg Kuumlmmerle et al 2005 Lung 2004 Mapedza et al 2003 Petit amp Lambin 2002 Petit et al 2001 Read et al 2001) are making use not only of satellite imagery but also of historical aerial photography and less often of topographic maps Geist amp Lambin (2001) performed a meta-analysis of drivers of tropical deforestation based on 152 subnational case studies thus highlighting the need for monitoring and modelling LUCC changes at regional scales Because only 19 case studies are from the African continent the authors conclude that considerably more African cases are needed especially East-African cases with only 1 case study included In order to understand the causes and drivers and perhaps to predict forest cover change processes there is the need to quantitatively analyse and characterise the spatial pattern of LUCC something field studies alone cannot accomplish (Petit et al 2001) Here the technique of geographic information systems (GIS) in combination with digital image processing (DIP) software for deriving information from remotely sensed imagery has emerged as the most useful tool for analysing spatial data and information from heterogeneous sources in an integrated manner

SPATIAL DATA PROCESSING For spatial data processing we are mainly making use of the widely used software tools ArcGIS 83 and ERDAS Imagine 87 The BIOTA-East-GIS online data catalogue (see httpwwwdlrdebiota-east or Walter amp Schaab 2005) can serve as an overview regarding data sources used but there are more datasets included than just the ones useful in setting-up LUCC time series Others are not included yet due to the wish to publish results first

Because Kakamega Forest has been the focus area of research already in the first project phase here a LUCC time series covering the last 30 years is already available (see Lung 2004 Lung amp Schaab 2004) For seven time steps between 1972 and 2001 Landsat ETM+ TM (both 30 m) and MSS data (60 m spatial resolution) have been acquired for the wider Kakamega Forest area considering also associated forest fragments or larger forest areas eg the Nandi

Figure 1 The forest areas investigated within the BIOTA East Africa project forming a disturbance gradient

Forests of comparable size Data processing involved georeferencing and radiometric corrections regarding atmospheric and terrain shading effects followed by a supervised multispectral classification Ground truthing for recent imagery was done via amateur photographs taken during an aeroplane flight in May 2001 For the historical imagery several maps showing vegetation and land coveruse information for distinct years were used (Vegetation map 1250000 from 1966 150000 topo map sheets from 1970 Forest Department forest map 110000 from 1972 KIFCON land cover map 125000 from 1991) These have been vectorized for inclusion in the GIS enabling visual and analytical overlays Thus 12 land cover classes could be distinguished of which 6 belong to forest formations The classification results have been thoroughly analysed to determine area numbers portions and changes in land cover including a complete change detection transition matrix A cluster analysis resulted in types of characteristic changes in land cover from 1972 to 2001 ie spatial regions have been formed that are alike regarding their specific forest history The striking developments over the past 30 years have thus been visualized in a single map presentation

Aerial photography mosaics for 194852 and 196567 have been created with the help of ERDAS tool Orthobase and a digital elevation model (DEM) processed from 150000 topo map sheet contours (see Herz 2004) in order to extend the time series back another 20 years These are currently being analysed via visual interpretation and on-screen digitizing Further forest cover as shown on old 1250000 topographic map sheets from 1916 (surveyed 1913) will provide information on an even earlier stage Because the Landsat time series can be extended with imagery from 2003 we are therefore able to tell the story of LUCC over almost the last 100 years

Similar LUCC time series and analyses are planned for the two new research sites in Uganda Landsat time series imagery has again been acquired for the past 30 years covering Mabira Forest from 1974 to 2001 via 5 time steps and Budongo Forest from 1972 to 2000 via 6 time steps The satellite images have been georeferenced by means of 150000 topo maps (from 19555860 and 1966 respectively) from which forest cover information is also taken Before classifying the satellite images for further preprocessing DEMs are needed The time series will be extended by recent Landsat satellite imagery as well as historical aerial photography and old topographic maps Again photography from the 1950s and 1960s should be available and the series of historic topographic maps appears to extend at least as far as the Kenyan examples

Figure 2 Forest extents of Kakamega Forest and Kisere fragment derived from different sources and valid for different years

SOME RESULTS ON SETTING-UP LAND USECOVER CHANGE TIME SERIES In the following we present example graphic presentations allowing for a visual judgment of spatial data and information available for setting-up LUCC time series of the three forest areas investigated within BIOTA East Africa The visually obvious changes in forest cover are described and forest area numbers derived from spatial data processing in GIS compared with each other and with values taken from literature

The visual overlay of forest extent information of Kakamega Forest and the fragment Kisere (see figure 2) for 3 different years (1933 1967 2001) on the left side enables a direct visual comparison of forest cover change The official forest boundary cannot be regarded as the actual forest extent in 1933 as it is clear that at that time the forest already included the numerous grassy glades for which the forest is well known (see eg Kokwaro 1988) The topo maps in 150000 scale include information on natural as well as on plantation forest By comparison to a relatively recent Landsat satellite image (Feb 2001) not only small-scale pattern of clearcut forest and plantation but also regeneration can be studied This is facilitated by delineating indigeneous old-growth forest cover the result of a visual interpretation on the basis of the satellite image as displayed Overall the very diverse story is told in more detail by the multispectral classification results of the Landsat time series which is not shown here for space-saving reasons (see Lung 2004) For Kakamega Forest most of the so far gathered spatial data have been processed for inclusion in GIS thus forest area numbers can be immediately compared While the gazetted area contains 23632 ha the topo maps cover 16875 ha of natural and 1672 ha of plantation forest for the year 1967 From the classification of the Landsat 2001 image an area of 17448 ha results when adding up the six forest formation classes or 12287 ha when considering just the two first real forest classes (for classes see figure 3) The area visually delineated as indigeneous old-growth forest results in 11106 ha The aerial photography mosaic of 194852 downscaled and contrast enhanced to suit the

Figure 3 Land usecover change for South Nandi Forest as revealed by a supervised multispectral classification of Landsat satellite imagery

requirements for inclusion in the paper (to the right of figure 2) can only serve here to demonstrate the need to include further time steps in order to cover those decades that have brought the most severe changes in forest cover to Kakamega Forest The interpretation of the historical aerial photography considering similar classes as treated in the multispectral classification of the satellite imagery is still in progress Therefore just a preliminary forest area value of 19800 ha has been determined for 194852 As already mentioned the time series will be extended even futher back by means of old topographic maps

For South Nandi Forest results of the multispectral Landsat time series classification are presented here (figure 3) including the official forest boundary as gazetted in the 1930s and covering an area of 19536 ha Very pronounced opposed dynamics regarding the forest formations are obvious While the area covered by Near natural + old secondary forest and Secondary forest is decreasing the area of Bushland shrubs is continously increasing since 1975 South Nandi Forest is marked by a dramatic loss of forested area 14000 ha of class 1 and 2 in 1972 as compared to 7200 ha in 2001 (Lung 2004) with a contrary trend for the bushland class Little information on the Nandi Forests can be found in the literature (eg Snell 1954) The Landsat time series analysis reveals not only a severe decrease in forest area due to clear fellings of larger areas but a marked portion has been lost due to selective logging opening the forest cover with numerous small gaps with only undergrowth vegetation left or subsequent encroaching of bushy vegetation These findings need to be checked in the field and confirmed by additional information most probably locked away in governmental archives

For Mabira Forest spatial data processing so far allows only a visual overlay of a Landsat sub-scene from 2001 with the topo maps in 150000 scale of 19555860 Due to the topo maps scale and the extent of Mabira Forest its presentation here is not feasible The vectorizing of the forest information from the map sheets is currently in work but not yet finalized Therfore we use figure 4 for a comparison of Mabira Forest as sensed by Landsat in 2001 and as drawn on an old topographic map from 1923 (Map of Buganda 1500000 Ordnance Survey 1923) Due to the needed cartographic generalization for the scale 1500000 but also likely to imperfect surveying of the actual forest cover by that time changes in forest cover between 1923 and 2001 have to be judged with care Rather disturbing is the fact that the 150000 topo map sheets of 19555860 reveal a forest edge to the east following the river Nile This is neither the case on the old topo map of 1923 nor on the satellite image of November 2001 However in the 1950s the forest seems to have covered at least twice as much area (40836 ha) compared to 1988 (see Westman et al 1989) Based on this value of 20420 ha forest in 1988 and an acompanying map the visually detectable differences among forest formations as revealed by the Landsat satellite from 2001 image leads to estimates of 20000 ha of established old-growth forest and less than 10000 ha of probably regenerating forest formations (of which 23 belong to one discrete section) ie former forested land once clearcut at a large scale seems to have regrown to bushland vegetation Forest plantation areas cannot be discerned

Our spatial data processing for Budongo Forest performed so far is in accordance with Plumptre (2002) showing that Budongo Forest has experienced very little change in forest cover extent over the years Due to the scale of figure 5 the overlay of the forest extent information from the 150000 topo maps on a Landsat image from 2000 it is difficult to make out the few minor changes Considering further the forest area numbers at hand ie 43000 ha as listed in

Figure 4 Mabira Forest as sensed by the Landsat satellite in 2001 and as drawn on an old topographic map from 1923

Plumptre (2002) and 44547 ha as derived from the 150000 topo map sheets after vectorization in GIS a similar value is to be expected from the classification of the Landsat scene Nevertheless a multispectral classification of the Landsat time series acquired is worth doing for the within-forest differentiation For 2000 the result is likely to produce a different picture compared to the land usecover map (to the right) this also to the fact of the maps making use of historical logging compartments and current forest utilisation zones respectively for reference units

However by comparing figure 2 and 5 (and with limitations also figure 4) differences in the state of East-African rainforest areas located on similar elevations are obvious These differences reflect their distinct forest use histories

Table 1 summarizes the forest area numbers as specified above among others for a straight-forward preliminary comparison of the four forest areas (here treating the Nandi Forests separately from Kakamega Forest) These values include some estimates since some remote sensing data andor GIS analyses are yet to be done and the figures must be handled with care Nevertheless they support the objectives of the BIOTA East Africa project to cover a disturbance gradient with research investigations in those three rainforest areas with Budongo Forest having experienced hardly any change in forest extent Mabira Forest having encountered large clearcuts and Kakamega Forest being marked by diverse small-scale changes including clearfelling and plantations To this set we add the Nandi Forests located ca 500 m higher in elevation which have recently experienced large-scale clearcuts as well as severe selective logging

CONCLUSIONS AND OUTLOOK In this paper we aimed at an overview of how LUCC time series for the three forest areas investigated in the BIOTA East Africa project are set up based on satellite imagery aerial photography mosaics and old topographic maps The derived time series must be homogeneous in regard of classification scheme and methods applied as well as sufficiently dense (see eg ICRAF 1996 and Brooks et al 1999 for time series that do not meet such standards) Only this way forest areas differing in disturbance or interference grades can be detected and their changing states reliably be assessed and compared over time The objective for truly comparable time series will be met for the information as derived from the Landsat time series data But already their extension via visual interpretation of historical aerial photography will very much depend on the imagerys overall quality (considering criteria such as image scale illumination etc) Making use of topographic maps for extracting forest extents of former times usually results in a complete loss of forest formation details Also one needs to be aware of decreasing positional surveying accuracy with increasing age of the maps and dependent on map scale But with cartographers the people are involved in the spatial data processing that are

Figure 5 Forest extent and land usecover of Budongo Forest

in particular aware of implications of scales and cartographic generalisation as well as of benefits from abtract as opposed to realistic representations These are important points to consider when various data and information on forest change from heterogeneous sources are integrated in a GIS and jointly analysed

By relating information from a descriptive forest history (see eg Mitchell 2004) with the spatial time series results we are optimistic to get further insights Questionnaires around the forests are conducted to gain the local oral histories of forest cover change Local placename surveys are performed for clues as to previous vegetation cover Archives are searched for forest records on commercial and governmental logging activities over the years collecting also historical forest sketch maps All this information will be assessed via the integration within a GIS in order to get a most complete semi-quantitative picture Thus our studies will contribute to a correction of the bias as assumed by Geist amp Lambin (2001) in their analysis of tropical deforestation being due to the fact that information is easier to get from farmers than on commercial or governmental logging behaviour and planting activities Further the time series are analysed with the help of landscape metrics (eg McGarigal 2001) Here we are not only considering standard landscape indices allowing eg for a comparison of neighbouring forest patches but also for their change over time or for statements on landscape diversity in an aggregated manner (see eg Lung 2004) By making use of a moving window approach images of spatially distributed indices are derived (featuring for example fragmentation see Wade et al 2003) thus having advantages regarding a direct linking with indicators applied for biodiversity monitoring on the ground as well as with field findings Such measures are considered as useful tools in conservation management

And finally only a spatially explicit understandable forest history instead of a mere description enables the linking of biodiversity change and human-induced environmental changes The review over time will ultimately allow for a biodiversity management that is sustainable in respect to the rainforests as well as the local people as is demanded by the Convention of Biological Diversity

REFERENCES Brooks TM SL Pimm amp JO Oyugi (1999) Time lag between deforestation and bird extinction in tropical forest fragments In Conservation Biology 13(5) 1140-1150

Defra (2004) African climate report A report commissioned by the UK Government to review African climate science policy and options for action URLwwwdefragovukenvironmentclimatechangeccafrica-studyindexhtm (11042005)

area [ha] Kakamega Forest with Kisere fragment

North Nandi and South Nandi Forests

Mabira Forest

Budongo Forest

gazetted forest area 23632 (1933) 458 (1930s)

11437 (1930s) 19536 (1930s)

30600 (1932) 82500 (1932-68)

forest cover from aerial photography

19800 (194852) 340 (194852)

natural forest cover from 150000 topo maps

16875 (1967) 380 (1967)

10777 (1967) 18719 (1967)

40836 (1955 5860)

44547 (1966)

forest cover from literature

11950 (1989) a 400 (1999) a

7915 (1991) b 15955 (1991) b

20420 (1988) c 43000 (2002) d

established old-growth forest cover from Landsat visual interpretation

11106 (2001) 420 (2001)

ca 6000 (2001) ca 9000 (2001) estimated

ca 20000 (2001) estimated


forest cover from Landsat classification (only first 2 forest classes)

12287 (2001) 394 (2001)

6225 (2001) 7214 (2001)



summary description of forest change

small-scale pattern of forest clearcut plantation and regeneration

clear felling of large areas severe selective logging throughout South Nandi Forest

clear felling of about one third of forested area with large-scale regeneration

hardly any change in forest extent

a Brooks et al 1999 b ICRAF 1996 c Westman et al 1989 d Plumptre 2002

Table 1 Forest area numbers for Kakamega the Nandi Mabira and Budongo Forests either analysed and estimated respectively from remotely-sensed imagery and topographic maps or as taken from literature

Geist HJ amp EF Lambin (2001) What drives tropical deforestation A meta-analysis of proximate and underlaying causes of deforestation based on subnational case study evidence LUCC Report Series No 4 CIACO Louvain-la-Neuve

Herz O (2004) Georeferenzierung und Mosaikierung von historischen Luftbildern fuumlr den Kakamega Forest und assoziierte Waldgebiete (Westkenia) sowie erste visuelle Auswertung bzgl Landbedeckungsaumlnderungen Unpublished Bachelor thesis course Cartography and Geomatics Karlsruhe University of Applied Sciences

ICRAF (1996) Tree cover changes in Kenyas West Rift ICRAF-Report Visions of Landscapes and Vegetation Changes June 1996 106-115

Koumlhler J (2004) Was hat Biodiversitaumltsforschung mit nachhaltiger Nutzung zu tun In Tier und Museum 8(3) 82-91

Kokwaro JO (1988) Conservation status of the Kakamega Forest in Kenya The easternmost relic of the equatorial rain forests of Africa In Monographs in Systematic Botany (Missouri Botanical Garden) 25 471-489

Kuumlmmerle T P Hostert A Damm amp V Radeloff (2005) Cross-border monitoring of landscape dynamics in Eastern Europe In Erasmi S B Cuffka amp M Kappas (ed) Remote sensing amp GIS for environmental studies Applications in geography Goumlttinger Geographische Abhandlungen 113 24-30

Lung T (2004) Landbedeckungsaumlnderungen im Gebiet Kakamega Forest und assoziierte Waldgebiete (Westkenia) - Multispektrale Klassifikation von Landsat-Satellitenbilddaten und Auswertung mittels Methoden im Raster-GIS In Karlsruher Geowissenschaftliche Schriften Rh A Bd 15 ed by G Schaab

Lung T amp G Schaab (2004) Change-detection in Western Kenya The documentation of fragmentation and disturbance for Kakamega Forest and associated forest areas by means of remotely-sensed imagery In ISPRS Archives Vol XXXV Part B (DVD) Proceedings of the ISPRS XXth Congress 12-23 July 2004 Istanbul

Mapezda E J Wright amp R Fawcett (2003) An investigation of land cover change in Mafungautsi Forest Zimbabwe using GIS and participatory mapping In Applied Geography 23(1) 1-21

McGarigal K (2001) Fragstats documentation part 3 (Fragstats Metrics) URL httpwwwumassedulandeco researchfragstatsdocumentsfragstats_documentshtml (25042005)

Mitchell N (2004) The exploitation and disturbance history of Kakamega Forest Western Kenya BIOTA East Report No 1 Bielefelder Oumlkologische Beitraumlge Bd 20 ed by B Bleher amp H Dalitz Petit CC amp EF Lambin (2002) Impact of data integration technique on historical land-useland-cover change comparing historical maps with remote sensing data in the Belgian Ardennes In Landscape Ecology 17(2) 117-132

Petit C T Scudder amp E Lambin (2001) Quantifying processes of land-cover change by remote sensing Resettlement and rapid land-cover changes in south-eastern Zambia In International Journal of Remote Sensing 22(17) 3435-3456

Plumptre A (2002) Extent and Status of the Forests in the Ugandan Albertine Rift Wildlife Conservation Society Albertine Rift Programme unpublished report to UNDPGEF

Read JM JS Denslow amp SM Guzman (2001) Documenting land cover history of a humid tropical environment in northeastern Costa Rica using timeseries remotely sensed data In Millington AC SJ Walsh amp PE Osborne (ed) GIS and remote sensing applications in biogeography and ecology Kluwer Academic Publishers Boston Dordrecht 69-89

Sala OE et al (2000) Global biodiversity scenarios for the year 2100 In Science 287 1770-1774

Schaab G T Kraus amp G Strunz (2004) GIS and remote sensing activities as an integrating link within the BIOTA-East Africa project In Sustainable use and conservation of biological diversity A challenge for society Proceedings of the International Symposium Berlin 1-4 December 2003 Berlin 161-168

Snell GS (1954) Nandi customary law MacMillan amp Co London

UNEP-CBD GBO (2001) Global biodiversity outlook URL httpwwwbiodivorg gbo (25042005) ed by Secretariat of the Convention on Biological Diversity

UNEP-DEWA GEO3 (2002) Global environment outlook 3 Past present and future perspectives URL http wwwuneporg GEOgeo3englishpdfhtm (25042005) ed by United Nations Environment Programme

UNEP-DEWA WRCF (2001) An assessment of the status of the worlds remaining closed forests URL http grid2crusgsgovpublicationsclosedforestpdf (25042005) ed by Division of Early Warning and Assessment United Nations Environment Programme

Wade TG KH Riitters DJ Wickham amp KB Jones (2003) Distribution and causes of global forest fragmentation In Conservation Ecology 7(2)7

Walter M amp G Schaab (2005) WebGIS zur Unterstuumltzung von Online-Geodatenkatalogen Realisierung fuumlr das BIOTA-Ostafrika-Projekt In Kartographische Nachrichten Fachzeitschrift fuumlr Geoinformation und Visualisierung 12005 17-25

Westman WE LL Strong amp BA Wilcox (1989) Tropical deforestation and species endangerment the role of remote sensing In Landscape Ecology 3(2) 97-109

BIOGRAPHY OF PRESENTING AUTHOR Since autumn 2002 Prof Dr-Ing Gertrud Schaab is a lecturer at the Faculty of Geoinformation of Karlsruhe University of Applied Sciences (FH Karlsruhe) Within the Diploma and Bachelor courses Cartography and Geomatics as well as in the International Geomatics Master Programme her lectures cover the complete range from basics in cartography to advanced thematic cartography but also the fields geographic information systems (GIS) remote sensing and environmental modelling Her background is a Diploma in Cartography from the same university a Master in Environmental Remote Sensing (University of Aberdeen) a doctoral degree from Dresden Technical University with an interdisciplinary work in the field of environmental modelling by means of GIS and several years of working experiences in various governmental and research institutions At FH Karlsruhe she enjoys the transfer of knowledge to students but she is also keen in funded research project work Here her scientific interests go generally for applied geoinformation processing in environmental sciences and more in particular for developing spatially explicit models for the simulation of processes and interactions Having been involved in the BIOTA project (Biodiversity Transect Analysis in Africa) since its start in 20002001 in June 2004 she became the head of the BIOTA East Africa subproject E02 GIS and Remote Sensing in Support of Biodiversity Research at the Landscape Scale In its second project phase the project E02 is now coordinated from FH Karlsruhe performed in collaboration with the German Aerospace Center (DLRDFD-UG Oberpfaffenhofen) and supported by counterparts from Kenya and Uganda (eg WAC NMK KWS MUIENR) This has allowed Gertrud Schaab to set-up a small research group at FH Karlsruhe with Dipl-Ing(FH) Tobias Lung whose work on his final thesis resulted in the LUCC time series from Landsat satellite imagery and Nicholas Mitchell MA who is currently working on more complete pictures of rainforest histories in East Africa for his PhD For this paper we like to acknowledge the help of Tanja Kraus from DLR regarding Landsat data acquisition and its georeferencing based on topographic map sheets

While climate change is difficult to describe for the African continent over the last decades due to deficiencies of the African observation sytem (Defra 2004) the creation of LUCC time series is expected to be less problematic due to the availability of satellite-based remote sensing since the early 1970s which can continue the series from earlier colonial maps In order to go back in time as far as possible a globally increasing number of case studies on LUCC (eg Kuumlmmerle et al 2005 Lung 2004 Mapedza et al 2003 Petit amp Lambin 2002 Petit et al 2001 Read et al 2001) are making use not only of satellite imagery but also of historical aerial photography and less often of topographic maps Geist amp Lambin (2001) performed a meta-analysis of drivers of tropical deforestation based on 152 subnational case studies thus highlighting the need for monitoring and modelling LUCC changes at regional scales Because only 19 case studies are from the African continent the authors conclude that considerably more African cases are needed especially East-African cases with only 1 case study included In order to understand the causes and drivers and perhaps to predict forest cover change processes there is the need to quantitatively analyse and characterise the spatial pattern of LUCC something field studies alone cannot accomplish (Petit et al 2001) Here the technique of geographic information systems (GIS) in combination with digital image processing (DIP) software for deriving information from remotely sensed imagery has emerged as the most useful tool for analysing spatial data and information from heterogeneous sources in an integrated manner

SPATIAL DATA PROCESSING For spatial data processing we are mainly making use of the widely used software tools ArcGIS 83 and ERDAS Imagine 87 The BIOTA-East-GIS online data catalogue (see httpwwwdlrdebiota-east or Walter amp Schaab 2005) can serve as an overview regarding data sources used but there are more datasets included than just the ones useful in setting-up LUCC time series Others are not included yet due to the wish to publish results first

Because Kakamega Forest has been the focus area of research already in the first project phase here a LUCC time series covering the last 30 years is already available (see Lung 2004 Lung amp Schaab 2004) For seven time steps between 1972 and 2001 Landsat ETM+ TM (both 30 m) and MSS data (60 m spatial resolution) have been acquired for the wider Kakamega Forest area considering also associated forest fragments or larger forest areas eg the Nandi

Figure 1 The forest areas investigated within the BIOTA East Africa project forming a disturbance gradient

























Mabira Forest

Budongo Forest


11437 (1930s) 19536 (1930s)

30600 (1932) 82500 (1932-68)


19800 (194852) 340 (194852)


16875 (1967) 380 (1967)

10777 (1967) 18719 (1967)

40836 (1955 5860)

44547 (1966)


11950 (1989) a 400 (1999) a

7915 (1991) b 15955 (1991) b

20420 (1988) c 43000 (2002) d


11106 (2001) 420 (2001)

ca 6000 (2001) ca 9000 (2001) estimated




12287 (2001) 394 (2001)

6225 (2001) 7214 (2001)


























































Mabira Forest

Budongo Forest


11437 (1930s) 19536 (1930s)

30600 (1932) 82500 (1932-68)


19800 (194852) 340 (194852)


16875 (1967) 380 (1967)

10777 (1967) 18719 (1967)

40836 (1955 5860)

44547 (1966)


11950 (1989) a 400 (1999) a

7915 (1991) b 15955 (1991) b

20420 (1988) c 43000 (2002) d


11106 (2001) 420 (2001)

ca 6000 (2001) ca 9000 (2001) estimated




12287 (2001) 394 (2001)

6225 (2001) 7214 (2001)


































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