Change-Detection in Western Kenya
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CHANGE-DETECTION IN WESTERN KENYA – THE DOCUMENTATION OF FRAGMENTATION AND DISTURBANCE FOR KAKAMEGA FOREST AND ASSOCIATED FOREST AREAS BY MEANS OF REMOTELY-SENSED IMAGERY T. Lung, G. Schaab * Department of Geoinformation, Karlsruhe University of Applied Sciences, Moltkestr. 30, D-76133 Karlsruhe, Germany, [email protected] KEY WORDS: Remote Sensing, Multisensor Imagery, Landsat, Classification, Land Cover, Change-Detection, GIS ABSTRACT: In order to understand causes and effects of disturbance and fragmentation on flora and fauna, a time series on land cover change is needed as basis for the BIOTA-East Africa project partners working in western Kenya. For 7 time steps over the past 30 years Land- sat data were collected for Kakamega Forest and its associated forest areas. Preprocessing involved georeferencing and radiometric corrections. In a first step the time series is evaluated via a threshold analysis distinguishing between “forest” and “non-forest”. Even though a temporally changing pattern of forest losses and replanting is observed, in total no major change in forest-covered area is revealed. Therefore, a supervised multispectral classification is performed distinguishing between classes at the ecosystem level. Ground truthing for the historical imagery is done with the help of maps showing vegetation types or land cover. Actual land cover verification is based on amateur photographs taken from an aeroplane as well as on terrain references. For classification the maxi- mum-likelihood decision rule is applied considering bands 3, 4, 5, 7 plus 7/2 for TM/ETM+ imagery and 1, 2, 3 and 4 for MSS-data, repectively. If available, scenes from both the rainy and dry seasons are made use of. From planned 17 land cover classes 12 can be realized, of which 6 belong to forest formations. A shortcome is that plantation forest of Maesopsis eminii (planted mixed in with other indigenous tree species) cannot be separated. Nevertheless, the classification results form a solid basis for a consistent and detailed evaluation of forest history between 1972 and 2001. Analyses presented include graphs of change in land cover class areas over time as well as such allowing for true change detection with transitions between the different classes. 1. INTRODUCTION 2. THE STUDY AREA Detecting land cover and its changes is needed in order to un- Kakamega Forest is known to be the species-richest forest in derstand global environmental change. Of particular interest are Kenya. It inhabits a large number of rare animals and even some the effects of tropical rain forest fragmentation which varies endemic plant species (KIFCON, 1994). The forest is sour- regarding intensity and extent. The increase in fragmentation is rounded by small forest fragments which might have been related either to natural or human sources (e.g. WADE et al., connected to the main forest in former times. The Nandi Forests 2003; Geist & Lambin, 2001). Both influence biodiversity, are of comparable size but placed on an escarpment some 200 to which is known to be immensly rich in tropical forests (Gaston, 300 m higher in elevation. There has been an ongoing debate 2000). In general, the BIOTA (Biodiversity Monitoring Tran- wether all these left forest islands have once formed one larger sect Analysis in Africa) project, funded by the German Ministry forest area if not even have belonged to the congo-guinean rain of Education and Research (BMBF), analyses changes in Afri- forest belt (see e.g. Kokwaro, 1988). The forests are placed in can biological diversity related to land cover and environmental one of the world’s most densely populated rural areas (Blackett, changes with the aim to develop recommendations for a sus- 1994: mean population density of 600 inh./km2) which is inten- tainable biodiversity management (see www.biota-africa.org). sily used for subsistence agriculture. Due to continualy increas- The research acticities of BIOTA-East Af- rica (see Köhler, 2004) are currently focus- ing on Kakamega Forest and fragments close by, located in western Kenya. Informa- Kakamega Forest and Associated Forest Areas Malava Taresia tion retrieval regarding land cover change Forest Forest and thus forest fragmentation and distur- Bunyala Forest Kisere bance is performed for the wider area of Forest North Nandi Kakamega Forest and associated forest areas Kenya Forest KAKAMEGA (see Figure 1). Here, the results of satellite TOWN Kakamega Kapteroi data analysis form a major contribution of Forest Forest geo-information processing for documenting Ururu anthropogenic influences on these rain Kaimosi Forest Forest forests over time. They will allow the differ- Maragoli Forest ent biological and/or ecological subprojects South Nandi Forest of BIOTA-East Africa to conclude on 0 5 10 km changes in forest ecosystems or biodiversity 0 100 200 km related to global change. With the study sites spread over a relatively small area there is Figure 1: Location of the study area and the different forests covered by remote the need and the chance to offer rather de- sensing analyses in western Kenya (34°37’5” – 35°9’25” east of Gr., tailed information, both in space and time. 0°32’24” north – 0°2’52” south of the equator) * corresponding author ing population numbers the pressure on the forests is growing. distinguishing „forest“ from „no forest“. This distinction could For the local people the forests play an important role in satisfy- be made use of later by performing multispectral classifications ing their daily needs (e.g. fire wood, house building material; independently for these two major land cover classes (Lillesand see Kokwaro, 1988). Other legal as well as illegal activities & Kiefer, 2000). The different spectral channels as well as since the early colonial time at the beginning of the 20th century several vegetation indices (e.g. NDVI, SAVI) are evaluated till today have resulted in forest degradation (Mitchell, in print). concerning their suitability. Band 2 and 1 (Green) in the case of Only small patches of intact forest are left. The heaviliy dis- TM/ETM+ and MSS, respectively, turned out to be best for turbed Kakamega Forest is said to have been reduced to ca. 120 separating ”forest“ and ”no forest“ (Lung, 2004). First numbers km² in 1980 (Kokwaro, 1988). KIFCON (1994) estimated the of forested areas are derived by overlay techniques combining offtake of fuel wood as ca. 100,000 m3 per year. the resulting threshold images and raster layers of the official forest areas (gazetted in the 1930s), the latter derived by digitiz- ing their boundaries from the already mentioned 1:50,000 topo 3. MATERIALS AND METHODS maps. However, even though a temporally changing pattern of forest losses and replanting is observed, in total no major 3.1 Data and software change in forest-covered area is revealed. What is needed for describing forest fragmentation and disturbances in detail is to Landsat satellite imagery was ordered with the aim to reach distinguish between more land cover classes in order to separate back as far as possible as well as to cover the time period in near natural forest from secondary forest or even plantation regular intervals. The achieved time series encompasses 7 time forest. Further, the results of the threshold analysis demonstrate steps between 1972 and 2001 with roughly one image every that a truely satisfying separation of “forest” and “no forest” is fifth year. For the time steps 1972, 1975, 1980, 1995 und 2001 not possible when considering just one spectral band. Therefore, two for most parts cloudless scenes from the dry and the rainy the subsequent multispectral classification is not to be per- season are available. This allows for taking seasonal variation in formed independantly for these two major classes. vegetation patterns into account. For the remaining time steps (1984 und 1989) only a single scene from the dry season is at 3.4 Supervised Classification hand for classification. The scenes were detected by different Landsat sensors, resulting in specific characteristics regarding The multispectral classification of the Landsat time series starts their spatial as well as radiometric resolutions. The data of the with the most actual time step (2001) and subsequently goes first three time steps (1972, 1975 und 1980) originate of LAND- back till the earliest time step (1972). It makes use of the maxi- SAT-MSS (Multispectral Scanner). The image material for mum-likelihood classificator, deriving training areas (several 1984, 1989 und 1995 were derived by LANDSAT-TM (The- per land cover class whereever possible) from a) different maps matic Mapper), whereas the data from 2001 were collected by with vegetation information, b) amateur photographs taken from LANDSAT-ETM+ (Enhanced Thematic Mapper Plus). Due to an aeroplane in 2001, and c) terrain references. While photo- the fact that the data was ordered in system-corrected format, graphs and terrain references are considered as ground truth for 2 the MSS-data had been resampled to 60 x 60 m spatial resolu- timestep 2001, the vegetation maps (Vegetation map 1:250,000 2 tion, that is a multiple of the 30 x 30 m TM/ETM+ data resolu- from 1966, Forest Department forest map 1:10,000 from 1972, tion. KIFCON land cover map 1:25,000 from 1991) are a valuable For digital image processing of the remotely-sensed imagery source for ground truthing in the past. The development of a including a supervised multispectral classification ERDAS methodolgy for a best-possible classification based on the satel- Imagine 8.5 is used. This software includes the module ATCOR lite imagery of 2001 can be subdivided in three steps: 1) Via 3 for correcting atmospheric and terrain effects. For analysing signature analysis of the training areas the spectral bands to be the classification results use is made of ArcGIS 8.3 that sup- considered are evaluated for the dry season image regarding the ports GIS overlay functionality with due accuracy.