Use of multitemporal satellite data for vegetation change detection in Namibia H. Wagenseil & C. Samimi Institute for Geography, Friedrich-Alexander-University Erlangen-Nuremberg, Germany hwagense@geographie.uni-erlangen.de Keywords: time series analysis, change detection, rainfall variability, NDVI, Namibia ABSTRACT: As water supply strongly influences plant growth in semiarid areas, precipitation events in their spatio-temporal variability are reflected in NDVI time series (AVHRR, MODIS) of individual rainy seasons and complicate a phenological delimitation of vegetation units. To overcome this problem and to develop a “near-to-real-time”-vegetation monitoring, relation- ships between NDVI and previous rainfall events are investigated, as it is assumed, that the dif- ferent vegetation units (e.g. grass savanna, tree savanna) show specific reactions on water avail- ability. Following a decision tree approach, homogenous rainfall-vegetation classes are separated from seasonal AVHRR-NDVI data and correlations to rainfall data including recent and past multi-day-sums are computed for each class. A supervised classification and a change map from two sets of Landsat data are used for final validation and class labeling. 1 BACKGROUND AND OBJECTIVE Signs of degradation in semi-arid regions of the world have long been the object of geographical research. The strong susceptibility of these areas to land degradation is attributed to the high vari- ability of the environment so that even insignificant changes in soil and climatic determinants cause marked changes in the density and composition of vegetation (Kempf 1996). An excessive increase in certain woody plants like Acacia mellifera and Dichrostachys cinerea can thus be observed in particular in rangeland but also in national parks as a result of overgrazing, or selective grazing and browsing. This process is termed bush encroachment and has already been recognized as a problem by Walter (1964), Strang (1973), and others. Moreover, vegetation cover and composition is a lead- ing and dominant factor for soil erosion, which in turn is the second relevant process in landscape degradation (Beugler-Bell 1996). As signs of degradation are indicated by the spatial composition of vegetation or conversely changes in plant cover could indicate future degradation processes, a continuous and “Near-to- Real-Time” monitoring of vegetation based on time series data of the Normalized Difference Vege- tation Index (NDVI) can therefore serve to detect such developments and could allow appropriate counter-measures to be adopted. Satellite images have routinely been used for such large-scale, quick, and cost effective analyses of vegetation. Vegetation studies of semi-arid areas have shown that restricted water supply is a limiting factor and strongly influences plant growth. Therefore precipitation events are reflected in the NDVI de- velopment in its variability over time and space (Nicholson & Farrar 1994, Grist et al. 1997, Rich- ard & Poccard 1998, Eiden 2000) and complicate the phenological delimitation of vegetation units 183 New Strategies for European Remote Sensing, Oluiþ (ed.) © 2005 Millpress, Rotterdam, ISBN 90 5966 003 X