| The application area of this study is southern Mauritania characterized by a northern Sahelian climate and a semi-arid ecosystem. Since the 1970s, the region has been under environmental stress due to the combined impacts of recurrent droughts and anthropogenic pressure. Although it represents only 12.5% of the country's land area, it concentrates nearly 37% of the population, 90% of whom practice substance agriculture and/or livestock keeping. The livestock population which has geometrically increased as a result of mass migrations from other parts of the country represents nearly 70% of the national herd. The degradation process that affects the life supporting base (soils, forage and forest resources) is a direct result of agricultural encroachment, overgrazing, fuelwood and building material collection. Such a situation needs to be assessed and monitored with a powerful data integration and analysis system in order to support environmental resources management decision making with accurate information.; This study is an investigation on the potential of Geographic Information Systems (GIS) and Remote Sensing digital image processing methods to exhibit the pattern of land degradation prone areas from geographical data and satellite imagery. GIS data integration and analysis techniques are applied to physical and socio-economic data for the cartographic and statistical characterization of land degradation prone areas. Statistical analyses are carried out on tabular and image data while spatial modelling procedures such as surface interpolation, distance analysis and various overlay operations are specifically performed on images to determine the spatial extent of the degradation processes. The digital image processing techniques used include spectral vegetation indices (VI) grouped into sloped-based (e.g., NDVI), distance-based (e.g., PVI) and orthogonal (e.g., GVI) models. These VIs are used in order to assess their ability to distinguish sparse green vegetation cover from its background soil. Land cover change and drought impact assessment was carried out using several approaches. Land cover change detection techniques such as image differencing, regression differencing, image ratio, classification comparisons, change vector analysis, and Principal Components Analysis (PCA) are applied to Landsat MSS data. Drought impact areas are assessed from NOAA/AVHRR 1.1 km images with Time Series Analysis (TSA) and the Vegetation Condition Index (VCI). (Abstract shortened by UMI.)... |
