Mapping North African landforms using continental scale unmixing of MODIS imagery

We describe the production of a landform map of North Africa utilizing moderate resolution satellite imagery and a methodology that is applicable for sub-continental to global scale landform mapping. A mosaic of Moderate Resolution Imaging Spectroradiometer (MODIS) apparent surface reflectance imagery was compiled for Africa north of 10° N. Landform image endmembers were chosen to characterize ten different types of vegetated and unvegetated desert surfaces: alluvial complexes, dunes, dry and ephemeral lakes, open water, basaltic volcanoes and flows, mountains, regs, stripped, low-angle bedrock surfaces, sand sheets, and Sahelian vegetation. Multiple Endmember Spectral Mixture Analysis (MESMA) was applied to the MODIS mosaic to estimate landform and vegetation endmember fractions. The major landform in each MODIS pixel was identified based on the majority endmember fraction in two- or three-endmember models. Accuracy assessment was conducted using two data sources: the historic Landform Map of North Africa [Raisz, E. (1952). Landform Map of North Africa. Environmental Protection Branch, Office of the Quartermaster General.] and Landsat Thematic Mapper (TM) data. Comparison with the Raisz landform map gave an overall classification accuracy of 54% with significant confusion between alluvial surfaces and regs, and between sandy and clayey surfaces and dunes. A second validation using 20 Landsat images in a stratified sampling scheme gave a classification accuracy of 70%, with confusion between dunes and sand sheets. Both accuracy assessment schemes indicated difficulty in vegetation classification at the margin of the Sahel. A comparison with minimum distance and maximum likelihood supervised classifications found that the MESMA approach produced significantly higher classification accuracies. This digital landform map is of sufficiently high quality to form the basis for geomorphic studies, including parameterization of the surface in global and regional dust models.