Multi-Biome Biomass Mapping in Sudan Using Remote Sensing.

Biomass production occupies a central role in shaping the human-environment interactions in Africa. Its assessment is vital for land use planning and investment in natural resource conservation and utilization.  This paper describes a case study in Sudan on the development of a simple operational methodology based on remote sensing for biomass estimation in tropical Africa covering different biomes. As basic data 19 scenes of Landsat ETM+ and 6 frames of MODIS 250m reflectance data were used.  A literature review was undertaken to identify available and relevant approaches for biomass estimation. This was followed by the development of regression models based on the calibration of actual canopy cover (CC) measurement with atmospherically corrected and reflectance-based Landsat vegetation indexes such as normalized difference vegetation index (NDVI), soil adjusted and atmospherically resistant vegetation index (SARVI), enhanced vegetation index (EVI), visible atmospherically resistant index (VARI) and wide dynamic range vegetation index (WDRVI). Among these, NDVI showed the best correlation with CC (r² = 0.8816) and was finally selected for biomass estimation. The next step was to outscale the models developed from Landsat ETM+ images to the whole of Sudan using MODIS data and a vegetation type mask for each major biome extracted from the FAO Land Cover Map of Sudan. For each MODIS frame 12 acquisitions between 4 July and 30 September 2007 were used to generate a map of the annual maximum or peak NDVI, to which the model for biomass estimation was applied. The results were found fully comparable with those obtained by other authors using different methods and field measurements. Therefore the estimation results are acceptable and the methodology can be considered for operational use for biomass characterization in other tropical African countries.