Remote sensing analysis of desert sensitive areas using MEDALUS model and GIS in the Niger River Basin

Abstract

Environmental degradation is a dynamic issue that requires ongoing monitoring to ensure ecological repair and environmental management for sustainable development. This study employed remote sensing techniques to ascertain the desertification sensitivity areas in the Niger River Basin (NRB) using the MEDALUS Model. The indicators for the model were divided into three categories: vegetation quality, climate quality, and soil quality index. The net primary productivity, estimated by the Carnegie Ames Stanford Approach model was substituted in the vegetation quality index. Each quality index was computed by calculating the geometric mean of selected sub-indices, while the geometric mean of the three quality indicators was utilized to determine the Environmental sensitivity to desertification in the study area. A total of 26.10% of the study area had low soil quality, while 26.16% and 17.41% of the study area had low vegetation and climate indicators, respectively. The study revealed that about 36.56% of the study area is highly sensitive while 27.65% and 35.78% are moderately and less sensitive respectively, to desertification in the NRB. The desertification-sensitivity index map shows that the northern part of the study area is highly susceptible to desertification and is encroaching southwards. The findings reveal that climate variables are major influencers in the study area. Our research is of importance to the Niger Basin Authority, planners, and other government agencies in charge of protecting the environment, to ascertain the sensitive areas to desertification, and to help cushion the effect of land degradation in the NRB.

Keywords

• Niger River Basin
• CASA model
• Net primary productivity
• Climate
• Land degradation