Mapping tree canopy cover in the semi-arid Sahel using satellite remote sensing and Google Earth imagery

Abstract: Tree vegetation is an essential element in the daily life of the people in the Sahel region of Africa. It is also considered as a robust indicator of the Sahel ecosystem status and health. In this thesis, a method to estimate tree vegetation was developed and used in mapping the tree canopy cover in semi-arid Sahel. The developed method utilized Normalized Difference Vegetation Index (NDVI) as the predictor variable coupled with estimations of tree canopy cover from Google Earth imagery as the response variable. The developed estimation regression was applied in the Sahel of Africa for the dry season (November to May). The results showed a strong correlation between NDVI derived from Landsat 8 imagery and Tree Canopy Cover (TCC) estimations from Google Earth imagery. The developed method errors were evaluated using two different validation approaches. Moreover, two estimation regressions were developed using NDVI products from Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Inventory Modeling and Mapping Studies (GIMMS). The correlation between MODIS and GIMMS NDVI was weak which could be due to the coarse spatial resolution of these NDVI products. Mapping tree cover in the Sahel using Landsat 8 derived NDVI require high computational power and large storage capacity. Therefore, Landsat 8 NDVI based estimation regression was applied to MODIS and GIMMS NDVI products to the map tree canopy cover for the entire Sahel. All the datasets used in this study is available for public use, and therefore this method is applicable for more development and improvements.