The vegetation of semi-arid savanna landscapes relies on the fragile equilibrium between rainfall, grazing and res [Trodd and Dougill, 1998]. For a sustainable management of their farms, farmers need to take into account this delicate equilibrium and correctly balance the amount of animals and the available food and water resources. These two quantities are estimated every year at the end of the growing season (end of May) to dene the yearly management plans [Reinhard, 2017]. Ground based techniques for animal counting and biomass estimation are long and time consuming processes that can hardly provide a good coverage of the farm [Reinhard, 2017]. Farmers are now more and more looking towards remote sensing techniques to overcome the eld work and achieve both these tasks. The main goal of this project was to classify the soil cover in Kuzikus Wildlife Reserve, in the north-eastern Kalahari region of Namibia, in order to provide an estimate of the food availability for wildlife at the end of the growing season. Ultra-high resolution UAV images and coarse resolution satellite images were classied using both supervised and unsupervised classication techniques. Random Forest and Support Vector Machine supervised classiers gave encouraging results and the research showed the great potential of UAV and satellite imagery for biomass estimation. Used together, these two complementary types of data allow to have both a detailed plant species recognition and a general overview of the study area.