Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesForests of the world provide an important ecosystem service in the fight against climate change by sequestering carbon from the atmosphere and storing them as biomass. However, cloud cover and terrain inaccessibility hamper studies of forest biomass using satellites, especially in the dense jungles of the tropics. This study investigated the use of UAS to complement existing satellite based approaches by exploring what information can be derived from UAS sensors and how their biomass estimates can be applied to satellite sensors to improve their accuracies. A biomass estimation model was built using on the ground measurements while GIS was used to generate biomass maps. The results from the model show that NDVI and tree heights were statistically significant explanatory variables for biomass in the Mixed Oak Forests of Davert, Germany. Estimates from UAS were the most accurate in terms of R2, compared to other sensor estimates from Sentinel 2, World View 3 and Orthophotos. Hence, two adjustment factors were proposed to improve the accuracy of World View 3 and Sentinel 2 estimates. UAS are thus a versatile sensor platform for biomass studies that complements satellite sensors to improve studies of global biomass of forests
