The aim of this EUFAR training course is to develop the special skills required for processing the new generation of airborne and satellite hyperspectral, thermal and LIDAR data for retrieving essential biodiversity variables in forest ecosystems. Forest management requires the use of comprehensive remote sensing data which enable monitoring biodiversity changes in response to calamities such as bark beetle infestation and other climate change induced phenomena. They also enable to predict the long-term impact of management decisions. Although the benefits of remote sensing for monitoring vegetation are well recognized, yet accurate and site specific monitoring of many essential biodiversity variables in forest ecosystems remain elusive. In forests, bidirectional effects mainly influence hyperspectral airborne signals and directly affect the accuracy of derived variables. Simultaneous acquisition of thermal, VIS/NIR hyperspectral and LIDAR data (See RS4forestEBV-B) allow accurate retrieval of vegetation parameters (e.g., LAI, chlorophyll, SLA, nitrogen, water content, species occurrence and 3D vegetation structural attributes) which have been recognized as essential biodiversity variables by GEO-BON and are crucial in forestry and national park management practices. Several ongoing projects will support this training course including the ESA Innovator III project (RS4EBV). The participants will be trained in remote sensing algorithms and retrieval of essential biodiversity variables. The BIOKLIM project which is coordinated by Bavarian Forest National Park (BFNP), will provide data and expert knowledge on forest structure, biodiversity and management issues as well as facilitate access to the field sites, flux towers and field data collection techniques.