Forest ecosystems respond very sensitively to climate and atmospheric changes. Feedback mechanisms can be measured via changes in albedo, energy balance and carbon storage. The Bavarian Forest National Park is a unique forest ecosystem with large non-intervention zones, which promote a large scale re-wilding process with low human interference. It provides important ecosystem services of clear water, carbon sequestration and recreation, and has fragile habitats with endangered forest species. The national park is therefore a very suitable field of research to study natural and near natural ecosystem processes. Under the leadership of the national park authority, experts from various European research institutions have joined forces to systematically establish a remote sensing data pool on the Bavarian Forest as a resource for their research. This collaborative effort provides an opportunity to combine various methodological approaches and data and to optimize products by sharing knowledge and expertise. The first objective of the data pool is to develop methods for the establishment of Essential Biodiversity Variables (EBV) based on a very sound and comprehensive data base. The recent advances in tighter collaboration of remote sensing and biodiversity science, especially with regard to the newly established EBV and RS-EBV concepts will help to improve the interdisciplinary research. However, such concepts and especially the underlying remote sensing data need to be developed, adapted and validated against biodiversity patterns. Such process needs an extensive set of in-situ and remotely sensed data in order to allow a thorough analysis. The Bavarian data pool fits these requirements through the commitment of all members and hence provides a variety of remote sensing data sets such as hyperspectral, Lidar as well as CIR and multispectral data, as well as a wealth of in-situ data of zoological and botanical transects. This combination allows setting sensor-specific, as well as species-specific analysis on different aspects, i.e. different processes between managed and natural forest, impact of climate change or species distribution mapping. The second objective is to develop concepts for EBV using Sentinel mission data combined with data from future contributing hyperspectral missions such as EnMAP. Spaceborne hyperspectral data has been identified by the remote sensing related biodiversity community as an important data source. However, the acquisition of airborne data is very expensive for regular coverage of forest stands and the entire forest ecosystem. This drawback will be overcome by the launch of the space-borne imaging spectroscopy mission EnMAP. It is a contributing mission to the Copernicus program and will be launched in 2018. EnMAP is expected to provide high quality imaging spectroscopy data on an operational basis and will be suitable for the retrieval of high resolution plant traits at local scales. First studies within the data pool have been focused on e.g. derivation of plant traits like chlorophyll, LAI and nitrogen and tree species classification with a special focus on rare species within the national park, just to name a few. Objective, purpose and content of the data pool will be shown as well as first selective developments.