RS4ForestEBV Training Course - Airborne remote sensing for monitoring essential biodiversity variables in forest ecosystems
Bavarian Forest National Park and DLR the German Aerospace Center in Oberpfaffenhofen, 3 to 14 July 2017
Monitoring biodiversity change in response to calamities such as bark beetle infestation and other climate change-induced phenomena for forest management requires utilization of remote sensing data. Although the role of remote sensing for monitoring forest ecosystem is well recognized, accurate and site-specific monitoring of many essential biodiversity variables (EBVs) in forest ecosystems remains elusive. Retrieval of these variables across different remote sensing systems and their dynamics remains an open challenge, and the uncertainty sources and multiple approaches have to be taken into account. In this training course, the skills required for processing the new generation of airborne and satellite hyperspectral, thermal infrared (TIR) and LiDAR data for retrieving EBVs in forest ecosystems were presented. In forests, bidirectional effects mainly influence hyperspectral airborne signals and directly affect the accuracy of derived variables. Simultaneous acquisition of thermal infrared (TIR), visible/near infrared (VNIR) and shortwave infrared (SWIR) hyperspectral and LiDAR data allows retrieval of vegetation parameters (e.g., LAI, chlorophyll, SLA, nitrogen, water content, species occurrence and 3D vegetation structural attributes) which have been recognized as EBVs by GEO-BON and are crucial in forestry and national park management practices.
Several ongoing projects supported this training course including the ESA Innovator III project (RS4EBV). The participants were trained to collect in situ measurements and use remote sensing algorithms for retrieval of EBVs. The BIOKLIM project, which was coordinated by Bavarian Forest National Park (BFNP), provided data and expert knowledge on forest structure, biodiversity and management issues as well as facilitated access to the field sites.
The aim of the training course was to demonstrate how different remote sensing data and in-situ measurements of plant traits 1) can be used to model vegetation and 2) be linked to image data inversion in order to retrieve plant variables and map their spatial patterns. The program of the training course was designed to enable the participants to achieve the following learning objectives:
- To map different vegetation parameters using hyperspectral VNIR, SWIR, TIR and LiDAR data ;
- To understand the advantage of each data sources and the best combinations of them for retrieving vegetation parameters;
- To understand data processing chains;
- To understand the challenge of collecting and integrating forest field data with remote sensing imagery;
20 trainees of 15 different nationalities participated in the training course; mainly PhD candidates, working in European universities and research institutes in 10 EU member states. The course was structured in three interlinked working groups: LiDAR, Thermal and the Hyperspectral, and on the first day of the training course, each participant was attributed to a specific group. Although each working group was piloted independently during the training course, the groups were all scientifically related, and each formed an important component of the research project. In this way, all the working groups were familiarized with the design of the flight and were involved in sampling design and field measurements of the plant traits. Furthermore, they all had the opportunity to learn tools and techniques used during vegetation field data collection.
To read the full scientific report, click here (available to registered EUFAR members only).
To view the presentations and have more information on the training course, click here.
To access the training course photo gallery, click here.
RS4forestEBV training course group picture taken during ICARE aircraft exhibition at DLR