Exploration of Space-borne LiDAR data for supporting Sentinel-1 forest parameter retrieval (SBL-S1-PR)
LiDAR is one of the most important earth observation technologies for forest monitoring and management as it provides 3D information not only on the canopy but also the internal forest structure. In addition to Airborne Laser Scanning (ALS), spaceborne LiDAR (SBL) sensors have become available in recent years, currently through the NASA missions ICESAT-2, opens an external URL in a new window and Global Ecosystem Dynamics Investigation (GEDI), opens an external URL in a new window. The objective of the SBL-S1-PR exploratory project is to assess the feasibility of SBL measurements for Alpine forest management. Sloped terrain limits the achievable accuracy of SBL derived canopy heights. Therefore, SBL-S1-PR will explore corrections within SBL processing (Gaussian waveform decomposition, etc.) based on terrain height from detailed ALS DTMs. To derive spatially continuous canopy height maps with high temporal resolution, SBL data will be used to calibrate machine learning models to estimate forest canopy parameters based on Sentinel-1 time series data including different polarizations, interferometric coherence and forest phenology. Demonstrating the feasibility of such a novel combined approach will promote the exploitation of Sentinel-1 and SBL data in an operational context in a challenging environment.
June 2021 - May 2022
Sentinel-1 InSAR processing
Machine Learning of forest parameters
CLIMERS staff involved