SYSSIFOSS is a joint project between the Institute of Geography and Geoecology (IFGG) of the Karlsruhe Institute of Technology (KIT) and the 3DGeo Research Group of Heidelberg University.

Funded by DFG Projektnummer 411263134 / 2019-2022

Airborne light detection and ranging (LiDAR) data provides reliable information on forest structure which relates to a range of forest inventory variables. LiDAR based forest inventory approaches recently evolved into operational tools. Today, further optimization of existing approaches is pursued to ensure high data quality of the inventory information and cost-efficiency over varied environmental and silvicultural conditions. Synthetic LiDAR data has been suggested as useful tool to better understand the interactions between forest canopies and LiDAR acquisitions and hence as a key instrument for identifying further optimization potential since it enables to create remote sensing datasets that cover the full variability of the investigated environmental, silvicultural and technical parameters. However, so far synthetic LiDAR data has either been simulated with a very high level of detail and for small areas or with simplistic approaches for larger areas.

In this project we suggest a new approach to create synthetic LiDAR data by combining the outputs of an established forest growth simulator with a to-be-created database of species-specific model trees extracted from real LiDAR point clouds. This approach will result in inventory information at the single tree level and a matching 3D forest structure for large areas. The 3D forest structure will serve as input to HELIOS (Heidelberg LiDAR Operations Simulator), a LiDAR ray-tracing tool with which accurate LiDAR acquisitions can be simulated. Based on the HELIOS simulations, we will on the one hand conduct a sensitivity analysis (considering e.g., field inventory design, field plot size, statistical model, LiDAR acquisition settings, etc.) to identify the most important factors influencing LiDAR based forest inventories and thereby identify optimization potentials. On the other hand, we will examine the potential of the created synthetic data to minimize the amount of field-collected reference data. The latter will be realized by developing a look-up table like approach where synthetic data matching the local conditions of the area for which real LiDAR data is available are used to calibrate models which can directly be applied to the real LiDAR dataset. The project will focus on central European forests, but the concepts developed in the project are applicable to forests worldwide.