Digital Forest
A real-time forest monitoring system
for climate change impacts

WP3 3D forest structure via laser scanning (PI Dominik Seidel)

Objective and approach

Objective: Stress-related structural changes of beech forests are often described based on the visual identification of bare patches in the crown (loss of leaves), branch dieback and bark losses. These subjective procedures lack reproducibility and involve expert knowledge. Recently, there has been great advancement in high-resolution 3D mensuration techniques for tree and forest structure. Using such quantitative approaches it could be shown that vegetation structures were sensitive to environmental conditions, such as changes in competition (e.g. Seidel et al. 2011). We hypothesize that state-of-the-art technology of forest mensuration, laser scanning in particular, is also suitable to observe structural changes in temperate forests occurring as a consequence of stress events (water stress, heat stress, storms, and infestations) and mortality. Using a detailed high frequency (monthly) monitoring, stress or mortality-related losses in structures will be made visible despite seasonal changes (phenology). In cooperation with WP4 machine learning approaches will be used to relate the observed small-scale observations from terrestrial, mobile and drone-based LiDAR with high time resolution data obtained in WP1 and WP2 and large-scale observations of preceding stress-related measures, e.g. changes in vegetation indices, drought, etc. 

Approach: We will make use of ground-based laser scanning as well as drone-based airborne laser scanning to conduct surveys of the entire study area. The two ground-based devices, namely a hand-held ZEB Horizon laser scanner (GEOSLAM Ltd., UK) and a tripod-based Faro Focus M70 (Faro Technologies, USA), will be used to measure the tree and forest structure monthly. These high-resolution techniques will provide detailed data on the individual tree architecture (e.g. Seidel 10 2019a, b, c). Overall stand structure will also be evaluated using indices presented in Ehbrecht et al. (2017), Juchheim et al. (2017) and Seidel et al. (2019b). In the summer months, we will make use of the airborne “birds-eye” perspective provided by an unmanned aerial vehicle (drone) carrying a laser scanner. This data will be merged with the ground-based data to provide a more complete information on the stand when all leaves are fully flushed. The study site is ideal for this approach as the climate tower (WP1) reaching through the canopy is a perfect reference object for the combination of the above-canopy and below-canopy measurements in 3D space.