The Swiss National Forest Inventory (NFI) has an official mandate to monitor the sustainability of forest services by assessing different indicators. Existing methods for assessing the indicators of the ecological and economical value of a tree stem are very time consuming and difficult to reproduce. The objective of this project is, thus, to develop an efficient and reproducible method using TLS for assessing stem structures on the most valuable part of the tree stem (first 8.5 m) of the main tree species in Switzerland (spruce, fir, beech and oak).
We use a semi-automated approach to detect, quantify and qualify the tree related microhabitats such as stem holes, cracks, bark damage, bark pockets, fungi and epiphytic structures (mosses, lichen and ivy). In a longer perspective, the potential of the TLS point clouds to provide general information about the ecological value of tree stems and stand over time should be tested.
The data for this study were collected in several forest reserves and managed forests across Switzerland. The models to detect and recognize the tree related microhabitats were trained using machine learning approaches.
2016 - 2019