A M.Sc thesis was written by Bastian Schumann under the supervision of Dr. Hooman Latifi and Prof. Christopher Conrad that focused on a LiDAR-based approach to combine structural metrics and forest habitat informaiton for causal and predictive models of understory canopy cover. The data base used consisted of a bi-temporal LiDAR dataset as well as two field datasets and two habitat maps. The entire data were initially edited, revealing that a bi-temporal treatment is only possible for understory layers. The statistical models used for modelling canopy cover density included random forest, logistic models and zero-and-one inflated beta regression.

The results revealed the most relevant LiDAR metrics which contribute to explain the canopy cover density. Furthermore it indicates that the habitat types have a significant influence on canopy cover density. In addition, it was shown that with the use of a denser point cloud a higher performance can be achieved in almost every vertical stand layer.

Wall-to-wall predictions of understory canopy cover usign high density point cloud, habitat types and a logistic model