MSc: predicting forest understory canopy cover

Wall-to-wall predictions of understory canopy cover usign high density point cloud, habitat types and a logistic modelThe M.Sc thesis by Bastian Schumann focused on a LiDAR-based approach to combine structural metrics and forest habitat information for causal and predictive models of under-story 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 under-story layers. The statistical models used for modeling 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.

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course on Theory and Practice of UAS Operation and Methods

course on Theory and Practice of UAS Operation and Methods

Last week our staff members Antonio Gomez Castaneda and Luisa Pflumm did an UAS course within out EAGLE M.Sc. program. The primary objective of this course is to prepare students — from having no prior experience — to safely operate drones for scientific applications....