UAV application for remote sensing course started

UAV application for remote sensing course started

The course on UAV application for Remote Sensing started successfully. The weather was good enough to do some first flights. In the next weeks and months more flights will be undertaken and data collected for different fields sites in order to gain more information concerning their ecology, geomorphology, archeology or forestry.  Several software solutions and fields of applications will be explored such as deriving 10cm Digital Elevation Models. Especially the application of UAV data for remote sensing application will be addressed and the students will conduct a project to outline the potential and limits of such aerial imagery for remote sensing tasks.

testing UAV and D-GPS application in the Steigerwald

testing UAV and D-GPS application in the Steigerwald

one of our field sites in the Steigerwald

For several upcoming EAGLE courses we visited potential field sites and tested our equipment. During this first field work of the year our UAV and D-GPS data collection were tested in the Steigerwald at the research station of the University Wuerzburg in Fabrik Schleichach. The research station is part of Prof. Jörg Müllers research department, his staff helped us with the field work and Dr. Simon Thorn, the deputy of the research station gave us a tour of the station and the ongoing research. The EAGLE students will have the opportunity to stay at this research station for the course work or later for an internship or M.Sc. thesis. This time we tested several methods to locate ground control points in the UAV imagery as well as the options to launch the UAV in different types of forest. Additionally, we took images of the research station itself and the plots to evaluate its suitability for later field work and courses. Interesting discussions and potential further collaborations with the biologists working on beetles, bird or fungi composition and distribution, as well as forest composition were also discussed. Tobias Ullmann, Hooman Latifi, Christian Büdel and Martin Wegmann conducted the field work and are now planning further field work in spring and summer as well as joint courses around the research station.

Research Station of the University of Wuerzburg in Steigerwald

MSc: Analysis of Airborne LiDAR Data for Deriving Terrain and Surface Models

MSc: Analysis of Airborne LiDAR Data for Deriving Terrain and Surface Models

DTMs Extracted using denser point cloud LiDAR data (leaf-on condition) using mirror points (left side) and without using mirror (right side)The M.Sc thesis by Raja Ram Aryal has been handed in (supervision of Dr. Hooman Latifi and Prof. Michael Hahn). The thesis focused on a comparative study on the variations of an adaptive TIN ground filtering algorithm  to extract DTM from discrete LiDAR point cloud captured in leaf-off and full wave LiDAR point cloud collected in leaf-on conditions. In addition Analysis of Variance (ANOVA) type II was used to assess the influential factors that are related to DTM random error.  The Accuracy assessment of extracted DTMs was done  at local and landscape levels in heterogeneous forest stands of Bavarian Forest National Park. The DTM generated using mirror points in leaf-off returned less RMSE (0.844 m) than in leaf-on (0.988 m) conditions. Furthermore RMSE values of 0.916 m (leaf-off) and 1.078 m (leaf-on) were observed the local level analysis when no mirror points were used. However, RMSE value of ca. 0.5 m was observed at the landscape level, with leaf-off DTM showing slightly higher error than leaf-on DTM. The DTM error increased with increasing slope. Deciduous habitat was found to significantly influence DTM error in both leaf-off and leaf-on conditions. Interaction effects were mainly observed between slope and forest habitat type.

DTMs Extracted using denser point cloud LiDAR data (leaf-on condition) using mirror points (left side) and without using mirror (right side)

MSc: predicting forest understory canopy cover

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.