1. Adantages of UAV-Copters for Environmental Applications Teja Kattenborn

2. Copter-UAVs as a plattform – In general enable heavier payloads / multiple sensors – Convertable payload enables more flexibility and versatile applications ( e.g. LIDAR, RGB-, NIR-, Thermal-, Hyperspectral-Cameras, Air-Quality sensors and data-loggers ) – Controllable camera mounting, viewing in any horizontal/vertical direction

3. Flight characteristics / planning Vertical take-off and landing, Feasibility to operate from very small sized areas (e.g. within forests, plantations) Nature of vertical take-off aircrafts enables various flight patterns, adaptable to application and terrain Take-off / landing area, ca. 6*6 m

4. UAV-based photogrammetric point clouds - tree stem mapping in open stands (A. Fritz, T. Kattenborn, B. Koch, 2013) tree stems = “vertical objects”  45° camera angle Forests = complex 3D-structures  zick zack flight pattern automatic detection of: tree position tree height diameter at breast height (dbh) Flight characteristics / planning: case study 3D-Reconstruction results:

5. Potential low cruise speeds alow for high image overlaps  High point densities  Less occlusion by objects as vegetation / buildings Very good image quality due to high stability High payload capacity enables SLR or digital System cameras  fast shooting speed (Lumix G3: ca. 90 images / min) Photogammetric point clouds

6. Photogammetric point clouds: case study Point cloud - RGB Potential of Unmanned Aerial Vehicle Based Photogrammetric Point Clouds for Automatic Single Tree Detection (M. Sperlich, T. Kattenborn, 2013) Test sites: a) Decidious forest b) coniferous forest Image flight: Altiutue 100m 250 nadir images / site Methods: Calculation of DSM / DTM / nDSM Pouring algorithm Point cloud – height gradient

7. Photogammetric point clouds: case study Test sites: a) Decidious forest b) Coniferous forest Objectives: Tree crown delination Single tree detection Results: 193/212 trees detected … Potential of Unmanned Aerial Vehicle Based Photogrammetric Point Clouds for Automatic Single Tree Detection (M. Sperlich, T. Kattenborn, 2013) Point cloud - RGBPoint cloud – height gradient

8. Replacement Possible ? Thanks...