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Adantages of UAV- C opters for Environmental A pplications. Teja Kattenborn. Copter -UAVs as a plattform. In general enable heavier payloads / multiple sensors
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Adantages of UAV-Copters for Environmental Applications Teja Kattenborn
Copter-UAVs as a plattform • In generalenableheavierpayloads / multiple sensors • Convertablepayloadenablesmoreflexibilityand versatile applications (e.g. LIDAR, RGB-, NIR-, Thermal-, Hyperspectral-Cameras, Air-Quality sensorsanddata-loggers) • Controllablecameramounting, viewing in any horizontal/verticaldirection
Flight characteristics / planning • Verticaltake-offandlanding, Feasibilitytooperatefromverysmallsizedareas (e.g. withinforests, plantations) • Nature ofverticaltake-offaircraftsenablesvariousflightpatterns, adaptabletoapplicationandterrain Take-off / landingarea, ca. 6*6 m
Flight characteristics / planning: casestudy 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 zickzack flight pattern 3D-Reconstruction results: automatic detection of: • tree position • tree height • diameter at breast height (dbh)
Photogammetricpointclouds • Potential low cruise speedsalowfor high imageoverlaps • High pointdensities • Lessocclusionbyobjectsasvegetation / buildings • Verygoodimagequality due to high stability • High payloadcapacityenables SLR or digital System cameras • fast shootingspeed (Lumix G3: ca. 90 images / min)
Photogammetricpointclouds: casestudy Potential of Unmanned Aerial Vehicle Based Photogrammetric Point Clouds for Automatic Single Tree Detection (M. Sperlich, T. Kattenborn, 2013) • Test sites: • a) Decidiousforest • b) coniferousforest • Image flight: • Altiutue 100m • 250 nadirimages / site • Methods: • Calculationof • DSM / DTM / nDSM • Pouringalgorithm Point cloud - RGB Point cloud – heightgradient
Photogammetricpointclouds: casestudy Potential of Unmanned Aerial Vehicle Based Photogrammetric Point Clouds for Automatic Single Tree Detection(M. Sperlich, T. Kattenborn, 2013) • Test sites: • a) Decidiousforest • b) Coniferousforest • Objectives: • Treecrowndelination • Single treedetection • Results: • 193/212 treesdetected • … Point cloud - RGB Point cloud – heightgradient
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