THERMAL REMOTE SENSING WITH UAV-BASED WORKFLOWS

Climate change will have a significant influence on vegetation health and growth. Predictions of higher mean summer temperatures and prolonged summer draughts may pose a threat to agriculture areas and forest canopies. Rising canopy temperatures can be an indicator of plant stress because of the closure of stomata and a decrease in the transpiration rate. Thermal cameras are available for decades, but still often used for single image analysis, only in oblique view manner or with visual evaluations of video sequences. Therefore remote sensing using a thermal camera can be an important data source to understand transpiration processes. Photogrammetric workflows allow to process thermal images similar to RGB data. But low spatial resolution of thermal cameras, significant optical distortion and typically low contrast require an adapted workflow. Temperature distribution in forest canopies is typically completely unknown and less distinct than for urban or industrial areas, where metal constructions and surfaces yield high contrast and sharp edge information. The aim of this paper is to investigate the influence of interior camera orientation, tie point matching and ground control points on the resulting accuracy of bundle adjustment and dense cloud generation with a typically used photogrammetric workflow for UAVbased thermal imagery in natural environments.