Abstract
Thermal infrared measurements acquired with unmanned aerial systems (UAS) allow for high spatial resolution and flexibility in the time of image acquisition to assess ground surface temperature. Nevertheless, thermal infrared cameras mounted on UAS suffer from low radiometric accuracy as well as low image resolution and contrast hampering image alignment. Our analysis aims to determine the impact of the sun elevation angle (SEA), weather conditions, land cover, image contrast enhancement, geometric camera calibration, and inclusion of yaw angle information and generic and reference pre-selection methods on the point cloud and number of aligned images generated by Agisoft Metashape. We, therefore, use a total amount of 56 single data sets acquired on different days, times of day, weather conditions, and land cover types. Furthermore, we assess camera noise and the effect of temperature correction based on air temperature using features extracted by structure from motion. The study shows for the first time generalizable implications on thermal infrared image acquisitions and presents an approach to perform the analysis with a quality measure of inter-image sensor noise. Better image alignment is reached for conditions of high contrast such as clear weather conditions and high SEA. Alignment can be improved by applying a contrast enhancement and choosing both, reference and generic pre-selection. Grassland areas are best alignable, followed by cropland and forests. Geometric camera calibration hampers feature detection and matching. Temperature correction shows no effect on radiometric camera uncertainty. Based on a valid statistical analysis of the acquired data sets, we derive general suggestions for the planning of a successful field campaign as well as recommendations for a suitable preprocessing workflow.
Highlights
Land surface temperature is a key factor of ecological, cryospheric, and climatic systems [1]
Our analysis aims to determine the impact of the sun elevation angle (SEA), weather conditions, land cover, image contrast enhancement, geometric camera calibration, and inclusion of yaw angle information and generic and reference pre-selection methods on the point cloud and number of aligned images generated by Agisoft Metashape
Different options for direct preprocessing of the images when aligning them may strongly influence the quality of the final product, independent of external influences as land cover, weather conditions, or temporal effects
Summary
Land surface temperature is a key factor of ecological, cryospheric, and climatic systems [1]. Present satellite missions offering thermal data with high temporal resolution suffer from low spatial resolution (e.g., MODIS 1 km × 1 km). To assess small scale variations of land surface temperature at plant level and flexible temporal resolution, unmanned aerial systems (UAS, referred as unmanned aerial vehicles (UAV)) as sensor platform are very promising and already in use to quantify plant water stress [7], canopy conductance [8], and turbulent heat fluxes [9,10]. Highest spatial resolution and radiometric accuracy are achieved with cooled TIR camera systems. Those are high in power consumption, weight, and cost. The more commonly used uncooled TIR cameras have a lower weight and consume less power while offering lower spatial resolution and measurement accuracy with manufacturer specifications of up to ±5 ◦C
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