Small unmanned aerial vehicle (UAV) and a prototype hyperspectral imaging camera (HSI) was used to measure the hemispherical directional reflectance factor (HDRF) of a test field with known light scattering properties. The HSI acquires a burst of 24 images within two seconds and all of these images are acquired with different spectral content. By using the autopilot of the UAV, the flight can be preplanned so that the target area is optimally covered with overlapping images from multiple view angles. Structure from motion (SFM) algorithm is used to accurately determine the view angles for each image. The HDRF is calculated for each ground pixel by determining view directions from all of the images for that particular pixel. The pixel intensity values are then processed to reflectance by using a reference panel, which has been measured in laboratory with Finnish Geodetic Institute Field Goniospectrometer (FIGIFIGO). The UAV flight was performed over a test field with different gravel targets. The targets have known HDRF and this allows us to validate the UAV results. Another test was performed over a crop field to display the potential of this method for crop monitoring.
Recent development in compact, lightweight hyperspectral imagers have enabled UAV-based remote sensing with reasonable costs. We used small hyperspectral imager based on Fabry-Perot interferometer for monitoring small freshwater area in southern Finland. In this study we shortly describe the utilized technology and the field studies performed. We explain processing pipeline for gathered spectral data and introduce target detection-based algorithm for estimating levels of algae, aquatic chlorophyll and turbidity in freshwater. Certain challenges we faced are pointed out.