Determination of tree heights using unmanned air vehicles

Unmanned aerial vehicles (UAV) have been widely used in a variety of fields in the last decade. In forestry, with different sensors, they have been used to estimate tree heights and crowns. This approach with a consumer-grade camera onboard system is becoming popular because it is cheaper and faster than traditional photogrammetric methods and UAV-Light Detecting and Ranging (UAV-LiDAR) systems. In this study, UAV-based imagery reconstruction, processing, and local maximum filter methods are used to obtain individual tree heights from an urban forest area which consists mostly of coniferous trees as scots and black pines and considered as very opened canopy. A low-cost onboard camera and a UAV with a 96-cm wingspan made it possible to acquire high resolution aerial images (6.41 cm average ground sampling distance), ortho-images, Digital Elevation Models (DEM), and point clouds in one flight. Canopy Height Models (CHM), obtained by extracting the Digital Surface Model (DSM) from the Digital Terrain Model (DTM), were filtered locally based on the pixel-based window size using the provided algorithm. For accuracy assessment, ground-based tree height measurements were made. There was a high 94 per cent correlation and a root mean square error of 28 cm. This study highlights the accuracy of the method and compares favorably to more expensive methods.