Single-tree Delineation by Instance Segmentation Using Drone-based Lidar and Multispectral Imagery: a Comparative Study in Various Forest Structures

Deep learning methods such as Mask R‑CNN enable the precise delineation of single-tree crowns from remote sensing data. However, their segmentation performance still depends on local stand conditions. Using UAV multispectral imagery and lidar canopy height models (CHM), we assessed the influence of tree species composition, stand density, and foliage condition on the robustness of deep-learning-based single-tree segmentation.
High-resolution laser data and multispectral data were collected over several hectares of forest area (Bavarian Forest National Park; DBU Natural Heritage, Schönau Foundation; Black Forest National Park, Kinzigtal) using a DJI 600 Pro drone. The Fraunhofer Lightweight Airborne Profiler collected a multispectral point cloud using a 905-nm laser and two integrated RGB cameras with 4112 × 3008 pixels. Another multispectral camera captured RGB imagery with 4112 × 3008 pixels and two monochrome bands (725 nm RE, 850 nm NIR; 2164 × 2056 pixels each). Flights were conducted at 80 m altitude with ≥ 50% lateral overlap, resulting in an average point density of 150 points/m2.
Different models were trained and validated using multispectral images (RGB, CIR), images derived from the CHM, and images fused from the CHM and two near-infrared channels (RE, NIR). Highly accurate tree positions and manually processed tree segments were available for accuracy analyses.