DendRobot: 2D-based tree-detection from LiDAR and photogrammetric point clouds of forest environments

Abstract Accurate and efficient assessment of forest structure is crucial for both ecological research and effective forest management. This paper introduces DendRobot, an innovative software pipeline developed to automate the inventory of forest sample plots or entire forest stands using terrestrial LiDAR scans or ground-based photogrammetric point clouds. DendRobot incorporates a novel 2D density-based tree-detection algorithm (Detection Rate = 93%) alongside a new vertical clustering approach for estimating tree height. Both methods are implemented together with established and widely trusted methods to process three-dimensional data into GIS layers. By leveraging these algorithms, DendRobot derives key forest inventory metrics of individual trees, including diameter at breast height (Mean Absolute Error = 3.4 cm), tree height (Mean Absolute Error = 0.7 m), tree locations, and crown projection areas at a fine spatial scale with the resolution of individual trees. Additionally, it produces Digital Terrain Models (DTMs), Digital Surface Models, and Canopy Height Models (CHMs) with user-defined resolution, supporting advanced spatial analyses of forest environments and providing information for forest management planning. Optionally, these data can be enriched with individual-tree point clouds, segmented by a novel approach. Designed as a comprehensive tool for forest researchers, managers, and students, DendRobot supports efficient, data-driven decision-making with minimal manual intervention. Initial tests conducted in complex forest environments demonstrate its capacity to streamline workflows and generate forest-stand-scale inventory data with accuracy comparable to state-of-the-art methods and software. DendRobot (available at https://www.dendrobot.czu.cz/) is a user-friendly, free and open-source solution for the practical application of terrestrial LiDAR scanning in real-world forestry challenges.