Extracting individual trees from lidar point clouds using treeseg

Abstract Recent studies have demonstrated the potential of lidar‐derived methods in plant ecology and forestry. One limitation to these methods is accessing the information content of point clouds, from which tree‐scale metrics can be retrieved. This is currently undertaken through laborious and time‐consuming manual segmentation of tree‐level point clouds from larger‐area point clouds, an effort that is impracticable across thousands of stems. Here, we present treeseg , an open‐source software to automate this task. This method utilises generic point cloud processing techniques including Euclidean clustering, principal component analysis, region‐based segmentation, shape fitting and connectivity testing. This data‐driven approach uses few a priori assumptions of tree architecture, and transferability across lidar instruments is constrained only by data quality requirements. We demonstrate the treeseg algorithm here on data acquired from both a structurally simple open forest and a complex tropical forest. Across these data, we successfully automatically extract 96% and 70% of trees, respectively, with the remainder requiring some straightforward manual segmentation. treeseg allows ready and quick access to tree‐scale information contained in lidar point clouds. treeseg should help contribute to more wide‐scale uptake of lidar‐derived methods to applications ranging from the estimation of carbon stocks through to descriptions of plant form and function.