Abstract
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.
Highlights
Advances in high-precision laser scanning have led to its application in a wide and growing range of fields across the environmental sciences
We successfully automatically extract 96% and 70% of trees, respectively, with the remainder requiring some straightforward manual segmentation. 4. 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 lidarderived methods to applications ranging from the estimation of carbon stocks through to descriptions of plant form and function
Several methods have looked to automate this process using a variety of techniques; the approaches of Raumonen et al (2015) and Trochta, Krůček, Vrška and Král (2017) organised the larger-area point clouds into clusters, from which tree-level point clouds were grown through fixed inter-cluster assumptions of distance and orientation to infer connectivity
Summary
Advances in high-precision laser scanning have led to its application in a wide and growing range of fields across the environmental sciences. Applications range from retrieval of traditional parameters of forest structure such as stem diameter and tree height (Maas, Bienert, Scheller & Keane, 2008), through to quantitative descriptions of plant material distribution and leaf area index (Jupp et al, 2009). New shape-fitting methods have been proposed to utilise such point clouds to construct models that describe the 3D woody structure of individual trees (Raumonen et al, 2013). These so-called quantitative structure models (QSMs) have primarily been used for the estimation of above-ground biomass and carbon stocks via volume estimation (Calders et al, 2015; Gonzalez de Tanago Menaca et al, 2018). New applications of QSMs include species identification (Åkerblom, Raumonen, Mäkipää & Kaasalainen, 2017), calibration and validation of remote sensing instrumentation (Armston et al, 2016), development of allometric
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