Individual tree extraction from terrestrial laser scanning data via graph pathing

• Published in: Forest ecosystems Vol. 8; no. 1; pp. 67 - 11
• Main Authors: Wang, Di, Liang, Xinlian, Mofack, Gislain II, Martin-Ducup, Olivier
• Format: Journal Article
• Language: English
• Published: Singapore Elsevier B.V 10.10.2021; Springer Singapore; Elsevier Limited; Springer; KeAi Communications Co., Ltd
• Subjects: 3D Remote Sensing for Forests - Progress and Perspective; Biodiversity; Biodiversity and Ecology; Biomedical and Life Sciences; Botanics; Cloud computing; Computational efficiency; Datasets; Ecology; Ecology, environment; Ecosystems; Environmental Sciences; Forestry; Forests; Graph pathing; Laser applications; Life Sciences; Point cloud; Quality control; Root-mean-square errors; Scanning; Segmentation; Systematics, Phylogenetics and taxonomy; Temperate forests; Terrestrial environments; Tree extraction; Trees; Vegetal Biology; Graph pathing; Point cloud; Segmentation; Tree extraction
• ISSN: 2197-5620; 2095-6355; 2197-5620
• DOI: 10.1186/s40663-021-00340-w

Background: Individual tree extraction from terrestrial laser scanning (TLS) data is a prerequisite for tree-scale estimations of forest biophysical properties. This task currently is undertaken through laborious and time-consuming manual assistance and quality control. This study presents a new fully automatic approach to extract single trees from large-area TLS data. This data-driven method operates exclusively on a point cloud graph by path finding, which makes our method computationally efficient and universally applicable to data from various forest types. Results: We demonstrated the proposed method on two openly available datasets. First, we achieved state-of-the-art performance on locating single trees on a benchmark dataset by significantly improving the mean accuracy by over 10% especially for difficult forest plots. Second, we successfully extracted 270 trees from one hectare temperate forest. Quantitative validation resulted in a mean Intersection over Union (mIoU) of 0.82 for single crown segmentation, which further led to a relative root mean square error (RMSE%) of 21.2% and 23.5% for crown area and tree volume estimations, respectively. Conclusions: Our method allows automated access to individual tree level information from TLS point clouds. The proposed method is free from restricted assumptions of forest types. It is also computationally efficient with an average processing time of several seconds for one million points. It is expected and hoped that our method would contribute to TLS-enabled wide-area forest qualifications, ranging from stand volume and carbon stocks modelling to derivation of tree functional traits as part of the global ecosystem understanding.