Individual tree extraction from terrestrial laser scanning data via graph pathing
| Autor: | Xinlian Liang, Olivier Martin-Ducup, Gislain Ii Mofack, Di Wang |
|---|---|
| Přispěvatelé: | Xidian University, Wuhan University [China], Université de Yaoundé I, Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0106 biological sciences
Graph pathing Mean squared error Computer science Point cloud computer.software_genre [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics Phylogenetics and taxonomy 010603 evolutionary biology 01 natural sciences [SDV.EE.ECO]Life Sciences [q-bio]/Ecology environment/Ecosystems Segmentation Forest plot Ecology Evolution Behavior and Systematics QH540-549.5 Nature and Landscape Conservation Ecology Intersection (set theory) Forestry 15. Life on land [SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics Tree (graph theory) Path (graph theory) Benchmark (computing) Graph (abstract data type) Data mining [SDE.BE]Environmental Sciences/Biodiversity and Ecology Tree extraction computer 010606 plant biology & botany |
| Zdroj: | Forest Ecosystems, Vol 8, Iss 1, Pp 1-11 (2021) Forest Ecosystems Forest Ecosystems, 2021, 8 (1), ⟨10.1186/s40663-021-00340-w⟩ Forest Ecosystems, Springer, 2021, 8 (1), ⟨10.1186/s40663-021-00340-w⟩ |
| ISSN: | 2197-5620 |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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