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Individual tree crown delineation using localized contour tree method and airborne LiDAR data in coniferous forests

机译:针叶林中使用局部轮廓树法和机载LiDAR数据进行单个树冠的描绘

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Individual tree crown delineation is of great importance for forest inventory and management. The increasing availability of high-resolution airborne light detection and ranging (LiDAR) data makes it possible to delineate the crown structure of individual trees and deduce their geometric properties with high accuracy. In this study, we developed an automated segmentation method that is able to fully utilize high-resolution LiDAR data for detecting, extracting, and characterizing individual tree crowns with a multitude of geometric and topological properties. The proposed approach captures topological structure of forest and quantifies topological relationships of tree crowns by using a graph theory-based localized contour tree method, and finally segments individual tree crowns by analogy of recognizing hills from a topographic map. This approach consists of five key technical components: (1) derivation of canopy height model from airborne LiDAR data; (2) generation of contours based on the canopy height model; (3) extraction of hierarchical structures of tree crowns using the localized contour tree method; (4) delineation of individual tree crowns by segmenting hierarchical crown structure; and (5) calculation of geometric and topological properties of individual trees. We applied our new method to the Medicine Bow National Forest in the southwest of Laramie, Wyoming and the HJ Andrews Experimental Forest in the central portion of the Cascade Range of Oregon, U.S. The results reveal that the overall accuracy of individual tree crown delineation for the two study areas achieved 94.21% and 75.07%, respectively. Our method holds great potential for segmenting individual tree crowns under various forest conditions. Furthermore, the geometric and topological attributes derived from our method provide comprehensive and essential information for forest management. (C) 2016 Elsevier B.V. All rights reserved.
机译:单个树冠的轮廓对于森林清查和管理非常重要。高分辨率机载光检测和测距(LiDAR)数据的可用性不断提高,可以描绘出单棵树的树冠结构并以高精度推导出其几何特性。在这项研究中,我们开发了一种自动分割方法,该方法能够充分利用高分辨率LiDAR数据来检测,提取和表征具有多种几何和拓扑特性的单个树冠。所提出的方法通过使用基于图论的局部轮廓树方法捕获森林的拓扑结构并量化树冠的拓扑关系,最后通过类比从地形图识别山丘来分割单个树冠。该方法包括五个关键技术组成部分:(1)从机载LiDAR数据推导冠层高度模型; (2)根据树冠高度模型生成轮廓; (3)采用局部轮廓树法提取树冠的层次结构; (4)通过分割分层树冠结构来描绘单个树冠; (5)计算单个树木的几何和拓扑特性。我们将这种新方法应用于怀俄明州拉勒米市西南的医学弓国家森林和美国俄勒冈州喀斯喀特山脉中部的HJ安德鲁斯实验森林。结果表明,该树的单个树冠轮廓的总体准确性两个研究领域分别达到94.21%和75.07%。我们的方法具有在各种森林条件下分割单个树冠的巨大潜力。此外,从我们的方法得出的几何和拓扑属性为森林管理提供了全面而必要的信息。 (C)2016 Elsevier B.V.保留所有权利。

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