| 结合DSM的机载LiDAR单木树高提取研究 |
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| 引用本文： | 张海清,李向新,王成,习晓环,王濮,陈正宇.结合DSM的机载LiDAR单木树高提取研究[J].地球信息科学,2021,23(10):1873-1881. |
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| 作者姓名： | 张海清 李向新 王成 习晓环 王濮 陈正宇 |
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| 作者单位： | 1.昆明理工大学国土资源工程学院,昆明 6500312.中国科学院空天信息创新研究院 中国科学院数字地球重点实验室,北京 1000943.中国能源建设集团江苏省电力设计院有限公司,南京 210000 |
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| 基金项目： | 广西自然科学基金—创新研究团队项目(2019GXNSFGA245001);国家自然科学基金项目(42071405) |
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| 摘 要： | 机载LiDAR在提取地形坡度较大区域的冠层高度模型（CHM）时易产生畸变,降低单木树高的提取精度,为此提出一种CHM与数字表面模型（DSM）相结合的树高估算方法。首先基于预处理后的点云生成的CHM,利用局部最大值算法和标记控制分水岭分割算法进行分割,得到单木树冠轮廓多边形;然后结合DSM,采用固定窗口的局部最大值算法探测树顶点并提取其高程,继而与使用狄洛尼三角网和高程内插得到的地面点相减获取树高;最后,以广西兴安县富江村附近地形起伏较大的针叶林为试验区,测试3种不同坡度下,在CHM、CHM结合DSM获得的树高与实测树高分别进行精度分析。结果表明,当树木分别位于平均坡度为32°、27°和15°的试验区时,CHM中提取的树高与实测数据拟合的R2分别为0.84、0.85和0.87,RMSE为1.48、1.41和1.58 m,结合DSM后R2为0.92、0.91和0.93,RMSE为0.93、1.02和1.16 m;在地形坡度较大的区域,本文方法可以有效提高单木树高的估算精度。
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| 关 键 词： | 机载LiDAR 单木分 树高 冠层高度模型 数字表面模型 标记控制分水岭分割算法 地形坡度 |
| 收稿时间： | 2021-01-20 |
Individual Tree Height Extraction from Airborne LiDAR Data by Combining with DSM |
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| ZHANG Haiqing,LI Xiangxin,WANG Cheng,XI Xiaohuan,WANG Pu,CHEN Zhengyu.Individual Tree Height Extraction from Airborne LiDAR Data by Combining with DSM[J].Geo-information Science,2021,23(10):1873-1881. |
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| Authors: | ZHANG Haiqing LI Xiangxin WANG Cheng XI Xiaohuan WANG Pu CHEN Zhengyu |
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| Institution: | 1. Academy College of Land Source and Engineering, Kunming University of Science and Technology, Kunming 650031, China2. Key Lab of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China3. China Energy Engineering Group Jiangsu Power Design Institute Co, LTD, Nanjing 210000, China |
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| Abstract: | The retrieval of tree height is very important for growth status evaluation and biomass estimation. The Canopy Height Models (CHMs) are commonly used to extract the heights of individual trees. However, airborne LiDAR-derived CHMs are prone to distortion in areas with complex terrain, which significantly limits the extraction accuracy of individual tree height. Therefore, this study aimed to propose a new method, which simultaneously utilized the CHM and Digital Surface Model (DSM) to extract the heights of individual trees. Firstly, the CHM was generated from the preprocessed point clouds using Inverse Distance Weighted (IDW) interpolation algorithm. Secondly, the local maximum algorithm and Mark-Controlled Watershed Segmentation (MCWS) algorithm were adopted to segment the CHM, and thereafter obtain the individual tree crown contour polygon. Thirdly, the local maximum algorithm with a fixed window was applied to the DSM to detect the tree vertices and extract its elevation. Lastly, the tree height was obtained by subtracting the ground elevation obtained by Delaunay triangulation interpolation algorithm. Taking the coniferous forest near Fujiang Village, Xing'an County, Guangxi Province as the test area, this study analyzed the accuracy of tree heights obtained by CHM and our proposed method. For trees located at different test sites with the average terrain slopes of 32°, 25°, and 15°, the coefficients of determination (R2) values of the estimated tree heights based on CHMs are 0.84, 0.85, and 0.87, respectively, while the Root Mean Square Error (RMSE) values are 1.48, 1.41, and 1.58m, respectively. In contrast, the R 2 values of the tree height extracted from our method and the measured tree height are 0.92, 0.91, and 0.93, respectively, while the RMSE values are 0.93, 1.12, and 1.16 m, respectively. Compared with the CHM-based tree height extraction method, the R 2 of our method increased by 0.08, 0.06, and 0.06, respectively, while the RMSE values decreased by 0.55, 0.29, and 0.42m, respectively. The results indicated that, compared with the traditional method, our proposed method can significantly improve the estimation accuracy of individual tree height in areas with large terrain slopes. |
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| Keywords: | LiDAR tree segmentation tree height canopy height model digital surface model mark-controlled watered segmentation terrain slope |
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