地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (10): 1873-1881.doi: 10.12082/dqxxkx.2021.210030

• 遥感科学与应用技术 • 上一篇    下一篇

结合DSM的机载LiDAR单木树高提取研究

张海清1,2(), 李向新1, 王成2, 习晓环2,*(), 王濮2, 陈正宇3   

  1. 1.昆明理工大学国土资源工程学院,昆明 650031
    2.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 100094
    3.中国能源建设集团江苏省电力设计院有限公司,南京 210000
  • 收稿日期:2021-01-20 修回日期:2021-03-19 出版日期:2021-10-25 发布日期:2021-12-25
  • 通讯作者: * 习晓环(1972— ),女,陕西乾县人,硕士,副研究员,主要从事激光雷达数据处理与应用。E-mail: xixh@radi.ac.cn
  • 作者简介:张海清(1995— ),女,河南洛阳人,硕士生,主要从事激光雷达数据处理与林业应用研究。E-mail: 903083885@qq.com
  • 基金资助:
    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)

Individual Tree Height Extraction from Airborne LiDAR Data by Combining with DSM

ZHANG Haiqing1,2(), LI Xiangxin1, WANG Cheng2, XI Xiaohuan2,*(), WANG Pu2, CHEN Zhengyu3   

  1. 1. Academy College of Land Source and Engineering, Kunming University of Science and Technology, Kunming 650031, China
    2. Key Lab of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    3. China Energy Engineering Group Jiangsu Power Design Institute Co, LTD, Nanjing 210000, China
  • Received:2021-01-20 Revised:2021-03-19 Online:2021-10-25 Published:2021-12-25
  • Supported by:
    Guangxi Natural Science Fund for Innovation Research Team(2019GXNSFGA245001);National Natural Science Foundation of China(42071405)

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。

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

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.

Key words: LiDAR, tree segmentation, tree height, canopy height model, digital surface model, mark-controlled watered segmentation, terrain slope