基于无人机LiDAR数据的荒漠梭梭林单木分割研究*

doi:10.7523/j.ucas.2024.006

中国科学院大学学报

• •

基于无人机LiDAR数据的荒漠梭梭林单木分割研究^*

熊世梅^1,2,3, 许文强^1,3†, 包安明^1,3, 王正宇^1,2,3, 陶泽涪^1,2,3

1.中国科学院新疆生态与地理研究所干旱区生态安全与可持续发展重点实验室荒漠与绿洲生态国家重点实验室, 乌鲁木齐 830011;
2.中国科学院大学, 北京 100049;
3.新疆遥感与地理信息系统应用重点实验室, 乌鲁木齐 830011

收稿日期:2023-10-18 修回日期:2024-03-04 发布日期:2024-04-03
通讯作者: ^†E-mail: xuwq@ms.xjb.ac.cn
基金资助:
^*新疆维吾尔自治区“天山英才”青年科技拔尖人才项目(2023TSYCCX0087)、新疆维吾尔自治区重点研发计划项目（2022B03021）和青海省“昆仑英才·高端创新创业人才-领军人才”项目 (2020-LCJ-02)

The individual tree segmentation of desert Haloxylon ammodendron forests based on UAV LiDAR

XIONG Shimei^1,2,3, XU Wenqiang^1,3, BAO Anming^1,3, WANG Zhengyu^1,2,3, TAO Zefu^1,2,3

1. State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. Key Laboratory of GIS and RS Application, Xinjiang Uygur Autonomous Region, Urumqi 830011, China

Received:2023-10-18 Revised:2024-03-04 Published:2024-04-03

摘要/Abstract

摘要： 激光雷达技术（LiDAR）在荒漠梭梭林单木分割和参数估算中的应用潜力尚未得到探索。本研究利用无人机LiDAR数据,采用不同插值方法构建了0.1m、0.25m、0.5m和1m空间分辨率的冠层高度模型(CHM),采用CHM种子点分割算法对3类不同生长情况的梭梭林样地进行单木分割,评估了空间分辨率和生长情况对分割精度（SA）的影响,并结合实测数据验证了树高和冠幅的提取精度。研究结果表明：在本研究中,基于反距离权重插值生成CHM的单木SA更高。空间分辨率是影响单木分割结果的关键因素,0.25m分辨率下分割效果最优。Ⅲ级样地SA最高,比Ⅱ级样地高27%、Ⅰ级样地高44%;Ⅰ级样地梭梭树冠的交错重叠使得树冠边界难以区分,而Ⅲ级样地树冠独立更容易得到准确的分割。3类样地的树高拟合模型R²均在0.80左右,均方根误差（RMSE）小于0.31m。Ⅰ、Ⅱ级样地冠幅提取拟合R²在0.70左右, RMSE误差略高,Ⅲ级样地半枯状态的梭梭枝条影响了冠幅的提取精度。本研究表明,应用无人机LiDAR数据对荒漠梭梭林进行单木分割具有巨大潜力,可为新疆荒漠植被碳汇估算提供数据支撑。

关键词: 荒漠灌木, LiDAR, 空间分辨率, 形态结构, 碳汇

Abstract: The potential of Light Detection And Ranging (LiDAR) technology in the application of individual tree segmentation and parameter estimation in desert Haloxylon ammodendron forests has not been explored. This study uses UAV LiDAR data to extract canopy height models (CHM) at spatial resolutions of 0.1 m, 0.25 m, 0.5 m, and 1 m on different interpolation methods, and applies the CHM seed point segmentation algorithm to segment individual trees in three types of Haloxylon ammodendron plots with different growth conditions. This study evaluates the impact of spatial resolution and growth conditions on segmentation accuracy, and verifies the extraction accuracy of tree height and crown width with field measurement data. The results show that the inverse distance weighting interpolation has a higher segmentation accuracy in this study. Spatial resolution is a key factor affecting the results of individual tree segmentation, with the best segmentation results obtained at a resolution of 0.25m.Class III plots had the highest segmentation accuracy, which was 27% higher than that of Class II plots and 44% higher than that of Class I sample plots. The overlapping crowns of Haloxylon ammodendron in plot I make it difficult to distinguish the crown boundaries, while the independent crowns in plot III make it easier to achieve accurate segmentation. The R² of the tree height fitting model for all three types of plots is around 0.80, with RMSE less than 0.31 m. The R² of the canopy extraction fit for the Class I and II plots is around 0.70, with a slightly higher RMSE error, and the branches in a half dead state of Haloxylon ammodendron in plot III affect the extraction accuracy of crown width. This study demonstrates that LiDAR data has great potential for individual tree segmentation in desert Haloxylon ammodendron forests, which can provide data support for desert forests carbon sink estimation in Xinjiang.

Key words: Desert shrub, LiDAR, spatial resolution, morphological structure, carbon sink

中图分类号:

S771.8

熊世梅, 许文强, 包安明, 王正宇, 陶泽涪. 基于无人机LiDAR数据的荒漠梭梭林单木分割研究^*[J]. 中国科学院大学学报, DOI: 10.7523/j.ucas.2024.006.

XIONG Shimei, XU Wenqiang, BAO Anming, WANG Zhengyu, TAO Zefu. The individual tree segmentation of desert Haloxylon ammodendron forests based on UAV LiDAR[J]. Journal of University of Chinese Academy of Sciences, DOI: 10.7523/j.ucas.2024.006.

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基于无人机LiDAR数据的荒漠梭梭林单木分割研究^*

The individual tree segmentation of desert Haloxylon ammodendron forests based on UAV LiDAR

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