基于无人机激光雷达的天然林林隙识别

齐志勇; 李世明; 岳巍; 刘清旺; 李増元

doi:10.12171/j.1000-1522.20210524

基于无人机激光雷达的天然林林隙识别

中国林业科学研究院资源信息研究所，国家林业和草原局林业遥感与信息技术重点实验室，北京 100091

基金项目: 高分专项（民用部分）项目（21-Y20B01-9001-19/22），国家自然科学基金面上项目（31370635）

详细信息

作者简介:
齐志勇。主要研究方向：无人机遥感应用。Email：535679943@qq.com　地址：100091 北京市海淀区中国林业科学研究院资源信息研究所

责任作者:
李世明，副研究员。主要研究方向：森林资源遥感监测、无人机遥感应用。Email：lism@ifrit.ac.cn　地址：下同

中图分类号: S754.1；S771.8
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出版历程
- 收稿日期: 2021-12-10
- 修回日期: 2021-12-27
- 录用日期: 2022-04-11
- 网络出版日期: 2022-04-13
- 发布日期: 2022-06-24

Forest gap identification in natural forest based on UAV LiDAR

Institute of Forest Resources Information Technique, Chinese Academy of Forestry, Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

摘要

摘要:
目的自然干扰引起的森林冠层林隙是天然林更新动态的主要驱动力，林隙的分布、形状和范围可以影响光照和土壤水分等生态因子。林隙的识别和特征描述对于理解森林的动态变化有着重要的意义。
方法以云南省普洱太阳河保护区无人机飞行区域为研究区，根据无人机激光雷达点云数据提取冠层高度模型；然后使用固定阈值法、相对高度阈值法和面向对象分类法对冠层高度模型数据进行林隙识别，通过图像目视解释获得独立验证样本进行精度评估；最后精度选取最优方法提取的林隙描述其空间特征。
结果固定阈值法的总体精度为92.00%，高于相对高度阈值法（66.00%）和面向对象分类法（88.00%）。研究区内林隙主要以中小林隙为主，干扰事件较少；研究区林隙的形状指数均值为1.97，多为形状指数较小、边缘效应不太明显的林隙，并且林隙的空间分布为聚集分布。
结论利用无人机激光雷达数据和固定阈值法可以准确绘制出小范围亚热带天然林的林隙空间分布特征。
- 无人机 /
- 激光雷达 /
- 冠层高度模型 /
- 林隙 /
- 固定阈值 /
- 空间特征
Abstract:
Objective The forest gaps caused by natural disturbance are the main driving force of the natural forest regeneration and the distribution, shape and extent of forest gaps can affect a series of ecological factors, such as sun light and soil moisture. The identification and characterization of forest gaps are of significance for understanding the dynamic changes of forests.
Method The remote sensing of UAV can quickly obtain the three-dimensional spatial information of the forest. The study site is located in the UAV flight coverage at the Puer Sun River Reserve in Yunnan Province of southwestern China. The canopy height model (CHM) was derived from the point cloud data of UAV LiDAR . The fixed threshold method, relative height threshold method and object-oriented classification were used to identify forest gaps in CHM. The reference data from visual interpretation of images were used for accuracy assessment of forest gap identification. Finally, the best method was selected to describe the spatial characteristics of forest gaps.
Result The experimental result showed that the overall accuracy of the fixed threshold method was 92.00%, which was higher than the relative height threshold method (66.00%) and object-oriented classification method (88.00%). The forest gaps in the study site area were mainly small and medium gaps, showing that there were fewer disturbance events. The average shape index of forest gaps in the study site was 1.97 and most of them with small shape index and less obvious edge effect. The spatial distribution of the gap was aggregation.
Conclusion The spatial distribution of forest gaps and its spatial characteristics in small-scale subtropical natural forests can be mapped by UAV LiDAR data and the fixed threshold method.
- UAV /
- LiDAR /
- canopy height model (CHM) /
- forest gap /
- fixed threshold /
- spatial characteristics

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图 1 研究区位置

Figure 1. Location of study area

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图 2 样点对比

Figure 2. Sample point comparison

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图 3 分割尺度参数对比

Figure 3. Comparison of segmentation scale parameters

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图 4 3种林隙识别方法剖面结果

Figure 4. Profile results of three forest gap identification methods

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图 5 不同方法林隙识别结果

CHM. 冠层高度模型 Canopy height model

Figure 5. Forest gap identification results by different methods

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图 6 林隙面积和数量散点图

Figure 6. Scatter plot of forest gap area and number

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图 7 林隙面积和周长散点图

Figure 7. Scatter plot of forest gap area and perimeter

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表 1 无人机和激光雷达传感器参数

Table 1 UAV and LiDAR sensor parameters

参数 Parameter	值 Value
飞行器类型 UAV type	六轴无人机 Six-axis UAV
RTK定位精度 RTK positioning accuracy/m	0.05
扫描角度 Scanning angle/（°）	45
脉冲频率 Pulse frequency/kHz	720
每秒激光点数量 Number of laser points per second	72 × 10⁴
定位精度 Positioning accuracy/m	水平0.01，垂直0.02 Horizontal 0.01, vertical 0.02
旁向重叠 Sidelap/%	50
飞行速度 Flight speed/(km·h⁻¹)	65
点云密度/(个·m⁻²) Point cloud density/(number·m⁻²)	53

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表 2 分类精度汇总

Table 2 Classification accuracy summary

林隙识别方法 Forest gap identification method	用户精度 User’s accuracy		生产者精度 Producer’s accuracy		总体精度 Overall accuracy/%	Kappa 系数 Kappa coefficient
林隙识别方法 Forest gap identification method	林隙 Forest gap/%	非林隙 Non forest gap/%	林隙 Forest gap/%	非林隙 Non forest gap/%	总体精度 Overall accuracy/%	Kappa 系数 Kappa coefficient
固定阈值法 Fixed threshold method	97.67	87.72	85.71	98.04	92.00	0.84
相对高度阈值法 Relative height threshold method	61.19	75.76	83.67	49.02	66.00	0.32
面向对象分类法 Object-based classification method	88.37	82.45	90.38	79.16	88.00	0.70

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表 3 不同方法识别的林隙面积统计

Table 3 Forest gap area statistics identified by different methods

林隙识别方法 Forest gap identification method	数量 Number	平均面积 Mean area/m²	面积的中位数 Median area/m²	面积的标准差 Standard deviation of area/m²	总面积 Total area/ m²	林隙所占研究区的比例 (总面积4 491 220 m²) Proportion of forest gap area in the study area (total area is 4491220 m²)/%
固定阈值法 Fixed threshold method	1 487	21.65	10.71	69.34	32 205.80	0.72
相对高度阈值法 Relative height threshold method	8 987	30.58	12.74	58.81	274 831.40	6.12
面向对象分类法 Object-based classification method	3 470	29.72	20.98	25.18	103 161.73	2.29

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表 4 林隙内高度信息统计

Table 4 Height information statistics in the forest gaps

林隙识别方法 Forest gap identification method	平均高度值 Average height value/m	高度标准差 Height standard deviation/m
固定阈值法 Fixed threshold method	2.23	0.69
相对高度阈值法 Relative height threshold method	10.24	3.66
面向对象分类法 Object-based classification method	5.23	2.29

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表 5 林隙形状指数统计

Table 5 Statistics of forest gap shape index

林隙识别方法 Forest gap identification method	平均值 Mean	最小值 Min. value	最大值 Max. value	下四分位数 1st quartile	中位数 Median	上四分位数 3rd quartile	标准差 Standard deviation	偏度 Skewness
固定阈值法 Fixed threshold method	1.97	1.19	5.78	1.64	1.88	2.21	0.48	1.69

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