基于不同点云密度LiDAR数据的天山云杉单木树高提取

doi:10.6048/j.issn.1001-4330.2025.04.016

摘要/Abstract

摘要：

【目的】针对不同点云密度LiDAR数据,结合地面每木定位调查,采用冠层高度模型法提取天山云杉单木树高,比较分析不同密度LiDAR点云数据对单木树高的提取精度,为新疆天山云杉单木树高提取提供理论依据。【方法】以新疆农业大学实习林场天山云杉为研究对象,结合样地每木检尺并使用RTK对样地内单株天山云杉每木定位,通过布料模拟滤波算法,提取数字表面模型(DSM)和数字高程模型(DEM),二者作差得到冠层高度模型(CHM),通过CHM获取天山云杉单木树高。【结果】提取天山云杉单木树高最优点云密度为57.66个/m²,平均精度为93.28%,提取效果最差点云密度为1.60 个/m²,拟合度仅有0.754 6,单木识别率最优点云密度为138.53个/m²,识别率为98.7%,单木识别率最差点云密度为1.6个/m²,单木识别率为70.8%。【结论】通过布料模拟滤波算法提取DSM和DEM,计算得到CHM,通过CHM提取天山云杉单木树高是一种可行的办法,点云密度在2.76个/m²左右即可有效作为调查范围较大、成本有限的单木树高提取点云密度。

关键词: 点云密度; 单木树高; 布料模拟滤波; RTK; 单木识别

Abstract:

【Objective】 Based on LiDAR point data of Tianshan sprucewith different densities, canopy height model is used to extract individual tree height of spruce in Tianshan Mountains, and the extraction accuracy of individual tree height from LiDAR point data with different densities was compared and analyzed in the hope of providing a theoretical basis for the extraction of individual tree height in Xinjiang mountain natural forests. 【Methods】 Based on the practice forest farm of Xinjiang Agricultural University, Tianshan spruce was selected as the research object. Combined with the measuring scale of each tree in the sample plot and the positioning of each tree in the sample plot by RTK, the digital surface model (DSM) and digital elevation model (DEM) were extracted by cloth analog filtering algorithm, and the canopy height model (CHM) was obtained by difference between the two. Finally,the single tree height of Tianshan spruce was obtained by the above model. 【Results】 The extracted Tianshan spruce density was 57.66 number/m², the average accuracy was 93.28%, the extraction effect was 1.60 number/m², the fit degree was only 0.754,6, the cloud density of single wood was 138.53 number/m², the recognition rate was 98.7%, the cloud density was 1.6 number/m², and the recognition rate was 70.8%. 【Conclusion】 DSM and DEM are extracted by cloth simulation filtering algorithm, and CHM has been calculated.If the point cloud density is about 2.76 number/m², it can be effectively used as the single tree height extraction point cloud density with large investigation scope and limited cost, and the relevant technical requirements of forestry resource investigation are met.

Key words: point cloud density; single tree height; cloth simulation filtering; RTK; single tree identification

中图分类号:

闫兆杰, 苏香玲, 王振锡, 胡天祺, 郝康迪, 霍延霄, 李凯旋, 马嘉龙. 基于不同点云密度LiDAR数据的天山云杉单木树高提取[J]. 新疆农业科学, 2025, 62(4): 917-928.

YAN Zhaojie, SU Xiangling, WANG Zhenxi, HU Tianqi, HAO Kangdi, HUO Yanxiao, LI kaixuan, MA Jialong. Single tree height extraction based on different LiDAR density data of Tianshan spruce[J]. Xinjiang Agricultural Sciences, 2025, 62(4): 917-928.

图/表 12

参考文献 28

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样地号 Plot number	平均胸径 Mean DBH (cm)	平均树高 Average tree height (m)	海拔 Elevation (m)	株数(棵) Number of individuals (Trees)	坡度 Slope (°)	坡向 Aspect
S₁	25.20	20.09	1 938.33	47	24	北偏东18
S₂	22.37	13.18	2 064.64	19	7	北偏东10
S₃	30.88	22.96	2 053.25	37	21	北偏东297
S₄	34.73	24.45	2 000.21	29	24	北偏东25
S₅	32.00	23.27	2 010.22	35	12	北偏东354
S₆	37.05	26.79	2 024.93	29	21	北偏东50
S₇	31.07	21.22	1 874.88	37	19	北偏东48

样地号 Plot number	平均胸径 Mean DBH (cm)	平均树高 Average tree height (m)	海拔 Elevation (m)	株数(棵) Number of individuals (Trees)	坡度 Slope (°)	坡向 Aspect
S₁	25.20	20.09	1 938.33	47	24	北偏东18
S₂	22.37	13.18	2 064.64	19	7	北偏东10
S₃	30.88	22.96	2 053.25	37	21	北偏东297
S₄	34.73	24.45	2 000.21	29	24	北偏东25
S₅	32.00	23.27	2 010.22	35	12	北偏东354
S₆	37.05	26.79	2 024.93	29	21	北偏东50
S₇	31.07	21.22	1 874.88	37	19	北偏东48

密度 Densities	实测株树(棵) Measured number of trees(Tree)	提取株树(棵) Extracted number of trees(Tree)	漏检数(棵) Missed number (Tree)	识别率 Recog-nition rate(%)
高密度High density(138.53/m²)	233	230	3	98.7
较高密度Higher density(100.57个/m²)	233	229	4	98.3
中密度Middle density(57.66个/m²)	233	220	13	94.4
低密度Low density(23.82个/m²)	233	218	15	93.5
较低密度Lower density(2.76个/m²)	233	213	20	91.4
极低密度Very low density(1.60个/m²)	233	165	68	70.8

密度 Densities	实测株树(棵) Measured number of trees(Tree)	提取株树(棵) Extracted number of trees(Tree)	漏检数(棵) Missed number (Tree)	识别率 Recog-nition rate(%)
高密度High density(138.53/m²)	233	230	3	98.7
较高密度Higher density(100.57个/m²)	233	229	4	98.3
中密度Middle density(57.66个/m²)	233	220	13	94.4
低密度Low density(23.82个/m²)	233	218	15	93.5
较低密度Lower density(2.76个/m²)	233	213	20	91.4
极低密度Very low density(1.60个/m²)	233	165	68	70.8

密度 Densities	漏测胸径cm Missed measure- ment of DBH(max)	漏测胸径cm Missed measure- ment of DBH(min)	漏测树高m Missed measure- ment of tree height(max)	漏测树高m Missed measure- ment of tree height(min)
高密度High density(138.53个/m²)	20.6	7.4	16.7	6.1
较高密度Higher density(100.57个/m²)	38.5	6.5	30	7.3
中密度Middle density(57.66个/m²)	24.3	6.3	22.3	5.9
低密度Low density(23.82个/m²)	23.5	6.9	24.3	6.2
较低密度Lower density(2.76个/m²)	28.4	6.1	28.7	5.8
极低密度Very low density(1.60个/m²)	32	6.5	26.5	6.4