基于机载LiDAR数据的崇礼冬奥核心区单木分割算法适宜性分析

doi:10.11707/j.1001-7488.20221012

Abstract

Abstract:

Objective: The suitability of three tree segmentation algorithms based on airborne LiDAR data in obtaining information of Pinus tabulaeformis and middle-aged Larix principis-rupprechtii in the core area of the Winter Olympic Games was studied, and the accuracy of individual tree segmentation and tree height extraction of the three methods were analyzed, with the aims to explore the optimal single tree segmentation method in the core area of the Winter Olympic Games and also to provide technical supports for mastering the forest structure information and formulating forest management measures in the core area of the Winter Olympic Games. Method: Based on the airborne LiDAR data of the sample plots in the core area of the Winter Olympic Games, the point cloud-based cluster segmentation algorithm, watershed segmentation algorithm and double-tangent crown recognition algorithm were applied, combined with sample plot survey data, orthophoto image and artificial visual interpretation, and tree crown detection rate(r), accuracy(p) and overall accuracy(F) were used to analyze the single tree segmentation accuracy of the algorithms; After individual tree registration, the correlation between field measured tree height and LiDAR estimated tree height was analyzed, and the suitability of three tree segmentation algorithms in the core area of Winter Olympic Games was comprehensively evaluated. Result: 1) The overall segmentation accuracy of the three tree segmentation methods was very high in newly afforestation Pinus tabulaeformis(F=0.90-0.93) and higher in middle-aged forest Larix principis-rupprechtii(F=0.72-0.75). 2) For newly afforestation Pinus tabulaeformis, the segmentation accuracy of point cloud-based cluster segmentation algorithm was the highest(F=0.93), which was better than that of watershed segmentation algorithm(F=0.90) and double-tangent crown recognition algorithm(F=0.90). For middle-aged Larix principis-rupprechtii, the segmentation accuracy of double-tangent crown recognition algorithm was the highest(F=0.75), which was better than that of the point cloud-based cluster segmentation algorithm(F=0.72) and watershed segmentation algorithm(F=0.70). 3) The correlation analysis between field measured tree height and airborne LiDAR estimated tree height showed that the correlation of single tree height extracted from newly afforestation Pinus tabulaeformis by point cloud-based cluster segmentation algorithm was the best, and the correlation of single tree height extracted by double-tangent crown recognition algorithm was the best in middle-aged Larix principis-rupprechtii. Conclusion: Through the single tree segmentation of the airborne LiDAR of different types of forest land in the core area of the Winter Olympic Games, the suitability of the point cloud-based cluster segmentation, watershed segmentation algorithm and double-tangent crown recognition algorithm could be reflected. The best segmentation was achieved using the point cloud-based cluster segmentation algorithm for newly planted Pinus tabulaeformis, and the best suitability segmentation was achieved using the double-tangent crown recognition algorithm for middle-aged Larix principis-rupprechtii.

Key words: laser LiDAR, single tree segmentation, Winter Olympic Games, watershed segmentation algorithm, crown recognition

CLC Number:

S757

Dongbo Xie,Qingwang Liu,Yakai Lei,Hang Yu,Xuping Yang,Liyong Fu. Suitability Analysis of Single Tree Segmentation Algorithm in the Core Area of Winter Olympic Games Based on Airborne LiDAR Data[J]. Scientia Silvae Sinicae, 2022, 58(10): 121-130.

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样地编号 No.	树种 Tree species	类型Types	株数 Plant number	年龄 Age/a	郁闭度 Canopy density	平均树高 Mean height/m	平均胸径 Mean DBH/cm	平均冠幅 Mean canopy/m
样地1 Sample 1	油松 Pinus tabulaeformis	新造林 Newly afforestation	68	7	0.30	2.98	4.68	1.62
样地2 Sample 2	油松 Pinus tabulaeformis	新造林 Newly afforestation	85	8	0.43	3.29	4.98	1.76
样地3 Sample 3	油松 Pinus tabulaeformis	新造林 Newly afforestation	92	8	0.50	3.24	5.75	1.96
样地4 Sample 4	油松 Pinus tabulaeformis	新造林 Newly afforestation	71	7	0.37	2.39	3.68	1.18
样地5 Sample 5	油松 Pinus tabulaeformis	新造林 Newly afforestation	60	7	0.21	2.73	3.36	1.00
样地6 Sample 6	华北落叶松 Larix principis-rupprechtii	中龄林 Middle-aged forest	66	38	0.82	12.91	19.89	4.64
样地7 Sample 7	华北落叶松 Larix principis-rupprechtii	中龄林 Middle-aged forest	91	40	0.90	17.60	20.40	5.20
样地8 Sample 8	华北落叶松 Larix principis-rupprechtii	中龄林 Middle-aged forest	50	41	0.75	11.80	17.75	3.88
样地9 Sample 9	华北落叶松 Larix principis-rupprechtii	中龄林 Middle-aged forest	60	41	0.80	12.62	19.68	5.10
样地10 Sample 10	华北落叶松 Larix principis-rupprechtii	中龄林 Middle-aged forest	100	40	0.86	13.22	15.54	3.50

精度验证指标 Index for accuracy assessments	基于点云的距离判别聚类法 Point cloud-based cluster segmentation algorithm	分水岭分割算法 Watershed segmentation algorithm	双正切角树冠识别算法 Double-tangent crown recognition algorithm
r	0.95	0.87	0.89
p	0.92	0.93	0.91
F	0.93	0.90	0.90

精度验证指标 Index for accuracy assessments	基于点云的距离判别聚类法 Point cloud-based cluster segmentation algorithm	分水岭分割算法 Watershed segmentation algorithm	双正切角树冠识别算法 Double-tangent crown recognition algorithm
r	0.62	0.64	0.78
p	0.85	0.77	0.72
F	0.72	0.70	0.75

样地编号 No.	基于点云的距离判别聚类法 Point cloud-based cluster segmentation algorithm	分水岭分割算法 Watershed segmentation algorithm	双正切角树冠识别算法 Double-tangent crown recognition algorithm
样地1 Sample 1	0.815^**	0.562^**	0.798^**
样地2 Sample 2	0.828^**	0.721^**	0.825^**
样地3 Sample 3	0.866^**	0.829^**	0.819^**
样地4 Sample 4	0.911^**	0.588^**	0.793^**
样地5 Sample 5	0.936^**	0.634^**	0.684^**
样地6 Sample 6	0.701^**	0.610^**	0.782^**
样地7 Sample 7	0.736^**	0.531^**	0.726^**
样地8 Sample 8	0.680^**	0.519^**	0.765^**
样地9 Sample 9	0.687^**	0.579^**	0.710^**
样地10 Sample 10	0.661^**	0.608^**	0.752^**

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[3]	Li Dan, Zhang Junjie, Zhao Mengxi. Extraction of Stand Factors in UAV Image Based on FCM and Watershed Algorithm [J]. Scientia Silvae Sinicae, 2019, 55(5): 180-187.

Suitability Analysis of Single Tree Segmentation Algorithm in the Core Area of Winter Olympic Games Based on Airborne LiDAR Data

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