基于无人机可见光遥感的单木树高提取方法研究

doi:10.13466/j.cnki.lyzygl.2021.01.021

摘要/Abstract

摘要：

利用目前流行的高分辨率可见光无人机遥感影像生成树木冠层高度模型,采用分水岭分割算法提取单木树高的研究具有重要理论和实践意义。以位于云南省富民县的天然云南松纯林为研究对象,通过大疆Phantom 4 Pro无人机获取低空可见光遥感影像,利用Pix4D Mapper对无人机影像进行预处理及三维重建,生成三维点云,利用LiDAR360处理三维点云,构建DSM,DEM并生成CHM;采用分水岭分割算法对不同郁闭度条件下获得的CHM进行单木分割及树高提取,对提取结果进行精度评价。结果表明:分水岭分割算法能够准确分割CHM,利用无人机可见光遥感影像进行单木树高提取是可行的;将基于无人机可见光影像提取的树高值与野外实地调查得到的树高值进行对比,R²为0.893,RMSE为1.23m,估测精度为87.58%;同时,林分郁闭度会对单木树高估测产生影响,根据不同郁闭度条件下提取的3组样木树高与实地测量树高的决定系数(R²)分别是0.857,0.939和0.921,RMSE分别为1.450,1.097,0.896m,在低郁闭度林分内树高估测的精度显著高于高郁闭度林分。

关键词: 树高提取, 无人机, 三维点云, 郁闭度

Abstract:

The feasibility of using the watershed segmentation algorithm to extract tree height based on the canopy height model(CHM) generated by high-resolution imagery of UAV was discussed.Tree height data were obtained from Pinus yunnanensis Franch in Fuming County of Yunnan Province.Remote sensing imagery in the study area of the near ground was obtained through the DJI Phantom 4 Pro drone remote sensing system.Pix4D Mapper software was used to preprocess UAV images and reconstruct the research area in three dimensions and 3D point cloud.LiDAR360 software was used to process 3D point cloud and build a DSM(Digital Surface Model),DEM(Digital elevation Model)and CHM were generated.Then,for different canopy density of CHM,the watershed segmentation algorithm was used to divide and extract tree height.The accuracies of the results were elevated.Results showed that CHM could be accurately divided by the watershed segmentation algorithm and it was feasible that UAV imagery was used to extract the height of Pinus yunnanensis Franch.The comparative analysis of the data on the height of trees acquired from the UAV image and the field measurement shows that the R ²,RMSE,rRMSE is 0.893,1.23m,12.42%.At the same time,the measurement of tree height was affected by canopy density with the R² and root mean squared error(RMSE)value for the least density being R²=0.939,RMSE=1.097m,for the next one was R²=0.921,RMSE=0.896m,and for the largest was R²=0.857,RMSE=1.450m.The accuracy of the measured height of the larger density was higher than that of the smaller density.

Key words: individual tree height, extraction, 3D point cloud, visual spectrum remote sensing, UAV

中图分类号:

S771.5

白明雄, 张超, 陈棋, 王娟, 李华玉, 史小蓉, 田湘云, 张玉薇. 基于无人机可见光遥感的单木树高提取方法研究[J]. 林业资源管理, 2021,(1): 164-172.

BAI Mingxiong, ZHANG Chao, CHEN Qi, WANG Juan, LI Huayu, SHI Xiaorong, TIAN Xianyun, ZHANG Yuwei. Study on the Extraction of Individual Tree Height Based on UAV Visual Spectrum Remote Sensing[J]. FOREST RESOURCES WANAGEMENT, 2021,(1): 164-172.

图/表 8

图1

图2

图3

图4

表1

表2

树木高度提取结果

树号	测量值/m	实测值/m	误差/m	相对误差	树号	测量值/m	实测值/m	误差/m	相对误差
1	6.20	7.93	1.73	0.2182	21	6.15	6.97	0.82	0.1176
2	6.32	8.30	1.98	0.2386	22	6.86	8.43	1.57	0.1862
3	6.60	7.65	1.05	0.1373	23	6.49	8.54	2.05	0.2400
4	6.10	7.65	1.55	0.2026	24	7.64	10.10	2.46	0.2436
5	9.10	11.15	2.05	0.1839	25	7.61	9.36	1.75	0.1870
6	7.44	8.60	1.16	0.1349	26	7.66	9.67	2.01	0.2079
7	7.63	7.80	0.17	0.0218	27	6.32	7.66	1.34	0.1749
8	6.90	7.63	0.73	0.0957	28	8.29	10.09	1.80	0.1784
9	6.30	8.00	1.70	0.2125	29	8.92	9.48	0.56	0.0591
10	5.84	7.30	1.46	0.2000	30	5.55	6.55	1.00	0.1527
11	5.56	6.90	1.34	0.1942	31	5.49	6.12	0.63	0.1029
12	11.82	13.60	1.78	0.1309	32	6.27	7.41	1.14	0.1538
13	8.60	9.19	0.59	0.0642	33	6.27	7.63	1.36	0.1782
14	5.76	7.80	2.04	0.2615	34	8.33	8.69	0.36	0.0414
15	7.90	8.50	0.60	0.0706	35	7.72	10.04	2.32	0.2311
16	9.30	9.70	0.40	0.0412	36	7.81	8.46	0.65	0.0768
17	8.90	9.30	0.40	0.0430	37	6.93	7.74	0.81	0.1047
18	8.90	10.60	1.70	0.1604	38	6.38	7.83	1.45	0.1852
19	6.63	8.16	1.53	0.1875	39	5.97	7.43	1.46	0.1965
20	11.02	13.33	2.31	0.1733	40	5.15	6.46	1.31	0.2028
41	5.71	6.22	0.51	0.0820	57	5.35	6.69	1.34	0.2003
42	8.52	8.78	0.26	0.0296	58	5.54	6.73	1.19	0.1768
43	9.52	9.89	0.37	0.0374	59	4.95	6.45	1.50	0.2326
44	9.15	9.87	0.72	0.0729	60	6.00	7.33	1.33	0.1814
45	9.29	9.90	0.61	0.0616	61	4.80	6.00	1.20	0.2000
46	8.28	9.44	1.16	0.1229	62	7.52	8.71	1.19	0.1366
47	4.95	6.45	1.50	0.2326	63	9.50	11.90	2.40	0.2017
48	4.05	4.50	0.45	0.1000	64	10.69	12.41	1.72	0.1386
49	5.93	7.15	1.22	0.1706	65	11.62	12.66	1.04	0.0821
50	4.12	4.12	0.00	0.0000	66	10.56	11.12	0.56	0.0504
51	8.30	8.44	0.14	0.0166	67	9.10	10.19	1.09	0.1070
52	8.19	8.45	0.26	0.0302	68	9.06	10.00	0.94	0.0940
53	8.44	8.45	0.01	0.0012	69	9.12	10.12	1.00	0.0988
54	7.40	8.59	1.19	0.1385	70	10.27	11.74	1.47	0.1252
55	7.40	8.59	1.19	0.1385	71	5.23	6.15	0.92	0.1496
56	7.03	7.61	0.58	0.0762	72	4.62	6.15	1.53	0.2488
73	7.20	7.49	0.29	0.0387	85	8.15	8.74	0.59	0.0675
74	5.46	7.08	1.62	0.2288	86	8.86	9.49	0.63	0.0664
75	5.46	6.20	0.74	0.1194	87	10.64	11.18	0.54	0.0483
76	5.46	5.91	0.45	0.0761	88	6.53	6.79	0.26	0.0383
77	9.38	10.10	0.72	0.0713	89	8.67	8.71	0.04	0.0046
78	7.30	7.68	0.38	0.0495	90	9.54	10.74	1.20	0.1117
79	7.15	9.45	2.30	0.2434	91	10.55	11.74	1.19	0.1014
80	2.78	3.36	0.58	0.1726	92	9.87	10.17	0.30	0.0295
81	8.50	9.80	1.30	0.1327	93	9.60	9.87	0.27	0.0274
82	9.45	10.80	1.35	0.1250	94	9.42	9.53	0.11	0.0115
83	8.27	9.36	1.09	0.1165	95	10.14	10.23	0.09	0.0088
84	6.95	7.76	0.81	0.1044	96	10.54	10.57	0.03	0.0028

表2

图5

表3

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