Study on the Extraction of Individual Tree Height Based on UAV Visual Spectrum Remote Sensing

doi:10.13466/j.cnki.lyzygl.2021.01.021

Abstract

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

CLC Number:

S771.5

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.

Figures/Tables 8

Fig.1

Fig.2

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Tab.1

Tab.1

Extraction result of tree height

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

Tab.1

Fig.5

Tab.3

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