Extracting Individual Tree Position of Chinese Fir Based on an Improved Local Maximum Method

doi:10.13466/j.cnki.lyzygl.2022.05.008

Abstract

Abstract:

Chinese fir is one of the important timber species in China.The extraction of individual tree position and density plays an important role in regulating its stand spatial structure and function,and improving stand quality.Based on the unmanned aerial vehicle(UAV)remote sensing image,taking the Chinese fir pure forest in Longquan City of Lishui as the research object,the improved local maximum method was used to extract the crown position and number of Chinese fir trees,which was compared and analyzed with the measured number of trees.The results show that the sampling interval parameter of the improved local maximum method had an important influence on the extraction accuracy of individual tree number.Under the appropriate sampling interval parameters,the overall accuracy of individual tree position detection for sample plots with dense and sparse tree density were 82.10% and 80.17%,commission errors were 24.12% and 18.18%,and omission errors were 17.90% and 19.83%,respectively.The detected tree density and measured tree density for sample plots with dense and sparse tree density were very similar,and the accuracy was 93.77% and 98.35%,respectively.The tree density had a negative correlation with the overall accuracy and commission error,and a positive correlation with the omission error.The improved local maximum method can accurately extract the number of individual trees of Chinese fir forest with different tree density,which provides a feasible method for intelligent,fast and accurate extraction of individual crown position and tree density of Chinese fir.

Key words: local maximum method, UAV, individual tree, tree density

CLC Number:

P237
S791.27

LI Suchun, LIN Luhua, XIA Lei, HU Lulu, XU Xiaojun. Extracting Individual Tree Position of Chinese Fir Based on an Improved Local Maximum Method[J]. FOREST RESOURCES WANAGEMENT, 2022, (5): 60-68.

Figures/Tables 10

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样方号	样地1			样地2			样方号	样地1			样地2
样方号	面积/ m²	株数/ 株	密度/ (株/hm²)	面积/ m²	株数/ 株	密度/ (株/hm²)	样方号	面积/ m²	株数/ 株	密度/ (株/hm²)	面积/ m²	株数/ 株	密度/ (株/hm²)
1	121.2	37	3053	120.6	17	1410	6	70.2	14	1994	89.5	13	1453
2	99.9	27	2703	85.2	10	1174	7	86.9	32	3683	118.6	15	1265
3	94.0	32	3404	122.9	11	895	8	76.7	28	3651	102.9	15	1458
4	72.4	35	4834	105.4	10	949	9	88.9	28	3150	98.0	21	2143
5	64.0	24	3750	77.3	9	1164	整体	774.2	257	3320	920.4	121	1315

样地	参考株数	监测株数	正确株数	错分株数	漏分株数	OA/%	CE/%	OE/%
样方1	37	53	36	17	1	97.30	45.95	2.70
样方2	27	29	23	6	4	85.19	22.22	14.81
样方3	32	29	24	5	8	75.00	15.63	25.00
样方4	35	28	25	3	10	71.43	8.57	28.57
样方5	24	21	18	3	6	75.00	12.50	25.00
样方6	14	18	12	6	2	85.71	42.86	14.29
样方7	32	32	25	7	7	78.13	21.88	21.88
样方8	28	31	23	8	5	82.14	28.57	17.86
样方9	28	32	25	7	3	89.29	25.00	10.71
整个样地	257	273	211	62	46	82.10	24.12	17.90

样地	参考株数	监测株数	正确株数	错分株数	漏分株数	OA/%	CE/%	OE/%
样方1	17	18	13	5	4	76.47	29.41	23.53
样方2	10	12	8	4	2	80.00	40.00	20.00
样方3	11	11	8	3	3	72.73	27.27	27.27
样方4	10	10	9	1	1	90.00	10.00	10.00
样方5	9	7	7	0	2	77.78	0.00	22.22
样方6	13	13	11	2	2	84.62	15.38	15.38
样方7	15	14	12	2	3	80.00	13.33	20.00
样方8	15	17	14	3	1	93.33	20.00	6.67
样方9	21	17	15	2	6	71.43	9.52	28.57
整个样地	121	119	97	22	24	80.17	18.18	19.83