Volume Estimation Method of High Canopy Density Pinus massoniana Forest Based on UAV Image

doi:10.13466/j.cnki.lczyyj.2024.02.009

Abstract

Abstract:

In order to reduce the cost of traditional field investigation and improve the efficiency of high canopy density forest resource investigation,multi-spectral UAV images combined with field sample survey data were used as the source data,and the Pinus massoniana pure stand was used as the research object.The stand stock volume in the study area was estimated by using the canopy height model(CHM)and six vegetation indices from multi-spectral images.The results show that:1)The assistance of a high-resolution digital elevation model can effectively compensate for the defect that UAV images can not extract ground points from in dense forests,improve the accuracy of CHM construction,and achieve accurate extraction of tree height in dense forests.2)When CHM was used to extract the tree height of a single tree in the study area and estimate the volume,292 Pinus massoniana were extracted from the plot,and the average tree height was 18.77 m.A total of 18 120 Pinus massoniana were extracted from the subcompartment area,and the average tree height was 17.02 m.The measured average tree height was 18.17 m.The average tree height extraction effect was good.The estimated volume was 7 466.74 m³,the measured volume was 9 024.40 m³,and the accuracy of the estimation was 82.90%.3)The RMSE of the vegetation index model is 0.39,R²=0.84,and the accuracy of the model is high.The volume is estimated to be 8 620.30 m³,and the estimation accuracy is 96.26%.By using UAV remote sensing technology,both of the two stock volume estimation methods can achieve rapid estimation of stock volume in high-canopy density forests.Among them,the effect of estimating stock volume by extracting vegetation index from multi-spectral data is better.This provides a strong scientific basis for further promotion and application of UAV images in surveys of forest resources with high canopy density.

Key words: UAV image, high canopy density, Pinus massoniana, canopy height model, vegetation index, stock volume

CLC Number:

S771.8

LUO Yaopei, LI Heping, YANG Guangbin, CEN Gang, LI Man, CAO Qianyang, WANG Renru, CHEN Panfang. Volume Estimation Method of High Canopy Density Pinus massoniana Forest Based on UAV Image[J]. Forest and Grassland Resources Research, 2024, (2): 68-79.

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样地编号	株数/ 株	郁闭度	平均坡度/(°)	胸径/cm			树高/m			蓄积量/m³
样地编号	株数/ 株	郁闭度	平均坡度/(°)	最大值	最小值	平均	最大值	最小值	平均	蓄积量/m³
1	60	0.75	8.04	26.50	10.70	18.79	18.20	13.00	15.95	14.36
2	74	0.75	5.29	33.90	11.00	19.61	19.00	12.90	17.05	20.45
3	71	0.75	2.78	33.60	11.10	20.83	20.50	15.00	17.98	23.26
4	56	0.70	2.21	32.80	13.90	22.32	21.70	16.30	18.85	21.69
5	48	0.70	3.84	41.40	14.30	26.25	23.90	18.80	21.01	28.43

波段名称	中心波长	波段范围
蓝	450	±16
绿	560	±16
红	650	±16
红边	730	±16
近红外	840	±26

植被指数	计算公式	公式变量含义
归一化植被指数(Normalized Differnce Vegetation Index,NDVI)	(R_NIR-R)/(R_NIR+R)	B:蓝光波段的反射值 G:绿光波段的反射值 R:红光波段的反射值 R_RedEdge:红边波段的反射值 R_NIR:近红外波段的反射值
可见光波段差异指数(Visible-band Difference Vegetation Index,VDVI)	(2×G-R-B)/(2×G+R+B)
植被颜色指数(Colour Index of Vegetation Extraction,CIVE)	0.44×R-0.88×G+0.39×G+18.79
归一化红绿差异指数(Normalized Green-Red Difference Index,NGRDI)	(G-R)/(G+R)
冠层叶绿素含量指数(Canopy Chlorophyll Content Index,CCCI)	(R_NIR-R_RedEdge)/(R_NIR+R_RedEdge)
红绿比值指数(Red-Green Ratio Index,RGRI)	R/G

样地编号	实测株数/株	实测平均树高/m	提取株数/株	提取平均树高/m	单木提取精度/%
1	60	15.95	58	12.50	96.67
2	74	17.05	66	17.98	89.19
3	71	17.98	68	20.77	95.77
4	56	18.85	55	20.84	98.21
5	48	21.01	45	21.76	93.75

小班编号	小班面积/hm²	株数/株	平均树高/m	平均胸径/cm	估算蓄积量/m³	实测蓄积量/m³	估算精度/%
00120	12.50	8 035	15.73	20.68	2 723.97	3 262.50	83.49
00086	15.70	10 085	18.31	23.34	4 742.77	5 761.90	82.31
合计	28.20	18 120	17.02	22.01	7 466.74	9 024.40	82.90