Inversion Research of Forest Stock Volume Using the Red Edge Bands of Sentinel-2A

doi:10.13466/j.cnki.lyzygl.2022.02.017

Abstract

Abstract:

Accurate and efficient estimation of forest stock volume is useful for measuring forest health and evaluating the carbon sequestration capacity of forests. The red-edge band is sensitive to vegetation chlorophyll changes,but its validity in forest stock volume estimation needs further verification. To explore the feasibility of red-edge band in estimating forest stock volume,the Xingning district of Nanning City was used as the study area,and different sets of modeling variables were constructed based on Sentinel-2A images to extract common band reflectance,red-edge band reflectance,common vegetation index and red-edge vegetation index,and the forest stock volume was estimated by multiple linear regression and random forest algorithm. The forest resources planning and design survey results data was used as the actual measurements for model accuracy evaluation. By comparing the modeling effects of the models and variable sets,the influence of the red-edge band on the estimation accuracy of the forest stock volume was analyzed. The results showed that the red-edge vegetation index was significantly correlated with the forest stock volume (P<0.01),and the forest stock volume estimation accuracy of the red-edge vegetation index variable set was significantly better than the other variable sets in different variable sets; among the two estimation models,the random forest model was more effective,and the accuracy of the random forest model was better than that of the multiple linear regression model in all variable sets. The random forest model using the variable set 4 achieved the highest estimation accuracy with R²,RMSE,and RRMSE of 0.66,28.63,and 23.54%,respectively. It can be concluded from the study that the red-edge band information of Sentinel-2A can be effectively used for remote sensing estimation of forest stock volume,which can provide a reference for efficient monitoring and management of forest resources by remote sensing.

Key words: forest stock volume, random forest, Sentinel-2A, red edge band, vegetation index

CLC Number:

LONG Zhihao, LUO Peng, XU Dengping, LI Zhen, DAI Huabin. Inversion Research of Forest Stock Volume Using the Red Edge Bands of Sentinel-2A[J]. FOREST RESOURCES WANAGEMENT, 2022, (2): 126-134.

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波段	描述	中心波长/nm	空间分辨率/m
蓝波段band2	Blue	490	10
绿波段band3	Green	560	10
红波段band4	Red	665	10
红边波段band5	Red Edge1	705	20
红边波段band6	Red Edge2	740	20
红边波段band7	Red Edge3	783	20
近红外波段band8	NIR	842	10
红边波段band8A	Red Edge4	865	20
短波红外band11	SWIR1	1610	20
短波红外band12	SWIR2	2190	20

植被指数	表达式
ARVI^[18]	$b a n d 8 - b a n d 4 + γ b a n d 2 - b a n d 4$ / $b a n d 8 + b a n d 4 + γ b a n d 2 - b a n d 4$
MSAVI^[19]	[2band8+1- $2 b a n d 8 + 1 2 - 8 b a n d 8 - b a n d 4$ ]/2
NDVI^[20]	$b a n d 8 - b a n d 4$ / $b a n d 8 + b a n d 4$
RECI^[21]	$b a n d 8 / b a n d 5$ -1
RENDVI^[22]	$b a n d 8 - b a n d 5$ / $b a n d 8 + b a n d 5$
RESR^[23]	band8/band5

变量	相关系数	变量	相关系数
band2	0.499^**	band11	0.426^**
band3	0.438^**	band12	0.579^**
band4	0.509^**	ARVI	-0.637^**
band5	0.481^**	MSAVI	-0.525^**
band6	-0.13	NDVI	-0.629^**
band7	-0.213^**	RECI	-0.565^**
band8	-0.251^**	RENDVI	-0.616^**
band8A	-0.213^**	RESR	-0.594^**

变量集	模型	决定系数 (R²)	均方根误差 (RMSE)/ (m³/hm²)	相对均方根误差 (RRMSE) /%
变量集1	多元线性回归	0.31	37.23	30.37
变量集2	多元线性回归	0.36	36.51	29.78
变量集3	多元线性回归	0.40	35.82	29.23
变量集4	多元线性回归	0.40	35.82	29.23
变量集1	随机森林	0.22	38.59	33.06
变量集2	随机森林	0.59	31.19	25.65
变量集3	随机森林	0.62	30.28	24.90
变量集4	随机森林	0.66	28.63	23.54