Research on Inversion of Combustible Moisture Content in the Pinus Tabulaeformis Canopy Based on Sentinel-2B

doi:10.13466/j.cnki.lyzygl.2023.04.017

Abstract

Abstract:

The occurrence of forest fires is closely related to the moisture content of vegetation canopy combustibles.Using high-precision,large-scale,and high-efficiency remote sensing image inversion to obtain the moisture content of vegetation canopy combustibles is of great significance for effective prevention and control of forest fires.Pinus tabulaeformis is one of the main tree species causing forest fires due to its physical and chemical properties.This study takes Pinus tabulaeformis in Chongli District,Zhangjiakou as the research object.Based on Sentinel 2B remote sensing images and measured moisture content dataof Pinus tabulaeformis,multiple linear regression models,nonlinear regression models and multiple nonlinear regression models were established for the moisture content of Pinus tabulaeformis canopy combustibles.Using the coefficient of determination(R²)and root mean square error(RMSE)to evaluate model accuracy.The results indicated that the nonlinear model was generally superior to the linear model;The multivariate nonlinear model established through multiple independent variable factors better reflected the moisture content of Pinus tabulaeformis canopy combustibles,and the model had higher inversion accuracy,which provided a certain theoretical basis for the selection of vegetation canopy fuel moisture inversion model methods.

Key words: Sentinel-2B, Pinus tabulaeformis, moisture content of combustibles in the canopy, principal component analysis, multiple nonlinear regression

CLC Number:

S762
S771.8

LIU Hongsheng, OUYANG Wenxin, WEI Yingjie, XIE Yiqiu, LI Jianjun. Research on Inversion of Combustible Moisture Content in the Pinus Tabulaeformis Canopy Based on Sentinel-2B[J]. FOREST RESOURCES WANAGEMENT, 2023, (4): 141-149.

Figures/Tables 13

Fig.1

Fig.2

Tab.1

Vegetation index calculation

指数	公式	变量释义
NDVI	NDVI= $(ρ N I R - ρ R) ρ N I R + ρ R$	NIR:近红外波段反射率 SWIR:短红外波段反射率 R:红波段反射率 B:蓝光波段反射率 L:土壤调节因子
EVI	EVI=2.5× $ρ N I R - ρ R ρ N I R + 6 ρ R - 7.5 ρ B + 1$
RVI	RVI= $ρ N I R ρ R$
DVI	DVI=ρ_NIR-ρ_R
SAVI	SAVI= $ρ N I R - ρ R ρ N I R + ρ R + ρ L$ (1+ρ_L)
NDWI	NDWI= $ρ N I R - ρ S W I R ρ N I R + ρ S W I R$

Tab.1

Tab.2

Tab.3

Tab.4

Fig.3

Tab.5

Fig.4

Tab.6

Tab.7

Tab.8

Fig.5

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	含水率	B4	NDVI	RVI	SAVI	NDWI
含水率	1
B4	-0.844	1
NDVI	0.882	-0.932	1
RVI	0.790	-0.792	0.922	1
SAVI	0.723	-0.663	0.881	0.935	1
NDWI	0.840	-0.754	0.830	0.744	0.733	1

成分	特征值	初始特征值方差百分比/%	累积百分比/%	总计/%	提取载荷平方和方差百分比/%	累积百分比/%
1	4.282	85.637	85.637	4.82	85.637	85.637
2	0.390	7.807	93.444
3	0.273	5.469	98.913
4	0.050	1.007	99.919
5	0.004	0.081	100.000

变量	主成分1
B4	-0.896
NDVI	0.988
RVI	0.952
SAVI	0.912
NDWI	0.875

自变量因子	一元线性回归模型	R²	RMSE
B4	y=0.395-1.390x	0.712	0.014
NDVI	y=0.161+0.234x	0.778	0.012
RVI	y=0.279+0.007x	0.624	0.016
SAVI	y=0.235+0.220x	0.523	0.018
NDWI	y=0.247+0.268x	0.706	0.014
Y	y=0.273+0.007x	0.643	0.016

自变量因子	二次多项式回归模型	R²	RMSE
B4	y=0.445+18.453x²-3.44x	0.748	0.013
NDVI	y=0.115-0.101x²+0.372x	0.780	0.012
RVI	y=0.219-0.001x²+0.024x	0.770	0.013
SAVI	y=0.032-0.998x²+1.135x	0.633	0.016
NDWI	y=0.258-0.261x²+0.362x	0.711	0.014
Y	y=0.207-0.001x²+0.024x	0.779	0.013