Estimating biomass of reclaimed vegetation in prairie mining area: Inversion method based on Worldview-3 and Sentinel-1 SAR data

doi:10.13745/j.esf.sf.2020.10.10

Abstract

Abstract:

Quantitative inversion of reclaimed vegetation biomass in prairie mining area based on the remote sensing technology is the basis of dynamic monitoring and evaluation of mining ecological environment. In this research, focussing on the reclaimed vegetation in the grassland open-pit coal mine in Inner Mongolia, we combine the advantages of optical and radar remote sensing to explore the inversion method for biomass estimation based on Worldview-3 and Sentinel-1 SAR data. The principal component-wavelet transform algorithm was selected for data fusion. We revealed the correlation between parameters such as band reflectivity, vegetation index, backscatter coefficient or texture feature and biomass, established multivariate biomass inversion models, and analyzed the spatial uncertainty of different biomass models. The results are as follows: (1) After image fusion using W-PCA method, both data entropy and average gradient of the fusion data were significantly improved, and the fused 8th band (NIR2) had the highest correlation coefficient, the lowest spectral distortion, and the highest spectral fidelity. (2) Correlation analysis revealed a significant positive correlation between biomass and EVI, NDVI, VH polarization scattering coefficient, VH mean texture or the 8th band after fusion. Compared with a single variable, using the joint variables, NDVI of WV-3 and VH mean texture of Sentinel-1, it achieved the highest model accuracy (R²=0.8340, RMSE=16.4646 g/m², Ac=81.52%), while the 8th band after fusion gave the highest verification accuracy (R²=0.7983, RMSE=22.8283 g/m², Ac=74.64%). (3) According to the residual uncertainty analysis of different models, the Sentinel-1 variables are more prone to overestimation and saturation, whereas the joint variables can achieve complementary advantages. Using fusion data significantly improved the biomass overestimation for biomass below 40 g/m² and saturation for biomass greater than 100 g/m², reducing the model uncertainty by 2.42-9.68 g/m² on average. It can be seen that the combination of optical and microwave cooperative remote sensing can effectively improve the estimation accuracy of vegetation biomass, thereby providing effective data support for fine monitoring of reclaimed vegetation in mining areas.

Key words: prairie mining area, reclaimed vegetation, Worldview-3, Sentinel-1 SAR, data fusion, biomass inversion

CLC Number:

X171.4
X87

LIU Yanhui, YANG Xiaoyu, BAO Nisha, GU Xiaowei. Estimating biomass of reclaimed vegetation in prairie mining area: Inversion method based on Worldview-3 and Sentinel-1 SAR data[J]. Earth Science Frontiers, 2021, 28(4): 219-228.

Figures/Tables 13

Fig.1 Locations of the research area and sampling sites

Fig.2 Types of reclaimed vegetation

Table 1 Remote sensing data parameters

传感器	分辨率	波段名称/极化方式	波长
		海岸波段	400~450 nm
		蓝波段	450~510 nm
		绿波段	510~580 nm
Worldview-3	1.24 m	黄波段	585~625 nm
		红波段	630~690 nm
		红边波段	705~745 nm
		近红外1	770~895 nm
		近红外2	860~1 040 nm
Sentinel-1	5 m×20 m	VH和VV极化	7.5~3.75 cm

Fig.3 Image fusion flow chart

Table 2 Calculation formulas and their advantages for 7 vegetable indexes

植被指数	计算公式	优点
NDVI	$NIR 1 - R NIR 1 + R$	NDVI指数是目前遥感影像植被分类研究应用最广泛的植被指数,是植被长势及覆盖度的最佳指标
DVI	NIR1-R	DVI指数对土壤背景变化敏感,适用植被发育早中期或植被覆盖度较低的区域
RVI	$NIR 1 R$	RVI指数对茂盛、覆盖度较高的植被敏感,绿色植被RVI值较高,非植被RVI值较低
NDGI	$NIR 1 - G NIR 1 + G$	NDGI指数在NDVI指数的基础上将红波段换成了绿波段,可用来对不同活力植被形式进行检验
ARVI	$NIR 1 - 2 R + B NIR 1 + 2 R - B$	ARVI指数根据蓝波段与红波段对大气响应的差异,用红蓝波段组合替代NDVI指数中的红光波段,以减少大气对植被指数的影响
EVI	$NIR 1 - R NIR 1 + R + B$	EVI指数不仅能克服大气对光谱反射的衰减,还能削弱土壤背景的影响
$NDV I 705$	$RE - R RE + R$	$NDV I 705$ 指数是NDVI指数的改进型,它对植被叶冠层的微小变化、林窗片段和衰老变化非常灵敏

Table 2 Calculation formulas and their advantages for 7 vegetable indexes

植被指数	计算公式	优点
NDVI	$NIR 1 - R NIR 1 + R$	NDVI指数是目前遥感影像植被分类研究应用最广泛的植被指数,是植被长势及覆盖度的最佳指标
DVI	NIR1-R	DVI指数对土壤背景变化敏感,适用植被发育早中期或植被覆盖度较低的区域
RVI	$NIR 1 R$	RVI指数对茂盛、覆盖度较高的植被敏感,绿色植被RVI值较高,非植被RVI值较低
NDGI	$NIR 1 - G NIR 1 + G$	NDGI指数在NDVI指数的基础上将红波段换成了绿波段,可用来对不同活力植被形式进行检验
ARVI	$NIR 1 - 2 R + B NIR 1 + 2 R - B$	ARVI指数根据蓝波段与红波段对大气响应的差异,用红蓝波段组合替代NDVI指数中的红光波段,以减少大气对植被指数的影响
EVI	$NIR 1 - R NIR 1 + R + B$	EVI指数不仅能克服大气对光谱反射的衰减,还能削弱土壤背景的影响
$NDV I 705$	$RE - R RE + R$	$NDV I 705$ 指数是NDVI指数的改进型,它对植被叶冠层的微小变化、林窗片段和衰老变化非常灵敏

Fig.4 Comparison of fusion results

Fig.5 Information entropy values for all available bands of WV-3 before and after image fusion and for Sentinel-1 VV-polarized SAR image

Fig.6 Average gradient values for all available bands of WV-3 before and after image fusion and for sentinel-1 VV-polarized SAR image

Fig.7 Similarity coefficient and spectral distortion between WV-3 image and fused image for all spectral bands

Table 3 Correlation analysis between biomass and different variables

Worldview-3
波段反射率		纹理特征					极化/纹理特征				融合波段
变量	相关性		变量		相关性		变量		相关性		变量		相关性
海岸波段	-0.323	波段4均值		-0.741^**		VH		0.504^**		波段1 (RHB1)		-0.759^**
蓝波段	-0.474^*	波段5均值		-0.792^**		VV		0.410^*		波段2 (RHB2)		-0.806^**
绿波段	-0.372	波段5相关性		-0.358^*		VV均值		0.412^**		波段3(RHB3)		-0.837^**
黄波段	-0.620^**	波段6均值		0.511^**		VV方差		0.174		波段4 (RHB4)		-0.834^**
红波段	-0.657^**	波段6相关性		-0.412^*		VV均一性		-0.238		波段5 (RHB5)		-0.800^**
红边波段	0.215	波段7均值		0.760^**		VV对比度		0.165		波段6 (RHB6)		0.564^**
近红外1(NIR1)	0.694^**	波段7方差		0.700^**		VV相异性		0.201		波段7 (RHB7)		0.842^**
近红外2(NIR2)	0.718^**	波段7均一性		-0.593^**		VV信息熵		0.257		波段8 (RHB8)		0.874^**
RVI	0.790^**	波段7相异性		0.535^*		VV二阶矩		-0.219
NGVI	0.871^**	波段7信息熵		0.510^**		VV相关性		0.169
NDVI₇₀₅	0.835^**	波段7二阶矩		-0.366^*		VH均值		0.505^**
NDVI	0.874^**	波段8均值		0.748^**		VH方差		-0.061
EVI	0.876^**	波段8方差		0.669^**		VH均一性		0.069
DVI	0.833^**	波段8均一性		-0.600^**		VH对比度		0.064
ARVI	0.845^**	波段8相异性		0.558^**		VH相异性		-0.016
		波段8信息熵		0.612^**		VH信息熵		-0.133
		波段8二阶矩		0.520^**		VH二阶矩		0.298
						VH相关性		-0.195

Table 4 Optimal model and its accuracy for each data set

变量	模型	建模精度 (n=21)			验证精度 (n=11)
变量	模型	R²	RMSE/ (g·m^-2)	Ac/%		R²		RMSE/ (g·m^-2)		Ac/%
EVI	$y = 2.840 8 × EVI + 16.829$	0.816 3	17.318 7	80.56	0.709 8		24.201 8		73.12
VH	$y = 2.272 6 × V H 2 + 96.275 × VH + 1 094.4$	0.430 6	30.488 8	65.78	0.324		42.110 4		53.22
NDVI,VH_ME	$y = 3.256 2 × NDVI - 0.622 4 × V H ME + 8.593$	0.834 0	16.464 6	81.52	0.696 3		23.680 1		73.69
RHB8	$y = 1.055 7 × RHB 8 - 26.676$	0.784 2	18.767 9	78.94	0.798 3		22.828 3		74.64

Table 4 Optimal model and its accuracy for each data set

变量	模型	建模精度 (n=21)			验证精度 (n=11)
变量	模型	R²	RMSE/ (g·m^-2)	Ac/%		R²		RMSE/ (g·m^-2)		Ac/%
EVI	$y = 2.840 8 × EVI + 16.829$	0.816 3	17.318 7	80.56	0.709 8		24.201 8		73.12
VH	$y = 2.272 6 × V H 2 + 96.275 × VH + 1 094.4$	0.430 6	30.488 8	65.78	0.324		42.110 4		53.22
NDVI,VH_ME	$y = 3.256 2 × NDVI - 0.622 4 × V H ME + 8.593$	0.834 0	16.464 6	81.52	0.696 3		23.680 1		73.69
RHB8	$y = 1.055 7 × RHB 8 - 26.676$	0.784 2	18.767 9	78.94	0.798 3		22.828 3		74.64

Fig.8 Residual plots for models

Fig.9 Distribution of estimation uncertainties for different biomass surface concentrations. (a) 60-100 g/m2; (b) 40-60 g/m2; (c) <40 g/m2; (d) >100 g/m2.

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