Study of the correlation between optical vegetation index and SAR data and the main affecting factors

doi:10.6046/gtzyyg.2020.02.17

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Abstract

Vegetation index is an important approach in vegetation monitoring and investigation. SAR data are free with weather condition observing data day and night. Building relationships between SAR data and vegetation indices can contribute to fusing two data to improve temporal monitoring in forest of mountain areas. Therefore, the authors made a statistical analysis between vegetation indices including NDVI, EVI, GVI, NDWI and C band SAR data and then made a comparison about difference of correlation between NDVI, NDWI and X, C, L band SAR data in different forest disturbances in Genhe forest region of Da Hinggan Mountains in Inner Mongolia. The results are as follows: ①PR and interferometry coefficients both have significant negative correlations with optical vegetation indices, PR has strong linear correlations with NDVI, EVI, GVI (R²=0.40~0.49), and interferometry coefficients have strong linear correlations with all optical indices (R²=0.43~0.51). ②Ground cover can affect linear regression between VH and NDVI. Scrub-grass land and fires scars with thick vegetation layer and forest land have a strong linear correlation with NDVI (R²=0.64~0.76). ③The correlations are different for different forest disturbances: In fires scars, NDVI has significant negative correlations with X- band HH, and C band PR and NDWI have a significant positive correlation with C band VH. In deforestation areas, L-band PR has significant negative correlations with NDVI, and L band VV and VH have significant positive correlations with NDWI. In undisturbed forest land, C-band PR has significant negative correlations with NDVI and NDWI.

Keywords vegetation index SAR remote sensing parameter fire scar deforestation correlation

TP79

Corresponding Authors: Huaguo HUANG E-mail: huaguo_huang@bjfu.edu.cn

Issue Date: 18 June 2020

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	Chuan WANG
	Jinghui FAN
	Simei LIN
	Yueming RAO
	Huaguo HUANG

Cite this article:

Chuan WANG,Jinghui FAN,Simei LIN, et al. Study of the correlation between optical vegetation index and SAR data and the main affecting factors[J]. Remote Sensing for Land & Resources, 2020, 32(2): 130-137.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.02.17 OR https://www.gtzyyg.com/EN/Y2020/V32/I2/130

Fig.1 Satellite image of the study area

Tab.1 The essential information of fire scars

Tab.2 The essential information of deforestation areas

Tab.3 The basic information and purpose of analysis about satellite sensors

Fig.2 Flow chart of correlation analysis between optical indices and SAR data

Fig.3 Influence of forest disturbance on correlation between optical indices and SAR data

植被指数	$R 1 ①$			$R 2 ②$
植被指数	VV	VH	PR	VV	VH	PR
NDVI	0.06	0.49^**	-0.71^**	-0.03	0.37^**	-0.67^**
EVI	0.09	0.47^**	-0.64^**	0.04	0.43^**	-0.68^**
GVI	0.10	0.48^**	-0.63^**	-0.03	0.36^**	-0.66^**
NDWI	0.31^**	0.59^**	-0.54^**	0.31^**	0.54^**	-0.49^**

Tab.4 Correlation analysis between SAR data of Sentinel-1B and optical indices of Landsat8 images

植被指数	8月14日		8月30日
植被指数	γ_VV	γ_VH	γ_VV	γ_VH
NDVI	-0.5 $1 ** ①$	-0.72^**	-0.49^**	-0.70^**
EVI	-0.49^**	-0.68^**	-0.47^**	-0.66^**
GVI	-0.50^**	-0.67^**	-0.48^**	-0.66^**
NDWI	-0.50^**	-0.68^**	-0.42^**	-0.59^**

Tab.5 Correlation analysis between interferometry coefficients and optical indices of Landsat8 images

植被指数	$R 12$		$R 22$		$R 32$		$R 42$
植被指数	VH	PR	VH	PR	γ_VV	γ_VH	γ_VV	γ_VH
NDVI	0.23	0.50	0.14	0.45	0.26	0.51	0.24	0.47
EVI	0.21	0.39	0.18	0.44	0.24	0.46	0.21	0.43
GVI	0.22	0.39	0.13	0.44	0.24	0.45	0.22	0.43
NDWI	0.34	0.28	0.29	0.23	0.24	0.46	0.18	0.35

Tab.6 Correlation analysis between VH, PR, interferometry coefficients of Sentinel-1B and optical indices of Landsat8 images

Fig.4 The scatter diagram between backscattering coefficient and NDVI

Fig.5 Quantitative statistic for pixels with different ground objects for AH and BH

Fig.6 Distribution characteristics of NDVI in shrub areas and fire scars

Fig.7 Recovering time of fires scars and distribution characteristics of NDVI

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