Analyzing multidimensional rural poverty alleviation at the county level in Guangxi based on remote sensing data of nighttime light

doi:10.6046/zrzyyg.2022355

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Abstract

A vital task for the current economic construction of China is to consolidate poverty alleviation achievements, implement rural revitalization strategies, and prevent poverty relapse. The 2022 Central Rural Work Conference emphasized that China must firmly prevent any large-scale relapse into poverty. The multidimensional poverty theory underlines the significant role of the dynamic and objective monitoring of the spatio-temporal evolution of rural poverty at the county level in China’s poverty-returning prevention. Rapid progress in remote sensing satellites has enabled the gradual enrichment and extensive application of high-quality remote sensing images that contain massive information. Compared to traditional statistical data, the remote sensing data of nighttime light exhibit a high correlation with socio-economic factors, pronounced objectivity, and a relatively long time span. This study constructed a model to describe the relationship between the nighttime light intensity index extracted from the DMSP/OLS and NPP/VIIRS remote sensing data, as well as the multidimensional poverty alleviation index. Using this model, this study explored the spatio-temporal evolution of rural poverty at the county level in Guangxi from 2010 to 2020 and analyzed the existing poverty-returning causes, providing a scientific reference and preventive measures for rural revitalization and poverty alleviation.

Keywords remote sensing data of nighttime light multidimensional poverty alleviation index poverty-returning prevention spatio-temporal pattern evolution analysis

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TP79

Issue Date: 13 March 2024

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	Yanling LU
	Yaqi HUANG
	Junfen ZHOU
	Jie WANG
	Jingshan WEI

Cite this article:

Yanling LU,Yaqi HUANG,Junfen ZHOU, et al. Analyzing multidimensional rural poverty alleviation at the county level in Guangxi based on remote sensing data of nighttime light[J]. Remote Sensing for Natural Resources, 2024, 36(1): 169-178.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022355 OR https://www.gtzyyg.com/EN/Y2024/V36/I1/169

Fig.1 Comparison of DMSP/OLS night light remote sensing images in Guangxi before and after correction in 2012

Tab.1 Index and weight of MPAI

Fig.2 Fitting results of TDNI and ADNI to MPAI in 2018

Tab.2 Comparison of parameters of each fitting regression model (2018)

Fig.3 Statistical analysis of TDNI

Tab.3 Standard ellipse parameters of light intensity in Guangxi counties

Fig.4 Change track of temporal and spatial pattern of light intensity in Guangxi counties from 2014 to 2020

Fig.5 Analysis results of Moran’s I index of counties in Guangxi

Fig.6 Lisa cluster map of MPAI in Guangxi

Fig.7 Fitting results of variogram of poverty degree in Guangxi counties

Tab.4 Fitting parameters of variogram of poverty degree in Guangxi counties

Tab.5 The variational fractal dimension of poverty degree in Guangxi counties

Fig.8 Temporal and spatial distribution of poverty in Guangxi county from 2014 to 2020

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