Socioeconomic development evaluation for Chinese poverty-stricken counties using indices derived from remotely sensed data

Abstract

Comprehensively and objectively evaluating the development of poverty-stricken counties is important for poverty alleviation. In this study, we propose a relatively objective and efficient method that utilizes indicators derived from remote sensing images to assess the socioeconomic development of poverty-stricken counties. Four indicators representing the area of developed land, area of forest vegetation cover, area of cultivated land, and average nighttime light intensity were integrated via the entropy method and used to construct a model for evaluation of the socioeconomic development of poverty-stricken counties (ESDPC). Then, 42 impoverished counties in Henan Province and Liangshan Yi Autonomous Prefecture of Sichuan Province in China were selected, and their ESDPC values for 2013, 2015, and 2017 were estimated. The average ESDPC value of the selected poverty-stricken counties increased from 0.29 to 0.34 between 2013 and 2017. The accuracy of this estimate was verified through an assessment based on the comprehensive development index (CDI) model constructed using 10 socioeconomic indices. The correlation coefficient between the ESDPC values and the CDI values was 0.60, and the fitting relationships between the two models and the remotely sensed indicators showed good consistency, indicating the potential for use of these remotely sensed indicators to assess regional socioeconomic development.