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Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data to Evaluate Poverty in Southwestern China

文献类型：期刊论文

作者Yong, Zhiwei3; Li, Kun4; Xiong, Junnan5,6; Cheng, Weiming6; Wang, Zegen3; Sun, Huaizhang1; Ye, Chongchong2
刊名REMOTE SENSING
出版日期2022-02-01
卷号14期号:3页码:19
关键词DMSP-OLS NPP-VIIRS nighttime light calibration multi-dimensional poverty poverty evaluation
DOI10.3390/rs14030600
通讯作者Xiong, Junnan(Xiongjn@swpu.edu.cn)
英文摘要Poverty alleviation is one of the most important tasks facing human social development. It is necessary to make accurate monitoring and evaluations for areas with poverty to improve capability of implementing poverty alleviation policies. Here, this study introduced nighttime light (NTL) data to estimate county-level poverty in southwest China. First, this study used particle swarm optimization-back propagation hybrid algorithm to explore the potential relationship between two NTL data (the Defense Meteorological Satellite Program's Operational Line Scan System data and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite data). Then, we integrated two NTL data at the pixel level to establish a consistent time-series of NTL dataset from 2000 to 2019. Next, an actual comprehensive poverty index (ACPI) was employed as an indicator of multidimensional poverty at county level based on 11 socioeconomic and natural variables, and which could be the reference to explore the poverty evaluation using NTL data. Based on the correlation between the ACPI and NTL characteristic variables, a poverty evaluation model was developed to evaluate the poverty situation. The result showed the great matching relationship between DMSP-OLS and NPP-VIIRS data (R-2 = 0.84). After calibration, the continuity and comparability of DMSP-OLS data were significantly improved. The integrated NTL data also reflected great consistency with socioeconomic development (r = 0.99). The RMSE between ACPI and the estimated comprehensive poverty index (ECPI) based on the integrated NTL data is approximately 0.19 (R-2 = 0.96), which revealed the poverty evaluation model was feasible and reliable. According to the ECPI, we found that the magnitude of poverty eradication increased in southwest China until 2011, but slowed down from 2011 to 2019. Regarding the spatial scale, geographic barriers are a key factor for poverty, with high altitude and mountainous areas typically having a high incidence of poverty. Our approach offers an effective model for evaluation poverty based on the NTL data, which can contribute a more reliable and efficient monitoring of poverty dynamic and a better understanding of socioeconomic development.
WOS关键词TIME-SERIES ; MULTIDIMENSIONAL POVERTY ; RURAL CHINA ; DYNAMICS ; GDP
资助项目Key R&D project of Sichuan Science and Technology Department[2021YFQ0042] ; National Key R&D Program of China[2020YFD1100701] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA20030302] ; Science and Technology Project of Xizang Autonomous Region[XZ201901-GA-07] ; National Flash Flood Investigation and Evaluation Project[SHZH-IWHR-57] ; Science and Technology Bureau of Altay Region in Yili Kazak Autonomous Prefecture[Y99M4600AL]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000756091400001
出版者MDPI
资助机构Key R&D project of Sichuan Science and Technology Department ; National Key R&D Program of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Science and Technology Project of Xizang Autonomous Region ; National Flash Flood Investigation and Evaluation Project ; Science and Technology Bureau of Altay Region in Yili Kazak Autonomous Prefecture
源URL[http://ir.igsnrr.ac.cn/handle/311030/170753]  
专题中国科学院地理科学与资源研究所
通讯作者Xiong, Junnan
作者单位1.Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou 510275, Peoples R China
2.Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource, Beijing 100875, Peoples R China
3.Southwest Petr Univ, Sch Geosci & Technol, Chengdu 610500, Peoples R China
4.PowerChina Sichuan Elect Power Engn Co Ltd, Chengdu 610041, Peoples R China
5.Southwest Petr Univ, Sch Civil Engn & Geomat, Chengdu 610500, Peoples R China
6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Yong, Zhiwei,Li, Kun,Xiong, Junnan,et al. Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data to Evaluate Poverty in Southwestern China[J]. REMOTE SENSING,2022,14(3):19.
APA Yong, Zhiwei.,Li, Kun.,Xiong, Junnan.,Cheng, Weiming.,Wang, Zegen.,...&Ye, Chongchong.(2022).Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data to Evaluate Poverty in Southwestern China.REMOTE SENSING,14(3),19.
MLA Yong, Zhiwei,et al."Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data to Evaluate Poverty in Southwestern China".REMOTE SENSING 14.3(2022):19.

入库方式： OAI收割

来源：地理科学与资源研究所

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