China’s poverty assessment and analysis under the framework of the UN SDGs based on multisource remote sensing data

ABSTRACT

Poverty has always been a global concern that has restricted human development. The first goal (SDG 1) of the United Nations Sustainable Development Goals (SDGs) is to eliminate all forms of poverty all over the world. The establishment of a scientific and effective localized SDG 1 evaluation and monitoring method is the key to achieving SDG 1. This paper proposes SDG 1 China district and county-level localization evaluation method based on multi-source remote sensing data for the United Nations Sustainable Development Framework. The temporal and spatial distribution characteristics of China’s poverty areas and their SDG 1 evaluation values in 2012, 2014, 2016, and 2018 have been analyzed. Based on the SDGs global indicator framework, this paper first constructed SDG 1 China’s district and county localization indicator system and then extracted multidimensional feature factors from nighttime light images, land cover data, and digital elevation model data. Secondly, we establish SDG 1 China’s localized partial least squares estimation model and SDG 1 China’s localized machine learning estimation model. Finally, we analyze and verify the spatiotemporal distribution characteristics of China’s poverty areas and counties and their SDG 1 evaluation values. The results show that SDG 1 China’s district and county localization indicator system proposed in this study and SDG 1 China’s localized partial least squares estimation model can better reflect the poverty level of China’s districts and counties. The estimated model R² is 0.65, which can identify 72.77% of China’s national poverty counties. From 2012 to 2018, the spatial distribution pattern of SDG evaluation values in China’s districts and counties is that the SDG evaluation values gradually increase from western China to eastern China. In addition, the average SDG 1 evaluation value of China’s districts and counties increased by 23% from 2012 to 2018. This paper is oriented to the United Nations SDGs framework, explores the SDG 1 localized evaluation method of China’s districts and counties based on multisource remote sensing data, and provides a scientific and rapid regional poverty monitoring and evaluation program for the implementation of the 2030 agenda poverty alleviation goals.

KEYWORDS:

• Multisource remote sensing data
• Sustainable Development Goals (SDGs)
• poverty indicator system
• partial least squares
• machine learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available in the public domain:

SRTM DEM data: (https://doi.org/10.1080/10095020.2022.2108346)

NPP-VIIRS-like nighttime light data: (https://doi.org/10.1080/10095020.2022.2108346)

Land cover data: (https://doi.org/10.1080/10095020.2022.2108346)

Socio-economic statistics data: (https://doi.org/10.1080/10095020.2022.2108346)

Consent for publication

All the coauthors consent the publication of this work.

Additional information

Funding

This work is supported by the National Natural Science Foundation of China [grant numbers 41971423 and 31972951], the Natural Science Foundation of Hunan Province [grant numbers 2020JJ3020 and 2020JJ5164], the Science and Technology Planning Project of Hunan Province [grant numbers 2019RS2043 and 2019GK2132], and the Postgraduate Scientific Research Innovation Project of Hunan Province [grant number CX20210991].

Notes on contributors

Mengjie Wang

Mengjie Wang is currently a master’s student at Hunan University of Science and Technology. His research interests include nighttime light remote sensing image processing and geographic modeling, socio-economic and urban ecological environment monitoring.

Yanjun Wang

Yanjun Wang is currently a professor at Hunan University of Science and Technology. He received the PhD in Photogrammetry and Remote Sensing at Wuhan University in 2012. His research interests include spatiotemporal geographic information analysis and mining, lidar remote sensing data processing, and modeling applications.

Fei Teng

Fei Teng is currently a master’s student at Hunan University of Science and Technology. His research interests include remote sensing data processing and geographic modeling, socioeconomic and urban ecological environment monitoring.

Shaochun Li

Shaochun Li is currently a master’s student at Hunan University of Science and Technology. His research interests include multi-source remote sensing data processing analysis and modeling analysis.

Yunhao Lin

Yunhao Lin is currently a master’s student at Hunan University of Science and Technology. His research interests include lidar point cloud intelligent processing and remote sensing image processing.

Hengfan Cai

Hengfan Cai is currently a master’s student at Hunan University of Science and Technology. His research interests include lidar point cloud intelligent processing.