DMSP/OLS night-time light intensity as an innovative indicator of regional sustainable development

ABSTRACT

This study proposes the use of DMSP/OLS night-time light data as a surrogate measure for comprehensive indicators to evaluate regional sustainable development. First, Sustainable Development Indicators (SDIs) were selected from annual multiple scores of sustainable development based on Principal Component Analysis (PCA), and annual Sum of Lights (SOL) of the study region were extracted for each year. Next, three time-series-based regression models were applied to the multiple score and SOL to verify their correlation. Lastly, based on time series, DMSP/OLS data is used to estimate regional sustainable development. Experiments using Shandong, Hubei, Henan, Anhui, Jiangxi, and Hunan Provinces, China as test datasets demonstrate that SOL is strongly related to multiple scores (R² ≥ 0.8). Moreover, the PCA results of sustainable development agree with the actual regional development conditions. In theory, night-time light can explain the key factors of SDIs. Hence, it is feasible to study regional sustainable development using SOL instead of SDIs.

Acknowledgments

The authors thank the National Natural Science Foundation of China (NSFC) (Grant No. 41771493 and 41101407), the Natural Science Foundation of Hubei Province (Grant Nos. 2014CFB377, ZRY2014001235, and 2010CDZ005), China, Wuhan Youth Science and technology plan (Grant No. 2016070204010137) and self-determined research funds of CCNU from the basic research and operation of MOE (Grant No. CCNU15A02001) for supporting this work. The authors are grateful for the comments and contributions of the anonymous reviewers and the members of the editorial team.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [41771493 and 41101407];Wuhan Youth Science and technology plan [2016070204010137];self-determined research funds of CCNU from the basic research and operation of MOE [CCNU15A02001];Natural Science Foundation of Hubei Province, China [2014CFB377 and 2010CDZ005].