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<p>The brightness and distribution of anthropogenic illumination at night can indirectly reveal the economic prosperity and development level in human settlement areas. Nighttime light (NTL) remote sensing can be used for statistics, intuitively and timely grasping the dynamic development of the region, without being limited by actual statistical time and administrative regions. Regional socio-economic development index (RDI) comprehensively considering society, economy, healthcare, education, and transportation factors was constructed in this paper. On this basis, the LJ-1 01 remote sensing image with high spatial resolution was used to explore its potential in estimating the RDI for fine-grained scale (at county and township levels). Subsequently, Zipf’s law was conducted to evaluate balance and distribution of regional development at the county and township levels for Yunnan Province, China in 2018.The results indicate that at the county-level spatial scale, the RDI estimation model constructed using the NTL data in this paper demonstrates relatively high stability for both the third-degree polynomial function (<i>R</i><sup><i>2</i></sup> = 0.7775, <i>RMSE</i> = 0.0677) and the power function (<i>R</i><sup><i>2</i></sup> = 0.762, <i>RMSE</i> = 0.0731). Then at the township-level spatial scale, the power function estimation performs the best, with an <i>R</i><sup><i>2</i></sup> reaching 0.6664. The power function is optimal for evaluating socio-economic development at fine-grained spatial scales. Furthermore, Yunnan Province exhibits relatively balanced development at the county level with a better performance of Zip’s value (<i>q</i> = 0.7851), nevertheless unbalanced development at the township level (<i>q</i> = 1.2421). Regions with imbalanced socio-economic development (lower RDI and high <i>q</i> values) are mainly located in areas with complex terrain and inconvenient transportation. This study can enrich the understanding of socio-economic regional development, narrowing regional disparities and achieving high-quality and sustainable development for future growth.</p>

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Fine-Grained Spatial Scale Evaluation of Regional Development Based on LJ-1 01 Nighttime Light Remote Sensing Image

  • 期刊论文

  • 发表日期：

    2025年1月8日

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  • Lingyan Bao,
  • Hua Pan,
  • Zhen Zhang,
  • Mengna Li,
  • Shengqing Guo

摘要

The brightness and distribution of anthropogenic illumination at night can indirectly reveal the economic prosperity and development level in human settlement areas. Nighttime light (NTL) remote sensing can be used for statistics, intuitively and timely grasping the dynamic development of the region, without being limited by actual statistical time and administrative regions. Regional socio-economic development index (RDI) comprehensively considering society, economy, healthcare, education, and transportation factors was constructed in this paper. On this basis, the LJ-1 01 remote sensing image with high spatial resolution was used to explore its potential in estimating the RDI for fine-grained scale (at county and township levels). Subsequently, Zipf’s law was conducted to evaluate balance and distribution of regional development at the county and township levels for Yunnan Province, China in 2018.The results indicate that at the county-level spatial scale, the RDI estimation model constructed using the NTL data in this paper demonstrates relatively high stability for both the third-degree polynomial function (R2 = 0.7775, RMSE = 0.0677) and the power function (R2 = 0.762, RMSE = 0.0731). Then at the township-level spatial scale, the power function estimation performs the best, with an R2 reaching 0.6664. The power function is optimal for evaluating socio-economic development at fine-grained spatial scales. Furthermore, Yunnan Province exhibits relatively balanced development at the county level with a better performance of Zip’s value (q = 0.7851), nevertheless unbalanced development at the township level (q = 1.2421). Regions with imbalanced socio-economic development (lower RDI and high q values) are mainly located in areas with complex terrain and inconvenient transportation. This study can enrich the understanding of socio-economic regional development, narrowing regional disparities and achieving high-quality and sustainable development for future growth.