4.7 Article

Monitoring the Distribution and Variations of City Size Based on Night-Time Light Remote Sensing: A Case Study in the Yangtze River Delta of China

期刊

REMOTE SENSING
卷 14, 期 14, 页码 -

出版社

MDPI
DOI: 10.3390/rs14143403

关键词

night-time light data; extraction of urban areas; rank-size rule; law of primate city

资金

  1. Fundamental Research Funds for the Central Universities [B200202017, B210201013, B220203008]
  2. National Natural Science Foundation of China [42071346]
  3. Natural Science Foundation of Jiangsu Province [BK20190495]
  4. Postgraduate Research and Practice Innovation Program of the Jiangsu Province [KYCX21_0529]

向作者/读者索取更多资源

Effectively monitoring city size in real time through nighttime light remote sensing can provide timely and rapid information for urban development planning. A case study in the Yangtze River Delta region in China shows that the variations in city size follow the rank-size rule, indicating the prominence of medium-small cities and a larger dispersed force compared to concentrated force. The use of urban areas extracted from nighttime light remote sensing instead of population data is validated by comparing the computed city size with statistical data, demonstrating the rationality and applicability of this method.
Effectively monitoring the size of a city in real time enables the scientific planning of urban development. Models that utilize the distribution and variations in city size generally use population data as inputs, which cannot be obtained in a timely and rapid manner. However, night-time light (NTL) remote sensing may be an alternative method. A case study was carried out on the Yangtze River Delta (YRD) in China, and the rank-size rule, the law of primate cities, and the Gini coefficient were employed to monitor the variation in city size in the study area. The urban areas extracted based on NTL remote sensing were utilized instead of the traditionally used population data to evaluate the variations in city size from 2012 to 2017. Considering the empiricism and subjectivity of the thresholding method, urban areas were extracted from NTL data combined with the normalized differential vegetation index and land-surface temperature data based on the artificial neural network algorithm. Based on the results, the YRD did not fit the distribution of the primate cities from 2012 to 2017. However, this region satisfied the rank-size rule well, which indicated that the development of medium-small cities was more prominent than that of larger cities, and the dispersed force was larger than the concentrated force. Notably, the city size reached a relatively balanced level in the study area. Further, sensitivity analysis revealed that the relatively low extraction accuracy of urban areas of few small cities had little effect on the results of city size variations. Moreover, the validation of city size computed from statistical population data and its comparison with results calculated based on the statistical data of urban areas aligned with the results of this study, which indicates the rationality and applicability of monitoring the variations in city size using the urban areas extracted from NTL remote sensing instead of population data.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据