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Extraction of Urban Built-Up Areas Using Nighttime Light (NTL) and Multi-Source Data: A Case Study in Dalian City, China

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  • Xueming Li

    (School of Geography, Liaoning Normal University, Dalian 116029, China
    Human Settlements Research Center, Liaoning Normal University, Dalian 116029, China)

  • Yishan Song

    (School of Geography, Liaoning Normal University, Dalian 116029, China
    Human Settlements Research Center, Liaoning Normal University, Dalian 116029, China)

  • He Liu

    (School of Geography, Liaoning Normal University, Dalian 116029, China
    Human Settlements Research Center, Liaoning Normal University, Dalian 116029, China)

  • Xinyu Hou

    (School of Geography, Liaoning Normal University, Dalian 116029, China
    Human Settlements Research Center, Liaoning Normal University, Dalian 116029, China)

Abstract

The rapid urban development associated with China’s reform and opening up has been the source of many urban problems. To understand these issues, it is necessary to have a deep understanding of the distribution of urban spatial structure. Taking the six districts of Dalian as an example, in this study, we integrated the enhanced vegetation index, points of interest, and surface temperature data into night light data. Furthermore, herein, we analyze the kernel density of the points of interest and construct three indices using image geometric mean: a human settlement index (HSI), a HSI-POI (HP) index, and a HSI-POI-LST (HPL) index. Using a support vector machine to identify the land type in Dalian’s built-up area, 1000 sampling points were created for verification. Then, the threshold boundary corresponding to the highest overall accuracy of each index and kappa coefficient was selected. The relevant conclusions are as follows: As compared with the other three types of data, the HPL index constructed in this study exhibited natural and social attributes, and the built-up area extracted using this method had the highest accuracy, a high image spatial resolution, and was able to overcome the omission issues observed when using one or two data sources. In addition, this method produces richer spatial details of the actual built-up area and provides more choices for assessing small-scale urban built-up areas in future research.

Suggested Citation

  • Xueming Li & Yishan Song & He Liu & Xinyu Hou, 2023. "Extraction of Urban Built-Up Areas Using Nighttime Light (NTL) and Multi-Source Data: A Case Study in Dalian City, China," Land, MDPI, vol. 12(2), pages 1-18, February.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:2:p:495-:d:1070912
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    References listed on IDEAS

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    1. Min Xu & Chunyang He & Zhifeng Liu & Yinyin Dou, 2016. "How Did Urban Land Expand in China between 1992 and 2015? A Multi-Scale Landscape Analysis," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-19, May.
    2. Nils-Bastian Heidenreich & Anja Schindler & Stefan Sperlich, 2013. "Bandwidth selection for kernel density estimation: a review of fully automatic selectors," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(4), pages 403-433, October.
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    Cited by:

    1. Nimesh Akalanka & Nayomi Kankanamge & Jagath Munasinghe & Tan Yigitcanlar, 2024. "Urban Big Data Analytics: A Novel Approach for Tracking Urbanization Trends in Sri Lanka," Land, MDPI, vol. 13(6), pages 1-45, June.

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