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Impervious Surface Mapping Based on Remote Sensing and an Optimized Coupled Model: The Dianchi Basin as an Example

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  • Yimin Li

    (School of Earth Sciences, Yunnan University, Kunming 650091, China
    Yunnan Provincial University Domestic High Score Satellite Remote Sensing Geological Engineering Research Center, Kunming 650091, China
    These authors contributed equally to this work.)

  • Xue Yang

    (School of Earth Sciences, Yunnan University, Kunming 650091, China
    These authors contributed equally to this work.)

  • Bowen Wu

    (School of Earth Sciences, Yunnan University, Kunming 650091, China)

  • Juanzhen Zhao

    (Institute of International Rivers and Ecological Security, Yunnan University, Kunming 650091, China)

  • Xuanlun Deng

    (School of Earth Sciences, Yunnan University, Kunming 650091, China)

Abstract

Accurately extracting impervious surfaces (IS) and continuously monitoring their dynamics are crucial practices for promoting sustainable development in regional ecological environments and resources. In this context, we conducted experiments to extract IS of the Dianchi Lake Basin by utilizing various features extracted from remote sensing images and applying three different machine learning algorithms. Through this process, we obtained the optimal combination of features and a machine learning algorithm. Utilizing this model, our objective is to map the evolution of IS in the Dianchi Lake Basin, from 2000 to 2022, and analyze its dynamic changes. Our results showed the following: (1) The optimal model for IS extraction in the Dianchi Lake Basin was IMG-SPESVM based on the support vector machine, remote sensing images, and spectral features. (2) From 2000 to 2022, the spatial distribution and shape of the IS in the Dianchi Lake Basin changed significantly, but they all developed in the area around Dianchi Lake. (3) From 2000 to 2015, the rate of expansion of IS gradually accelerated, while from 2015 to 2022, it contracted. (4) From 2000 to 2022, the center of mass of IS moved to the northeast, and the standard deviation ellipse shifted greatly in the south–north direction. (5) Natural factors negatively affected the expansion of IS, while social factors positively affected the distribution of the IS.

Suggested Citation

  • Yimin Li & Xue Yang & Bowen Wu & Juanzhen Zhao & Xuanlun Deng, 2023. "Impervious Surface Mapping Based on Remote Sensing and an Optimized Coupled Model: The Dianchi Basin as an Example," Land, MDPI, vol. 12(6), pages 1-26, June.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:6:p:1210-:d:1168338
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    References listed on IDEAS

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    1. Shaohua Zhang & Kun Yang & Yuling Ma & Mingchan Li, 2021. "The Expansion Dynamics and Modes of Impervious Surfaces in the Guangdong-Hong Kong-Macau Bay Area, China," Land, MDPI, vol. 10(11), pages 1-16, October.
    2. Daehyon Kim & Seungjae Lee & Seongkil Cho, 2007. "Input Vector Normalization Methods in Support Vector Machines for Automatic Incident Detection," Transportation Planning and Technology, Taylor & Francis Journals, vol. 30(6), pages 593-608, July.
    3. Christian P. Giardina & Michael G. Ryan, 2000. "Evidence that decomposition rates of organic carbon in mineral soil do not vary with temperature," Nature, Nature, vol. 404(6780), pages 858-861, April.
    4. Ning Zhang & Kangning Xiong & Hua Xiao & Juan Zhang & Chuhong Shen, 2023. "Ecological Environment Dynamic Monitoring and Driving Force Analysis of Karst World Heritage Sites Based on Remote-Sensing: A Case Study of Shibing Karst," Land, MDPI, vol. 12(1), pages 1-15, January.
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