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Production–Living–Ecological Spatial Function Identification and Pattern Analysis Based on Multi-Source Geographic Data and Machine Learning

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  • Ziqiang Bu

    (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Jingying Fu

    (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Dong Jiang

    (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Natural Resources, Beijing 100101, China)

  • Gang Lin

    (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

Land use cannot be simply understood as land cover. The same land may carry different functions, such as production, living, and ecological applications; the dominant function of land will affect and restrict other uses. Disorderly urbanization and industrialization have led to an intensification of conflicts among the production, living, and ecological functions of land, which is a major constraint on regional sustainable development. This paper took the perspective of land-use function and used multi-source data such as Sentinel remote-sensing imagery, VIIRS night-time light data, and POIs to classify land-use functions on a large scale in the Beijing–Tianjin–Hebei (BTH) urban agglomeration. The specific research process was as follows. Firstly, the BTH region was multi-scale-segmented based on Sentinel remote-sensing data. Then, the spectral, texture, shape, and socio-economic features of each small area after segmentation were extracted. Moreover, a PLES land-use classification system oriented towards land-use function was established, and a series of representative samples were selected. Subsequently, a random forest model was trained using these samples; then, the trained model was used for the large-scale analysis of land use in the entire BTH region. Finally, the spatial distribution patterns and temporal–spatial evolution characteristics of PLES in the BTH region from 2016 to 2021 were analyzed from the macro level to the micro level.

Suggested Citation

  • Ziqiang Bu & Jingying Fu & Dong Jiang & Gang Lin, 2023. "Production–Living–Ecological Spatial Function Identification and Pattern Analysis Based on Multi-Source Geographic Data and Machine Learning," Land, MDPI, vol. 12(11), pages 1-17, November.
  • Handle: RePEc:gam:jlands:v:12:y:2023:i:11:p:2029-:d:1275707
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    References listed on IDEAS

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    1. Fu, Jingying & Bu, Ziqiang & Jiang, Dong & Lin, Gang & Li, Xiang, 2022. "Sustainable land use diagnosis based on the perspective of production–living–ecological spaces in China," Land Use Policy, Elsevier, vol. 122(C).
    2. Chen, Xin & Yu, Le & Du, Zhenrong & Liu, Zhu & Qi, Yuan & Liu, Tao & Gong, Peng, 2022. "Toward sustainable land use in China: A perspective on China’s national land surveys," Land Use Policy, Elsevier, vol. 123(C).
    Full references (including those not matched with items on IDEAS)

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