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Classification of Industrial Heat Source Objects Based on Active Fire Point Density Segmentation and Spatial Topological Correlation Analysis in the Beijing–Tianjin–Hebei Region

Author

Listed:
  • Caihong Ma

    (Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

  • Xin Sui

    (Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    School of Information, Beijing Forestry University, Beijing 100083, China)

  • Yi Zeng

    (Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    School of Information, Beijing Forestry University, Beijing 100083, China)

  • Jin Yang

    (Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

  • Yanmei Xie

    (Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    School of Environment and Surveying, China University of Mining and Technology, Xuzhou 221116, China)

  • Tianzhu Li

    (Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

  • Pengyu Zhang

    (Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    School of Information, Beijing Forestry University, Beijing 100083, China)

Abstract

The development of industrial infrastructure in the Beijing–Tianjin–Hebei(BTH) region has been accompanied by a disorderly expansion of industrial zones and other inappropriate development. Accurate industrial heat source classification data become important to evaluate the policies of industrial restructuring and air quality improvement. In this study, a new classification of industrial heat source objects model based on active fire point density segmentation and spatial topological correlation analysis in the BTH Region was proposed. First, industrial heat source objects were detected with an active fire point density segmentation method using NPP-VIIRS active fire/hotspot data. Then, industrial heat source objects were classified into five categories based on a spatial topological correlation analysis method using POI data. Then, identification and classification results were manually validated based on Google Earth imagery. Finally, we evaluated the factors influencing the number of industrial heat sources based on an OLS regression model. A total of 493 industrial heat source objects were identified in this study with an identification accuracy of 96.14%(474/493). Compared with results for nighttime fires, the number of industrial heat source objects that were identified was higher, and the spatial coverage was greater; the minimum size of the detected objects was also smaller. Based on the function of the identified industrial heat source objects, the objects in the BTH region were then divided into five categories: cement plants (21.73%), steel plants (53.80%), coal and chemical industry (12.66%), oil and gas developments (7.81%), and other (4.01%). An analysis of their operations showed that the number of industrial heat source objects in operation in the BTH region tended to first rise and then decline during the 2012–2021 period, with the peak being reached in 2013. The results of this study will aid the rationalization of industrial infrastructure in the BTH region and, by extension, in China as a whole.

Suggested Citation

  • Caihong Ma & Xin Sui & Yi Zeng & Jin Yang & Yanmei Xie & Tianzhu Li & Pengyu Zhang, 2022. "Classification of Industrial Heat Source Objects Based on Active Fire Point Density Segmentation and Spatial Topological Correlation Analysis in the Beijing–Tianjin–Hebei Region," Sustainability, MDPI, vol. 14(18), pages 1-19, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11228-:d:909336
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    References listed on IDEAS

    as
    1. Caihong Ma & Jin Yang & Fu Chen & Yan Ma & Jianbo Liu & Xinpeng Li & Jianbo Duan & Rui Guo, 2018. "Assessing Heavy Industrial Heat Source Distribution in China Using Real-Time VIIRS Active Fire/Hotspot Data," Sustainability, MDPI, vol. 10(12), pages 1-17, November.
    2. Christopher D. Elvidge & Mikhail Zhizhin & Kimberly Baugh & Feng-Chi Hsu & Tilottama Ghosh, 2015. "Methods for Global Survey of Natural Gas Flaring from Visible Infrared Imaging Radiometer Suite Data," Energies, MDPI, vol. 9(1), pages 1-15, December.
    3. Yi Zhou & Fei Zhao & Shixin Wang & Wenliang Liu & Litao Wang, 2018. "A Method for Monitoring Iron and Steel Factory Economic Activity Based on Satellites," Sustainability, MDPI, vol. 10(6), pages 1-21, June.
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