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Improvement of Monitoring Production Status of Iron and Steel Factories Based on Thermal Infrared Remote Sensing

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  • Fang Han

    (Hebei Remote Sensing Technology Identification Innovation Center for Environmental Change, Hebei Laboratory of Environmental Evolution and Ecological Construction, School of Geographic Sciences, Hebei Normal University, Shijiazhuang 050024, China
    Hebei Collaborative Innovation Center for Urban-Rural Integrated Development, School of Public Management, Hebei University of Economics and Business, Shijiazhuang 050061, China)

  • Fei Zhao

    (China Satellite Communications Co., Ltd. Beijing, Beijing 100190, China)

  • Fuxing Li

    (Hebei Remote Sensing Technology Identification Innovation Center for Environmental Change, Hebei Laboratory of Environmental Evolution and Ecological Construction, School of Geographic Sciences, Hebei Normal University, Shijiazhuang 050024, China)

  • Xiaoli Shi

    (Hebei Remote Sensing Technology Identification Innovation Center for Environmental Change, Hebei Laboratory of Environmental Evolution and Ecological Construction, School of Geographic Sciences, Hebei Normal University, Shijiazhuang 050024, China)

  • Qiang Wei

    (Hebei Remote Sensing Technology Identification Innovation Center for Environmental Change, Hebei Laboratory of Environmental Evolution and Ecological Construction, School of Geographic Sciences, Hebei Normal University, Shijiazhuang 050024, China)

  • Weimiao Li

    (Hebei Remote Sensing Technology Identification Innovation Center for Environmental Change, Hebei Laboratory of Environmental Evolution and Ecological Construction, School of Geographic Sciences, Hebei Normal University, Shijiazhuang 050024, China)

  • Wei Wang

    (Hebei Remote Sensing Technology Identification Innovation Center for Environmental Change, Hebei Laboratory of Environmental Evolution and Ecological Construction, School of Geographic Sciences, Hebei Normal University, Shijiazhuang 050024, China)

Abstract

Thermal infrared remote satellite (TIRS) images combined with high-resolution optical images in a time series can be used to analyze the production status of iron and steel factories (ISF) in China, which is more objective compared with statistical data. In previous studies, based on the land surface temperature (LST) data retrieved from Landsat-8 TIRS data, the heat island intensity index of an ISF (hereinafter referred to as ISHII) evaluates the LST difference between the main production area and other areas, and it can show the production status partly in one ISF. However, deviations in the LST due to seasonal changes can cause inaccuracies in the monitoring production status. In this study, we propose a modified method that introduces a seasonal-trend decomposition procedure based on regression (hereinafter referred to as STR) into the ISHII data to build a seasonal decomposition model. First, on the basis of a previously proposed time series of ISHII data from January 2013 to October 2017 for three ISF samples, the seasonal decomposition of the ISHII model was used to decompose the ISHII data into three components: trend, seasonality, and random. Then, we analyzed the relationships between these three components and the production status in the three ISFs. Additionally, to verify the precision of this method, we used high-resolution optical images to visually detect surface changes in the facilities at specific times. Finally, results showed that the trend curve can represent the entire factory development status, the seasonality curve shows the regular seasonal fluctuation, and the random component sensitively reflects the production status changes of one ISF. After decomposition, the capacity of a random component to reflect production changes has doubled or tripled compared to previous methods. In conclusion, this study provides a modified method with a seasonal decomposition model to improve prediction accuracy on the long-term production status of ISFs. Then, based on the change obtained from high-resolution optical images and Internet data on the ISF production status, we verified the accuracy of this modified method. This research will provide powerful supports for sustainable industrial development and policy decision-making in China.

Suggested Citation

  • Fang Han & Fei Zhao & Fuxing Li & Xiaoli Shi & Qiang Wei & Weimiao Li & Wei Wang, 2023. "Improvement of Monitoring Production Status of Iron and Steel Factories Based on Thermal Infrared Remote Sensing," Sustainability, MDPI, vol. 15(11), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8575-:d:1155401
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    References listed on IDEAS

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    1. Alexander Dokumentov & Rob J. Hyndman, 2015. "STR: A Seasonal-Trend Decomposition Procedure Based on Regression," Monash Econometrics and Business Statistics Working Papers 13/15, Monash University, Department of Econometrics and Business Statistics.
    2. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 127-152, April.
    3. Yue Jiang & Wenpeng Lin, 2021. "A Comparative Analysis of Retrieval Algorithms of Land Surface Temperature from Landsat-8 Data: A Case Study of Shanghai, China," IJERPH, MDPI, vol. 18(11), pages 1-18, May.
    4. 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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