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Satellite-based ANN identification and spatiotemporal evolution analysis of industrial heat sources coupled with temperature characteristics

  • role： First author第一作者
  • Affiliation：

    School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

  • Email：195011046@csu.edu.cn
  • Introduction：张钦挺，研究方向为大气污染制图与迁移特征分析。E-mail： 195011046@csu.edu.cn
ZHANG Qinting1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

  • Email：210010@csu.edu.cn
  • Introduction：邹滨，研究方向为大气污染遥感制图、环境地理信息系统建模与服务。E-mail： 210010@csu.edu.cn
ZOU Bin1*,  
  • Affiliation：

    School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

LIU Ning1,  
  • Affiliation：

    College of Geomatics, Xi'an University of Science & Technology, Xi'an 710054, China

MA Xuying2,  
  • Affiliation：

    School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

LI Shenxin1,  
  • Affiliation：

    School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

LI Mengtao1

Resümee

As one of main air pollution sources, the spatial-temporal distribution and category dependent determination of industrial heat sources are critical for policy making of air pollution control. However, due to the lack of identified characteristics, it is difficult to clearly differentiate the sub categories of the industrial heat sources in large geographical area using remote sensing technology. For that, we proposed a satellite-based Artificial Neural Network (ANN) identification method for industrial heat sources by coupling with temperature characteristics in this study by taking the whole China as a case. The Suomi-NPP Nightfire products containing location and temperature information in China from 2013 to 2020 were firstly collected and screened as industrial heat source clusters based on DBSCAN clustering algorithm and land use data. Then, four types of temperature characteristic templates depended on industrial heat source clusters were generated by combining the frequency statistical analysis with Gaussian function. Finally, a temperature characteristic template enhanced ANN model was developed to discriminate the sub categories of the recognized industrial heat sources and subsequently analyze their spatio-temporal changes. Results illustrate that there are significant differences in temperature frequency, distribution pattern and major-minor peaks among four types of industry heat sources (i.e. coal processing (CP), Metal Smelting and Rolling (MSR), Cement Lime and Gypsum Manufacturing (CLGM) and Refined Petroleum Products Manufacturing (RPPM)) with their major peak temperatures being 795 K、830 K、760 K and 1725 K, respectively. Moreover, with the enhancement of temperature characteristic template, the ANN model performs very well in identify the category depended industrial heat sources, with the training and verification accuracy of 99% and 88.17%, respectively. Besides, spatial-temporal distribution of industrial heat sources in China demonstrates the dual characteristics of “regional concentration” and “decreasing fluctuations”. Industrial heat sources are mainly concentrated in the northern region, accounting for 85.4% of the whole country. The main locations of CP, MSR, RPPM, and CLGM are Shanxi, Hebei, Xinjiang, and Anhui, respectively. In the period of 2013 to 2020, the overall trend of fluctuations is “descent - ascension - descent”, taking 2015 and 2018 as the turning time.There are obviously difference in temperature frequency, distribution pattern and distribution statistics among four types of industrial heat sources. Based on these differences, the temperature characteristic templates constructed are reliable and credible to discriminate the sub categories of industrial heat sources. Temperature characteristic template enhanced ANN model would provide a newly promising way for satellite-based precise identification of industrial heat sources by combining the temperature feature of industrial source and the super self-learning ability of ANN method.

Schlüsselwort

industrial heat source;temperature characteristic templates;Artificial neural Network;VIIRS;atmospheric remote sensing

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