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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (4) : 173-183     DOI: 10.6046/zrzyyg.2024146
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Gaussian mixture model and its application in remote sensing identification of industrial heat sources
LI Lelin1,2(), WANG Wenxi3, YANG Wentao1, CHEN Hao2, PENG Huanhua2, ZHAO Qian3
1. National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China
2. Institute for Local Sustainable Development Goals, Hunan University of Science and Technology, Xiangtan 411201, China
3. School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
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Abstract  

Precisely extracting the information of industrial heat source activities serves as a significant prerequisite for the prevention and control of air pollution and the prediction of industrial economy in China. However, due to unclear heat source characteristics and inaccurate type determination, the remote sensing monitoring of industrial heat sources fails to be widely applied. This study investigated Hunan Province based on the Suomi-NPP VIIRS Nightfire data from 2015 to 2021. First, this study extracted nighttime industrial heat sources from the data using spatial filtering and the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm. Second, this study constructed temperature characteristic template functions for different industrial heat sources using the Gaussian mixture model. Third, this study determined the subcategories of industrial heat sources according to temperature similarity of the same categories, achieving an overall classification accuracy of 86.31 %. Finally, this study obtained the layout of industrial heat sources in Hunan Province. The results indicate that the industry in Hunan Province was dominated by petrochemical plants, with the smallest number of coal-to-chemical plants. Metallurgical enterprises, showing the highest heat radiation intensity, were primarily distributed in the Loudi-Xiangtan-Zhuzhou area. From 2015 to 2019, industrial heat sources in Hunan Province showed a decreasing trend, indicating that relevant government departments effectively rectified the scattered, non-compliant, and polluting factories in Hunan Province during the 13th Five-Year Plan period. During the COVID-19 pandemic, the number of heat sources changed slightly since work and production were gradually resumed under the effective regulation of the government. This study analyzed the grey relational degrees between the heat radiation emission intensity and the relevant indicators of energy consumption and industrial pollutant emissions. Based on the comprehensive industrial energy consumption, industrial sulfur dioxide emissions, and heat radiation emission intensity, this study explored the relevant situation of energy consumption, pollution, and heat emissions in Hunan Province, dividing the cities and prefectures into seven types accordingly. Overall, this study provides information sources and data support for local governments to dynamically monitor the production activities of local key industrial enterprises. Ascertaining the spatial distribution patterns and evolutionary trends of different industrial enterprises will contribute to the formulation of industrial transformation policies by the government and relevant departments and the practice of sustainable development.
Keywords Suomi-NPP VIIRS Nightfire data      industrial heat source      temperature characteristics      Gaussian mixture model      remote sensing identification     
ZTFLH:  TP79  
Issue Date: 03 September 2025
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Lelin LI
Wenxi WANG
Wentao YANG
Hao CHEN
Huanhua PENG
Qian ZHAO
Cite this article:   
Lelin LI,Wenxi WANG,Wentao YANG, et al. Gaussian mixture model and its application in remote sensing identification of industrial heat sources[J]. Remote Sensing for Natural Resources, 2025, 37(4): 173-183.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024146     OR     https://www.gtzyyg.com/EN/Y2025/V37/I4/173
Fig.1  Distribution of industrial heat sources extracted based on VNF data
Fig.2  Temperature characteristics of industrial heat sources in different categories
企业类别 GMM模型 R2 调整后R2 RMSE/%
冶金 f ( x ) = 4.55 e - x - 874.2 85.41 2 + 3.315 e - x - 1094 1443 2 + 0.2803 e - x - 1531 1302 2 0.993 7 0.993 2 0.114 1
水泥 f ( x ) = 11.52 e - x - 840 93.58 2 0.937 2 0.934 8 0.870 4
石化 f ( x ) = 4.894 e - x - 1191 168 2 + 2.275 e - x - 858.4 119.6 2 + 0.1839 e - x - 1586 206.1 2 0.988 0 0.986 9 0.218 5
煤化 f ( x ) = 4.533 e - x - 1752 103.8 2 + 2.041 e - x - 1776 324.1 2 0.970 9 0.967 6 0.277 8
Tab.1  GMM for temperature characteristics of industrial heat source subclasses
分类类别 参考类别 用户精度/%
冶金 水泥 石化 煤化
冶金 34 2 3 1 85.00
水泥 2 38 3 2 84.44
石化 3 4 64 0 90.14
煤化 0 1 2 9 75.00
制图精度/% 89.47 84.44 88.89 75.00 —
总体精度/% 86.31
Tab.2  Classification accuracy of industrial heat sources
Fig.3  Distribution of various types of industrial heat sources in study area
市级行政区 冶金 水泥 石化 煤化 共计
长沙市 4 8 6 0 18
株洲市 2 2 6 0 10
湘潭市 5 5 7 1 18
衡阳市 9 1 9 2 21
邵阳市 0 3 4 1 8
岳阳市 0 5 11 1 17
常德市 1 6 4 0 11
张家界市 0 2 0 0 2
益阳市 0 2 2 1 5
郴州市 8 1 11 1 21
永州市 1 2 4 0 7
怀化市 1 2 4 0 7
娄底市 6 5 3 4 18
湘西土家族苗族自治州 2 1 1 1 5
共计 39 45 72 12 168
Tab.3  Statistics on the number of industrial heat sources(个)
Fig.4  Analysis of standard distance circles for industrial heat sources in key economic zones
Fig.5-1  Distribution of heat emissions from various types of industrial heat sources in study area
Fig.5-2  Distribution of heat emissions from various types of industrial heat sources in study area
Fig.6  Variation trend of industrial heat sources in in study area in different years
评价项 关联度 排名
X8 0.846 1
X3 0.788 2
X5 0.761 3
X7 0.756 4
X1 0.726 5
X4 0.712 6
X6 0.704 7
X2 0.700 8
Tab.4  Results of grey relational degree
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