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
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.
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LI Lelin, WANG Wenxi, YANG Wentao, CHEN Hao, PENG Huanhua, ZHAO Qian. Gaussian mixture model and its application in remote sensing identification of industrial heat sources. Remote Sensing for Natural Resources, 2025, 37(4): 173-183.
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