Abstract
Rapid urbanization and economic development have led to the development of heavy industry and structural re-equalization in mainland China. This has resulted in scattered and disorderly layouts becoming prominent in the region. Furthermore, economic development has exacerbated pressures on regional resources and the environment and has threatened sustainable and coordinated development in the region. The NASA Land Science Investigator Processing System (Land-SIPS) Visible Infrared Imaging Radiometer (VIIRS) 375-m active fire product (VNP14IMG) was selected from the Fire Information for Resource Management System (FIRMS) to study the spatiotemporal patterns of heavy industry development. Furthermore, we employed an improved adaptive K-means algorithm to realize the spatial segmentation of long-order VNP14IMG and constructed heat source objects. Lastly, we used a threshold recognition model to identify heavy industry objects from normal heat source objects. Results suggest that the method is an accurate and effective way to monitor heat sources generated from heavy industry. Moreover, some conclusions about heavy industrial heat source distribution in mainland China at different scales were obtained. Those can be beneficial for policy-makers and heavy industry regulation.
Highlights
Heavy industry is an important component of China’s basic industry and provides technical equipment, power, and raw materials for all sectors of the national economy
The spatial distribution of 4143 heavy industrial heat sources in China’s seven mainland regions (Figure 3) revealed that heavy industrial heat sources had been mainly focused in North China (NC), East China (EC), and Northwest China (NWC) in the past six years
The max number of NWH values occurred in 2013 and 2014 but have declined since 2014. This suggests that smalland medium-sized heavy industrial heat sources were limited or shut down due to environmental protection policies
Summary
Heavy industry is an important component of China’s basic industry and provides technical equipment, power, and raw materials for all sectors of the national economy. Schroeder et al [11] and Giglio et al [12] applied NPP VIIRS data with a 375-m resolution to daily/nighttime thermal anomaly extraction to make fire point product data more in line with Earth’s true fire point distribution, generating a global scale product This active fire product provided a greater response for fires in small areas due to its higher spatial resolution and improved nighttime performance. We introduced a clustering method that is widely used in pattern recognition, data analysis, and image processing [13] to detect heat source objects. The key issue for heavy industry heat source discovery and detection is choosing the best method for setting the cluster num for K-means segmentation based on the characteristics of VIIRS active fire hotspots.
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