Spatial-Temporal Distribution Analysis of Industrial Heat Sources in the US with Geocoded, Tree-Based, Large-Scale Clustering

Abstract

Heavy industrial burning contributes significantly to the greenhouse gas (GHG) emissions. It is responsible for almost one-quarter of the global energy-related CO2 emissions and its share continues to grow. Mostly, those industrial emissions are accompanied by a great deal of high-temperature heat emissions from the combustion of carbon-based fuels by steel, petrochemical, or cement plants. Fortunately, these industrial heat emission sources treated as thermal anomalies can be detected by satellite-borne sensors in a quantitive way. However, most of the dominant remote sensing-based fire detection methods barely work well for heavy industrial heat source discernment. Although the object-oriented approach, especially the data clustering-based approach, has guided a novel method of detection, it is still limited by the costly computation and storage resources. Furthermore, when scaling to a national, or even global, long time-series detection, it is greatly challenged by the tremendous computation introduced by the incredible large-scale data clustering of tens of millions of high-dimensional fire data points. Therefore, we proposed an improved parallel identification method with geocoded, task-tree-based, large-scale clustering for the spatial-temporal distribution analysis of industrial heat emitters across the United States from long time-series active Visible Infrared Imaging Radiometer Suite (VIIRS) data. A recursive k-means clustering method is introduced to gradually segment and cluster industrial heat objects. Furthermore, in order to avoid the blindness caused by random cluster center initialization, the time series VIIRS hotspots data are spatially pre-grouped into GeoSOT-encoded grid tasks which are also treated as initial clustering objects. In addition, some grouped parallel clustering strategy together with geocoding-aware task tree scheduling is adopted to sufficiently exploit parallelism and performance optimization. Then, the spatial-temporal distribution pattern and its changing trend of industrial heat emitters across the United States are analyzed with the identified industrial heat sources. Eventually, the performance experiment also demonstrated the efficiency and encouraging scalability of this approach.

Analysis of spatial and temporal changes of industrial heat sources in Ukraine from 2012 to 2022

“…The active thermal anomaly data used in this study were obtained from the NPP-VIIRS (Visible Infrared Imaging Radiometer Suite) sensor on the Suomi-NPP (NASA/NOAA Suomi-National Polar-orbiting Partnership) and NOAA-20 joint satellites. Sensor with a medium resolution of 375 m (NPP-VIIRS active fire/hotpot data) [7], the data were downloaded from NASA's FIRMS website and acquired for the period of January 20, 2012 to December 31, 2022. The obtained active thermal anomaly data for Ukraine was also used for the auxiliary validation with Google Earth high spatial resolution satellite image data [8].…”

Real-time monitoring and analysis of industrial heat sources based on data: Hangzhou City as an example

“…This situation has been further exacerbated by rapid urbanization and economic development, leading to the proliferation of heavy industries and an unplanned regional layout [2]. Therefore, real-time monitoring and analysis of the quantity and distribution of industrial heat sources have become crucial for heat source management, energy conservation, emission reduction, environmental protection, and sustainable development [3].…”