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.
Highlights
The emissions from the energy-intensive industrial sectors are quite significant contributors to greenhouse gas (GHG) release
In order to avoid the blindness caused by random cluster center initialization, the time series Visible Infrared Imaging Radiometer Suite (VIIRS) hotspots data are spatially pre-grouped into GeoSOT-encoded grid tasks which are treated as initial clustering objects
Though the satellite-borne sensors offer a quantitive way of detecting thermal anomalies like fires, most of the available fire detection methods barely work well for heavy industrial heat source identification
Summary
The emissions from the energy-intensive industrial sectors are quite significant contributors to greenhouse gas (GHG) release. The combustion of gas- and oil-based fossil fuels during the modern industrial processes in several heavy-pollution industrial sectors, such as the steel industries, petrochemical industries, and cement industries are the major emitters. They account for nearly one-quarter of the global energy-related carbon dioxide (CO2) emissions and their share continues to grow [1]. According to the C2ES [3] analysis, the US’s emissions cuts may still off track to meeting the agreement under the Paris climate accord [4] These industrial emissions have already posed a serious threat to the urban environment and even natural ecosystems [5]. Accurate and up-to-date observations and tracking of the distribution patterns of industrial activities over time is crucial to a better understanding of the national or even global climate change trends
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