Spatial-Temporal Distribution Analysis of Industrial Heat Sources in the US with Geocoded, Tree-Based, Large-Scale Clustering Article Swipe
Yan Ma
,
Caihong Ma
,
Peng Liu
,
Jin Yang
,
Yuzhu Wang
,
Yueqin Zhu
,
Xiaoping Du
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/rs12183069
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/rs12183069
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs12183069
- OA Status
- gold
- Cited By
- 5
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3087528721
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3087528721Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs12183069Digital Object Identifier
- Title
-
Spatial-Temporal Distribution Analysis of Industrial Heat Sources in the US with Geocoded, Tree-Based, Large-Scale ClusteringWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-09-19Full publication date if available
- Authors
-
Yan Ma, Caihong Ma, Peng Liu, Jin Yang, Yuzhu Wang, Yueqin Zhu, Xiaoping DuList of authors in order
- Landing page
-
https://doi.org/10.3390/rs12183069Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/rs12183069Direct OA link when available
- Concepts
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Visible Infrared Imaging Radiometer Suite, Cluster analysis, Environmental science, Computer science, Change detection, Remote sensing, Meteorology, Satellite, Geography, Engineering, Machine learning, Aerospace engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
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2025: 2, 2024: 1, 2023: 1, 2022: 1Per-year citation counts (last 5 years)
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41Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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