1. National-scale nighttime high-temperature anomalies from Landsat-8 OLI images.
- Author
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Wu, Huansha, Liu, Yongxue, Pu, Yulin, Liu, Peng, Zhao, Wanjing, and Guo, Xiaoxiao
- Subjects
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EMISSIONS (Air pollution) , *SOLAR radiation , *DEEP learning , *CONUS , *LAND cover , *CHARCOAL - Abstract
• Landsat-8 nighttime OLI data can map nighttime high-temperature anomalies (N-HTAs). • N-HTAs were detected/classified using object-oriented and deep-learning methods. • Different types of N-HTAs were analyzed over the contiguous United States (CONUS). • Gas flaring dominates the 30-m CONUS industrial N-HTA inventory derived from OLI data. • Fires from living sources dominate all N-HTAs in CONUS and have a holiday effect. Identifying and characterizing the distribution, abundance, and structural composition of national high-temperature anomalies (HTAs) are essential for mitigating natural disasters, regulating industrial emissions, and assessing societal impacts. The Landsat-8 Operational Land Imager (OLI) provides a unique capability for monitoring nighttime HTAs (N-HTAs) at high spatial resolutions without contamination from reflected solar radiation. However, the extraction of N-HTAs from nighttime OLI imagery poses significant challenges due to the interference of bright up-light, especially for smaller and cooler hotspots, and the absence of spectral information on backgrounds complicates the classification of N-HTAs at night. Here, we explored the background noise characteristics of Bands 2–7 in nighttime OLI imagery and developed an object-oriented approach for the enhanced detection of small and cool N-HTAs. Furthermore, we introduced a deep learning model that leverages high-resolution aerial imagery and land cover data to categorize N-HTAs into industrial sources, living sources, vegetation fires, and others. Applying these methodologies to nighttime OLI imagery from 2013 to 2020 allowed us to map and classify N-HTAs across the contiguous United States (CONUS), facilitating the creation of a detailed spatiotemporal inventory of industrial N-HTAs. Our analysis revealed that N-HTAs from living sources dominate the total N-HTAs, with a pronounced holiday effect, and gas flaring activities in oil and gas development areas account for >85 % of industrial N-HTAs. The outcomes of this national-scale investigation underscore the significant potential and practical utility of nighttime OLI imagery for N-HTA monitoring and advocate for expanding the spatial and temporal coverage of nighttime OLI observations globally. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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