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Article

Detection of Fire Smoke Plumes Based on Aerosol Scattering Using VIIRS Data over Global Fire-Prone Regions

1
Geospatial Sciences Center of Excellence, Department of Geography & Geospatial Sciences, South Dakota State University, Brookings, SD 57007, USA
2
Appalachian Laboratory, University of Maryland Center for Environmental Science, Frostburg, MD 21532, USA
3
IMSG Inc. & NOAA/NESDIS/STAR, 5825 University Research Court, College Park, MD 20740, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(2), 196; https://doi.org/10.3390/rs13020196
Received: 16 December 2020 / Revised: 4 January 2021 / Accepted: 5 January 2021 / Published: 8 January 2021
(This article belongs to the Special Issue Advances in Remote Sensing of Biomass Burning)
Smoke from fires significantly influences climate, weather, and human health. Fire smoke is traditionally detected using an aerosol index calculated from spectral contrast changes. However, such methods usually miss thin smoke plumes. It also remains challenging to accurately separate smoke plumes from dust, clouds, and bright surfaces. To improve smoke plume detections, this paper presents a new scattering-based smoke detection algorithm (SSDA) depending mainly on visible and infrared imaging radiometer suite (VIIRS) blue and green bands. The SSDA is established based on the theory of Mie scattering that occurs when the diameter of an atmospheric particulate is similar to the wavelength of the scattered light. Thus, smoke commonly causes Mie scattering in VIIRS blue and green bands because of the close correspondence between smoke particulate diameters and the blue/green band wavelengths. For developing the SSDA, training samples were selected from global fire-prone regions in North America, South America, Africa, Indonesia, Siberia, and Australia. The SSDA performance was evaluated against the VIIRS aerosol detection product and smoke detections from the ultraviolet aerosol index using manually labeled fire smoke plumes as a benchmark. Results show that the SSDA smoke detections are superior to existing products due chiefly to the improved ability of the algorithm to detect thin smoke and separate fire smoke from other surface types. Moreover, the SSDA smoke distribution pattern exhibits a high spatial correlation with the global fire density map, suggesting that SSDA is capable of detecting smoke plumes of fires in near real-time across the globe. View Full-Text
Keywords: fire smoke detection; aerosol index; aerosol scattering; spectral signature; spatial standard deviation fire smoke detection; aerosol index; aerosol scattering; spectral signature; spatial standard deviation
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MDPI and ACS Style

Lu, X.; Zhang, X.; Li, F.; Cochrane, M.A.; Ciren, P. Detection of Fire Smoke Plumes Based on Aerosol Scattering Using VIIRS Data over Global Fire-Prone Regions. Remote Sens. 2021, 13, 196. https://doi.org/10.3390/rs13020196

AMA Style

Lu X, Zhang X, Li F, Cochrane MA, Ciren P. Detection of Fire Smoke Plumes Based on Aerosol Scattering Using VIIRS Data over Global Fire-Prone Regions. Remote Sensing. 2021; 13(2):196. https://doi.org/10.3390/rs13020196

Chicago/Turabian Style

Lu, Xiaoman, Xiaoyang Zhang, Fangjun Li, Mark A. Cochrane, and Pubu Ciren. 2021. "Detection of Fire Smoke Plumes Based on Aerosol Scattering Using VIIRS Data over Global Fire-Prone Regions" Remote Sensing 13, no. 2: 196. https://doi.org/10.3390/rs13020196

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