Detection of fire smoke plumes based on aerosol scattering using GOCI-II data

Fangtian Niu; Lin Sun; Chao Ma

doi:10.1117/12.2681301

8 June 2023 Detection of fire smoke plumes based on aerosol scattering using GOCI-II data

Fangtian Niu, Lin Sun, Chao Ma

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 1270727 (2023) https://doi.org/10.1117/12.2681301
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

The smoke and particulate matter emitted from forest fires will have a significant impact on air quality and climate change, as well as human health and the ecological environment. Fire smoke can be detected from satellite imagery based on changes in its spectral properties. However, previous remote sensing satellites used for smoke detection have low to medium resolution and long revisit intervals. In response to this deficiency, this paper proposes a method for extracting fire smoke based on the multi-channel threshold of GOCI-II (Geostationary Ocean Color Imager-II) data through the analysis of the fire smoke plume sensitive band. Firstly, the separability index of reflection of smoke and other objects in the ultraviolet, visible, and infrared bands was analyzed. Experiments have shown that the Rayleigh scattering of smoke is stronger in the ultraviolet and blue bands. This difference can be used to separate smoke from clouds. Finally, we compared the monitoring results of the multi-band threshold method with the SSDA monitoring results of VIIRS. Through comparison, it is found that the multi-band threshold method is better for smoke plumes extraction and can obtain more detailed and comprehensive smoke information.

Citation Download Citation

Fangtian Niu, Lin Sun, and Chao Ma "Detection of fire smoke plumes based on aerosol scattering using GOCI-II data", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 1270727 (8 June 2023); https://doi.org/10.1117/12.2681301

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KEYWORDS

Clouds

Forest fires

Detection and tracking algorithms

Ultraviolet radiation

Satellites

Reflectivity

Aerosols

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