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Near Real-Time Wildfire Progression Monitoring with Sentinel-1 SAR Time Series and Deep Learning

Authore(s) : Yifang Ban || Division of GeoinformaticsKTH Royal Institute of Technology10044StockholmSweden

Volume : (16), Issue : 210, January - 2020

Abstract : In recent years, the world witnessed many devastating wildfires that resulted in destructive human and environmental impacts across the globe. Emergency response and rapid response for mitigation calls for effective approaches for near real-time wildfire monitoring. Capable of penetrating clouds and smoke, and imaging day and night, Synthetic Aperture Radar (SAR) can play a critical role in wildfire monitoring. In this communication, we investigated and demonstrated the potential of Sentinel-1 SAR time series with a deep learning framework for near real-time wildfire progression monitoring. The deep learning framework, based on a Convolutional Neural Network (CNN), is developed to detect burnt areas automatically using every new SAR image acquired during the wildfires and by exploiting all available pre-fire SAR time series to characterize the temporal backscatter variations. The results show that Sentinel-1 SAR backscatter can detect wildfires and capture their temporal progression as demonstrated for three large and impactful wildfires: the 2017 Elephant Hill Fire in British Columbia, Canada, the 2018 Camp Fire in California, USA, and the 2019 Chuckegg Creek Fire in northern Alberta, Canada. Compared to the traditional log-ratio operator, CNN-based deep learning framework can better distinguish burnt areas with higher accuracy. These findings demonstrate that spaceborne SAR time series with deep learning can play a significant role for near real-time wildfire monitoring when the data becomes available at daily and hourly intervals with the launches of RADARSAT Constellation Missions in 2019, and SAR CubeSat constellations.

Keywords :Yifang Ban would like to KTH Royal Institute of Technology for the sabbatical funding support that enabled her to expand her and her team’s research into SAR-based wildfire monitoring. She would also like to thank Michael A. Wulder at Canadian Forest Service (Pacific Forestry Centre) for hosting her sabbatical. Puzhao Zhang is grateful to the Chinese Scholarship Council for the scholarship to study in KTH Royal Institute of Technology.

Article: Download PDF Journal DOI : 311/714

Cite This Article:

Near Real-Time Wildfire Progression Monitoring

Vol.I (16), Issue.I 210

Article No : 10040

Number of Downloads : 112

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