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Remote Sens. 2018, 10(10), 1654; https://doi.org/10.3390/rs10101654

Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data

1
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
2
Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China
3
Fenner School of Environment and Society, The Australian National University, Canberra, ACT 2601, Australia
4
Bushfire & Natural Hazards Cooperative Research Centre, Melbourne, VIC 3002, Australia
*
Authors to whom correspondence should be addressed.
Received: 5 September 2018 / Revised: 6 October 2018 / Accepted: 13 October 2018 / Published: 17 October 2018
(This article belongs to the Special Issue Remote Sensing of Wildfire)
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Abstract

Fire Spread Rate (FSR) can indicate how fast a fire is spreading, which is especially helpful for wildfire rescue and management. Historically, images obtained from sun-orbiting satellites such as Moderate Resolution Imaging Spectroradiometer (MODIS) were used to detect active fire and burned area at the large spatial scale. However, the daily revisit cycles make them inherently unable to extract FSR in near real­-time (hourly or less). We argue that the Himawari-8, a next generation geostationary satellite with a 10-min temporal resolution and 0.5–2 km spatial resolution, may have the potential for near real-time FSR extraction. To that end, we propose a novel method (named H8-FSR) for near real-time FSR extraction based on the Himawari-8 data. The method first defines the centroid of the burned area as the fire center and then the near real-time FSR is extracted by timely computing the movement rate of the fire center. As a case study, the method was applied to the Esperance bushfire that broke out on 17 November, 2015, in Western Australia. Compared with the estimated FSR using the Commonwealth Scientific and Industrial Research Organization (CSIRO) Grassland Fire Spread (GFS) model, H8-FSR achieved favorable performance with a coefficient of determination (R2) of 0.54, mean bias error of –0.75 m/s, mean absolute percent error of 33.20% and root mean square error of 1.17 m/s, respectively. These results demonstrated that the Himawari-8 data are valuable for near real-time FSR extraction, and also suggested that the proposed method could be potentially applicable to other next generation geostationary satellite data. View Full-Text
Keywords: fire spread rate; fire center; fire behavior; Himawari-8; near real-time fire spread rate; fire center; fire behavior; Himawari-8; near real-time
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Liu, X.; He, B.; Quan, X.; Yebra, M.; Qiu, S.; Yin, C.; Liao, Z.; Zhang, H. Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data. Remote Sens. 2018, 10, 1654.

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