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.
Highlights
Wildfires are the essential factor in the formation and evolution of ecosystems [1,2]
The H8-Fire Spread Rate (FSR) compared favorably to the Commonwealth Scientific and Industrial Research Organization (CSIRO) Grassland Fire Spread (GFS) model when it came to the extraction of the near real-time FSR in the Esperance wildfire and produced errors that were acceptable within the literature (~30% mean absolute percent error (MAPE) [28])
This paper first defined the fire center as the burned area centroid and proposed a novel method for near real-time FSR extraction based on the movement rate of the fire center which is extracted from the high-temporal resolution Himawari-8 data
Summary
Wildfires are the essential factor in the formation and evolution of ecosystems [1,2] They shape the creation of a natural landscape, ensure the diversity and stability of the organism and change the biophysical characteristics of the soil [1,3,4,5]. According to the Emergency Events Database (EM-DAT), the global annual average economic losses due to wildfires by 2015, reached 2677 million US dollars, and around six million people are affected worldwide, every year [11,12]. These numbers highlight the importance of a quantitative understanding of wildfire behavior. Estimating near real-time FSR during wildfires can provide timely useful information for fire monitoring and management and improve the efficiency of operational firefighting [17,18,19]
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