# Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data
## 1 Introduction
[19] used the spatial-temporal information of the MODIS active fire product (MCD14 ML) and the official burned area database for Portugal between 2001 and 2009 to get the major fire routes and correspondent mean spread directions. However, the temporal resolution of the solar orbit MODIS products is one-day or longer [36] which makes the retrieval of near real-time FSR unfeasible. Some studies have used sequential airborne data to extract the FSR [34]. However, the flight of an airplane is generally limited in time and space. Compared with solar orbit satellites, geostationary satellites provide higher temporal resolution, usually ranging from 10 to 30 min [37; 38]. Therefore, these satellites have the potential to provide near real-time monitoring of wildfire behavior. [PERSON] et al. [39] proposed an algorithm to simulate the diurnal burned area using the Geostationary Operational Environmental Satellites (GOES) active fire data. The results demonstrated its potential value in near real-time burned area estimation. [PERSON] and [PERSON] et al. [40; 41] successfully used data from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) with a temporal resolution of 15 min, to detect forest fires in near real-time in Greece and Portugal. [PERSON] et al. [42] used the Korean geostationary meteorological satellite to identify wildfires in Korea in 2013 with an overall accuracy of over 85% of the fire activity detected. [PERSON] et al. [38] used multi-geostationary satellites (GEOS-West and East, MSG SEVIRI and Multifunctional Transport Satellite (MTSAT)) to obtain hourly data on global biomass burning emissions products. However, the spatial resolution of the geostationary data used in these studies was generally above 4 km, making them suitable only for extremely large-scale wildfire detection and limiting the near real-time FSR extraction. With the launch of the Himawari-8 stationary meteorology satellite on October 17 th 2014, near-infrared (NIR) and infrared band images with a temporal resolution of ten minutes (data distribution with 15-20-min latency) and higher spatial resolution of 2 km were available (500 m for the red band and 1 km for other visible bands) [43], making the near real-time monitoring and detection of fire behaviors more feasible. [PERSON] et al. [44] used Sataid software [45] to combine the Red-Green-Blue (RGB) image of Himawari-8 data from channel 3, 4 and 6, to detect forest fire smoke in Indonesia from August to October 2015. [PERSON] et al. [46] also presented a straightforward algorithm based on the Himawari-8 data to detect and track near real-time wildfires in Australia. [PERSON] et al. [47] applied the geostationary FRP (Fire Radiative Power) algorithm designed for the MSG SEVIRI to Himawari-8 and other geostationary sensors, such as Feng Yun-2 (FY-2) and MTSAT. The latter study showed that data from the Himawari-8 achieved the best results. [PERSON] et al. [48] added NIR and red reflectance to a traditional active fire detection algorithm and developed a multi-spatial resolution approach for Himawari-8, which could map fire lines with a 500 m spatial resolution. However, up to now there have been no studies using the Himawari-8 data for near real-time FSR extraction. This paper presents the first study to assess the capability of Himawari-8 data in estimating near real-time FSR. To that end, a novel method is presented based on the movement rate of the fire center that is extracted from Himawari-8 (H8-FSR). The method was applied to the Esperance bushfire which ignited on 17 November 2015, in Western Australia. The results were compared with the FSR obtained from the CSIRO GFS model broadly used by Australian rural fire authorities.