Himawari-8 Satellite Based Dynamic Monitoring of Grassland Fire in China-Mongolia Border Regions

Abstract

In this study, we used bands 7, 4, and 3 of the Advance Himawari Imager (AHI) data, combined with a Threshold Algorithm and a visual interpretation method to monitor the entire process of grassland fires that occurred on the China-Mongolia border regions, between 05:40 (UTC) on April 19th to 13:50 (UTC) on April 21st 2016. The results of the AHI data monitoring are evaluated by the fire point product data, the wind field data, and the environmental information data of the area in which the fire took place. The monitoring result shows that, the grassland fire burned for two days and eight hours with a total burned area of about 2708.29 km2. It mainly spread from the northwest to the southeast, with a maximum burning speed of 20.9 m/s, a minimum speed of 2.52 m/s, and an average speed of about 12.07 m/s. Thus, using AHI data can not only quickly and accurately track the dynamic development of a grassland fire, but also estimate the spread speed and direction. The evaluation of fire monitoring results reveals that AHI data with high precision and timeliness can be highly consistent with the actual situation.

Detection and Monitoring of Forest Fires Using Himawari-8 Geostationary Satellite Data in South Korea

“…The MIR band (i.e., band 7) is effective in observing radiative emissions from objects radiating at temperatures similar to those of forest fires [13]. Thus, it has been used in most existing fire detection algorithms [10,11,13,15,30]. Many factors such as land cover type, topographic characteristics, time of day, and day of the year affect the threshold [4].…”

“…However, this method produces a relatively high number of false alarms and often misses fires because of the varied characteristics of forests, topography, and climate between different regions [4]. Contextual algorithms, which were developed from the threshold-based algorithm, use local maxima and other multispectral criteria based on the difference between fire pixels and the background temperature [6][7][8][9][10][11][12][13][14][15]. Furthermore, the modeling of the fire pixel diurnal temperature cycle (DTC), which shows a diurnal variation of the brightness temperature of the pixel, has been also used [16][17][18][19].…”

“…Early detection and monitoring of forest fires are crucial to reduce damage and save human lives and property. Recently, some researchers have used Himawari-8 to detect forest fires over Asia and Australia using its multiple spectral bands and high temporal resolution [10,13,15,19]. These studies were based on contextual algorithms targeting large forest fires in East Asia and Australia, which were not evaluated for detecting small fires in South Korea.…”

Monitoring the Agung (Indonesia) Ash Plume of November 2017 by Means of Infrared Himawari 8 Data

“…The improved features of the AHI instrument, in terms of spectral, spatial and temporal resolution, should guarantee a more efficient monitoring of rapidly changing weather/environmental phenomena in comparison with imagers of the previous MTSAT series [18]. In particular, the AHI infrared bands 7 (3.74-3.96 µ m), 11 (8.44-8. Although a number of studies up to now have been performed exploiting Himawari-8 observations (e.g., [21][22][23][24]), only a few of them focused on volcanic ash (e.g., [25,26]). Some authors In this work, we investigate the ash events of 25-28 November 2017 from space by implementing the well-established RST ASH algorithm [14][15][16], which was previously tested over the Asiatic region using infrared MTSAT-1R/2 (Multi-Functional Transport Satellite-1R/2) observations [17], for the first time on Himawari-8 data.…”

“…The improved features of the AHI instrument, in terms of spectral, spatial and temporal resolution, should guarantee a more efficient monitoring of rapidly changing weather/environmental phenomena in comparison with imagers of the previous MTSAT series [18]. In particular, the AHI infrared bands 7 (3.74-3.96 µm), 11 (8.44-8 Although a number of studies up to now have been performed exploiting Himawari-8 observations (e.g., [21][22][23][24]), only a few of them focused on volcanic ash (e.g., [25,26]). Some authors have used, for instance, a qualitative ash RGB product designed by the JMA (Japan Meteorological Agency) [27] to discriminate ash and SO2 plumes from meteorological clouds, emphasizing the advantages of using data from Himawari-8 for monitoring those features in a timely manner [25].…”