Use of Remote Sensing in Wildfire Management

Leblon, Brigitte; Bourgeau‐Chavez, Laura; San-Miguel-Ayanz, Jesús

doi:10.5772/45829

Cited by 45 publications

(32 citation statements)

References 75 publications

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“…To increase the probability of detection of small-scale forest fires, the threshold-based algorithm of this study modified the existing threshold-based forest fire detection algorithms, which used 4 and 11 µm bands [9,13], considering the characteristics of forest fires in South Korea. Since average temperatures of active fires range from 800 K to 1200 K, the fires are detectable in the mid-infrared and thermal bands with high intensities [5]. Himawari-8 AHI band 5 (1.61 µm), 7 (3.85 µm) and 14 (11.2 µm) data were used in the threshold-based algorithm in this study (Table 1).…”

Section: Threshold-based Algorithmmentioning

confidence: 99%

“…BT differences and ratios between band 7 and thermal bands were considered important variables. While the peak radiation at thermal wavelengths (8-12 µm) is related to a normal environmental temperature, hot temperature by forest fires can be detected at a shorter wavelength than the Earth's surface, especially 3-4 µm (band 7) [5]. The large BT difference between the shortwave (3-4 µm) and thermal bands can be observed in fire pixels, and thus the BT difference has been used in other fire detection algorithms [9,15,33,34].…”

Section: Forest Fire Detectioinmentioning

confidence: 99%

“…An alternative to such field monitoring is satellite-based monitoring, which can cover vast areas including inaccessible regions with fine temporal resolution [4]. Satellite data have been widely used in forest fire management, such as pre-fire condition management, forest fire hot spot detection, smoke detection, and burn severity mapping [5]. Various satellite sensors have been used for forest fire detection, such as polar-orbiting satellite sensors (Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR), the Landsat series, and the Visible Infrared Imaging Radiometer Suite (VIIRS)), and geostationary satellite sensor systems (Geostationary Operational Environmental Satellite (GOES), Spinning Enhanced Visible and Infrared Imager (SEVIRI), Communication, Ocean and Meteorological Satellite (COMS), and Himawari-8).…”

Section: Introductionmentioning

confidence: 99%

“…Fires are typically detected through their high surface temperature, which is easily distinguishable in mid-infrared and thermal remote sensing data [5]. One of the most widely used methods for detecting forest fires is a simple threshold-based algorithm, which distinguishes fire pixels based on given empirical threshold values applied to band radiance, brightness temperature (BT), or the band ratio of specific wavelengths.…”

Section: Introductionmentioning

confidence: 99%

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

Jang

Kang

et al. 2019

Remote Sensing

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Geostationary satellite remote sensing systems are a useful tool for forest fire detection and monitoring because of their high temporal resolution over large areas. In this study, we propose a combined 3-step forest fire detection algorithm (i.e., thresholding, machine learning-based modeling, and post processing) using Himawari-8 geostationary satellite data over South Korea. This threshold-based algorithm filtered the forest fire candidate pixels using adaptive threshold values considering the diurnal cycle and seasonality of forest fires while allowing a high rate of false alarms. The random forest (RF) machine learning model then effectively removed the false alarms from the results of the threshold-based algorithm (overall accuracy~99.16%, probability of detection (POD) 93.08%, probability of false detection (POFD)~0.07%, and 96% reduction of the false alarmed pixels for validation), and the remaining false alarms were removed through post-processing using the forest map. The proposed algorithm was compared to the two existing methods. The proposed algorithm (POD~93%) successfully detected most forest fires, while the others missed many small-scale forest fires (POD~50-60%). More than half of the detected forest fires were detected within 10 min, which is a promising result when the operational real-time monitoring of forest fires using more advanced geostationary satellite sensor data (i.e., with higher spatial and temporal resolutions) is used for rapid response and management of forest fires.

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Section: Threshold-based Algorithmmentioning

confidence: 99%

Section: Forest Fire Detectioinmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Jang

Kang

et al. 2019

Remote Sensing

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“…Titled "Use of Remote Sensing in Wildfire Management" [10], the chapter elaborates wildfire management in a total different way for pre-fire and post-fire conditions monitoring, which is space-borne remotely sensed imagery, consist of optical remote sensing, thermal infrared remote sensing and radar remote sensing. Authors admit that further studies are required to establish models to estimate fuel moisture, and further to assess optical and thermal infrared images, radar images for monitoring pre-fire conditions too.…”

Section: Introductionmentioning

confidence: 99%