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

1
School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea
2
Environmental Satellite Centre, Climate and Air Quality Research Department, National Institute of Environmental Research, Incheon 22689, Korea
*
Author to whom correspondence should be addressed.
The first two authors equally contributed to the paper.
Remote Sens. 2019, 11(3), 271; https://doi.org/10.3390/rs11030271
Received: 23 December 2018 / Revised: 26 January 2019 / Accepted: 28 January 2019 / Published: 30 January 2019
(This article belongs to the Special Issue Advances in Remote Sensing-based Disaster Monitoring and Assessment)
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Abstract

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. View Full-Text
Keywords: forest fire; Himawari-8; threshold-based algorithm; machine learning forest fire; Himawari-8; threshold-based algorithm; machine learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Jang, E.; Kang, Y.; Im, J.; Lee, D.-W.; Yoon, J.; Kim, S.-K. Detection and Monitoring of Forest Fires Using Himawari-8 Geostationary Satellite Data in South Korea. Remote Sens. 2019, 11, 271.

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