File Download

  • Find it @ UNIST can give you direct access to the published full text of this article. (UNISTARs only)
Related Researcher

임정호

Im, Jungho
Intelligent Remote sensing and geospatial Information Science Lab.
Read More

Views & Downloads

Detailed Information

Cited time in webofscience Cited time in scopus
Metadata Downloads

Detection and monitoring of forest fires using Himawari-8 geostationary satellite data in South Korea

Author(s)
Jang, EunnaKang, YoojinIm, JunghoLee, Dong-WonYoon, JongminKim, Sang-Kyun
Issued Date
2019-02
DOI
10.3390/rs11030271
URI
https://scholarworks.unist.ac.kr/handle/201301/26442
Fulltext
https://www.mdpi.com/2072-4292/11/3/271
Citation
REMOTE SENSING, v.11, no.3, pp.271
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.
Publisher
MDPI AG
ISSN
2072-4292
Keyword (Author)
Forest fireHimawari-8Machine learningThreshold-based algorithm
Keyword
Threshold elementsAdaptive threshold valuesForest firesHigh temporal resolutionAlarm systemsDecision treesDeforestationErrorsFire hazardsHimawari-8Machine learning modelsProbability of detectionSatellite remote sensing systemsGeostationary satellitesLearning algorithmsLearning systemsSpatial and temporal resolutionsFiresFire detectorsMachine learningPixelsRemote sensing

qrcode

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.