{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T02:28:29Z","timestamp":1773714509287,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,1,30]],"date-time":"2019-01-30T00:00:00Z","timestamp":1548806400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2017M1A3A3A02015981"],"award-info":[{"award-number":["NRF-2017M1A3A3A02015981"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Institute of Environmental Research, South Korea","award":["NIER-2017-01-02-063"],"award-info":[{"award-number":["NIER-2017-01-02-063"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u201360%). 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.<\/jats:p>","DOI":"10.3390\/rs11030271","type":"journal-article","created":{"date-parts":[[2019,1,30]],"date-time":"2019-01-30T10:58:27Z","timestamp":1548845907000},"page":"271","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":99,"title":["Detection and Monitoring of Forest Fires Using Himawari-8 Geostationary Satellite Data in South Korea"],"prefix":"10.3390","volume":"11","author":[{"given":"Eunna","family":"Jang","sequence":"first","affiliation":[{"name":"School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea"}]},{"given":"Yoojin","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4506-6877","authenticated-orcid":false,"given":"Jungho","family":"Im","sequence":"additional","affiliation":[{"name":"School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea"}]},{"given":"Dong-Won","family":"Lee","sequence":"additional","affiliation":[{"name":"Environmental Satellite Centre, Climate and Air Quality Research Department, National Institute of Environmental Research, Incheon 22689, Korea"}]},{"given":"Jongmin","family":"Yoon","sequence":"additional","affiliation":[{"name":"Environmental Satellite Centre, Climate and Air Quality Research Department, National Institute of Environmental Research, Incheon 22689, Korea"}]},{"given":"Sang-Kyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Environmental Satellite Centre, Climate and Air Quality Research Department, National Institute of Environmental Research, Incheon 22689, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,30]]},"reference":[{"key":"ref_1","unstructured":"Ryu, G. (2016). 2015 Forest Standard Statistics, Korea Forest Service."},{"key":"ref_2","unstructured":"Kim, G. (2015). A Study on Wildfire Detection Using Geostationary Meteorological Satellite. [Master\u2019s Thesis, Pukyoung National University]."},{"key":"ref_3","unstructured":"Kim, J., Lee, S., and Nam, M. (2018). 2017 Statistical Yearbook of Forest Fire, Korea Forest Service."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Leblon, B., San-Miguel-Ayanz, J., Bourgeau-Chavez, L., and Kong, M. (2016). Remote sensing of wildfires. Land Surface Remote Sensing, Elsevier.","DOI":"10.1016\/B978-1-78548-105-5.50003-7"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Leblon, B., Bourgeau-Chavez, L., and San-Miguel-Ayanz, J. (2012). Use of remote sensing in wildfire management. Sustainable Development-Authoritative and Leading Edge Content for Environmental Management, InTech.","DOI":"10.5772\/45829"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Di Biase, V., and Laneve, G. (2018). Geostationary sensor based forest fire detection and monitoring: An improved version of the SFIDE algorithm. Remote Sens., 10.","DOI":"10.20944\/preprints201801.0007.v1"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e2","DOI":"10.1016\/j.rse.2017.01.019","article-title":"RST-FIRES, an exportable algorithm for early-fire detection and monitoring: Description, implementation, and field validation in the case of the MSG-SEVIRI sensor","volume":"192","author":"Filizzola","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0034-4257(03)00184-6","article-title":"An enhanced contextual fire detection algorithm for MODIS","volume":"87","author":"Giglio","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.rse.2016.02.054","article-title":"The collection 6 MODIS active fire detection algorithm and fire products","volume":"178","author":"Giglio","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Na, L., Zhang, J., Bao, Y., Bao, Y., Na, R., Tong, S., and Si, A. (2018). Himawari-8 satellite based dynamic monitoring of grassland fire in China-Mongolia border regions. Sensors, 18.","DOI":"10.3390\/s18010276"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.rse.2013.12.008","article-title":"The new VIIRS 375 m active fire detection data product: Algorithm description and initial assessment","volume":"143","author":"Schroeder","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.rse.2015.08.032","article-title":"Active fire detection using Landsat-8\/OLI data","volume":"185","author":"Schroeder","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wickramasinghe, C.H., Jones, S., Reinke, K., and Wallace, L. (2016). Development of a multi-spatial resolution approach to the surveillance of active fire lines using Himawari-8. Remote Sens., 8.","DOI":"10.3390\/rs8110932"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1080\/2150704X.2018.1484955","article-title":"Implementation of a new algorithm resulting in improvements in accuracy and resolution of SEVIRI hotspot products","volume":"9","author":"Wickramasinghe","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1080\/2150704X.2017.1350303","article-title":"Real-time wildfire detection and tracking in Australia using geostationary satellite: Himawari-8","volume":"8","author":"Xu","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"65","DOI":"10.5194\/isprs-archives-XLI-B8-65-2016","article-title":"Assessment of the utility of the advanced Himawari imager to detect active fire over Australia","volume":"41","author":"Hally","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hally, B., Wallace, L., Reinke, K., Jones, S., and Skidmore, A. (2018). Advances in active fire detection using a multi-temporal method for next-generation geostationary satellite data. Int. J. Digit. Earth, 1\u201316.","DOI":"10.1080\/17538947.2018.1497099"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.rse.2014.06.020","article-title":"Development of a multi-temporal Kalman filter approach to geostationary active fire detection & fire radiative power (FRP) estimation","volume":"152","author":"Roberts","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xie, Z., Song, W., Ba, R., Li, X., and Xia, L. (2018). A spatiotemporal contextual model for forest fire detection using Himawari-8 satellite data. Remote Sens., 10.","DOI":"10.3390\/rs10121992"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Miller, J., Borne, K., Thomas, B., Huang, Z., and Chi, Y. (2013). Automated wildfire detection through Artificial Neural Networks. Remote Sensing and Modeling Applications to Wildland Fires, Springer.","DOI":"10.1007\/978-3-642-32530-4_20"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Atwood, E.C., Englhart, S., Lorenz, E., Halle, W., Wiedemann, W., and Siegert, F. (2016). Detection and characterization of low temperature peat fires during the 2015 fire catastrophe in Indonesia using a new high-sensitivity fire monitoring satellite sensor (FireBird). PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0159410"},{"key":"ref_22","unstructured":"Kim, S. (2009). Development of an Algorithm for Detecting Sub-Pixel Scale Forest Fires Using MODIS Data. [Ph.D. Thesis, Inha University]."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3375","DOI":"10.1080\/01431161.2017.1295481","article-title":"Enhanced contextual forest fire detection with prediction interval analysis of surface temperature using vegetation amount","volume":"38","author":"Huh","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","unstructured":"(2019, January 09). Seoul Broadcasting System (SBS) News Website. Available online: http:\/\/news.sbs.co.kr\/news\/endPage.do?news_id=N1004722267&plink=ORI&cooper=NAVER."},{"key":"ref_25","unstructured":"(2019, January 10). Korean Statistical Information Service Home Page. Available online: http:\/\/kosis.kr\/statHtml\/statHtml.do?orgId=101&tblId=DT_2KAA101&conn_path=I2."},{"key":"ref_26","unstructured":"Ryu, G. (2018). 2018 National Park Standard Statistics, Korea National Park Service."},{"key":"ref_27","unstructured":"(2018, August 27). Korea Meteorological Administration. Available online: http:\/\/www.weather.go.kr\/weather\/climate\/average_south.jsp."},{"key":"ref_28","unstructured":"(2019, January 17). Korea Forest Service Home Page. Available online: http:\/\/www.forest.go.kr\/newkfsweb\/html\/HtmlPage.do?pg=\/policy\/policy_0401.html&mn=KFS_38_05_04."},{"key":"ref_29","unstructured":"(2019, January 09). Environmental Geographic Information Service Home Page. Available online: http:\/\/www.index.go.kr\/search\/search.jsp."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.rse.2012.10.036","article-title":"A sub-pixel-based calculation of fire radiative power from MODIS observations: 1: Algorithm development and initial assessment","volume":"129","author":"Peterson","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1859","DOI":"10.5194\/amt-10-1859-2017","article-title":"Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data","volume":"10","author":"Lee","year":"2017","journal-title":"Atmos. Meas. Tech."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1016\/j.rse.2016.07.021","article-title":"The development and first validation of the GOES Early Fire Detection (GOES-EFD) algorithm","volume":"184","author":"Koltunov","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1016\/j.rse.2009.12.019","article-title":"Detection and monitoring of African vegetation fires using MSG-SEVIRI imagery","volume":"114","author":"Amraoui","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5503","DOI":"10.1109\/TGRS.2016.2566665","article-title":"Improving nocturnal fire detection with the VIIRS day\u2013night band","volume":"54","author":"Polivka","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1080\/15481603.2017.1370169","article-title":"Landsat-8 vs. Sentinel-2: Examining the added value of Sentinel-2\u2019s red-edge bands to land-use and land-cover mapping in Burkina Faso","volume":"55","author":"Forkuor","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Jang, E., Im, J., Park, G.-H., and Park, Y.-G. (2017). Estimation of fugacity of carbon dioxide in the East Sea using in situ measurements and Geostationary Ocean Color Imager satellite data. Remote Sens., 9.","DOI":"10.3390\/rs9080821"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1080\/15481603.2018.1426091","article-title":"Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system","volume":"55","author":"Liu","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1080\/15481603.2017.1302181","article-title":"A comparison of geographic datasets and field measurements to model soil carbon using random forests and stepwise regressions (British Columbia, Canada)","volume":"54","author":"Richardson","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1080\/15481603.2017.1364837","article-title":"Evaluation of Goddard\u2019s lidar, hyperspectral, and thermal data products for mapping urban land-cover types","volume":"55","author":"Zhang","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1080\/15481603.2016.1250328","article-title":"Mining parameter information for building extraction and change detection with very high-resolution imagery and GIS data","volume":"54","author":"Guo","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Park, S., Im, J., Park, S., Yoo, C., Han, H., and Rhee, J. (2018). Classification and mapping of paddy rice by combining Landsat and SAR time series data. Remote Sens., 10.","DOI":"10.3390\/rs10030447"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.isprsjprs.2018.01.018","article-title":"Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data","volume":"137","author":"Yoo","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.agrformet.2017.02.022","article-title":"Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula","volume":"237","author":"Park","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1080\/15481603.2017.1351149","article-title":"Assessing the suitability of data from Sentinel-1A and 2A for crop classification","volume":"54","author":"Sonobe","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, D., Liu, X., Wu, X., Yao, Y., Wu, X., and Chen, Y. (2018). Multiple intra-urban land use simulations and driving factors analysis: A case study in Huicheng, China. GISci. Remote Sens., 1\u201327.","DOI":"10.1080\/15481603.2018.1507074"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1080\/15481603.2017.1408892","article-title":"Less is more: Optimizing classification performance through feature selection in a very-high-resolution remote sensing object-based urban application","volume":"55","author":"Georganos","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.agrformet.2017.02.011","article-title":"Meteorological drought forecasting for ungauged areas based on machine learning: Using long-range climate forecast and remote sensing data","volume":"237","author":"Rhee","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Sim, S., Im, J., Park, S., Park, H., Ahn, M.H., and Chan, P.W. (2018). Icing detection over East Asia from geostationary satellite data using machine learning approaches. Remote Sens., 10.","DOI":"10.3390\/rs10040631"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.rse.2016.02.027","article-title":"HOTMAP: Global hot target detection at moderate spatial resolution","volume":"177","author":"Murphy","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_51","unstructured":"(2019, January 10). China Forest Fire Management Home Page, Available online: http:\/\/www.slfh.gov.cn."},{"key":"ref_52","unstructured":"(2019, January 10). China Forest Fire Management Home Page, Available online: http:\/\/www.slfh.gov.cn\/Item\/24197.aspx."},{"key":"ref_53","unstructured":"(2019, January 10). China Forest Fire Management Home Page, Available online: http:\/\/www.slfh.gov.cn\/Item\/25469.aspx."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Liu, X., He, B., Quan, X., Yebra, M., Qiu, S., Yin, C., Liao, Z., and Zhang, H. (2018). Near real-time extracting wildfire spread rate from Himawari-8 satellite data. Remote Sens., 10.","DOI":"10.3390\/rs10101654"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/3\/271\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:29:46Z","timestamp":1760185786000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/3\/271"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,30]]},"references-count":54,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["rs11030271"],"URL":"https:\/\/doi.org\/10.3390\/rs11030271","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,30]]}}}