{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T23:59:05Z","timestamp":1780444745794,"version":"3.54.1"},"reference-count":58,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,6,10]],"date-time":"2018-06-10T00:00:00Z","timestamp":1528588800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003629","name":"Korea Meteorological Administration","doi-asserted-by":"publisher","award":["NMSC-2017-01"],"award-info":[{"award-number":["NMSC-2017-01"]}],"id":[{"id":"10.13039\/501100003629","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The worst forest fire in South Korea occurred in April 2000 on the eastern coast. Forest recovery works were conducted until 2005, and the forest has been monitored since the fire. Remote sensing techniques have been used to detect the burned areas and to evaluate the recovery-time point of the post-fire processes during the past 18 years. We used three indices, Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), and Gross Primary Production (GPP), to temporally monitor a burned area in terms of its moisture condition, vegetation biomass, and photosynthetic activity, respectively. The change of those three indices by forest recovery processes was relatively analyzed using an unburned reference area. The selected unburned area had similar characteristics to the burned area prior to the forest fire. The temporal patterns of NBR and NDVI, not only showed the forest recovery process as a result of forest management, but also statistically distinguished the recovery periods at the regions of low, moderate, and high fire severity. The NBR2.1 for all areas, calculated using 2.1 \u03bcm wavelengths, reached the unburned state in 2008. The NDVI for areas with low and moderate fire severity levels became significantly equal to the unburned state in 2009 (p &gt; 0.05), but areas with high severity levels did not reach the unburned state until 2017. This indicated that the surface and vegetation moisture conditions recovered to the unburned state about 8 years after the fire event, while vegetation biomass and health required a longer time to recover, particularly for high severity regions. In the case of GPP, it rapidly recovered after about 3 years. Then, the steady increase in GPP surpassed the GPP of the reference area in 2015 because of the rapid growth and high photosynthetic activity of young forests. Therefore, the concluding scientific message is that, because the recovery-time point for each component of the forest ecosystem is different, using only one satellite-based indicator will not be suitable to understand the post-fire recovery process. NBR, NDVI, and GPP can be combined. Further studies will require more approaches using various terms of indices.<\/jats:p>","DOI":"10.3390\/rs10060918","type":"journal-article","created":{"date-parts":[[2018,6,11]],"date-time":"2018-06-11T11:01:01Z","timestamp":1528714861000},"page":"918","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["Satellite-Based Evaluation of the Post-Fire Recovery Process from the Worst Forest Fire Case in South Korea"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5609-2739","authenticated-orcid":false,"given":"Jae-Hyun","family":"Ryu","sequence":"first","affiliation":[{"name":"Department of Applied Plant Science, Chonnam National University, 77 Yongbong-ro, Gwangju 61186, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyung-Soo","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Spatial Information Engineering, Pukyong National University, 45 Yongsoro, Namgu, Busan 48513, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5518-9478","authenticated-orcid":false,"given":"Sungwook","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Environment, Energy, and Geoinfomatics, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9778-3624","authenticated-orcid":false,"given":"No-Wook","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Geoinformatic Engineering, Inha University, 100 Inha-ro, Nam-gu, Incheon 22212, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang-Won","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Spatial Information Engineering, Pukyong National University, 45 Yongsoro, Namgu, Busan 48513, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3375-4357","authenticated-orcid":false,"given":"Jaeil","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Applied Plant Science, Chonnam National University, 77 Yongbong-ro, Gwangju 61186, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1111\/j.1440-1703.2003.00607.x","article-title":"Forest responses to the large-scale east coast fires in Korea","volume":"19","author":"Choung","year":"2004","journal-title":"Ecol. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1007\/s10021-014-9763-7","article-title":"Recovery of Ecosystem Carbon Stocks in Young Boreal Forests: A Comparison of Harvesting and Wildfire Disturbance","volume":"17","author":"Seedre","year":"2014","journal-title":"Ecosystems"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1071\/WF16211","article-title":"High post-fire mortality of resprouting woody plants in Tasmanian Mediterranean-type vegetation","volume":"26","author":"Nicholson","year":"2017","journal-title":"Int. J. Wildland Fire"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1139\/cjfr-2017-0236","article-title":"Recovery of carbon pools a decade after wildfire in black spruce forests of interior Alaska: Effects of soil texture and landscape position","volume":"48","author":"Houle","year":"2018","journal-title":"Can. J. For. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1641\/0006-3568(2001)051[0723:CCAFD]2.0.CO;2","article-title":"Climate change and forest disturbances: Climate change can affect forests by altering the frequency, intensity, duration, and timing of fire, drought, introduced species, insect and pathogen outbreaks, hurricanes, windstorms, ice storms, or landslides","volume":"51","author":"Dale","year":"2001","journal-title":"BioScience"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1023\/A:1020221123884","article-title":"Using remote sensing to assess Russian forest fire carbon emissions","volume":"55","author":"Isaev","year":"2002","journal-title":"Clim. Chang."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"873","DOI":"10.2134\/jeq2004.0873","article-title":"Plant and Soil Responses to Biosolids Application following Forest Fire","volume":"33","author":"Meyer","year":"2004","journal-title":"J. Environ. Qual."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1071\/WF05044","article-title":"Influence of vegetation recovery on soil hydrology and erodibility following fire: An 11-year investigation","volume":"14","author":"Doerr","year":"2005","journal-title":"Int. J. Wildland Fire"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1071\/WF09015","article-title":"\u2018SINAMI\u2019: A tool for the economic evaluation of forest fire management programs in Mediterranean ecosystems","volume":"19","year":"2010","journal-title":"Int. J. Wildland Fire"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1111\/aec.12404","article-title":"Post-fire recovery of litter detritivores is limited by distance from burn edge","volume":"42","author":"Arnold","year":"2016","journal-title":"Austral Ecol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1294","DOI":"10.1111\/jbi.12947","article-title":"Assessing variability in post-fire forest structure along gradients of productivity in the Canadian boreal using multi-source remote sensing","volume":"44","author":"Bolton","year":"2017","journal-title":"J. Biogeogr."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1139\/x00-025","article-title":"Net primary productivity following forest fire for Canadian ecoregions","volume":"30","author":"Amiro","year":"2000","journal-title":"Can. J. For. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.rse.2006.01.011","article-title":"Using satellite time-series data sets to analyze fire disturbance and forest recovery across Canada","volume":"101","author":"Goetz","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1071\/WF07049","article-title":"Fire intensity, fire severity and burn severity: A brief review and suggested usage","volume":"18","author":"Keeley","year":"2009","journal-title":"Int. J. Wildland Fire"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/s11355-013-0214-y","article-title":"Effects of heterogeneity of pre-fire forests and vegetation burn severity on short-term post-fire vegetation density and regeneration in Samcheok, Korea","volume":"10","author":"Lee","year":"2013","journal-title":"Landsc. Ecol. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.rse.2016.05.018","article-title":"Assessing postfire recovery of chamise chaparral using multi-temporal spectral vegetation index trajectories derived from Landsat imagery","volume":"183","author":"Storey","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2006.12.006","article-title":"Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR)","volume":"109","author":"Miller","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_18","first-page":"42","article-title":"Multiscale mapping of burn area and severity using multisensor satellite data and spatial autocorrelation analysis","volume":"20","author":"Lanorte","year":"2013","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.rse.2015.03.011","article-title":"Global burned area mapping from ENVISAT-MERIS and MODIS active fire data","volume":"163","author":"Chuvieco","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"7712","DOI":"10.3390\/rs70607712","article-title":"A Remote Sensing and GIS Approach to Study the Long-Term Vegetation Recovery of a Fire-Affected Pine Forest in Southern Greece","volume":"7","author":"Nioti","year":"2015","journal-title":"Remote Sens."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"6273","DOI":"10.1080\/01431161.2010.508057","article-title":"Enhancement of a fire-detection algorithm by eliminating solar contamination effects and atmospheric path radiance: Application to MODIS data","volume":"32","author":"He","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1109\/TGRS.2008.2009000","article-title":"Southern Africa Validation of the MODIS, L3JRC, and GlobCarbon Burned-Area Products","volume":"47","author":"Roy","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","unstructured":"Key, C.H., and Benson, N.C. (1999, January 15\u201317). Measuring and remote sensing of burn severity: The CBI and NBR. Proceedings of the Joint Fire Science Conference and Workshop, Boise, ID, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1071\/WF05097","article-title":"Remote sensing techniques to assess active fire characteristics and post-fire effects","volume":"15","author":"Lentile","year":"2006","journal-title":"Int. J. Wildland Fire"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1080\/01431160600908924","article-title":"Characterizing post-fire vegetation recovery of California chaparral using TM\/ETM+ time-series data","volume":"28","author":"Hope","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.rse.2015.10.024","article-title":"Effects of fire severity and post-fire climate on short-term vegetation recovery of mixed-conifer and red fir forests in the Sierra Nevada Mountains of California","volume":"171","author":"Meng","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1071\/WF08078","article-title":"Monitoring post-wildfire vegetation response with remotely sensed time-series data in Spain, USA and Israel","volume":"19","author":"Casady","year":"2010","journal-title":"Int. J. Wildland Fire"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/14498596.2015.974227","article-title":"Using MODIS data to analyse post-fire vegetation recovery in Australian eucalypt forests","volume":"60","author":"Caccamo","year":"2014","journal-title":"J. Spat. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1111\/ele.12820","article-title":"Metabolic compensation constrains the temperature dependence of gross primary production","volume":"20","author":"Padfield","year":"2017","journal-title":"Ecol. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"41366","DOI":"10.1038\/srep41366","article-title":"Dominant role of plant physiology in trend and variability of gross primary productivity in North America","volume":"7","author":"Zhou","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2806","DOI":"10.1109\/JSTARS.2016.2528127","article-title":"Development and Validation of Fire Damage-Severity Indices in the Framework of the PREFER Project","volume":"9","author":"Laneve","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1016\/j.rse.2008.11.009","article-title":"Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA","volume":"113","author":"Miller","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.rse.2005.07.008","article-title":"Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands","volume":"98","author":"Chen","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1080\/10106049109354290","article-title":"Mapping burns and natural reforestation using thematic Mapper data","volume":"6","author":"Caselles","year":"1991","journal-title":"Geocarto Int."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"899","DOI":"10.14358\/PERS.69.8.899","article-title":"AVHRR-Based Spectral Vegetation Index for Quantitative Assessment of Vegetation State and Productivity","volume":"69","author":"Kogan","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"723","DOI":"10.2135\/cropsci2000.403723x","article-title":"Remote Sensing of Biomass and Yield of Winter Wheat under Different Nitrogen Supplies","volume":"40","author":"Serrano","year":"2000","journal-title":"Crop Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3364","DOI":"10.3390\/rs4113364","article-title":"How Normalized Difference Vegetation Index (NDVI) Trendsfrom Advanced Very High Resolution Radiometer (AVHRR) and Syst\u00e8me Probatoire d\u2019Observation de la Terre VEGETATION (SPOT VGT) Time Series Differ in Agricultural Areas: An Inner Mongolian Case Study","volume":"4","author":"Yin","year":"2012","journal-title":"Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2296","DOI":"10.1016\/j.asr.2014.08.031","article-title":"Evaluation of MODIS GPP over a complex ecosystem in East Asia: A case study at Gwangneung flux tower in Korea","volume":"54","author":"Shim","year":"2014","journal-title":"Adv. Space Res."},{"key":"ref_41","first-page":"109","article-title":"Quantitative Study of CO2 based on Satellite Image for Carbon Budget on Flux Tower Watersheds","volume":"57","author":"Jung","year":"2015","journal-title":"J. Korean Soc. Agric. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5162","DOI":"10.3390\/su6085162","article-title":"Forest Policy and Law for Sustainability within the Korean Peninsula","volume":"6","author":"Park","year":"2014","journal-title":"Sustainability"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s11355-012-0203-6","article-title":"Temporal changes in the breeding bird community caused by post-fire treatments after the Samcheok forest fire in Korea","volume":"10","author":"Choi","year":"2013","journal-title":"Landsc. Ecol. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1007\/s11355-013-0212-0","article-title":"Effects of forest fires on forest ecosystems in eastern coastal areas of Korea and an overview of restoration projects","volume":"10","author":"Ahn","year":"2013","journal-title":"Landsc. Ecol. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s10310-008-0072-x","article-title":"Estimation of fire severity by use of Landsat TM images and its relevance to vegetation and topography in the 2000 Samcheok forest fire","volume":"13","author":"Lee","year":"2008","journal-title":"J. For. Res."},{"key":"ref_46","unstructured":"Key, C.H., and Benson, N.C. (2006). Landscape Assessment Sampling and Analysis Methods."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1896","DOI":"10.1016\/j.rse.2010.03.013","article-title":"Estimating burn severity from Landsat dNBR and RdNBR indices across western Canada","volume":"114","author":"Soverel","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/15481603.2017.1354803","article-title":"Evaluating and comparing Sentinel 2A and Landsat-8 Operational Land Imager (OLI) spectral indices for estimating fire severity in a Mediterranean pine ecosystem of Greece","volume":"55","author":"Mallinis","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.scitotenv.2008.03.034","article-title":"Effects of soil conservation measures in a partially vegetated area after forest fires","volume":"399","author":"Kim","year":"2008","journal-title":"Sci. Total Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1080\/15481603.2015.1055451","article-title":"Post-wildfire assessment of vegetation regeneration in Bastrop, Texas, using Landsat imagery","volume":"52","author":"Lee","year":"2015","journal-title":"GISci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"230","DOI":"10.2307\/1941323","article-title":"Experiments on disturbance in old-field plant communities: Impact on species richness and abundance","volume":"66","author":"Armesto","year":"1985","journal-title":"Ecology"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s11355-012-0208-1","article-title":"Growth response of Pinus densiflora seedlings to different fertilizer compound ratios in a recently burned area in the eastern coast of Korea","volume":"10","author":"Kim","year":"2012","journal-title":"Landsc. Ecol. Eng."},{"key":"ref_53","unstructured":"(2018, May 13). Korea Meteorological Administration, Available online: https:\/\/data.kma.go.kr."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"7065","DOI":"10.1080\/01431160802226034","article-title":"Sensitivity studies of the moisture effects on MODIS SWIR reflectance and vegetation water indices","volume":"29","author":"Wang","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1071\/WF12058","article-title":"Monitoring post-fire vegetation recovery in the Mediterranean using SPOT and ERS imagery","volume":"23","author":"Polychronaki","year":"2014","journal-title":"Int. J. Wildland Fire"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1111\/j.1365-2486.2008.01613.x","article-title":"Long-term impact of a stand-replacing fire on ecosystem CO2 exchange of a ponderosa pine forest","volume":"14","author":"Dore","year":"2008","journal-title":"Glob. Chang. Biol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1139\/x86-067","article-title":"Biomass of tropical tree plantations and its implications for the global carbon budget","volume":"16","author":"Brown","year":"1986","journal-title":"Can. J. For. Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"430","DOI":"10.3832\/ifor1733-009","article-title":"Heuristic forest planning model for optimizing timber production and carbon sequestration in teak plantations","volume":"10","year":"2017","journal-title":"iForest"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/6\/918\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:08:04Z","timestamp":1760195284000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/6\/918"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,10]]},"references-count":58,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2018,6]]}},"alternative-id":["rs10060918"],"URL":"https:\/\/doi.org\/10.3390\/rs10060918","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,10]]}}}