{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T19:37:24Z","timestamp":1770752244024,"version":"3.50.0"},"reference-count":77,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,7,30]],"date-time":"2018-07-30T00:00:00Z","timestamp":1532908800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31560599"],"award-info":[{"award-number":["31560599"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31560118"],"award-info":[{"award-number":["31560118"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005273","name":"Natural Science Foundation of Yunnan Province","doi-asserted-by":"publisher","award":["2015FB157"],"award-info":[{"award-number":["2015FB157"]}],"id":[{"id":"10.13039\/501100005273","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote mountainous regions are among the Earth\u2019s last remaining wild spots, hosting rare ecosystems and rich biodiversity. Because of access difficulties and low population density, baseline information about natural and human-induced disturbances in these regions is often limited or nonexistent. Landsat time series offer invaluable opportunities to reconstruct past land cover changes. However, the applicability of this approach strongly depends on the availability of good quality, cloud-free images, acquired at a regular time interval, which in mountainous regions are often difficult to find. The present study analyzed burn scar detection capabilities of 11 widely used spectral indices (SI) at 1 to 5 years after fire events in four dominant vegetation groups in a mountainous region of northwest Yunnan, China. To evaluate their performances, we used M-statistic as a burned-unburned class separability index, and we adapted an existing metric to quantify the SI residual burn signal at post-fire dates compared to the maximum severity recorded soon after the fire. Our results show that Normalized Burn Ratio (NBR) and Normalized Difference Moisture Index (NDMI) are always among the three best performers for the detection of burn scars starting 1 year after fire but not for the immediate post-fire assessment, where the Mid Infrared Burn Index, Burn Area Index, and Tasseled Cap Greenness were superior. Brightness and Wetness peculiar patterns revealed long-term effects of fire in vegetated land, suggesting their potential integration to assist other SI in burned area detection several years after the fire event. However, in general, class separability of most of the SI was poor after one growing season, due to the seasonal rains and the relatively fast regrowth rate of shrubs and grasses, confirming the difficulty of assessment in mountainous ecosystems. Our findings are meaningful for the selection of a suitable SI to integrate in burned area detection workflows, according to vegetation type and time lag between image acquisitions.<\/jats:p>","DOI":"10.3390\/rs10081196","type":"journal-article","created":{"date-parts":[[2018,7,30]],"date-time":"2018-07-30T11:55:08Z","timestamp":1532951708000},"page":"1196","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":86,"title":["Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2045-2800","authenticated-orcid":false,"given":"Davide","family":"Fornacca","sequence":"first","affiliation":[{"name":"Institute of Eastern-Himalaya Biodiversity Research, Dali University Hongsheng Rd. 2, Dali 671003, China"},{"name":"EnviroSPACE Lab, Institute for Environmental Sciences, University of Geneva, 66 Boulevard Carl Vogt, 1205 Geneva, Switzerland"},{"name":"Collaborative Innovation Center for Biodiversity and Conservation in the Three Parallel Rivers Region of China, Dali 671003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3381-3166","authenticated-orcid":false,"given":"Guopeng","family":"Ren","sequence":"additional","affiliation":[{"name":"Institute of Eastern-Himalaya Biodiversity Research, Dali University Hongsheng Rd. 2, Dali 671003, China"},{"name":"Collaborative Innovation Center for Biodiversity and Conservation in the Three Parallel Rivers Region of China, Dali 671003, China"}]},{"given":"Wen","family":"Xiao","sequence":"additional","affiliation":[{"name":"Institute of Eastern-Himalaya Biodiversity Research, Dali University Hongsheng Rd. 2, Dali 671003, China"},{"name":"Collaborative Innovation Center for Biodiversity and Conservation in the Three Parallel Rivers Region of China, Dali 671003, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2016.03.008","article-title":"Optical remotely sensed time series data for land cover classification: A review","volume":"116","author":"White","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.rse.2012.01.010","article-title":"Opening the archive: How free data has enabled the science and monitoring promise of Landsat","volume":"122","author":"Wulder","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0167-5877(05)80004-2","article-title":"Interpreting vegetation indices","volume":"11","author":"Jackson","year":"1991","journal-title":"Prev. Vet. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/02757259509532298","article-title":"A review of vegetation indices","volume":"13","author":"Bannari","year":"1995","journal-title":"Remote Sens. Rev."},{"key":"ref_5","first-page":"227","article-title":"Towards an efficacious method of using Landsat TM imagery to map forest in complex mountain terrain in Northwest Yunnan, China","volume":"48","author":"Yang","year":"2007","journal-title":"Trop. Ecol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xue, J., and Su, B. (2017). Significant remote sensing vegetation indices: a review of developments and applications. J. Sens., 2017.","DOI":"10.1155\/2017\/1353691"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1080\/01431160701281072","article-title":"Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM\/ETM images","volume":"29","author":"Escuin","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1080\/19475705.2014.925982","article-title":"Mapping a burned forest area from Landsat TM data by multiple methods","volume":"7","author":"Chen","year":"2016","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_9","first-page":"109","article-title":"Cartograf\u00eda De Grandes Incendios Forestales En La Pen\u00ednsula Ib\u00e9rica a Partir De Im\u00e1genes Noaa-Avhrr","volume":"7","author":"Chuvieco","year":"1998","journal-title":"Ser. Geogr."},{"key":"ref_10","unstructured":"Bailly, J.-S., Griffith, D., and Josselin, D. (2016). Evaluating performances of spectral indices for burned area mapping using object-based image analysis. Proceedings of Spatial Accuracy, International Spatial Accuracy Research Association."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1016\/j.rse.2010.12.005","article-title":"Mapping burned areas from landsat TM\/ETM+ data with a two-phase algorithm: Balancing omission and commission errors","volume":"115","author":"Bastarrika","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.rse.2017.03.035","article-title":"A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series","volume":"194","author":"White","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"12360","DOI":"10.3390\/rs61212360","article-title":"BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data","volume":"6","author":"Bastarrika","year":"2014","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.1016\/j.rse.2010.07.008","article-title":"Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr-Temporal segmentation algorithms","volume":"114","author":"Kennedy","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/j.rse.2017.06.027","article-title":"Mapping burned areas using dense time-series of Landsat data","volume":"198","author":"Hawbaker","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.rse.2009.08.017","article-title":"An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks","volume":"114","author":"Huang","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6950","DOI":"10.3390\/rs70606950","article-title":"Standardized time-series and interannual phenological deviation: New techniques for burned-area detection using long-term MODIS-NBR dataset","volume":"7","author":"Silva","year":"2015","journal-title":"Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5103","DOI":"10.1080\/01431160210153129","article-title":"Assessment of different spectral indices in the red\u2014Near-infrared spectral domain for burned land discrimination","volume":"23","author":"Chuvieco","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1071\/WF09069","article-title":"Evaluating Landsat Thematic Mapper spectral indices for estimating burn severity of the 2007 Peloponnese wildfires in Greece","volume":"19","author":"Veraverbeke","year":"2010","journal-title":"Int. J. Wildland Fire"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3521","DOI":"10.1080\/01431161003752430","article-title":"Evaluation of pre\/post-fire differenced spectral indices for assessing burn severity in a Mediterranean environment with Landsat Thematic Mapper","volume":"32","author":"Veraverbeke","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.3390\/rs3112403","article-title":"Evaluating Spectral Indices for Assessing Fire Severity in Chaparral Ecosystems (Southern California) Using MODIS\/ASTER (MASTER) Airborne Simulator Data","volume":"3","author":"Harris","year":"2011","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1007\/s11069-015-2115-x","article-title":"A comparison of forest fire burned area indices based on HJ satellite data","volume":"81","author":"Liu","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.rse.2005.03.002","article-title":"Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM+","volume":"96","author":"Epting","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.rse.2013.03.003","article-title":"Mapping fire extent and burn severity in Alaskan tussock tundra: An analysis of the spectral response of tundra vegetation to wildland fire","volume":"134","author":"Loboda","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1071\/WF15098","article-title":"Evaluation of spectral indices for estimating burn severity in semiarid grasslands","volume":"25","author":"Lu","year":"2016","journal-title":"Int. J. Wildland Fire"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Huang, H., Roy, D.P., Boschetti, L., Zhang, H.K., Yan, L., Kumar, S.S., Gomez-Dans, J., and Li, J. (2016). Separability analysis of Sentinel-2A Multi-Spectral Instrument (MSI) data for burned area discrimination. Remote Sens., 8.","DOI":"10.3390\/rs8100873"},{"key":"ref_27","unstructured":"Melchiori, A.E., C\u00e2ndido, P., Libonati, R., Morelli, F., Setzer, A., de Jesus, S.C., Garcia Fonseca, L.M., and K\u00f6rting, T.S. (2015, January 25\u201329). Spectral indices and multi-temporal change image detection algorithms for burned area extraction in the Brazilian Cerrado. Proceedings of the Anais XVII Simp\u00f3sio Brasileiro de Sensoriamento Remoto\u2014SBSR, Jo\u00e3o Pessoa-PB, Brasil."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1803","DOI":"10.3390\/rs6031803","article-title":"Burned area detection and burn severity assessment of a heathland fire in belgium using airborne imaging spectroscopy (APEX)","volume":"6","author":"Schepers","year":"2014","journal-title":"Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hislop, S., Jones, S., Soto-Berelov, M., Skidmore, A.K., Haywood, A., and Nguyen, T. (2018). Using Landsat Spectral Indices in Time-Series to Assess Wildfire Disturbance and Recovery. Remote Sens., 10.","DOI":"10.3390\/rs10030460"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"7905","DOI":"10.1080\/01431161.2010.524678","article-title":"Detecting post-fire burn severity and vegetation recovery using multitemporal remote sensing spectral indices and field-collected composite burn index data in a ponderosa pine forest","volume":"32","author":"Chen","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lozano, F.J., Su\u00e1rez-Seoane, S., and de Luis-Calabuig, E. (2012). Does fire regime affect both temporal patterns and drivers of vegetation recovery in a resilient Mediterranean landscape? A remote sensing approach at two observation levels. Int. J. Wildland Fire, 21.","DOI":"10.1071\/WF10072"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1080\/2150704X.2015.1126375","article-title":"Forest recovery trends derived from Landsat time series for North American boreal forests","volume":"37","author":"Pickell","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","unstructured":"Key, C.H., and Benson, N.C. (2006). Landscape Assessment: Ground Measure of Severity, the Composite Burn Index; and Remote Sensing of Severity, the Normalized Burn Ratio."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.1749-8198.2008.00200.x","article-title":"Remote Sensing of Mountain Environments","volume":"3","author":"Weiss","year":"2009","journal-title":"Geogr. Compass"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6767","DOI":"10.3390\/rs5126767","article-title":"Evaluation of different topographic corrections for landsat TM data by prediction of foliage projective cover (FPC) in topographically complex landscapes","volume":"5","author":"Ediriweera","year":"2013","journal-title":"Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1080\/01431160500182992","article-title":"The role of topographic correction in mapping recently burned Mediterranean forest areas from LANDSAT TM images","volume":"27","author":"Gitas","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.rse.2016.07.002","article-title":"Multi-criteria evaluation of topographic correction methods","volume":"184","author":"Sola","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1016\/j.rse.2013.05.013","article-title":"Improved forest change detection with terrain illumination corrected Landsat images","volume":"136","author":"Tan","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.foreco.2015.05.015","article-title":"The role of fire in the Central Yunnan Plateau ecosystem, southwestern China","volume":"356","author":"Su","year":"2015","journal-title":"For. Ecol. Manag."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Qin, X., Li, Z., and Zhang, Z. (2010, January 28). Distribution Pattern of Fires in China Based on Satellite Data. Proceedings of the 2010 Second lITA International Conference on Geoscience and Remote Sensing, Qingdao, China.","DOI":"10.1109\/IITA-GRS.2010.5602671"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1071\/WF14031","article-title":"Comparison of forest burned areas in mainland China derived from MCD45A1 and data recorded in yearbooks from 2001 to 2011","volume":"24","author":"Li","year":"2014","journal-title":"Int. J. Wildland Fire"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Fornacca, D., Ren, G., and Xiao, W. (2017). Performance of Three MODIS fire products (MCD45A1, MCD64A1, MCD14ML), and ESA Fire_CCI in a mountainous area of Northwest Yunnan, China, characterized by frequent small fires. Remote Sens., 9.","DOI":"10.3390\/rs9111131"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s10265-009-0221-0","article-title":"Ecology of subtropical evergreen broad-leaved forests of Yunnan, southwestern China as compared to those of southwestern Japan","volume":"122","author":"Tang","year":"2009","journal-title":"J. Plant Res."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Tang, C.Q. (2015). The Subtropical Vegetation of Southwestern China: Plant Distribution, Diversity and Ecology, Springer. Plant and Vegetation.","DOI":"10.1007\/978-94-017-9741-2"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1109\/36.581987","article-title":"Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: An Overview","volume":"35","author":"Vermote","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS Symposium, NASA."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/BF00031911","article-title":"GEMI: A non-linear index to monitor global vegetation from satellites","volume":"101","author":"Pinty","year":"1992","journal-title":"Vegetatio"},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1080\/014311600210506","article-title":"Burned area mapping using logistic regression modeling of a single post-fire Landsat-5 Thematic Mapper image","volume":"21","author":"Koutsias","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/S0034-4257(01)00318-2","article-title":"Detection of forest harvest type using multiple dates of Landsat TM imagery","volume":"80","author":"Wilson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Mart\u00edn, M.P., G\u00f3mez, I., and Chuvieco, E. (2006). Burnt Area Index (BAIM) for burned area discrimination at regional scale using MODIS data. For. Ecol. Manag., 234.","DOI":"10.1016\/j.foreco.2006.08.248"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2641","DOI":"10.1080\/01431160110053185","article-title":"An evaluation of different bi-spectral spaces for discriminating burned shrub-savannah","volume":"22","author":"Trigg","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1109\/TGRS.1984.350619","article-title":"A phisically-based transformation of Thematic Mapper data\u2014The TM Tasseled Cap","volume":"GE-22","author":"Crist","year":"1984","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/0034-4257(85)90102-6","article-title":"A TM Tasseled Cap equivalent transformation for reflectance factor data","volume":"17","author":"Crist","year":"1985","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"64","DOI":"10.4996\/fireecology.0301064","article-title":"The relationship of multispectral satellite imagery to immediate fire effects","volume":"3","author":"Hudak","year":"2007","journal-title":"Fire Ecol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.rse.2011.06.027","article-title":"Monitoring gradual ecosystem change using Landsat time series analyses: Case studies in selected forest and rangeland ecosystems","volume":"122","author":"Vogelmann","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.rse.2014.11.015","article-title":"Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia","volume":"158","author":"Schmidt","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1080\/01431160802220219","article-title":"An automatic method for burn scar mapping using support vector machines","volume":"30","author":"Cao","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/S0034-4257(02)00021-4","article-title":"Radiometric analysis of SPOT-VEGETATION images for burnt area detection in Northern Australia","volume":"82","author":"Stroppiana","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.ecolmodel.2006.02.025","article-title":"Estimating spectral separability of satellite derived parameters for burned areas mapping in the Calabria region by using SPOT-Vegetation data","volume":"196","author":"Lasaponara","year":"2006","journal-title":"Ecol. Model."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Mazher, A. (2013). Comparative analysis of mapping burned areas from landsat TM images. J. Phys. Conf. Ser., 439.","DOI":"10.1088\/1742-6596\/439\/1\/012038"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"3101","DOI":"10.1080\/01431160152558279","article-title":"Mapping fire-induced vegetation depletion in the Peloncillo Mountains, Arizona and New Mexico","volume":"22","author":"Rogan","year":"2001","journal-title":"Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2003.10.019","article-title":"Spectral indices and fire behavior simulation for fire risk assessment in savanna ecosystems","volume":"91","author":"Mbow","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.ecolind.2017.04.038","article-title":"Assessment of forest recovery at Wu-Ling fire scars in Taiwan using multi-temporal Landsat imagery","volume":"79","author":"Chompuchan","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"34","DOI":"10.4996\/fireecology.0202034","article-title":"Ecological and Sampling Constraints on Defining Landscape Fire Severity","volume":"2","author":"Key","year":"2006","journal-title":"Fire Ecol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1109\/36.297984","article-title":"Detection of Forests Using Mid-IR Reflectance: An Application for Aerosol Studies","volume":"32","author":"Kaufman","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1016\/j.rse.2011.02.006","article-title":"On a new coordinate system for improved discrimination of vegetation and burned areas using MIR\/NIR information","volume":"115","author":"Libonati","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2753","DOI":"10.1080\/01431160600954704","article-title":"Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: Comparison of methods and application to MODIS","volume":"28","author":"Smith","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.ecolmodel.2003.12.037","article-title":"Vegetation recovery assessment at the Jou-Jou Mountain landslide area caused by the 921 Earthquake in Central Taiwan","volume":"176","author":"Lin","year":"2004","journal-title":"Ecol. Model."},{"key":"ref_70","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_71","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.rse.2015.09.014","article-title":"Estimation and evaluation of multi-decadal fire severity patterns using Landsat sensors","volume":"170","author":"Parker","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.rse.2007.01.017","article-title":"Regionally adaptable dNBR-based algorithm for burned area mapping from MODIS data","volume":"109","author":"Loboda","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/rs3081680","article-title":"Timing constraints on remote sensing of wildland fire burned area in the southeastern US","volume":"3","author":"Picotte","year":"2011","journal-title":"Remote Sens."},{"key":"ref_74","first-page":"19","article-title":"The analysis of regional climate change features over Yunnan in recent 50 years","volume":"27","author":"Cheng","year":"2008","journal-title":"Prog. Geogr."},{"key":"ref_75","unstructured":"Stubbendieck, J., Volesky, J., and Ortmann, J. (2007). Grassland management with prescribed fire. Extension, 1\u20136."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/LGRS.2005.858485","article-title":"Remote sensing of fire severity: Assessing the performance of the normalized burn ratio","volume":"3","author":"Roy","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1071\/WF08007","article-title":"Using Landsat data to assess fire and burn severity in the North American boreal forest region\u2014An overview and summary of results","volume":"17","author":"French","year":"2008","journal-title":"Int. J. Wildland Fire"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/8\/1196\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:15:19Z","timestamp":1760195719000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/8\/1196"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,30]]},"references-count":77,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2018,8]]}},"alternative-id":["rs10081196"],"URL":"https:\/\/doi.org\/10.3390\/rs10081196","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,30]]}}}