{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T07:02:17Z","timestamp":1774335737764,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,2,6]],"date-time":"2019-02-06T00:00:00Z","timestamp":1549411200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2018YFC0807000"],"award-info":[{"award-number":["2018YFC0807000"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["WK2320000040"],"award-info":[{"award-number":["WK2320000040"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Grant Council of the Hong Kong Special Administrative Region, China","award":["CityU 11300815"],"award-info":[{"award-number":["CityU 11300815"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Since wildfires have occurred frequently in recent years, accurate burned area mapping is required for wildfire severity assessment and burned land reconstruction. Satellite remote sensing is an effective technology that can provide valuable information for wildfire assessment. However, the common approaches based on using a single satellite image to promptly detect the burned areas have low accuracy and limited applicability. This paper develops a new burned area mapping method that surpasses the detection accuracy of previous methods, while still using a single Moderate Resolution Imaging Spectroradiometer (MODIS) sensor image. The key innovation is integrating optimal spectral indices and a neural network algorithm. We used the traditional empirical formula method, multi-threshold method and visual interpretation method to extract the sample sets of five typical types (burned area, vegetation, cloud, bare soil, and cloud shadow) from the MODIS data of several wildfires in the American states of Nevada, Washington and California in 2016. Afterward, the separability index M was adopted to assess the capacity of seven spectral bands and 13 spectral indices to distinguish the burned area from four unburned land cover types. Based on the separability analysis between the burned area and unburned areas, the spectral indices with an M value higher than 1.0 were employed to generate the training sample sets that were assessed to have an overall accuracy of 98.68% and Kappa coefficient of 97.46%. Finally, we utilized a back-propagation neural network (BPNN) to learn the spectral differences of different types from the training sample sets and obtain the output burned area map. The proposed method was applied to three wildfire cases in the American states of Idaho, Nevada and Oregon in 2017. A comparison of detection results between the new MODIS-based burned area map and the reference burned area map compiled from Landsat-8 Operational Land Imager (OLI) data indicates that the proposed method can effectively exploit the spectral characteristics of various land cover types. Also, this new method can achieve higher accuracy with the reduction of commission error (CE, &gt;10%) and omission error (OE, &gt;6%) compared to the traditional empirical formula method. The new burned area mapping method could help managers and the public perform more effective wildfire assessments and emergency management.<\/jats:p>","DOI":"10.3390\/rs11030326","type":"journal-article","created":{"date-parts":[[2019,2,6]],"date-time":"2019-02-06T11:51:12Z","timestamp":1549453872000},"page":"326","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Integration of Multiple Spectral Indices and a Neural Network for Burned Area Mapping Based on MODIS Data"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8889-3111","authenticated-orcid":false,"given":"Rui","family":"Ba","sequence":"first","affiliation":[{"name":"State Key Laboratory of Fire Science, University of Science and Technology of China, Jinzhai 96, Hefei 2300026, China"},{"name":"Department of Civil and Architectural Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong"}]},{"given":"Weiguo","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Fire Science, University of Science and Technology of China, Jinzhai 96, Hefei 2300026, China"}]},{"given":"Xiaolian","family":"Li","sequence":"additional","affiliation":[{"name":"College of Ocean Science and Engineering, Shanghai Maritime University, Haigang Ave 1550, Shanghai 201306, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6483-095X","authenticated-orcid":false,"given":"Zixi","family":"Xie","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Fire Science, University of Science and Technology of China, Jinzhai 96, Hefei 2300026, China"}]},{"given":"Siuming","family":"Lo","sequence":"additional","affiliation":[{"name":"Department of Civil and Architectural Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,6]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/S0034-4257(99)00026-7","article-title":"An algorithm for extracting burned areas from time series of AVHRR GAC data applied at a continental scale","volume":"69","author":"Barbosa","year":"1999","journal-title":"Remote Sensi. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.5194\/bg-7-1991-2010","article-title":"The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: Results from a process-based model","volume":"7","author":"Thonicke","year":"2010","journal-title":"Biogeosciences"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Melchiorre, A., and Boschetti, L. (2018). Global Analysis of Burned Area Persistence Time with MODIS Data. Remote Sens., 10.","DOI":"10.3390\/rs10050750"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Fornacca, D., Ren, G., and Xiao, W. (2018). Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China. Remote Sens., 10.","DOI":"10.3390\/rs10081196"},{"key":"ref_6","first-page":"64","article-title":"Ten years of global burned area products from spaceborne remote sensing-A review: Analysis of user needs and recommendations for future developments","volume":"26","author":"Mouillot","year":"2014","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Pereira, A.A., Pereira, J., Libonati, R., Oom, D., Setzer, A.W., Morelli, F., Machado-Silva, F., and de Carvalho, L.M.T. (2017). Burned area mapping in the brazilian savanna using a one-class support vector machine trained by active fires. Remote Sens., 9.","DOI":"10.3390\/rs9111161"},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"4003","DOI":"10.1080\/0143116031000103835","article-title":"Mapping burned surfaces in Sub-Saharan Africa based on multi-temporal neural classification","volume":"24","author":"Brivio","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1109\/TGRS.2003.808898","article-title":"An algorithm for mapping burnt areas in Australia using SPOT-VEGETATION data","volume":"41","author":"Stroppiana","year":"2003","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/0034-4257(93)00074-J","article-title":"Locating and estimating the areal extent of wildfires in alaskan boreal forests using multiple-season avhrr ndvi composite data","volume":"51","author":"Kasischke","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/0034-4257(95)00154-9","article-title":"Methodology for estimating burned area from AVHRR reflectance data","volume":"54","author":"Razafimpanilo","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1109\/36.739156","article-title":"A comparative evaluation of NOAA\/AVHRR vegetation indexes for burned surface detection and mapping","volume":"37","author":"Pereira","year":"1999","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/S0034-4257(00)00078-X","article-title":"Hotspot and NDVI differencing synergy (HANDS): A new technique for burned area mapping over boreal forest","volume":"74","author":"Fraser","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.rse.2011.10.017","article-title":"Burned area mapping time series in Canada (1984\u20131999) from NOAA-AVHRR LTDR: A comparison with other remote sensing products and fire perimeters","volume":"117","author":"Ruiz","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.rse.2008.10.006","article-title":"An active-fire based burned area mapping algorithm for the MODIS sensor","volume":"113","author":"Giglio","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"1300","DOI":"10.1109\/TGRS.2004.826801","article-title":"A technique for detecting burn scars using MODIS data","volume":"42","author":"Li","year":"2004","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Guindos-Rojas, F., Arbelo, M., Garc\u00eda-L\u00e1zaro, J.R., Moreno-Ruiz, J.A., and Hern\u00e1ndez-Leal, P.A. (2018). Evaluation of a Bayesian Algorithm to Detect Burned Areas in the Canary Islands\u2019 Dry Woodlands and Forests Ecoregion Using MODIS Data. Remote Sens., 10.","DOI":"10.3390\/rs10050789"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1109\/LGRS.2009.2020067","article-title":"Analysis and Interpretation of Spectral Indices for Soft Multicriteria Burned-Area Mapping in Mediterranean Regions","volume":"6","author":"Stroppiana","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Boschetti, M., Stroppiana, D., and Brivio, P.A. (2010). Mapping Burned Areas in a Mediterranean Environment Using Soft Integration of Spectral Indices from High-Resolution Satellite Images. Earth Interact., 14.","DOI":"10.1175\/2010EI349.1"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2702","DOI":"10.1016\/j.rse.2011.06.010","article-title":"Evaluating spectral indices for burned area discrimination using MODIS\/ASTER (MASTER) airborne simulator data","volume":"115","author":"Veraverbeke","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-L\u00e1zaro, J., Moreno-Ruiz, J., Ria\u00f1o, D., and Arbelo, M. (2018). Estimation of Burned Area in the Northeastern Siberian Boreal Forest from a Long-Term Data Record (LTDR) 1982\u20132015 Time Series. Remote Sens., 10.","DOI":"10.3390\/rs10060940"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1109\/LGRS.2011.2167953","article-title":"Positive and Negative Information for Assessing and Revising Scores of Burn Evidence","volume":"9","author":"Stroppiana","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.3390\/rs70201320","article-title":"Integration of Optical and SAR Data for Burned Area Mapping in Mediterranean Regions","volume":"7","author":"Stroppiana","year":"2015","journal-title":"Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1016\/j.rse.2010.12.005","article-title":"Mapping burned areas from Landsat TM\/ETM plus data with a two-phase algorithm: Balancing omission and commission errors","volume":"115","author":"Bastarrika","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.isprsjprs.2012.03.001","article-title":"A method for extracting burned areas from Landsat TM\/ETM+ images by soft aggregation of multiple Spectral Indices and a region growing algorithm","volume":"69","author":"Stroppiana","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Axel, A.C. (2018). Burned Area Mapping of an Escaped Fire into Tropical Dry Forest in Western Madagascar Using Multi-Season Landsat OLI Data. Remote Sens., 10.","DOI":"10.3390\/rs10030371"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1111\/gcb.13841","article-title":"Spatial evaluation of Indonesia\u2019s 2015 fire affected area and estimated carbon emissions using Sentinel-1","volume":"24","author":"Lohberger","year":"2018","journal-title":"Glob. Chang. Biol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Engelbrecht, J., Theron, A., Vhengani, L., and Kemp, J. (2017). A simple normalized difference approach to burnt area mapping using multi-polarisation C-Band SAR. Remote Sens., 9.","DOI":"10.3390\/rs9080764"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4473","DOI":"10.3390\/rs70404473","article-title":"Forest Fire Smoke Detection Using Back-Propagation Neural Network Based on MODIS Data","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Santana, N., de Carvalho J\u00fanior, O., Gomes, R., and Guimar\u00e3es, R.J.R.S. (2018). Burned-Area Detection in Amazonian Environments Using Standardized Time Series Per Pixel in MODIS Data. Remote Sens., 10.","DOI":"10.3390\/rs10121904"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ryu, J.-H., Han, K.-S., Hong, S., Park, N.-W., Lee, Y.-W., and Cho, J. (2018). Satellite-Based Evaluation of the Post-Fire Recovery Process from the Worst Forest Fire Case in South Korea. Remote Sens., 10.","DOI":"10.3390\/rs10060918"},{"key":"ref_34","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_35","unstructured":"(2018, December 15). Level-1 and Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC), Available online: https:\/\/modis.gsfc.nasa.gov\/."},{"key":"ref_36","unstructured":"(2019, January 18). United States Geological Survey (USGS) Earth Explorer Website, Available online: https:\/\/earthexplorer.usgs.gov\/."},{"key":"ref_37","unstructured":"(2018, December 20). Incident Information System (InciWeb), Available online: https:\/\/inciweb.nwcg.gov\/."},{"key":"ref_38","unstructured":"Capata, A., Marella, A., and Russo, R. (2008, January 4\u20137). A geo-based application for the management of mobile actors during crisis situations. Proceedings of the 5th International ISCRAM Conference, Washington, DC, USA."},{"key":"ref_39","unstructured":"(2018, December 20). GeoMAC Website (by the Geospatial Multi-Agency Coordination Group), Available online: https:\/\/www.geomac.gov\/."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Parson, A., Robichaud, P.R., Lewis, S.A., Napper, C., and Clark, J.T. (2010). Field Guide for Mapping Post-Fire Soil Burn Severity.","DOI":"10.2737\/RMRS-GTR-243"},{"key":"ref_41","unstructured":"(2018, November 18). Monitoring Trends in Burn Severity (MTBS) Website, Available online: https:\/\/www.mtbs.gov\/."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/s11069-013-0564-7","article-title":"Use of remote sensing-derived variables in developing a forest fire danger forecasting system","volume":"67","author":"Chowdhury","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1080\/014311600210173","article-title":"The global fire product: Daily fire occurrence from April 1992 to December 1993 derived from NOAA AVHRR data","volume":"21","author":"Stroppiana","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"2367","DOI":"10.1080\/01431160701236795","article-title":"Smoke plume detection in the eastern United States using MODIS","volume":"28","author":"Xie","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1859","DOI":"10.1109\/36.951076","article-title":"Automatic detection of fire smoke using artificial neural networks and threshold approaches applied to AVHRR imagery","volume":"39","author":"Li","year":"2001","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_47","first-page":"44","article-title":"Atmospheric correction module: Quac and flaash user\u2019s guide","volume":"4","author":"Module","year":"2009","journal-title":"Version"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3181","DOI":"10.1080\/01431160500044713","article-title":"A practical split-window algorithm for retrieving land-surface temperature from MODIS data","volume":"26","author":"Mao","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5103","DOI":"10.1080\/01431160210153129","article-title":"Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination","volume":"23","author":"Chuvieco","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.rse.2005.04.014","article-title":"Testing the potential of multi-spectral remote sensing for retrospectively estimating fire severity in African Savannahs","volume":"97","author":"Smith","year":"2005","journal-title":"Remote Sens. Environ."},{"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","unstructured":"Key, C., and Benson, N. (1999, January 15\u201317). Measuring and remote sensing of burn severity. Proceedings of the Joint Fire Science Conference and Workshop, Boise, ID, USA."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_55","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_56","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":"Plant Ecol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/36.124218","article-title":"Classification of multispectral remote sensing data using a back-propagation neural network","volume":"30","author":"Heermann","year":"1992","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/0954-1810(94)00011-S","article-title":"Back-propagation neural networks for modeling complex systems","volume":"9","author":"Goh","year":"1995","journal-title":"Artif. Intell. Eng."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1985). Learning Internal Representations by Error Propagation, California Univ San Diego La Jolla Inst for Cognitive Science.","DOI":"10.21236\/ADA164453"},{"key":"ref_61","unstructured":"Yu, C.-C., and Liu, B.-D. (2002, January 12\u201317). A backpropagation algorithm with adaptive learning rate and momentum coefficient. Proceedings of the 2002 International Joint Conference on the Neural Networks, Honolulu, HI, USA."},{"key":"ref_62","unstructured":"Key, C.H., and Benson, N.C. (2006). Landscape Assessment, FIREMON: Fire Effects Monitoring and Inventory System."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/j.rse.2016.07.022","article-title":"A MODIS-based burned area assessment for Russian croplands: Mapping requirements and challenges","volume":"184","author":"Hall","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"243","DOI":"10.2307\/2402322","article-title":"An evaluation of long grass as a bird deterrent on British airfields","volume":"17","author":"Brough","year":"1980","journal-title":"J. Appli. Ecol."},{"key":"ref_65","unstructured":"(2018, December 20). Tallgrass Prairie. Available online: https:\/\/en.wikipedia.org\/wiki\/Tallgrass_prairie."},{"key":"ref_66","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_67","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2008). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press.","DOI":"10.1201\/9781420055139"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/S0034-4257(98)00006-6","article-title":"Remote sensing of biomass burning in tropical regions: Sampling issues and multisensor approach","volume":"64","author":"Eva","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/S0034-4257(02)00076-7","article-title":"The MODIS fire products","volume":"83","author":"Justice","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_70","unstructured":"Pagano, T.S., and Durham, R.M. (1993, January 13\u201314). Moderate resolution imaging spectroradiometer (MODIS). Proceedings of the Sensor Systems for the Early Earth Observing System Platforms, Orlando, FL, USA."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/3\/326\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:31:25Z","timestamp":1760185885000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/3\/326"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,6]]},"references-count":70,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["rs11030326"],"URL":"https:\/\/doi.org\/10.3390\/rs11030326","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,6]]}}}