{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T09:08:45Z","timestamp":1776071325408,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,6,5]],"date-time":"2021-06-05T00:00:00Z","timestamp":1622851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Sentinel-2 (S2) multi-spectral instrument (MSI) images are used in an automated approach built on fuzzy set theory and a region growing (RG) algorithm to identify areas affected by fires in Mediterranean regions. S2 spectral bands and their post- and pre-fire date (\u0394post-pre) difference are interpreted as evidence of burn through soft constraints of membership functions defined from statistics of burned\/unburned training regions; evidence of burn brought by the S2 spectral bands (partial evidence) is integrated using ordered weighted averaging (OWA) operators that provide synthetic score layers of likelihood of burn (global evidence of burn) that are combined in an RG algorithm. The algorithm is defined over a training site located in Italy, Vesuvius National Park, where membership functions are defined and OWA and RG algorithms are first tested. Over this site, validation is carried out by comparison with reference fire perimeters derived from supervised classification of very high-resolution (VHR) PlanetScope images leading to more than satisfactory results with Dice coefficient &gt; 0.84, commission error &lt; 0.22 and omission error &lt; 0.15. The algorithm is tested for exportability over five sites in Portugal (1), Spain (2) and Greece (2) to evaluate the performance by comparison with fire reference perimeters derived from the Copernicus Emergency Management Service (EMS) database. In these sites, we estimate commission error &lt; 0.15, omission error &lt; 0.1 and Dice coefficient &gt; 0.9 with accuracy in some cases greater than values obtained in the training site. Regression analysis confirmed the satisfactory accuracy levels achieved over all sites. The algorithm proposed offers the advantages of being least dependent on a priori\/supervised selection for input bands (by building on the integration of redundant partial burn evidence) and for criteria\/threshold to obtain segmentation into burned\/unburned areas.<\/jats:p>","DOI":"10.3390\/rs13112214","type":"journal-article","created":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T01:56:40Z","timestamp":1623031000000},"page":"2214","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["A Burned Area Mapping Algorithm for Sentinel-2 Data Based on Approximate Reasoning and Region Growing"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7022-5921","authenticated-orcid":false,"given":"Matteo","family":"Sali","sequence":"first","affiliation":[{"name":"Institute for Electromagnetic Sensing of the Environment (IREA), Consiglio Nazionale delle Ricerche, 20133 Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erika","family":"Piaser","sequence":"additional","affiliation":[{"name":"Institute for Electromagnetic Sensing of the Environment (IREA), Consiglio Nazionale delle Ricerche, 20133 Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2156-4166","authenticated-orcid":false,"given":"Mirco","family":"Boschetti","sequence":"additional","affiliation":[{"name":"Institute for Electromagnetic Sensing of the Environment (IREA), Consiglio Nazionale delle Ricerche, 20133 Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5477-3194","authenticated-orcid":false,"given":"Pietro Alessandro","family":"Brivio","sequence":"additional","affiliation":[{"name":"Institute for Electromagnetic Sensing of the Environment (IREA), Consiglio Nazionale delle Ricerche, 20133 Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4456-4628","authenticated-orcid":false,"given":"Giovanna","family":"Sona","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering (DICA), Politecnico di Milano, 20133 Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6775-753X","authenticated-orcid":false,"given":"Gloria","family":"Bordogna","sequence":"additional","affiliation":[{"name":"Institute for Electromagnetic Sensing of the Environment (IREA), Consiglio Nazionale delle Ricerche, 20133 Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5619-4305","authenticated-orcid":false,"given":"Daniela","family":"Stroppiana","sequence":"additional","affiliation":[{"name":"Institute for Electromagnetic Sensing of the Environment (IREA), Consiglio Nazionale delle Ricerche, 20133 Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5545","DOI":"10.1016\/j.atmosenv.2011.05.010","article-title":"Forest fires in a changing climate and their impacts on air quality","volume":"45","author":"Carvalho","year":"2011","journal-title":"Atmos. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"267","DOI":"10.5194\/bg-13-267-2016","article-title":"Climate, CO2 and human population impacts on global wildfire emissions","volume":"13","author":"Knorr","year":"2016","journal-title":"Biogeosciences"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7537","DOI":"10.1038\/ncomms8537","article-title":"Climate-induced variations in global wildfire danger from 1979 to 2013","volume":"6","author":"Jolly","year":"2015","journal-title":"Nat. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1029\/2018GL080959","article-title":"Global emergence of anthropogenic climate change in fire weather indices","volume":"46","author":"Abatzoglou","year":"2019","journal-title":"Geophys. Res. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s13595-020-00933-5","article-title":"Climate change impact on future wildfire danger and activity in southern Europe: A review","volume":"77","author":"Dupuy","year":"2020","journal-title":"Ann. Forest Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"11707","DOI":"10.5194\/acp-10-11707-2010","article-title":"Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997\u20132009)","volume":"10","author":"Randerson","year":"2010","journal-title":"Atmos. Chem. Phys."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.5194\/gmd-8-1321-2015","article-title":"Modelling the role of fires in the terrestrial carbon balance by incorporating SPITFIRE into the global vegetation model ORCHIDEE\u2014part 2: Carbon emissions and the role of fires in the global carbon balance","volume":"8","author":"Yue","year":"2015","journal-title":"Geosci. Model Dev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2293","DOI":"10.5194\/bg-10-2293-2013","article-title":"Quantifying the role of fire in the Earth system\u2014Part 1: Improved global fire modeling in the Community Earth System Model (CESM1)","volume":"10","author":"Li","year":"2013","journal-title":"Biogeosciences"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.5194\/bg-11-1345-2014","article-title":"Quantifying the role of fire in the Earth system\u2014Part 2: Impact on the net carbon balance of global terrestrial ecosystems for the 20th century","volume":"11","author":"Li","year":"2014","journal-title":"Biogeosciences"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"15782","DOI":"10.3390\/rs71115782","article-title":"An algorithm for burned area detection in the Brazilian cerrado using 4 \u00b5m MODIS imagery","volume":"7","author":"Libonati","year":"2015","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.rse.2019.02.013","article-title":"Historical background and current developments for mapping burned area from satellite earth observation","volume":"225","author":"Chuvieco","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","unstructured":"Shimabukuro, Y.E., Dutra, A.C., Arai, E., Duarte, V., Cassol, H.L.G., Pereira, G., and Cardozo, F.S. (2020). Mapping burned areas of mato grosso state Brazilian amazon using multisensor datasets. Remote Sens., 12.","DOI":"10.3390\/rs12223827"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1111\/geb.12440","article-title":"A new global burned area product for climate assessment of fire impacts","volume":"25","author":"Chuvieco","year":"2016","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Pereira, A.A., Pereira, J.M.C., 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_17","doi-asserted-by":"crossref","unstructured":"Randerson, J.T., Chen, Y., van der Werf, G.R., Rogers, B.M., and Morton, D.C. (2012). Global burned area and biomass burning emissions from small fires. Biogeoscience, 117.","DOI":"10.1029\/2012JG002128"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Filipponi, F. (2019). Exploitation of sentinel-2 time series to map burned areas at the national level: A case study on the 2017 Italy wildfires. Remote Sens., 11.","DOI":"10.3390\/rs11060622"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2018.12.011","article-title":"Development of a sentinel-2 burned area algorithm: Generation of a small fire database for sub-Saharan Africa","volume":"222","author":"Roteta","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_20","first-page":"221","article-title":"Automated burned scar mapping using sentinel-2 imagery","volume":"12","author":"Stavrakoudis","year":"2020","journal-title":"J. Geogr. Inf. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111254","DOI":"10.1016\/j.rse.2019.111254","article-title":"Landsat-8 and Sentinel-2 burned area mapping\u2014a combined sensor multi-temporal change detection approach","volume":"231","author":"Roy","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.rse.2015.01.022","article-title":"MODIS\u2013Landsat fusion for large area 30 m burned area mapping","volume":"161","author":"Boschetti","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, J., and Roy, D.P. (2017). A Global analysis of sentinel-2a, sentinel-2b andlandsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sens., 9.","DOI":"10.3390\/rs9090902"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1080\/22797254.2020.1738900","article-title":"A novel fire index-based burned area change detection approach using Landsat-8 OLI data","volume":"53","author":"Liu","year":"2020","journal-title":"Eur. J. Remote Sens."},{"key":"ref_25","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_26","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_27","first-page":"101951","article-title":"Towards an automated approach to map flooded areas from Sentinel-2 MSI data and soft integration of water spectral features","volume":"84","author":"Goffi","year":"2020","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1109\/21.87068","article-title":"On ordered weighted averaging aggregation operators in multi-criteria decision making","volume":"18","author":"Yager","year":"1988","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1955","DOI":"10.1002\/2018GL077253","article-title":"June 2017: The earliest european summer mega-heatwave of reanalysis period","volume":"45","author":"Barriopedro","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"13886","DOI":"10.1038\/s41598-019-50281-2","article-title":"Climate drivers of the 2017 devastating fires in Portugal","volume":"9","author":"Turco","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ranghetti, L., and Busetto, L. (2021, May 01). Sen2r: An R Toolbox to Find, Download and Preprocess Sentinel-2 Data. Available online: http:\/\/sen2r.ranghetti.info.","DOI":"10.1016\/j.cageo.2020.104473"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Main-Knorn, M., Pflug, B., Louis, J., Debaecker, V., M\u00fcller-Wilm, U., and Gascon, F. (2017, January 10\u201314). Sen2Cor for sentinel-2. Proceedings of the SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, Warsaw, Poland.","DOI":"10.1117\/12.2278218"},{"key":"ref_33","unstructured":"Louis, J., Charantonis, A., and Berthelot, B. (July, January 28). Cloud detection for sentinel-2. Proceedings of the ESA Living Planet Symposium, Bergen, Norway."},{"key":"ref_34","unstructured":"Planet Team (2021, May 01). Planet Application Program Interface: In Space for Life on Earth. Available online: https:\/\/api.planet.com."},{"key":"ref_35","unstructured":"Lemajic, S., Vajsov\u00e1, B., and Aastrand, P. (2018). New Sensors Benchmark Report on PlanetScope: Geometric Benchmarking Test for Common Agricultural Policy (CAP) Purposes, Publications Office of the European Union. JRC111221."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ramo, R., and Chuvieco, E. (2017). Developing a random forest algorithm for MODIS global burned area classification. Remote Sens., 9.","DOI":"10.3390\/rs9111193"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forest","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Boschetti, M., Nutini, F., Manfron, G., Brivio, P.A., and Nelson, A. (2014). Comparative analysis of normalised difference spectral indices derived from MODIS for detecting surface water in flooded rice cropping systems. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0088741"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1080\/13658810701703183","article-title":"A flexible multi-source spatial-data fusion system for environmental status assessment at continental scale","volume":"22","author":"Carrara","year":"2008","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Goffi, A., Bordogna, G., Stroppiana, D., Boschetti, M., and Brivio, P.A. (2020). Knowledge and data-driven mapping of environmental status indicators from remote sensing and VGI. Remote Sens., 12.","DOI":"10.3390\/rs12030495"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2008). Assessing the Accuracy of Remotely Sensed Data, CRC Press. [2nd ed.].","DOI":"10.1201\/9781420055139"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.rse.2005.04.007","article-title":"Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data","volume":"97","author":"Roy","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_44","first-page":"344","article-title":"Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3","volume":"23","author":"Clevers","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Saulino, L., Rita, A., Migliozzi, A., Maffei, C., Allevato, E., Garonna, A.P., and Saracino, A. (2020). Detecting burn severity across mediterranean forest types by coupling medium-spatial resolution satellite imagery and field data. Remote Sens., 12.","DOI":"10.3390\/rs12040741"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"111490","DOI":"10.1016\/j.rse.2019.111490","article-title":"Global validation of the collection 6 MODIS burned area product","volume":"235","author":"Boschetti","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_47","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_48","doi-asserted-by":"crossref","unstructured":"Pulvirenti, L., Squicciarino, G., Fiori, E., Fiorucci, P., Ferraris, L., Negro, D., Gollini, A., Severino, M., and Puca, S. (2020). An automatic processing chain for near real-time mapping of burned forest areas using sentinel-2 data. Remote Sens., 12.","DOI":"10.3390\/rs12040674"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Smiraglia, D., Filipponi, F., Mandrone, S., Tornato, A., and Taramelli, A. (2020). Agreement index for burned area mapping: Integration of multiple spectral indices using sentinel-2 satellite images. Remote Sens., 12.","DOI":"10.3390\/rs12111862"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Seydi, S.T., Akhoondzadeh, M., Amani, M., and Mahdavi, S. (2021). Wildfire damage assessment over Australia using sentinel-2 imagery and MODIS land cover product within the google earth engine cloud platform. Remote Sens., 13.","DOI":"10.3390\/rs13020220"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/11\/2214\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:11:13Z","timestamp":1760163073000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/11\/2214"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,5]]},"references-count":50,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["rs13112214"],"URL":"https:\/\/doi.org\/10.3390\/rs13112214","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,5]]}}}