{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:37:15Z","timestamp":1760236635419,"version":"build-2065373602"},"reference-count":70,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T00:00:00Z","timestamp":1639958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["SFRH\/BD\/104663\/2014"],"award-info":[{"award-number":["SFRH\/BD\/104663\/2014"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100013239","name":"CESAM","doi-asserted-by":"publisher","award":["UID\/AMB\/50017\/2019"],"award-info":[{"award-number":["UID\/AMB\/50017\/2019"]}],"id":[{"id":"10.13039\/100013239","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This work presents a change detection method (MINDED-BA) for determining burned extents from multispectral remote sensing imagery. It consists of a development of a previous model (MINDED), originally created to estimate flood extents, combining a multi-index image-differencing approach and the analysis of magnitudes of the image-differencing statistics. The method was implemented, using Landsat and Sentinel-2 data, to estimate yearly burn extents within a study area located in northwest central Portugal, from 2000\u20132019. The modelling workflow includes several innovations, such as preprocessing steps to address some of the most important sources of error mentioned in the literature, and an optimal bin number selection procedure, the latter being the basis for the threshold selection for the classification of burn-related changes. The results of the model have been compared to an official yearly-burn-extent database and allow verifying the significant improvements introduced by both the pre-processing procedures and the multi-index approach. The high overall accuracies of the model (ca. 97%) and its levels of automatization (through open-source software) indicate potential for being a reliable method for systematic unsupervised classification of burned areas.<\/jats:p>","DOI":"10.3390\/rs13245164","type":"journal-article","created":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T08:43:32Z","timestamp":1639989812000},"page":"5164","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A New Method (MINDED-BA) for Automatic Detection of Burned Areas Using Remote Sensing"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9276-9145","authenticated-orcid":false,"given":"Eduardo R.","family":"Oliveira","sequence":"first","affiliation":[{"name":"COPING TEAM\u2014Coastal and Ocean Planning Governance, CESAM\u2014Centre for Environmental and Marine Studies, Department of Environment and Planning, University of Aveiro, 3810-193 Aveiro, Portugal"},{"name":"Dipartimento di Scienze Fisiche, della Terra e dell\u2019Ambiente, Universit\u00e0 degli Studi di Siena, 53100 Siena, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6510-2575","authenticated-orcid":false,"given":"Leonardo","family":"Disperati","sequence":"additional","affiliation":[{"name":"Dipartimento di Scienze Fisiche, della Terra e dell\u2019Ambiente, Universit\u00e0 degli Studi di Siena, 53100 Siena, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7304-6953","authenticated-orcid":false,"given":"F\u00e1tima L.","family":"Alves","sequence":"additional","affiliation":[{"name":"COPING TEAM\u2014Coastal and Ocean Planning Governance, CESAM\u2014Centre for Environmental and Marine Studies, Department of Environment and Planning, University of Aveiro, 3810-193 Aveiro, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1071\/WF12052","article-title":"Integrating geospatial information into fire risk assessment","volume":"23","author":"Chuvieco","year":"2014","journal-title":"Int. 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