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Therefore, it is imperative to map burned areas accurately. Currently, there are few burned-area products around the world. Researchers have mapped burned areas directly at the pixel level that is usually a mixture of burned area and other land cover types. In order to improve the burned area mapping at subpixel level, we proposed a Burned Area Subpixel Mapping (BASM) workflow to map burned areas at the subpixel level. We then applied the workflow to Sentinel 2 data sets to obtain burned area mapping at subpixel level. In this study, the information of true fire scar was provided by the Department of Emergency Management of Hunan Province, China. To validate the accuracy of the BASM workflow for detecting burned areas at the subpixel level, we applied the workflow to the Sentinel 2 image data and then compared the detected burned area at subpixel level with in situ measurements at fifteen fire-scar reference sites located in Hunan Province, China. Results show the proposed method generated successfully burned area at the subpixel level. The methods, especially the BASM-Feature Extraction Rule Based (BASM-FERB) method, could minimize misclassification and effects due to noise more effectively compared with the BASM-Random Forest (BASM-RF), BASM-Backpropagation Neural Net (BASM-BPNN), BASM-Support Vector Machine (BASM-SVM), and BASM-notra methods. We conducted a comparison study among BASM-FERB, BASM-RF, BASM-BPNN, BASM-SVM, and BASM-notra using five accuracy evaluation indices, i.e., overall accuracy (OA), user\u2019s accuracy (UA), producer\u2019s accuracy (PA), intersection over union (IoU), and Kappa coefficient (Kappa). The detection accuracy of burned area at the subpixel level by BASM-FERB\u2019s OA, UA, IoU, and Kappa is 98.11%, 81.72%, 74.32%, and 83.98%, respectively, better than BASM-RF\u2019s, BASM-BPNN\u2019s, BASM-SVM\u2019s, and BASM-notra\u2019s, even though BASM-RF\u2019s and BASM-notra\u2019s average PA is higher than BASM-FERB\u2019s, with 89.97%, 91.36%, and 89.52%, respectively. We conclude that the newly proposed BASM workflow can map burned areas at the subpixel level, providing greater accuracy in regards to the burned area for post-forest fire management and assessment.<\/jats:p>","DOI":"10.3390\/rs14153546","type":"journal-article","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T01:42:13Z","timestamp":1658713333000},"page":"3546","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Development of a Novel Burned-Area Subpixel Mapping (BASM) Workflow for Fire Scar Detection at Subpixel Level"],"prefix":"10.3390","volume":"14","author":[{"given":"Haizhou","family":"Xu","sequence":"first","affiliation":[{"name":"College of Forestry, Central South University of Forestry and Technology, Changsha 410004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gui","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Forestry, Central South University of Forestry and Technology, Changsha 410004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoming","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Geological Engineering, Montana Technological University, Butte, MT 59701, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4135-7634","authenticated-orcid":false,"given":"Xiaobing","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Geological Engineering, Montana Technological University, Butte, MT 59701, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Forestry, Central South University of Forestry and Technology, Changsha 410004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cui","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Forestry, Central South University of Forestry and Technology, Changsha 410004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1071\/WF16056","article-title":"Understanding forest fire patterns and risk in Nepal using remote sensing, geographic information system and historical fire data","volume":"26","author":"Matin","year":"2017","journal-title":"Int. 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