{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T04:31:41Z","timestamp":1792729901141,"version":"4.5.0"},"reference-count":65,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,2]],"date-time":"2022-01-02T00:00:00Z","timestamp":1641081600000},"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>Peat fires differ from other wildfires in their duration, carbon losses, emissions of greenhouse gases and highly hazardous products of combustion and other environmental impacts. Moreover, it is difficult to identify peat fires using ground-based methods and to distinguish peat fires from forest fires and other wildfires by remote sensing. Using the example of catastrophic fires in July\u2013August 2010 in the Moscow region (the center of European Russia), in the present study, we consider the results of peat-fire detection using Terra\/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) hotspots, peat maps, and analysis of land cover pre- and post-fire according to Landsat-5 TM data. A comparison of specific (for detecting fires) and non-specific vegetation indices showed the difference index \u0394NDMI (pre- and post-fire normalized difference moisture Index) to be the most effective for detecting burns in peatlands according to Landsat-5 TM data. In combination with classification (both unsupervised and supervised), this index offered 95% accuracy (by ground verification) in identifying burnt areas in peatlands. At the same time, most peatland fires were not detected by Terra\/Aqua MODIS data. A comparison of peatland and other wildfires showed the clearest differences between them in terms of duration and the maximum value of the fire radiation power index. The present results may help in identifying peat (underground) fires and their burnt areas, as well as accounting for carbon losses and greenhouse gas emissions.<\/jats:p>","DOI":"10.3390\/rs14010194","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:06:15Z","timestamp":1641769575000},"page":"194","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Remote Sensing Mapping of Peat-Fire-Burnt Areas: Identification among Other Wildfires"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4615-565X","authenticated-orcid":false,"given":"Andrey","family":"Sirin","sequence":"first","affiliation":[{"name":"Institute of Forest Science, Russian Academy of Sciences, Uspenskoye, 143030 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3511-6535","authenticated-orcid":false,"given":"Maria","family":"Medvedeva","sequence":"additional","affiliation":[{"name":"Institute of Forest Science, Russian Academy of Sciences, Uspenskoye, 143030 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,2]]},"reference":[{"key":"ref_1","unstructured":"Shukla, P.R., Skea, J., Calvo Buendia, E., Masson-Delmotte, V., P\u00f6rtner, H.-O., Roberts, D.C., Zhai, P., Slade, R., Connors, S., and van Diemen, R. 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