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Article

Remote Sensing Mapping of Peat-Fire-Burnt Areas: Identification among Other Wildfires

Institute of Forest Science, Russian Academy of Sciences, Uspenskoye, 143030 Moscow, Russia
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Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(1), 194; https://doi.org/10.3390/rs14010194
Submission received: 10 November 2021 / Revised: 25 December 2021 / Accepted: 29 December 2021 / Published: 2 January 2022
(This article belongs to the Special Issue State-of-the-Art Technology of Remote Sensing in Russia)

Abstract

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–August 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 ΔNDMI (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.
Keywords: remote sensing; multispectral data; thermal data; peatlands; hotspots; vegetation cover; peat fires; wildfires; Landsat-5 TM; Terra/Aqua MODIS; vegetation indices remote sensing; multispectral data; thermal data; peatlands; hotspots; vegetation cover; peat fires; wildfires; Landsat-5 TM; Terra/Aqua MODIS; vegetation indices
Graphical Abstract

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MDPI and ACS Style

Sirin, A.; Medvedeva, M. Remote Sensing Mapping of Peat-Fire-Burnt Areas: Identification among Other Wildfires. Remote Sens. 2022, 14, 194. https://doi.org/10.3390/rs14010194

AMA Style

Sirin A, Medvedeva M. Remote Sensing Mapping of Peat-Fire-Burnt Areas: Identification among Other Wildfires. Remote Sensing. 2022; 14(1):194. https://doi.org/10.3390/rs14010194

Chicago/Turabian Style

Sirin, Andrey, and Maria Medvedeva. 2022. "Remote Sensing Mapping of Peat-Fire-Burnt Areas: Identification among Other Wildfires" Remote Sensing 14, no. 1: 194. https://doi.org/10.3390/rs14010194

APA Style

Sirin, A., & Medvedeva, M. (2022). Remote Sensing Mapping of Peat-Fire-Burnt Areas: Identification among Other Wildfires. Remote Sensing, 14(1), 194. https://doi.org/10.3390/rs14010194

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