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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 5, pp. 157-166

Applicability of different spectral indexes based on satellite data for peat fire area estimation

M.A. Medvedeva 1 , D.A. Makarov 1 , A.A. Sirin 1 
1 Institute of Forest Science RAS, Moscow Oblast, Uspenskoye, Russia
Accepted: 12.09.2020
DOI: 10.21046/2070-7401-2020-17-5-157-166
The possibility to detect burned areas after peat fires on the basis of cartographic information about the borders of peat bogs and anthropogenically modified peatlands with the analysis of data on wild fires was shown. For more accurate detection of burnt areas on the example of Landsat-5 data, the possibilities of applying the indexes NBR, NBR2, BAI, MIRBI, NDVI and NDMI for the next after fire vegetation season with field-check data after the 2010 fires in the Moscow region have been studied. These areas had a diverse pre-fire land cover, which is typical of both peat bogs and used or abandoned drained peatlands. Ranges of burned areas detection values were determined for each of the post-fire indexes and the differences in the indexes as compared to the pre-fire one. The difference index ΔNDMI was determined as the most accurate one, which was further used for detecting all 2010 peat fires in the Moscow Region. The ΔNDMI index in combination with classification without training and with training made it possible to identify with a high accuracy (95 %) peat burned areas. At the same time, most of the burned areas were not previously detected by thermal anomalies according to MODIS data with spatial resolution of 1 km. The tested approach of detection of burned areas can be applied on the basis of satellite data with similar Landsat-5 spectral characteristics not only for peat, but also for other wild fires, including forest fires.
Keywords: remote sensing, multispectral images, peatlands, vegetation cover, peat fires, Landsat-5, vegetation indexes
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