Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 2, pp. 150-159
Potential of different multispectral satellite data for monitoring abandoned fire hazardous peatlands and rewetting effectiveness
M.A. Medvedeva
1 , A.E. Vozbrannaya
2 , A.A. Sirin
1 , A.A. Maslov
1 1 Institute of Forest Science RAS, Uspenskoye, Moscow Region, Russia
2 Meschera State National Park, Gus–Khrustalny, Vladimir Region, Russia
Accepted: 10.04.2019
DOI: 10.21046/2070-7401-2019-16-2-150-159
Use of multispectral satellite data is instrumental for monitoring abandoned fire hazardous man-disturbed peatlands that are hardly accessible, difficult for ground survey, or have a rapid plant succession, as well as for assessment of rewetting effectiveness on such peatlands. To solve the problem, we developed and tested a technique that used Landsat data to identify six land cover classes. However, data from one scanner are often insufficient to successfully implement this technique in large areas and for long periods of time. In this study, we used the Meschera National Park (Vladimir Region) as test area to compare land cover classifications based on data from Spot-5, -6, Landsat-7, -8, and Sentinel-2 satellites. The Spot-6 data were insufficient, despite higher spatial resolution, due to the lack of shortwave infrared (SWIR) band. A high classification accuracy attained using data from other sensors enabled their combined use to provide an acceptable accuracy of the final product. The classification accuracy was similar between satellite images when using minimum distance Erdas Imagine and the object-oriented ScanEx Image Processor, which facilitates transitions from one method to the other without quality loss. The proposed and tested approach can be used to analyze the status of abandoned peatlands in other locations for inventory purposes and prioritization of sites for rewetting and restoration, as well as for monitoring status changes. The comparability of data from different sensors allows for combinations of classified images and creates new possibilities for time series analysis.
Keywords: remote sensing; multispectral images; peat extraction lands; vegetation cover; peat fires; rewetting, Spot-5, Spot-6, Landsat-7, Landsat-8, Sentine-2
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