Abstract
Since the 1990s, many peatlands that were drained for peat extraction and agriculture in Russia have been abandoned with high CO2 emissions and frequent fires, such as the enormous fires around Moscow in 2010. The fire hazard in these peatlands can be reduced through peatland rewetting and wetland restoration, so monitoring peatland status is essential. However, large expanses, poor accessibility, and fast plant succession pose as challenges for monitoring these areas without satellite images. In this study, a technique involving multispectral satellite data was used to identify six land cover classes that meet the requirements for peatland monitoring using the Meschera National Park as the testing area. This park is the largest area of once-exploited and now rewetted peatlands. However, data from one scanner are often insufficient to successfully implement this technique. In this study, we compared the land cover classifications obtained by using data from Spot-5, Spot-6, Landsat-7, Landsat-8, and Sentinel-2 satellites. The Spot-6 data were insufficient, despite having a higher spatial resolution, due to the lack of a shortwave infrared (SWIR) band. The high classification accuracy attained using data from other sensors enabled their combined use to provide an acceptable accuracy in the final product. The classification results were compared using minimum distance Erdas Imagine and the object-oriented ScanEx Image Processor, and the classification accuracy was similar between satellite images, which facilitates the transition from one method to another without quality loss. The proposed and tested approach can be used to analyze the status of abandoned and rewetted peatlands in other locations for the inventory and prioritization of sites for rewetting and restoration, monitoring status changes, and assessing restoration efficacy. The comparability of the data from different sensors allows for the combination of classified images and creates new possibilities for time series analysis.
Highlights
Peatlands are wetland ecosystems characterized by the accumulation of organic matter derived from dead and decaying plant material under high water saturation [1,2]
The results of this study demonstrate the possibility of the long–term analysis of the fire hazard of abandoned peatlands and the results of rewetting and restoration based on freely available Landsat, Sentinel, and commercial Spot remote sensing data (RSD)
Since the 1990s, extensive areas of peatlands that were drained for peat extraction and agriculture in European Russia are to a large extent, abandoned
Summary
Peatlands are wetland ecosystems characterized by the accumulation of organic matter (peat) derived from dead and decaying plant material under high water saturation [1,2]. The water, plants, and peat in peatlands are strongly interconnected. If any of these components are altered, the nature of the peatland changes. Peatlands are the most efficient terrestrial ecosystem in terms of carbon storage; they are a growing source of greenhouse gas (GHG) emissions after degradation [2,4,5,6]. Degradation of drained peatlands and peat fires are important sources of greenhouse gases that contribute to climate change [7]. The rewetting of unused drained peatlands and mire restoration are recognized as the most important measures to re-establish a range of mire ecosystem services, including their contribution to climate stability [3,8,9] and biological diversity [10,11,12]. Covering less than 0.3% of the earth’s surface, drained peatlands produce 5% of the global carbon dioxide (CO2 ) emissions [13]
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