Assessing the Land and Vegetation Cover of Abandoned Fire Hazardous and Rewetted Peatlands: Comparing Different Multispectral Satellite Data

Abstract

Since the 1990s, many peatlands that were drained for peat extraction and agriculture in Russia have been abandoned with high CO 2 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.

Rewetting Offers Rapid Climate Benefits for Tropical and Agricultural Peatlands But Not for Forestry‐Drained Peatlands

“…Additionally, if climate change leads to increasing occurrence of severe droughts (Dai, 2013; Jolly et al, 2015), the risk of releasing great amounts of C to the atmosphere in forest and peat fires increases. Peatland fires are already common in continental areas, for example, in many parts of Canada (Turetsky et al, 2004) and Russia (Sirin et al, 2018).…”

Reviews and syntheses: Arctic fire regimes and emissions in the 21st century

“…Fourth, increased lightning strikes will increase fire risk in Alaska (Veraverbeke et al 2017) but also northern Europe (Púčik et al, 2017). Fifth, the interaction between climatedriven changes in fire regimes and permafrost will compel a decrease in and a northern migration of Siberian taiga, which will result in the transition of tundra to taiga in northern Siberia (Tchebakova et al, 2009(Tchebakova et al, , 2011Sizov et al, 2021). Permafrost is not predicted to thaw deep enough to sustain dark-needled taiga (Pinus sibirica, Abies sibirica, and Picea obovata); nonetheless light-needled coniferous Larix is predicted to continue to dominate in eastern Siberia, maintaining a higher fire risk according to the Russian fire hazard rankings (Melekhov, 1980).…”

Comparison of Satellite and Drone-Based Images at Two Spatial Scales to Evaluate Vegetation Regeneration after Post-Fire Treatments in a Mediterranean Forest

“…The assessment of wildfire effects and post-fire regeneration using remote sensing has been done through the calculation of spectral vegetation indices [28]. These indices are calculated using the electromagnetic wave reflectance data of vegetation using passive sensors [29]. Several studies have successfully developed and applied vegetation indices (e.g., the Normalized Difference Vegetation Index, NDVI; the Composite Burn Index, CBI; the Differenced Normalized Burn Ratio, DNBR, and the Visible Atmospherically Resistant Index, VARI [24].…”

“…Bhagat et al [26] reported that UAVs provide data at fine resolution with the desired temporal resolution, which make them cost-effective and efficient data collectors. Regarding satellite images, Sirin et al [29] was able to compare different multispectral satellite data to assess vegetation cover in abandoned lands and rewetted peatlands. Bright et al [30] analyzed post-wildfire recovery in different coniferous forest types in North America using the Normalized Burn Ratio (NBR) derived from LandTrendr images.…”