Assessing the Land and Vegetation Cover of Abandoned Fire Hazardous and Rewetted Peatlands: Comparing Different Multispectral Satellite Data
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 r...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-06-01
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| Series: | Land |
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| Online Access: | http://www.mdpi.com/2073-445X/7/2/71 |
| _version_ | 1819048417881489408 |
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| author | Andrey Sirin Maria Medvedeva Alexander Maslov Anna Vozbrannaya |
| author_facet | Andrey Sirin Maria Medvedeva Alexander Maslov Anna Vozbrannaya |
| author_sort | Andrey Sirin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T11:15:56Z |
| format | Article |
| id | doaj.art-958b83484c404d50b162096457693b35 |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-12-21T11:15:56Z |
| publishDate | 2018-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-958b83484c404d50b162096457693b352022-12-21T19:05:55ZengMDPI AGLand2073-445X2018-06-01727110.3390/land7020071land7020071Assessing the Land and Vegetation Cover of Abandoned Fire Hazardous and Rewetted Peatlands: Comparing Different Multispectral Satellite DataAndrey Sirin0Maria Medvedeva1Alexander Maslov2Anna Vozbrannaya3Institute of Forest Science, Russian Academy of Sciences, Moscow Region, 143030 Uspenskoye, RussiaInstitute of Forest Science, Russian Academy of Sciences, Moscow Region, 143030 Uspenskoye, RussiaInstitute of Forest Science, Russian Academy of Sciences, Moscow Region, 143030 Uspenskoye, RussiaMeschera State National Park, Ministry of Natural Resources of Russian Federation, Vladimir Oblast, 601500 Gus-Khrustalny, RussiaSince 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.http://www.mdpi.com/2073-445X/7/2/71remote sensingmultispectral imagespeat extraction landsvegetation coverpeat firesrewetting |
| spellingShingle | Andrey Sirin Maria Medvedeva Alexander Maslov Anna Vozbrannaya Assessing the Land and Vegetation Cover of Abandoned Fire Hazardous and Rewetted Peatlands: Comparing Different Multispectral Satellite Data Land remote sensing multispectral images peat extraction lands vegetation cover peat fires rewetting |
| title | Assessing the Land and Vegetation Cover of Abandoned Fire Hazardous and Rewetted Peatlands: Comparing Different Multispectral Satellite Data |
| title_full | Assessing the Land and Vegetation Cover of Abandoned Fire Hazardous and Rewetted Peatlands: Comparing Different Multispectral Satellite Data |
| title_fullStr | Assessing the Land and Vegetation Cover of Abandoned Fire Hazardous and Rewetted Peatlands: Comparing Different Multispectral Satellite Data |
| title_full_unstemmed | Assessing the Land and Vegetation Cover of Abandoned Fire Hazardous and Rewetted Peatlands: Comparing Different Multispectral Satellite Data |
| title_short | Assessing the Land and Vegetation Cover of Abandoned Fire Hazardous and Rewetted Peatlands: Comparing Different Multispectral Satellite Data |
| title_sort | assessing the land and vegetation cover of abandoned fire hazardous and rewetted peatlands comparing different multispectral satellite data |
| topic | remote sensing multispectral images peat extraction lands vegetation cover peat fires rewetting |
| url | http://www.mdpi.com/2073-445X/7/2/71 |
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