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

机译：评估废弃的火灾隐患和湿润的泥炭地的土地和植被覆盖：比较不同的多光谱卫星数据

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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.
机译：自1990年代以来，俄罗斯许多因泥炭开采和农业而被排干的泥炭地已被大量CO 2排放和频繁的火灾所抛弃，例如2010年莫斯科周围的大火。和湿地恢复，因此监测泥炭地状态至关重要。但是，大面积，可及性差以及植物快速繁殖是在没有卫星图像的情况下监测这些地区的挑战。在这项研究中，使用了一种涉及多光谱卫星数据的技术来确定六个以Meschera国家公园为试验区的泥炭地覆盖类别，这些类别满足泥炭地监测的要求。这个公园是曾经被开采过且现在重新湿润的泥炭地的最大区域。但是，来自一个扫描仪的数据通常不足以成功实施此技术。在这项研究中，我们比较了使用Spot-5，Spot-6，Landsat-7，Landsat-8和Sentinel-2卫星的数据获得的土地覆盖类别。尽管缺少较高的空间分辨率，但由于缺乏短波红外（SWIR）波段，Spot-6数据还是不够的。使用来自其他传感器的数据可获得很高的分类精度，因此可以将它们组合使用以在最终产品中提供可接受的精度。使用最小距离Erdas Imagine和面向对象的ScanEx图像处理器比较了分类结果，并且卫星图像之间的分类精度相似，这有助于从一种方法过渡到另一种方法而不会造成质量损失。提出并经过测试的方法可用于分析其他位置废弃和重新湿陷的泥炭地的状况，以便对重新润湿和恢复的地点进行清点和优先排序，监视状态变化并评估恢复效果。来自不同传感器的数据的可比性允许分类图像的组合，并为时间序列分析创造了新的可能性。

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