Usability of one-class classification in mapping and detecting changes in bare peat surfaces in the tundra

Arctic areas have experienced greening and changes in permafrost caused by climate change during recent decades. However, there has been a lack of automated methods in mapping changes in fine-scale patterns of permafrost landscapes. We mapped areal coverage of bare peat areas and changes in them in a peat plateau located in north-western Russia between 2007 and 2015. We utilized QuickBird and WorldView-3 satellite image data in an object-based setting. We compared four different one-class classifiers (one-class support vector machine, binary support vector machine, random forest, rotation forest) both in a fully supervised binary setting and with positive and unlabelled training data. There was notable variation in classification performance. The bare peat area F-score varied between 0.77 and 0.96 when evaluated by cross-validated training data and between 0.22 and 0.57 when evaluated by independent test data. Overall, random forest performed the most robustly but all classifiers performed well in some classifications. During the 8 year period, there was a 21%-26% decrease in the bare peat areal coverage. We conclude that (1) tested classifiers can be used in one-class settings and (2) there is a need to develop methods for tracking changes in single land cover types.Peer reviewe

Comparison and assessment of coarse resolution land cover maps for Northern Eurasia

Information on land cover at global and continental scales is critical for addressing a range of ecological, socioeconomic and policy questions. Global land cover maps have evolved rapidly in the last decade, but efforts to evaluate map uncertainties have been limited, especially in remote areas like Northern Eurasia. Northern Eurasia comprises a particularly diverse region covering a wide range of climate zones and ecosystems: from arctic deserts, tundra, boreal forest, and wetlands, to semi-arid steppes and the deserts of Central Asia. In this study, we assessed four of the most recent global land cover datasets: GLC-2000, GLOBCOVER, and the MODIS Collection 4 and Collection 5 Land Cover Product using cross-comparison analyses and Landsat-based reference maps distributed throughout the region. A consistent comparison of these maps was challenging because of disparities in class definitions, thematic detail, and spatial resolution. We found that the choice of sampling unit significantly influenced accuracy estimates, which indicates that comparisons of reported global map accuracies might be misleading. To minimize classification ambiguities, we devised a generalized legend based on dominant life form types (LFT) (tree, shrub, and herbaceous vegetation, barren land and water). LFT served as a necessary common denominator in the analyzed map legends, but significantly decreased the thematic detail. We found significant differences in the spatial representation of LFT's between global maps with high spatial agreement (above 0.8) concentrated in the forest belt of Northern Eurasia and low agreement (below 0.5) concentrated in the northern taiga-tundra zone, and the southern dry lands. Total pixel-level agreement between global maps and six test sites was moderate to fair (overall agreement: 0.67-0.74, Kappa: 0.41-0.52) and increased by 0.09-0.45 when only homogenous land cover types were analyzed. Low map accuracies at our tundra test site confirmed regional disagreements and difficulties of current global maps in accurately mapping shrub and herbaceous vegetation types at the biome borders of Northern Eurasia. In comparison, tree dominated vegetation classes in the forest belt of the region were accurately mapped, but were slightly overestimated (10%-20%), in all maps. Low agreement of global maps in the northern and southern vegetation transition zones of Northern Eurasia is likely to have important implications for global change research, as those areas are vulnerable to both climate and socio-economic changes. (C) 2011 Elsevier Inc. All rights reserved.Keywords: Land cover, MODIS, Eurasia, Global, Validation, GLC-2000, LCCS, GLOBCOVERKeywords: Land cover, MODIS, Eurasia, Global, Validation, GLC-2000, LCCS, GLOBCOVE

Arctic Greening Caused by Warming Contributes to Compositional Changes of Mycobiota at the Polar Urals

The long-term influence of climate change on spatio-temporal dynamics of the Polar mycobiota was analyzed on the eastern macro slope of the Polar Urals (Sob River valley and Mountain Slantsevaya) over a period of 60 years. The anthropogenic impact is minimal in the study area. Effects of environmental warming were addressed as changes in treeline and forest communities (greening of the vegetation). With warming, permafrost is beginning to thaw, and as it thaws, it decomposes. Therefore, we also included depth of soil thawing and litter decomposition in our study. Particular attention was paid to the reaction of aphyllophoroid fungal communities concerning these factors. Our results provide evidence for drastic changes in the mycobiota due to global warming. Fungal community composition followed changes of the vegetation, which was transforming from forest-tundra to northern boreal type forests during the last 60 years. Key fungal groups of the ongoing borealization and important indicator species are discussed. Increased economic activity in the area may lead to deforestation, destruction of swamps, and meadows. However, this special environment provides important services such as carbon sequestration, soil formation, protecting against flood risks, and filtering of air. In this regard, we propose to include the studied territory in the Polarnouralsky Natural Park

Distribution of the invasive plant species Heracleum sosnowskyi Manden. in the Komi Republic (Russia)

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