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

Räsänen, Aleksi, Elsakov, Vladimir, Virtanen, Tarmo
International journal of remote sensing 2019 v.40 no.11 pp. 4083-4103
automation, climate change, forests, landscapes, peat, permafrost, remote sensing, support vector machines, tundra, Arctic region, Russia
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.