Land cover change detection in the Aralkum with multi-source satellite datasets
Fabian Löw, Dimo Dimov, Shavkat Kenjabaev, Sherzod Zaitov, Galina Stulina, V. Dukhovny
Abstract
The Aral Sea, once the fourth largest freshwater lake on Earth, has lost circa 90% of its original water surface in 1960. Maps of different land cover categories provide a suitable baseline to plan and implement effective measures to combat ongoing desertification, such as reforestation of dried out Aral Sea soils. In this study, we used satellite-based remote sensing data and applied a machine learning method (Random Forest) to map land cover in the Aralkum in 2020. We tested different satellite data from optical (Landsat-8, Sentinel-2) and Radar instruments (Sentinel-1) and trained a random forest model for classifying different combinations of these data sets into ten distinct land cover classes. We further calculated per-pixel uncertainty based on posterior classification probability scores. An accuracy assessment, based on in-situ data, revealed that the average overall accuracy of land cover maps was 86.8%. Fusing optical and radar instruments achieved the highest overall accuracy (88.8%, with lower/higher 95% confidence interval values of 87.6%/89.9%, and a Kappa value of 0.865. Classification uncertainty was lower in more homogeneous landscapes (i.e. large expanses of a single land cover class like water or shrubland). Only around 9% of the study area was still water in 2020, while 32% was covered by saline soils with high erosion risk. Several potential applications of this land cover map in the Aralkum exist – spanning many areas of environmental impact assessment, policy, and planning and management or afforestation. This methodological framework can similarly provide a useful template for more broadly assessing large-scale, land dynamics at high-resolution in the entire Aralkum and surrounding areas.