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.

摘要

咸海曾经是地球上第四大淡水湖，在 1960 年已经失去了大约 90% 的原始水面。不同土地覆盖类别的地图为规划和实施有效措施以应对持续的荒漠化提供了合适的基线，例如重新造林干涸的咸海土壤。在这项研究中，我们使用基于卫星的遥感数据并应用机器学习方法（随机森林）绘制了 2020 年 Aralkum 的土地覆盖图。我们测试了来自光学（Landsat-8、Sentinel-2）和雷达的不同卫星数据仪器（Sentinel-1）并训练了一个随机森林模型，用于将这些数据集的不同组合分类为十个不同的土地覆盖类别。我们基于后验分类概率分数进一步计算了每像素的不确定性。基于现场数据的准确性评估，显示土地覆盖图的平均整体准确率为 86.8%。融合光学和雷达仪器实现了最高的整体准确度（88.8%，较低/较高的 95% 置信区间值分别为 87.6%/89.9%，Kappa 值为 0.865。在更均匀的景观中（即大面积的一个单一的土地覆盖类别，如水或灌木丛）。到 2020 年，只有大约 9% 的研究区域仍然是水，而 32% 被具有高侵蚀风险的盐渍土壤覆盖。该土地覆盖图在 Aralkum 存在几个潜在的应用– 跨越环境影响评估、政策、规划和管理或植树造林的许多领域。该方法框架同样可以为更广泛地评估大规模、