Abstract. Land degradation is a cause of many social, economic, and environmental problems. Therefore identification and monitoring of high-risk areas for land degradation are necessary. Despite the importance of land degradation, the topic receives often relatively little attention. The present study aims to create a land degradation map in terms of soil erosion caused by wind and water erosion of semi-dry land. We focus on the Lut watershed in Iran encompassing the Lut Desert that is influenced by both monsoon rainfalls and dust storms. Dust sources are identified using MODIS satellite images with the help of four different indices to quantify uncertainty. The dust source maps are assessed with three machine learning algorithms encompassing artificial neural network (ANN), random forest (RF), and flexible discriminant analysis (FDA) to map dust sources paired with soil erosion susceptibility due to water. We assess the accuracy of the maps from the machine learning results with the metric Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC). The maps for water and aeolian soil erosion are used to identify different classes of land degradation risks. The results show that 43 % of the watershed is prone to land degradation in terms of both aeolian and water erosion. Most regions (45 %) have a risk of water erosion and some regions (7 %) a risk of aeolian erosion. Only a small fraction (4 %) of the total area of the region had a low to very low susceptibility for land degradation. The results of this study underline the risk of land degradation for an inhabited region in Iran. Future work should focus on land degradation associated with soil erosion from water and storms in larger regions to evaluate the risks also elsewhere.

中文翻译：

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绘制风蚀和水蚀造成的土地退化风险图

摘要。土地退化是许多社会、经济和环境问题的原因。因此，有必要确定和监测土地退化的高风险区域。尽管土地退化很重要，但这个话题往往受到的关注相对较少。本研究旨在根据半旱地的风蚀和水蚀引起的水土流失创建土地退化图。我们关注伊朗的卢特分水岭，包括受季风降雨和沙尘暴影响的卢特沙漠。借助四个不同的指数来量化不确定性，使用 MODIS 卫星图像识别尘源。尘源图使用三种机器学习算法进行评估，包括人工神经网络 (ANN)、随机森林 (RF)、和灵活的判别分析 (FDA) 来绘制与水引起的土壤侵蚀敏感性配对的尘源。我们使用接受者操作特征 (ROC) 的曲线下面积 (AUC) 度量从机器学习结果中评估地图的准确性。水土流失图用于识别不同类别的土地退化风险。结果表明，43% 的流域在风蚀和水蚀方面容易发生土地退化。大多数地区 (45%) 存在水蚀风险，部分地区 (7%) 存在风蚀风险。该地区总面积中只有一小部分 (4%) 对土地退化具有低至极低的敏感性。这项研究的结果强调了伊朗有人居住地区土地退化的风险。