Increased use and increasing demands pose serious threats to rangelands. In this study, we document a pronounced downward trend in rangeland quality in the Alborz Mountains in Firozkuh County, Iran using analysis of three machine‐learning models (MLMs). A total of 1,147 transects were established to evaluate the rangeland quality trends from field data collected over a 7‐year period. Twelve independent conditional factors were analyzed for their relationships to range quality through three MLMs—Random Forest (RF), classification and regression tree (CART), and support vector machine (SVM). Based on assessments of the trained and validated models, RF, with a ROC‐AUC = 0.96, was determined to be the most robust. The results show that about 20% of the rangeland in the study area is in a critically degraded condition. Distances from roads and livestock density are the two factors most strongly linked to degradation. These results, in combination with field observations, indicate that the rangelands of the study area face two major challenges (overgrazing and early grazing) that require new strategies to mitigate and prevent damages. This study may provide important guidance for evaluating rangeland conditions in other regions of the world.

中文翻译：

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使用机器学习技术评估土地退化：牧场减少的情况

使用量的增加和需求的增加对牧场构成了严重威胁。在这项研究中，我们通过对三种机器学习模型（MLM）的分析，记录了伊朗费罗兹库县Alborz山脉的牧场质量显着下降趋势。共建立了1147个样点，以评估7年期间收集的田间数据对牧场质量趋势的影响。通过三个MLM（随机森林（RF），分类和回归树（CART）和支持向量机（SVM））分析了十二个独立条件因子与范围质量的关系。根据对经过训练和经过验证的模型的评估，确定ROC-AUC = 0.96的RF是最可靠的。结果表明，研究区域中约有20％的牧场处于严重退化状态。距道路的距离和牲畜密度是与退化最密切相关的两个因素。这些结果与实地观察相结合，表明研究区域的牧场面临两个主要挑战（过度放牧和早期放牧），这需要新的策略来减轻和防止损害。这项研究可能为评估世界其他地区的牧场状况提供重要指导。