There is a need for the up‐to‐date assessment of desertification/land degradation maps that are dynamic in nature at different scales for comprehensive planning and preparation of action plans. This paper aims to develop the desertification vulnerability index (DVI) and predict the different desertification processes operating in Anantapur District, India, based on machine language techniques. Climate, land use, soil, and socioeconomic parameters were used to prepare DVI by a multivariate index model. The computed DVI along with climate, terrain, and soil properties was used as explanatory variable to predict the desertification processes by using a random forest model. About 14.2% of the area was created as a training dataset in 9 places for modeling and remaining area was tested for prediction of desertification processes. We used desertification status map (DSM) of Anantapur District prepared under Desertification status mapping of India–2nd cycle as a reference dataset for calculation of accuracy indices. Kappa and classification accuracy index were calculated for training and validation datasets. We recorded overall accuracy rate and kappa index of 85.5% and 75.8% for training datasets and 71.0% and 51.8% for testing datasets. The results of variable importance analysis of random forest model showed that DVI was the most important predictor followed by potential evapotranspiration and Normalized Difference Vegetation Index for prediction of desertification processes. The results from this work given new insight into using the existing knowledge on prediction of desertification in unvisited areas and also quick update of DSM maps.

中文翻译：

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荒漠化脆弱性指数-一种评估荒漠化进程的有效方法：以印度安得拉邦阿南塔普尔地区为例

有必要对各种规模的，动态变化的荒漠化/土地退化图进行最新评估，以进行全面规划和制定行动计划。本文旨在开发基于机器语言技术的荒漠化脆弱性指数（DVI），并预测在印度阿南塔普尔地区开展的不同荒漠化过程。通过多元指数模型，使用气候，土地利用，土壤和社会经济参数来准备DVI。通过使用随机森林模型，将计算出的DVI以及气候，地形和土壤特性用作解释变量，以预测荒漠化过程。在9个地方创建了约14.2％的区域作为训练数据集进行建模，并对剩余区域进行了测试以预测荒漠化过程。我们使用在印度第二周期的荒漠化状况图下准备的阿南塔普尔地区的荒漠化状况图（DSM），作为计算准确度指标的参考数据集。计算了训练和验证数据集的Kappa和分类准确性指数。对于训练数据集，我们记录的总体准确率和kappa指数分别为85.5％和75.8％，对于测试数据集，记录的总体准确率和kappa指数为71.0％和51.8％。随机森林模型的变量重要性分析结果表明，对于荒漠化过程的预测，DVI是最重要的预测因子，其次是潜在蒸散量和归一化植被指数。这项工作的结果使人们有了新的见解，可以利用现有的知识来预测未到访地区的荒漠化情况，还可以快速更新DSM地图。