Abstract：
Drought events threaten freshwater reservoirs and agricultural productivity, particularly in semi-arid regions characterized by erratic rainfall. This study evaluates a novel technique for assessing the impact of drought on LULC variations in the context of climate change from 2018 to 2022. Various data sources were harnessed, encompassing Sentinel-2 satellite imagery for LULC classification, climate data from the CHIRPS and AgERA5 databases, geomorphological data from JAXA\'s ALOS satellite, and a drought indicator (Vegetation Health Index (VHI)) derived from MODIS data. Two classifier models, namely gradient tree boost (GTB) and random forest (RF), were trained and assessed for LULC classification, with performance evaluated by overall accuracy (OA) and kappa coefficient (K). Notably, the GTB model exhibited superior performance, with OA > 90% and a K > 0.9. Over the period from 2018 to 2022, Fez experienced LULC changes of 19.92% expansion in built-up areas, a 34.86% increase in bare land, a 17.86% reduction in water bodies, and a 37.30% decrease in agricultural land. Positive correlations of 0.81 and 0.89 were observed between changes in agricultural LULC, rainfall, and VHI. Furthermore, mild drought conditions were identified in the years 2020 and 2022. This study emphasizes the importance of AI and remote sensing techniques in assessing drought and environmental changes, with potential applications for improving existing drought monitoring systems.
摘要：
干旱事件威胁淡水水库和农业生产力，特别是在降雨量不稳定的半干旱地区。这项研究评估了一种新技术，用于评估2018年至2022年气候变化背景下干旱对LULC变化的影响。利用了各种数据源，包括用于LULC分类的Sentinel-2卫星图像，来自CHIRPS和AgERA5数据库的气候数据，来自JAXAALOS卫星的地貌数据，和从MODIS数据得出的干旱指标(植被健康指数(VHI))。两个分类器模型，即梯度树增强(GTB)和随机森林(RF)，进行了LULC分类的培训和评估，通过总体精度(OA)和卡帕系数(K)评估性能。值得注意的是,GTB模型表现出卓越的性能，OA>90%，K>0.9。在2018年至2022年期间，非斯经历了LULC在建成区扩张19.92%的变化，裸地增加34.86%，水体减少17.86%，农业用地减少37.30%。在农业LULC的变化之间观察到0.81和0.89的正相关，降雨,和VHI。此外，在2020年和2022年确定了轻度干旱条件。这项研究强调了人工智能和遥感技术在评估干旱和环境变化中的重要性，具有改善现有干旱监测系统的潜在应用。
