pone.0344835.t002 -

Monitoring land cover dynamics and understanding vegetation responses to climate change are critical for ecological assessment and management in dryland regions. This study systematically analyzes land cover dynamics, vegetation type transitions, and their climatic drivers across Asian drylands from 2001 to 2022 by integrating MODIS land cover data, TerraClimate climate reanalysis datasets, and the Google Earth Engine (GEE) platform. Using a unified framework that combines land cover dynamic indices, transition probability and transfer matrix analyses, and climate attribution, we quantify spatiotemporal change patterns and identify dominant vegetation transition pathways. The results reveal pronounced land cover changes across Asian drylands over the past two decades, characterized by expansions of grasslands (GRA), savannas (SAV), croplands (CRO), and water, snow, and ice (WSI), alongside contractions of shrublands (SH), mixed forests (MF), permanent wetlands (WET), and barren land (BAR). Land cover transition analysis indicates that the most prominent conversion pathways are from barren land to grasslands and from grasslands to croplands, reflecting the combined influences of climate variability and land use processes. Climate attribution analyses further demonstrate that vegetation dynamics across different stability zones exhibit distinct responses to long-term climate trends, with increasing maximum temperature, soil moisture, and vapor-related variables, together with declining precipitation, drought indices, and surface radiation, jointly shaping vegetation persistence, expansion, or degradation. By integrating long-term multi-source datasets and cloud-based geospatial computing, this study provides a scalable and reproducible framework for assessing land cover change and vegetation stability in arid and semi-arid regions. The findings enhance understanding of dryland ecosystem dynamics under climate change and support large-scale ecological assessment in data-scarce environments.监测土地覆被动态、厘清植被对气候变化的响应，对于干旱区的生态评估与管理而言至关重要。本研究整合中分辨率成像光谱仪（MODIS）土地覆被数据、TerraClimate气候再分析数据集与谷歌地球引擎（GEE）平台，系统分析了2001至2022年亚洲干旱区的土地覆被动态、植被类型转换及其气候驱动因子。本研究采用融合土地覆被动态指数、转换概率与转移矩阵分析及气候归因的统一分析框架，量化了时空变化格局并识别出主导性植被转换路径。研究结果显示，近二十年来亚洲干旱区的土地覆被变化显著，具体表现为草地（GRA）、稀树草原（SAV）、耕地（CRO）以及水冰雪（WSI）的扩张，同时伴随灌丛（SH）、混交林（MF）、永久湿地（WET）与裸地（BAR）的缩减。土地覆被转换分析表明，最显著的转换路径为裸地向草地、草地向耕地的转换，这反映了气候变异性与土地利用过程的共同影响。气候归因分析进一步表明，不同稳定区的植被动态对长期气候趋势呈现出差异化响应：最高气温、土壤湿度与水汽相关变量的升高，叠加降水、干旱指数与地表辐射的下降，共同塑造了植被的存续、扩张或退化状态。本研究通过整合长期多源数据集与云端地理空间计算技术，为干旱半干旱区的土地覆被变化与植被稳定性评估提供了可扩展、可复现的分析框架。本研究结果深化了对气候变化背景下干旱区生态系统动态的认知，并可为数据匮乏地区的大规模生态评估提供支撑。