通过探究东帕米尔高原植被覆盖度的时空演变规律及驱动力对植被覆盖度的驱动机制，为研究区的植被保护提供科学的数据支撑和参考。基于1993年、2000年、2007年、2014年、2021年Landsat遥感影像、气象、数字高程模型（digital elevation model, DEM）等数据，使用像元二分模型、马尔科夫模型、空间自相关分析和地理探测器，开展东帕米尔高原植被覆盖度时空演变及检测驱动力的研究。结果表明：①1993—2021年东帕米尔高原植被覆盖度呈上升趋势，在空间分布上总体表现为“东部高，西部低，中部极低”的特点；②1993—2021年东帕米尔高原植被覆盖度空间自相关分析表现为显著的正空间自相关，全局莫兰指数为0.27~0.40，局部空间自相关表明，植被覆盖度在局部地区以低-低聚集和高-高聚集为主的聚集；③因子探测结果表明，1993—2021年间，研究区各驱动因子对植被覆盖度空间异质性的解释力（q）表现出不同程度的波动，土地利用和DEM是东帕米尔高原植被覆盖度的主要驱动因素。交互探测结果表明，驱动因子之间交互的影响力大于单驱动因子的影响力，交互结果均表现为增强。

Exploring the spatiotemporal evolution of FVC(fractional vegetation cover) in the East Pamirs Plateau and the driving mechanisms of FVC by the driving forces, providing scientific data support and reference for vegetation protection in the study area. Based on Landsat remote sensing image data, meteorological data, DEM (digital elevation model) data and other data in 1993, 2000, 2007, 2014 and 2021, pixel dichotomous model, Markov transition model, spatial auto-correlation analysis and geodetector were used to research on the spatiotemporal evolution and detection driving forces of vegetation coverage in the East Pamirs Plateau. Results show as follows. The FVC in the East Pamirs Plateau is exhibiting a fluctuating upward trend, in terms of spatial distribution, is overall characterized by "higher in the east, lower in the west and extremely low in the medium". The spatial autocorrelation analysis of vegetation coverage in the East Pamirs Plateau from 1993 to 2021 showed a significant positive spatial autocorrelation, with the global Moran 's index of FVC 0.27 ~ 0.40. Local spatial autocorrelation shows that the vegetation coverage in local areas is dominated by low-low aggregation and high-high aggregation. Factor detection results showed that the explanatory power (q) of various driving factors on the spatial heterogeneity of vegetation coverage in the study area fluctuated to varying degrees from 1993 to 2021, with land use and DEM were the main driving factors of FVC in the East Pamirs Plateau. Results of the interaction detection showed that the influence of interaction between driving factors is greater than that of a single driving factor, and the interaction results are all enhanced.