Abstract:Under the influence of China’s rapid urbanization process, the landscape pattern of the natural landscape, such as “ rivers, forests, fields, lakes, and grass “ in the suburbs of Shanghai, has undergone significant changes. Revealing the rule of their spatial-temporal evolution can provide a scientific basis for the preparation of territorial spatial planning, ecological special planning, and urban and rural landscape planning under the new situation. Taking the natural landscape of Jiading District in suburban Shanghai as the research object, this paper selects three time nodes from 2000 to 2020 to explore the characteristics and driving factors of the spatial-temporal evolution of natural landscape pattern with the help of GIS technology, land use transfer matrix, and geographical detector analysis. The results show that: (1) The loss of cultivated land, grassland, and forest in Jiading is intensified, and the transfer of land to construction is large. (2) The diversity of natural landscape gradually decreased, the fragmentation degree increased, and the complexity increased. (3) The primary driving force influencing the natural landscape pattern has gradually transitioned from natural environmental factors to human intervention, particularly construction activities. Notably, the interaction between policy regulation and vegetation status has progressively intensified across the three periods, suggesting that the synergistic effects of urban expansion and policy direction increasingly shape the regional ecological pattern. The identification of the evolutionary trend of the dominant driving force can provide theoretical support for the delineation of the ecological protection boundary of the suburban transition zone, the identification of the conservation priority area and the formulation of the ecological control strategy of zoning classification, which can promote the fine management and sustainable development of the region, and provide a reference for the research and planning practice of similar areas.