A new approach to land-use simulation that integrates macro-and microspatial characteristics:A case study of Wuhan,China

一种顾及宏观和微观空间特征的土地利用模拟新方法——以中国武汉为例

Land-use and land-cover change(LUCC)simulations are powerful tools for evaluating and predicting future landscape dynamics amid rapid human-nature interactions to support decision-making.However,existing models often overlook spatial heterogeneity and temporal dependencies when modeling LUCC at both the macro and microscales.In this paper,we propose a new model,a self-calibrated convolutional neural network-based cellular automata(SC–CNN–CA)model,which integrates macro-and microspatial characteristics to simulate complex interactions among land-use types.The SC-CNN-CA model incorporates a self-calibration module using Gaussian functions to capture macrotrend such as urban sprawl while accounting for microlevel land-use interactions such as neighborhood effects.The results indicated that(1)the neighborhood effect between agricultural land and urban land tended to“increase followed by a decrease.”(2)Urban sprawl in Wuhan was highly compact,with a relatively high intensity of urban expansion at distances between 11.96 km and 24.44 km.(3)Compared with the other CA models tested,the SC-CNN-CA model demonstrated superior performance,achieving an overall accuracy of 84.12% and a figure of merit of 20.20%.This new model can enhance our understanding of historical LUCC trajectories and improve predictions of spatially explicit information for efficient land resource and urban management.