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 ...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.展开更多
基金National Natural Science Foundation of China,No.42101259,No.42371101,No.42301455Young Science and Technology New Star Project of Shaanxi Province,No.2024ZC-KJXX-013+3 种基金Qin Chuangyuan Cites High-level Innovation or Entrepreneurship Talent Project,No.QCYRCXM-2023-066Fifth Batch Special Funding(Pre-Station)from China Postdoctoral Science Foundation,No.2023TQ0207Fundamental Research Funds for the Central Universities,No.GK202304024,No.1110011297,No.1112010355Teaching Reform Project of Shaanxi Normal University,No.23GGYS-JG06。
文摘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.
