Abstract:

Land use change and prediction are crucial for land resource management and optimization. In this paper, based on remote sensing spatial and temporal series data, a synergistic landscape index and long short-term memory (LSTM) model were used to characterize and predict the evolution of spatial and temporal patterns of land use in the Manasi region over a long time series. Results showed that (1) from 1992 to 2020, the cropland, grassland, and building land increased, and tree cover, water bodies, and unused land decreased. (2) The degree of fragmentation of arable land gradually decreased. The landscape indices of tree cover and water bodies were in a state of slight fluctuation. Grassland aggregation increased, fragmentation decreased, and the shape exhibited regularization. The landscape index of building land showed the most dramatic state of continuous expansion, with a deepening fragmentation and a tendency for complex shapes. The fragmentation of unused land gradually increased, but the shape tended to be regular. (3) Different models for predicting land use change, including the LSTM, multi-layer perception artificial neural network, logistic regression, and CA-Markov models, were compared. The Kappa coefficient of the LSTM model was 95.31%, which is more accurate than that of other models and consistent with the actual land use pattern. The LSTM model suggests that in 2025, land use types will still be dominated by cropland, grassland, and unused land.

Key words: spatial and temporal pattern change, land use prediction, deep learning, LSTM model, landscape index