Change Detection and Future Land Use Projections in Zhejiang Province, China: A Case Study

Abstract. Zhejiang Province is experiencing rapid land use/land cover (LULC) transitions driven by urban expansion, infrastructure development, and increasing environmental pressures. Understanding historical dynamics and future trajectories of these changes is essential for informed regional planning and ecological management. This study analyzes land use changes from 2000 to 2020 and forecasts future patterns for 2025 to 2040 by integrating multi-temporal land use data with key spatial drivers, including elevation, slope, aspect, Normalized Difference Vegetation Index (NDVI), and proximity to roads and built-up areas. Change detection results reveal substantial declines in croplands and green spaces alongside rapid urban expansion, particularly around Hangzhou and Shaoxing and along major transportation corridors, reflecting an early phase of accelerated urbanization from a relatively small baseline. Future landuse dynamics were simulated using a hybrid Convolutional Neural Network - Long Short Term Memory (CNN-LSTM)-Cellular Automata (CA)-Markov framework that captures complex spatiotemporal dependencies and neighbourhood interactions under physical and anthropogenic constraints. Model projections indicate a more moderate growth regime from 2025 to 2040, with urban land increasing by 1.7%, croplands decreasing by 2.2%, and modest gains in water bodies (1.9%) and forest cover (1.1%), suggesting landscape saturation and policy-influenced land management. Validation using the observed 2025 land use map demonstrates strong predictive performance, achieving an overall accuracy of 86% and a Kappa coefficient of 79%. The results provide spatially explicit insights to support balanced development and enhanced ecological resilience.