Spatiotemporal pattern analysis of land use in Jiangsu Province based on long-term time series remote sensing images - Zhendong Ji; Lingzhi Yin; Jinhong Wang (Journal of Applied Science and Engineering)
Studying spatiotemporal patterns of land use is crucial for optimal land resource allocation and sustainable development. This study utilizes the Google Earth Engine (GEE) platform and long-term remote sensing imagery data, selecting Jiangsu Province as a case study area. Principal Component Analysis (PCA) was applied to reduce feature dimensionality, and the Random Forest classification algorithm was optimized with Bayesian Optimization and Tree-structured Parzen Estimators (TPE) for improved performance. The classification achieved an overall accuracy of 92% and a Kappa coefficient of 0.89. Spatiotemporal clustering was conducted at the optimal scale, determined using landscape pattern indices, to analyze the land use evolution from 2000 to 2020. The study results indicate that: (1) PCA effectively reduced feature redundancy, enabling a more robust classification process, while Bayesian optimization improved the model’s predictive performance. (2) Cropland area continuously declined, built-up land expanded significantly, waterbody areas decreased slightly, and forest coverage remained stable. The main transitions occurred between built-up land and cropland, as well as between waterbodies and both cropland and built-up land. (3) From 2000 to 2010, rapid urbanization led to substantial expansion of built-up land, particularly in coastal areas, south of the Yangtze River, and northern cities, causing significant cropland loss and ecological degradation. Post-2010, land use policies helped curb cropland loss. These findings offer valuable insights into land use patterns in Jiangsu, supporting effective land resource management and planning.