Evaluating Spatiotemporal Changes and Predicting Land-Use Dynamics Using an Artificial Neural Network in Worcester City, Massachusetts

作者：M. J. Alam, Mijanur Rahman, Nusrut Sharmin, M. N. Molla, Hossain Al Mahbub, Muhammad Muniruzzaman, Md Nadim Rahman, Edris Alam · 发表于：Earth Systems and Environment · 年份：2026 · DOI：10.1007/s41748-026-01099-2 · 研究领域：Land Use and Ecosystem Services、Remote Sensing in Agriculture、Urban Green Space and Health

Abstract The land use and land cover (LULC) change has a remarkable impact on the urban ecosystems due to its effect on the environmental processes, natural resources, and urban sustainability. The aim of this study was to conduct the spatiotemporal analysis of LULC in Worcester City, Massachusetts, and to identify the dynamics of forest cover and justify its causes in the given time range of 2003–2023. LULC classification and forest change detection were done with high-resolution datasets provided by the National Land Cover Database (NLCD). The MLP-ANN outperformed logistic regression and achieved higher in overall accuracy percentages and location Kappa values. Furthermore, a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) model embedded in the MOLUSCE plug-in of QGIS was used in order to forecast future LULC scenarios in 2033, 2043, and 2053. The findings showed that the built-up areas moved up from 82.1% to 83.2%, and the forest cover reduced from 12.4% to 11.9% over the period studied. Water bodies were relatively stable, wetlands were slightly raised, whereas the grasslands and barren lands recorded slight reductions. Forest change detection showed a total of 0.81% loss of forest, 0.03% gain of forest, and 99.10% change in forest that remained the same. The model validation was high (Kappa = 0.96), which proved that it can be used in the future to simulate it. The future LULC projections showed further urbanization and slow forest cover loss due to urbanizati...