Machine learning-based spatio-temporal assessment of land use/land cover change in Barishal district of Bangladesh between 1988 and 2024

Abstract

Barishal, known as the “Land of Paddy, Rivers, and Canals,” bears immense historical, social, and economic importance as a major agricultural, cultural, and financial hub of Bangladesh. However, the absence of a comprehensive LULC inventory, high landscape variability, and the limited availability of high quality remotely sensed data make the quantification of LULC changes in the region extremely challenging. This study is the first of its kind in Bangladesh to propose a cloud-based, open-source, machine learning framework that integrates spectral, textural, and topographic information to assess the spatio-temporal dynamics of LULC change in Barishal over 35 years. The performance of four machine learning algorithms (Support Vector Machine, Classification and Regression Tree, K-Nearest Neighbor, and Random Forests) were evaluated to ensure classification reliability. Results indicate that Random Forest outperforms other classifiers, achieving an average accuracy of 99 % across all study periods, making it the most suitable model for classifying heterogeneous landscapes. An analysis of multi-temporal LULC maps reveals a net increase in wetland (0.35 %), built-up (1.81 %), vegetation (8.48 %), and a net decrease in agriculture (−10.33 %) and bare soil (−0.36 %), primarily due to indiscriminate land use transitions. The study establishes a comprehensive and reliable baseline for Barishal’s LULC and introduces a rapid, open data driven approach for mapping complex, heterogeneous coastal landscapes globally. The spatio-temporal patterns of LULC underscore the urgent need for climate-resilient planning in Barishal and provide valuable insights for evidence-based policymaking necessary in implementing SDG 11: Sustainable Cities and Communities and SDG 15: Life on Land.