Rapid replacement of vegetated land with impermeable land (built-up areas) is a major factor in the increase in Land Surface Temperature (LST), while increased LST worsens the temperature in cities and creates the Surface Urban Heat Island (SUHI) effect. The study aims to measure vegetation loss and Land Surface Temperature of the Gazipur district between 2000 and 2020 and explore the relationship among Normalized Difference Vegetation Index (NDVI), LST, and Urban Thermal Field Variance Index (UTFVI). The Landsat TM/OLI images with minimum cloud coverage have been used to derive different indices. The mean NDVI values are 0.21, 0.16, and 0.22 in 2000, 2010, and 2020 respectively which indicates a general improvement in the health of the vegetation. Besides, the highest LST values throughout 20 years, represent a general increasing trend. As a consequence, different land covers have experienced fluctuations in mean temperature. The result shows that the mean temperature of bare land, buildup, vegetation, and waterbody has increased by 4.77, 2.01, 2.25, and 2.23 °C respectively from 2000 to 2020. The strongest SUHI zone’s area grew by about 28% between 2000 and 2020. Additionally, the highest index value of UTFVI was 0.39 in 2000 and grew to 0.43 in 2010. It changed to 0.49 in 2020, or ten years later. Thus, the SUHI effect’s increasing intensity is visible. Also, regression analysis has been used to explore the correlation between the derived indices. Stakeholders from different sectors like urban planners and policymakers may take insights from this study to work to promote greenery for a healthy urban environment.

Rapid and unplanned urbanization in megacities of the Global South has accelerated land use and land cover (LULC) transformation, with profound implications for environmental sustainability. This study examines LULC change dynamics in the Detailed Area Plan (DAP) area of Dhaka, Bangladesh, which is one of the most rapidly urbanizing regions in South Asia; using multi-temporal Landsat imagery for the years 2001, 2011, and 2021. Two machine learning classifiers, Support Vector Machine (SVM) and Random Forest (RF), were independently applied to four land cover classes (built-up, water body, vegetation, and others) across multiple spectral band combinations, and their classification accuracies were systematically compared. SVM consistently outperformed RF across all three time periods, achieving overall accuracies of 99%, 91%, and 98% with kappa coefficients of 0.99, 0.88, and 0.97, respectively, compared to RF accuracies of 95%, 90%, and 97%. The SVM-derived LULC maps were subsequently used as inputs to an Artificial Neural Network (ANN) transition potential model to simulate future LULC scenarios for 2031 and 2041. The ANN model was calibrated using 2001 and 2011 imagery and validated against the 2021 classification, achieving a correctness of 95.67% and an overall kappa of 0.9429. Classification results reveal a dramatic expansion of built-up area from 395.75 km² in 2001 to 808.87 km² in 2021, accompanied by steep vegetation loss, trends that are projected to intensify under current trajectories. These findings, which should be interpreted with appropriate caution given the assumption of trend continuity, provide a scientific basis for sustainable land use policy in RAJUK DAP region.