Mahmudul Hasan

Landscape Ecological Risk (LER) reflects the potential adverse effects of landscape change on ecological structure, function, and stability. In rapidly urbanizing megacities such as Dhaka, vegetation loss and built-up expansion have intensified environmental pressure over recent decades. This study examines the spatiotemporal dynamics of LER in Dhaka from 2004 to 2024 under the combined influence of vegetation change and urban expansion. Multi-temporal remote sensing data were used to generate land cover maps, derive Fractional Vegetation Cover (FVC), and quantify urbanization intensity using Nighttime Light (NTL) data. The Landscape Ecological Risk Index (LERI) was calculated using landscape pattern metrics, while bivariate spatial autocorrelation and geographically weighted regression (GWR) were applied to examine spatial associations and local spatial heterogeneity. The results show that vegetation degradation affected 34.39% of the study area during 2004–2024, while high-risk zones increased from 24.36% in 2004 to 42.95% in 2024. Land cover analysis further indicates a substantial expansion of built-up areas, accompanied by the contraction and fragmentation of vegetation, agricultural land, and lowland classes. Spatial analyses reveal that the relationships among vegetation cover, urbanization intensity, and ecological risk vary across the city and became increasingly spatially differentiated over time. These findings suggest that vegetation loss and urban expansion are spatially associated with increasing ecological risk in Dhaka. However, the results should be interpreted with caution because of uncertainties related to remotely sensed data, unsupervised land cover classification, resampling procedures, and limited ground validation. Despite these limitations, the study provides a spatially explicit framework for understanding ecological risk dynamics and offers useful evidence for green-space conservation, ecological restoration, and sustainable urban planning in rapidly urbanizing regions.

Rapid urbanization and climate change intensify heat stress in Bangladeshi cities through urban heat islands. Although evidence shows cooling benefits of blue and green spaces, most studies in Bangladesh focus only on direct effects on surface albedo and land surface temperature, ignoring neighborhood spillovers. This study addresses this gap by analyzing the thermal dynamics of urban water bodies and vegetation in Dhaka, Rajshahi, and Chattogram using remote sensing data and a Spatial Durbin Error Model. Surface reflectance from Sentinel-2 and land-surface temperature from Landsat 8 informed land-use classifications and albedo calculations, while road network data provided spatial covariates. Random forest-based LULC maps achieved accuracies above 89 percent, and SDEM quantified both direct cooling within blue and green spaces and their indirect cooling in adjacent areas. Results demonstrate water bodies consistently provide the strongest cooling, lowering LST by 0.36–1.17°C directly and exhibiting significant negative spillovers. Vegetation directly reduced LST by up to 0.64°C, with substantial cooling spillover (e.g., −0.91°C in Dhaka). Conversely, barren land increased LST directly (up to +1.24°C) and via positive spillovers (+0.80°C). Albedo increases paradoxically correlated with LST warming (+0.26°C per unit in Dhaka) due to the absence of evaporative cooling. Seasonal variations were pronounced, with winter water cooling intensifying by 45–118%. Validation confirmed model robustness (albedo Pseudo R2 = 59–68%; LST Pseudo R2 = 37–51%). These findings underscore the superior thermodynamic efficiency of blue and green infrastructure via latent heat flux rather than reflectivity alone for urban heat mitigation.

Rapid urbanization and climate change intensify heat stress in Bangladeshi cities through urban heat islands. Although evidence shows cooling benefits of blue and green spaces, most studies in Bangladesh focus only on direct effects on surface albedo and land surface temperature, ignoring neighborhood spillovers. This study addresses this gap by analyzing the thermal dynamics of urban water bodies and vegetation in Dhaka, Rajshahi, and Chattogram using remote sensing data and a Spatial Durbin Error Model. Surface reflectance from Sentinel-2 and land-surface temperature from Landsat 8 informed land-use classifications and albedo calculations, while road network data provided spatial covariates. Random forest-based LULC maps achieved accuracies above 89 percent, and SDEM quantified both direct cooling within blue and green spaces and their indirect cooling in adjacent areas. Results demonstrate water bodies consistently provide the strongest cooling, lowering LST by 0.36–1.17°C directly and exhibiting significant negative spillovers. Vegetation directly reduced LST by up to 0.64°C, with substantial cooling spillover (e.g., −0.91°C in Dhaka). Conversely, barren land increased LST directly (up to +1.24°C) and via positive spillovers (+0.80°C). Albedo increases paradoxically correlated with LST warming (+0.26°C per unit in Dhaka) due to the absence of evaporative cooling. Seasonal variations were pronounced, with winter water cooling intensifying by 45–118%. Validation confirmed model robustness (albedo Pseudo R2 = 59–68%; LST Pseudo R2 = 37–51%). These findings underscore the superior thermodynamic efficiency of blue and green infrastructure via latent heat flux rather than reflectivity alone for urban heat mitigation.

The thermal consequences of industrial land transformation remain underexplored in rapidly urbanizing regions of Bangladesh. This study presents a novel approach of how extensive industrial expansion in Narayanganj, a major manufacturing hub dominated by textile, knitwear and dyeing industries, has altered land surface temperature (LST) dynamics over the past three decades, including its variation across classes, relationships with biophysical indices and future patterns. Landsat 5 TM and Landsat 8 OLI imagery from 1991, 2007, and 2023 were utilized to map LULC using winter-season images through supervised classification, while multi-seasonal thermal bands were used to derive LST. LST variations were further evaluated using cross-sectional profiles across different land cover types, and correlations were examined with indices including the greenness index (NDVI), moisture index (NDMI), built-up index (NDBI), and barrenness index (NDBAI). Additionally, a future LST map for 2039 was generated using the cellular automata–artificial neural network (CA-ANN) model. Results show that between 1991 and 2023, built-up area and bare land expanded by 16.72% and 14.15%, while vegetation area and water bodies decreased by 26.62% and 4.25%. Average LST increased from 25.94 °C in 1991 to 28.68 °C in 2023, with projections indicating an additional 2 °C rise by 2039. Cross-sectional analysis found that built-up areas consistently showed the maximum surface temperatures, followed by bare land, vegetation and water bodies. In addition, correlation analysis revealed that LST showed an inverse relation with NDVI and NDMI, while showing a positive relationship with NDBI and NDBAI. These findings show the necessity of sustainable urban planning and green infrastructure to reduce surface heating in rapidly urbanizing areas.

Conventional data-driven models require a substantial volume of meteorological and hydrological variables as input for the accurate prediction of river water level, which may not be feasible for many river basins having data scarcity. More particularly, the accurate prediction of river water level using a univariate approach often becomes difficult employing the conventional models. Placing this challenge as the key motivator, this study aims to propose a novel data-driven approach by combining five sophisticated preprocessing techniques, EMD, EEMD, CEEMDAN, VMD, and SVMD with a recently proposed model, namely cross-variable linear integrated enhanced Transformer (CLIENT), for improving the univariate predictions of river water level. The Rupsa-Pasur River, located at the southwestern coastal region of Bangladesh, was chosen as the study region due to its highly stochastic hydrological dynamics. Four statistical metrics, i.e., NRMSE, MAE, KGE, and NSE, were computed for all the developed models at both the training and testing phases following a uniform train-test ratio of 8:2. The Borda count strategy was implemented with a view to identifying the optimal data-driven model. Compared to the traditional lag features-based input combinations, the proposed framework achieved superior performance at both stations. Results exhibited that the SVMD-enhanced CLIENT model (referred to as the C6 model) outperformed all other models for both stations. The C6 model demonstrated better capability of recognizing the major portion of the hidden and advantageous signals within the time series. This led to in an NSE value of 0.999 during both the training and testing phases, indicating a notable enhancement compared to existing literature, while also achieving considerably lower computational costs for both stations. The framework’s capability to provide precise predictions based solely on historical water level data underscores its applicability in regions with limited data availability. Thereafter, an interactive graphical user interface (GUI) was developed with the C6 model following a five-fold cross validation and external validation.