Members’ Publication

In this work, we analyzed the locations of existing bus stops in the Halifax Regional Municipality (HRM) area and then calculated the walking distance in time in order to understand the accessibility for pedestrians. In doing so, we employed geographic information systems (e.g., spatial and network analysis tools) to generate accessibility models from bus stops at 5, 10, 15, and 20 min of walking distance. After generating the service area of bus stops on maps, we overlaid socioeconomic variables (i.e., age group, income, and bus-stop accessibility) to better understand the HRM public transport network. We found that population density was an important consideration in providing the number of bus stops in specific communities, which may be related to the facilities offered by the urban hierarchy. Furthermore, we established a relationship between transit stops and accessibility for people in age groups of between 0 and 19 (e.g., school-going children) and 65+ (i.e., the older population) so as to obtain an understanding of the time required for them to access the bus stops on foot. Overall, 77% of the population in the HRM was served by public transport within 5 min of walking; however, for the 65+ cohort, this number was higher (82%). A significant amount of the young population (23%) was served over longer distances than 10′ of walking. We summarize that our findings are critical for planners, practitioners, and researchers in order for them to understand the present transit system in place based on accessibility to the bus stops within walking distance.

Limited access to fossil fuels in Bangladesh is becoming increasingly expensive, leading to high prices. Compared to fossil fuels, renewable energy sources are better regarding several factors worth mentioning: cost-effectiveness and availability. To secure sustainable energy infrastructure and transit towards green economies, Bangladesh must pursue clean energy. The study proposes employing GIS-based multi-criteria suitability analysis to determine Bangladesh's potential locations for solar power plants. Topography, climate, the distance to power stations, and environmental preservation are among the selection criteria. The research area was evaluated using a Geographic Information System (GIS) and the Analytic Hierarchy Process (AHP) framework. As a result, it was divided into three distinct suitability categories: least suitable, moderate suitable, and most suitable areas. It is noteworthy that Bangladesh's southeast has a disproportionately high concentration of the majority of potential sites for constructing solar power plants.

Meteorological drought, driven by inadequate precipitation, has significant repercussions for water resources, agriculture, and human well-being. This study conducted an extensive assessment of meteorological drought in northern Bangladesh, employing remote sensing indices and machine learning techniques. The main aim was to evaluate meteorological drought occurrences in northern Bangladesh from 2010 to 2019, utilizing seven drought parameters and a machine learning model. Utilizing a Random Forest (RF) model, this study employed the Standardized Precipitation Index (SPI) as the dependent variable and seven remote sensing indices as independent variables. Through this methodology, the study assessed the significance of these indices generated by the model and integrated them, culminating in the creation of a meteorological drought distribution map spanning 2010 to 2019. This approach offers novel insights by probing the interplay and collective impacts of these indices, shedding light on previously unexplored aspects of regional drought patterns of northern Bangladesh. The major findings showed that precipitation strongly influenced both short-term and long-term meteorological drought episodes. Moreover, land surface-related indices, such as Evapotranspiration (ET) and Normalized Difference Water Index (NDWI), exhibited a more pronounced impact on short-term drought occurrences, while vegetation-related indices like Normalized Multi-band Drought Index (NMDI) and Normalized Difference Vegetation Index (NDVI) demonstrated greater influence over long-term drought events. During this timeframe, the Rajshahi division experienced frequent extreme and severe drought events. Moderate droughts and abnormally dry conditions were widespread. The Barind tract area consistently faced moderate to extreme droughts, with exceptions in 2011, 2014, and 2019. On average, over 5% of the region had extreme droughts, while more than 12% experienced severe droughts during this decade. Long-term drought indicators (SPI 6 and SPI 9) consistently showed higher frequencies of extreme and severe droughts compared to short-term indicators (SPI 1 and SPI 3), emphasizing the influence of prolonged rainfall deficits on extreme droughts and the relevance of longer time frames for severe drought dynamics. The RF model demonstrated strong performance with accuracy ranging from 81% to 95%. Low prediction errors (RMSE 6% to 31%) and high out-of-bag (OOB) accuracy ranging from 76% to 98% highlighted its accuracy. The F1 score consistently exceeded 76%, indicating high precision and recall. Cross-validation values ranged from 78% to 94%, affirming reliable generalization to new data. Incorporating the main findings, this study contributes valuable insights for the formulation of targeted drought mitigation strategies in northern Bangladesh. It is imperative to note that the scope of this study is confined to the northern region of Bangladesh, and generalizing these findings to other regions should be exercised with caution. Nevertheless, the research methodology and approach can serve as a model for future studies in related fields, advancing knowledge of how to assess droughts using remote sensing and machine learning methods.

Land use land cover change (LULC) significantly impacts urban sustainability, urban planning, climate change, natural resource management, and biodiversity. The Chattogram Metropolitan Area (CMA) has been going through rapid urbanization, which has impacted the LULC transformation and accelerated the growth of urban sprawl and unplanned development. To map those urban sprawls and natural resources depletion, this study aims to monitor the LULC change using Landsat satellite imagery from 2003 to 2023 in the cloud-based remote sensing platform Google Earth Engine (GEE). LULC has been classified into five distinct classes: waterbody, build-up, bare land, dense vegetation, and cropland, employing four machine learning algorithms (random forest, gradient tree boost, classification & regression tree, and support vector machine) in the GEE platform. The overall accuracy (kappa statistics) and the receiver operating characteristic (ROC) curve have demonstrated satisfactory results. The results indicate that the CART model outperforms other LULC models when considering efficiency and accuracy in the designated study region. The analysis of LULC conversions revealed notable trends, patterns, and magnitudes across all periods: 2003–2013, 2013–2023, and 2003–2023. The expansion of unregulated builtup areas and the decline of croplands emerged as primary concerns. However, there was a positive indication of a significant increase in dense vegetation within the study area over the 20 years.

River channel migration frequently takes place in the river in the floodplain zones. The morphological dynamics of rivers in the floodplain zones act as an important ecological indicator of its profound effect on both ecological and human existence. Living close to rivers puts one’s life and property in danger owing to fluctuations in LULC brought on by channel dynamics. A detailed analysis of the causes and impacts of such changes is necessary for establishing better policy and decision-making in the areas of catastrophe risk reduction and management. The study aims to assess the spatiotemporal variation of the Padma River bank line as well as surroundings LULC from 1991 to 2021 with predicting backlines migration and LULC till 2031 and 2041 respectively. Additionally, the study determines the impact of riverbank shifting on LULC of Padma River surroundings upazilas. The bank line was extracted using GEE to compute the NDWI and MNDWI indexes, also ArcGIS was used to complete the rest of the DSAS model before the value was visually performed. The outstanding accuracy of the model between the actual and forecasted riverbank movement was also revealed by the validation results. On the other hand, LULC was carried out in GEE, and QGIS was used to develop the prediction model. Last but not least, the Chisquare test demonstrated that there is no statistically significant difference between the actual and predicted LULC for 2021. The data demonstrate that the rate of erosion and accretion fluctuates from 20 to 90 m per year, while the predicted rate remains constant. The findings of the research made it evident that 9 out of the 37 upazilas that were chosen had experienced the LULC transition, and that by 2041, 4 new upazilas will replace the previous 2 summing up to 11. Four upazilas, namely Bagha, Daulatpur, Goalanda, and Faridpur Sadar, have been affected by the LULC since 1991, and the findings of the study came to a verdict that this degradation will continue until 2041. Protecting aquatic ecosystems is one of the main objectives of mapping river morphological dynamics, but it’s also important to recognize the effects that any change in ecological indicators will have on people and the surroundings. The study addresses the issues of ecosystem degradation in the river-adjacent upazilas and provides a spatial strategy for monitoring trends in river migration and LULC variations over time in order to possibly use the study’s findings as a framework for planning and policymaking to improve human quality of life.