Members’ Publication

The study aimed at deeply engaging the local community to identify and address their alarming issues.This research showed the results of a meticulous participatory planning initiative conducted in Dakatia Village, Khulna, employing the robust framework of participatory rural appraisal (PRA). Utilizing an extensive array of PRA tools, such as social mapping, resource mapping, transect walks, network diagrams, and problem tree analyses, the initiative provided profound insights into the complex socioeconomic and environmental issues confronting the residents. The study revealed several critical issues of the village: severe waterlogging worsened by inadequate drainage systems, inefficient waste management practices, poor road infrastructure leading to accessibility problems, high unemployment rates, and a noticeable lack of effective NGO engagement in addressing these issues. In response, A developed and proposed a series of solutions designed to address these specific challenges comprehensively. These solutions included the construction of drainage systems tailored to the village's topography, the implementation of sustainable community-based waste management programs, and significant improvements to road connectivity to enhance access to essential services and economic opportunities. Moreover, the project placed a strong emphasis on capacity building through skill development programs aimed at enhancing employability and economic self-sufficiency among the villagers. Enhanced involvement of local and international NGOs was also proposed to provide the necessary support and resources, thereby strengthening the community's ability to sustain these initiatives. The participatory approach of PRA strengthens a sense of ownership and commitment among the village residents. This was crucial for the sustainability of the initiatives, as it ensured that the interventions were not only accepted but also actively maintained by the community. Furthermore, this project demonstrated the value of community involvement in planning and decision-making processes, which led to more informed and consensual solutions. The successful application of PRA methodologies in Dakatia Village highlights the success of participatory planning in understanding and transforming rural settings in a sustainable and inclusive manner.

Since the advent of the IKONOS satellite in 1999, remote sensing has witnessed remarkable enhancements in spatial resolution, propelled further by satellites such as QuickBird, the WorldView series, GeoEye-1, Pleiades, and others, including UAVs. This technological advancement has facilitated the development of high-resolution images that significantly differ from moderate- or low-resolution ones by enabling the identification of distinct land cover features, or “objects,” crucial for Geographic Object-Based Image Analysis (GEOBIA). Central to GEOBIA, image segmentation partitions images into coherent regions representing real-world elements like buildings and vegetation, which is crucial for accurate classification. This chapter reviews segmentation algorithms, categorizing them into traditional and deep learning (DL)-based semantic segmentation. Traditional methods group pixels into objects without class assignment, requiring additional classification steps, whereas DL-based semantic segmentation combines segmentation and classification, enhancing precision. The chapter traces the evolution of these methods, noting a significant increase in traditional segmentation with high-resolution imagery and an exponential rise in DL-based approaches. It explores state-of-the-art DL-based algorithms, their concepts, advantages, limitations, and applications. Additionally, it highlights the application of segmentation in various imaging contexts, including multispectral, hyperspectral, and LiDAR images, offering a comprehensive analysis of the current trends and future directions in remote sensing image segmentation.

As climate change intensifies coastal hazards globally, understanding and quantifying community vulnerability is crucial for effective adaptation strategies. This study examines the application of indicators in planning adaptive capacity strategies to reduce community vulnerability to tsunami hazards (THs). Using a case study approach in Mehuin, a coastal community in southern Chile susceptible to tsunamis, we identified and validated specific indicators through both quantitative and qualitative methods. Secondary data sources were used to initially identify indicators, which were then validated through 12 in-depth interviews with community representatives and local emergency management actors. The Analytic Hierarchy Process was employed to determine the relative weights of indicators in adaptive capacity planning. A total of 25 specific indicators were identified for Mehuin, grouped into 7 key issues: human exposure, physical exposure, geographical factors, socioeconomic conditions, psychological factors, governance, and planning capacities. The most influential indicators for adaptive capacity planning were found to be preparedness and planning-related, including the existence of emergency plans (14.33% weight), quality of urban/regional plans (14.20%), and building codes (10.79%). Socioeconomic factors like collective action (9.06%) and social networks (5.61%) were also significant. The least influential factors were building structure (0.16%) and housing density (0.36%). The indicators provide insights into community vulnerabilities and can inform targeted strategies to enhance adaptive capacity. This study concludes that local-scale indicators are crucial for identifying community-specific vulnerabilities and priorities in tsunami preparedness. However, their application requires careful contextual consideration. The findings demonstrate the importance of integrating community perspectives in vulnerability assessments and adaptive planning for coastal hazards. This research advances evidence-based approaches for disaster risk reduction and sustainable development in coastal regions, aligning with UN Sustainable Development Goals 11 (Sustainable Cities and Communities) and 13 (Climate Action).

Groundwater depletion in arid regions poses a significant threat to agricultural sustainability and rural livelihoods. This study employs geospatial analysis and economic modeling to address groundwater depletion in the arid Barind region of Northwest Bangladesh, where 84% of the rural population depends on agriculture. Using remote sensing and GIS, we developed an elevation map revealing areas up to 60 m above sea level, exacerbating evaporation and aquifer dryness. Field data collected through Participatory Rural Appraisal tools showed farmers exhibiting “ignorant myopic” behavior, prioritizing short-term profits over resource conservation. To address this, an Increasing Block Tariff (IBT) water pricing model was developed, dividing water usage into three blocks based on irrigation hours: 1–275 h, 276–550 h, and 551+ h. The proposed IBT model significantly increases water prices across the three blocks: 117 BDT/hour for the first block (from current 100–110 BDT/hour), 120 BDT/hour for the second block, and 138 BDT/hour for the third block. A demand function (y = −0.1178x + 241.8) was formulated to evaluate the model’s impact. The results show potential reductions in groundwater consumption: 59 h in the first block, 26 h in the second block, and 158 h in the third block. These reductions align with the principles of integrated water resource management (IWRM): social equity, economic efficiency, and environmental integration. The model incorporates economic externalities (e.g., well lifting costs) and environmental externalities (e.g., crop pattern shifts), with total costs reaching 92,709,049 BDT for environmental factors. This research provides a framework for sustainable groundwater management in arid regions, potentially reducing overextraction while maintaining agricultural productivity. The proposed IBT model offers a locally driven solution to balance resource conservation with the livelihood needs of farming communities in the Barind tract. By combining remote sensing, GIS, and economic modeling, this research provides a framework for sustainable groundwater management in arid regions, demonstrating the power of geospatial technologies in addressing complex water resource challenges.

Access to land is very important for socially and economically disadvantaged people, from thecountries where agriculture is the main source of employment to improve their livelihoods. Itbrings an opportunity for producing their livelihoods and, at the same time, it affects incentivesto make investments and the ability to access financial markets. Furthermore, socially &economically disadvantaged people’s access to land contributes in reducing poverty andimproving economic growth of a country like Bangladesh. Land reform is a tool beingemployed by the governments around the world to contribute in this respect. Theimplementation of land reform programs takes place through either of three major approaches;state-led, community-based, and market-assisted. The limitations and weaknesses with theseapproaches hinder the success of the programs. The paper focuses on how land reformapproaches can contribute to benefit socially and economically disadvantaged people of Bangladesh.

Bangladesh is among the countries with the highest concentrations of particulate matter and other air pollutants, according to the World Health Organization. The Department of Environment of Bangladesh has installed air monitoring systems in Dhaka, Chattogram, Khulna, Rajshahi, Barishal, Gazipur, and Narayanganj to observe daily gaseous pollutants and particulate matter (PM) concentrations in those cities. This study analyzed the concentration of gaseous and particulate pollutants from 2018 to 2023 in these urban areas of Bangladesh. Ambient air concentrations of PM2.5, PM10, carbon monoxide (CO), oxides of nitrogen (NOx) and ozone (O3) were monitored in the dry and wet seasons for these cities. Temporal variability including hourly, day of the week, monthly, and seasonal variations of particulate matter and gaseous pollutants (except seasonal variation) were assessed. Both PM2.5 and PM10 exceeded the Bangladesh National Ambient Air Quality Standard (BNAAQS) and the World Health Organization (WHO) air quality guidelines during the observed period for all the observed regions. PM2.5 mean concentration was maximum in Narayanganj i.e., 109.7 µg/m3 (313% of the limit of the air quality standard), and PM10 mean concentration was maximum in Narayanganj i.e., 203.3 µg/m3 (407%). Among the observed cities, Khulna had the better air quality although it was not satisfactory at all. However, the gaseous pollutants were within permissible limits. The temporal patterns suggested that vehicles, brick kilns, and industries were responsible for the poor air quality in Bangladesh. The air quality during the period in which COVID-19 was most prevalent (2020–2021) was compared to the two years prior and post to that interval. In general, air quality improved during the COVID period, but in the post-COVID period, they returned to concentrations similar to the pre-COVID period. After identifying possible pollutant sources, these results will assist decision-makers in taking action and implementing policies to control air pollution in cities.