Background: This study focuses on Dhaka City and its impact on urban surface water. Cities, ecosystems, and agriculture need surface water. It is crucial for water resource planning and environmental preservation. The primary aim is to study how urbanization has affected surface water in Dhaka City over 30 years using satellite imagery. Methods: The study analyzed three decades of urban surface water shifts using Landsat 5 TM and Landsat 8 OLI/TIRS satellite imagery and Google Earth Engine (GEE) with JavaScript code for water ratio index detection. To investigate water level changes, field observation surveys and secondary data analysis were conducted. This integrated methodology simplified surface water data extraction and analysis, making remote sensing easier and allowing cloud-based satellite data processing. Results: The study demonstrates that the amount of surface water in cities is going down, from 36.23 km² in 1990 to 5.83 km² in 2021, which is an enormous decrease. This means that about 20 square kilometers, or 45 percent of the water's surface, have been lost in the last 30 years. The main reasons for the drop are unplanned expansion of cities, accelerated real estate development, and more trade and economic activities in the study area. Conclusions: The GEE algorithms provide useful insights into surface water's maximum and minimum extent, enabling appropriate planning and management. These findings aid Dhaka City's water resource management and environmental protection.

Background This research focuses on monitoring the spatiotemporal changes of urban surface water in Dhaka City from 1990 to 2021, utilizing satellite imagery and the Google Earth Engine (GEE) platform. Surface water is essential for urban, environmental, and agricultural ecosystems, and its dynamics have significant implications for water resource planning and environmental management. Objectives The main objectives of this study are to assess the extent of urban surface water coverage over the last three decades and identify trends of water loss or gain in the study area. Methods The study employs Landsat 5 TM and Landsat 8 OLI/TIRS imagery, integrating GEE with machine learning coding and WRI techniques to extract and analyze surface water data efficiently. Traditional remote sensing methods for dynamic monitoring are time-consuming and cumbersome, but GEE offers a user-friendly and accurate approach, providing easy access to satellite data and cloud-based processing. Results The results reveal a concerning trend in urban surface water coverage, indicating a significant reduction from 36.23 km² in 1990 to 5.83 km² in 2021, representing a loss of approximately 20 square kilometers or 45 percent of surface water over the last three decades. Conclusions The decline is attributed to factors such as unplanned urban expansion, rapid real estate development, and increased industrial and economic activities in the study area. The developed algorithms utilizing GEE offer valuable insights into the maximum and minimum extent of surface water, enabling effective surface water planning and management. These findings contribute to sustainable water resource management and environmental preservation in Dhaka City.

This study examines community participation in integrated solid waste management (ISWM) at Jahangirnagar University in Bangladesh. The university faces significant challenges in waste management, with negative social, environmental and economic consequences. The goal is to identify barriers to community involvement in ISWM and provide recommendations for enhancing engagement. The research uses a mixed-methods approach, surveying 397 respondents using cluster random sampling from the university's population of 20,000. Data collection methods include interviews, observations, focus groups, and document analysis. The findings highlight obstacles to community participation, such as limited community empowerment, mobilization, organization, and resource coordination. Household waste segregation is practiced by only 30% of respondents. However, 74% of participants express willingness to oversee door-to-door waste collection and dispose of waste at designated points, influenced by socioeconomic factors. Non-compliance with ISWM regulations is observed in 26% of respondents, and 12% lack sufficient knowledge about ISWM, indicating a need for improved organization and coordination. Additionally, some university authorities rely on outdated methods, resulting in weak enforcement of environmental regulations. Ineffective policies exacerbate the situation, failing to align with the market economy. The study recommends fostering cooperation between university authorities and the community, revising and enforcing regulations, and emphasizing waste generators' responsibility for proper disposal. Addressing these challenges can significantly improve solid waste management and environmental outcomes at Jahangirnagar University.

This research focuses on monitoring the spatiotemporal changes of urban surface water in Dhaka City from 1990 to 2021, utilizing satellite imagery and the Google Earth Engine (GEE) platform. Surface water is essential for urban, environmental, and agricultural ecosystems, and its dynamics have significant implications for water resource planning and environmental management. The main objectives of this study are to assess the extent of urban surface water coverage over the last three decades and identify trends of water loss or gain in the study area. The study employs Landsat 5 TM and Landsat 8 OLI/TIRS imagery, integrating GEE with machine learning coding and WRI techniques to extract and analyze surface water data efficiently. Traditional remote sensing methods for dynamic monitoring are time-consuming and cumbersome, but GEE offers a user-friendly and accurate approach, providing easy access to satellite data and cloud-based processing. The results reveal a concerning trend in urban surface water coverage, indicating a significant reduction from 36.23 km² in 1990 to 5.83 km² in 2021, representing a loss of approximately 20 square kilometers or 45 percent of surface water over the last three decades. The decline is attributed to factors such as unplanned urban expansion, rapid real estate development, and increased industrial and economic activities in the study area. The developed algorithms utilizing GEE offer valuable insights into the maximum and minimum extent of surface water, enabling effective surface water planning and management. These findings contribute to sustainable water resource management and environmental preservation in Dhaka City.

Flash floods are recurring disasters that cause losses to human life, agriculture and infrastructure in north-eastern haor region of Bangladesh. Cloud-based geospatial analytics were applied to assess flood damages after the extreme flooding event of June 20th, 2022 – the worst haor flooding event in recent decade. Flood inundation extents were mapped with Sentinel-1 SAR imagery on Google Earth Engine platform, while MODIS land cover and GHSL population datasets were used for multidimensional damage and exposure assessment. Results estimated a total inundated area of 1,141,615 ha, about 57.09% of total study area. From a total mapped inundation area, approximately 573,051 ha of MODIS-derived cropland and 6,811 ha of urban land were overlain. Furthermore, the GHSL population surface assessed that about 4,036,019 people were exposed. Accuracy assessment with 300 independent reference points achieved an overall accuracy of 92.33%. The study concludes by proposing integrated flood mitigation strategies, including both engineering solutions and non-structural approaches. Recommendations include Sabo Dams and Flash Flood Dampers, improving early warning systems, implementing nature-based solutions, community resilience and adaptive infrastructure. This case study broadly illustrates the applicability of cloud-based geospatial technology for rapid flood assessment and evidence-based mitigation strategies in flood-prone wetlands globally.