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The intensity and formation of urban heat island (UHI) phenomena are closely related to land use/land cover (LULC) and land surface temperature (LST) change. The effect of UHI can be described quantitatively by urban thermal field variance index (UTFVI). For measuring urban health and ensuring sustainable development, the analysis of LST and UTFVI are receiving boosted attention. This study predicted LULC, seasonal (summer & winter) LST, and UTFVI variations using machine learning algorithms (MLAs) in Cumilla City Corporation (CCC), Bangladesh. Landsat 4–5 TM and Landsat 8 OLI satellite images were used for 1999, 2009, and 2019 to predict future scenarios for 2029 and 2039. MLAs such as Cellular Automata (CA) and Artificial Neural Network (ANN) methods were used to predict the future change in LULC, LST, and UTFVI. The result suggests that, in the year 2029 and 2039, the urban area will likely to be increased by around 8 % and 11 %, where significant decrease will be taken place in green cover by 9 % and 14 %. If the rapid urban growth continues, more than 30 % of the CCC area will likely to be experienced more than 33 ◦C temperature and strongest UTFVI effect in the year 2029 and 2039. In addition, an average 4 ◦C higher LST was recorded in the urban area compared with vegetation cover. In urban construction practice, avoiding concentrated impermeable layers (built-up areas) and increasing green covers, are effective ways of mitigating the effect of UTFVI. This study will contribute in achieving sustainable development and provide useful insights to understand the complex relationship among different elements of urban environments and promotion of city competence.

According to the statement of Intergovernmental Panel on Climate Change (IPCC), since the late 19th century, the global mean land surface temperature (LST) has increased 20C due to the increase in greenhouse gas emissions and rapid land use/land cover change (LULCC) from human activities. The dramatic increase in LST leads to sever urban heat island (UHI) effect. In the last few decades, massive LULCC transition leads to higher LST in Bangladesh's different city space. The dramatic decrease in green land and water bodies with a massive increase in the urban builtup area influence LST in Chattogram city. This study estimates the existing and future dynamic of LST using remote sensing techniques. A series of Landsat TM/OLI images collected in the summer season were used to explore LST variation from 1999 to 2019. Also, the correlation between LST and normalized difference water index (NDWI), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and normalized difference bare soil index (NDBSI) is estimated. Finally using artificial neural network predict the future LST for the year 2029 and 2039 to identify the impact of future LST increase in climate change. The study results suggest that an overall increase in LST is 2.50C for the last 20 years. Correlation results indicate that LST shows a strong positing correlation with NDBI and a negative correlation with NDWI and NDVI. Simulation results indicate that LST will increase 20C (2029) and 30C (2039) compare with 2019. A significant increase in LST will create environmental degradation and ecological imbalance in Chattogram city. Higher LST hotspot areas were found in urban built-up areas, which are the most vulnerable to the UHI effect. The findings will help the city authorities to prepare suitable strategies to reduce the UHI effect and improve the city's climate change scenario.

In the last few decades, rapid urban development has a significant impact on land use/landcover (LULC) parameters, i.e., water bodies, built-up area, agricultural and bare land. According to the yearly report of the Rajshahi district, almost 10% of the total agricultural land has been lost in the last 20 years due to the tremendous development pressure and massive LULC change. Monitoring and prediction of urban growth and the LULC change scenario is the essential element in current strategies for conserving agricultural land and natural resources for ensuring sustainable urbanization and food security in the future. This study aims to identify the loss of agricultural land using maximum likelihood supervised image classification algorithm and LULC indexes such as normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI) and normalized difference bare soil index (NDBSI) for the year 1999, 2009 and 2019 using Landsat 4-5 TM and Landsat 8 OLI satellite images in Rajshahi district. The study also simulates the urban growth and loss of agricultural land for the year 2029 using machine learning-based cellular automata (CA) approach. The result suggests that almost 12% of the built-up area is increased in the last 20 years, and based on the simulation, the built-up area will be increased to 15% in 2029 in the Rajshahi district. 13% of agricultural land already converted to built-up areas, and in 2029 the percentage will increase by almost 21%. The loss in agricultural land will become unstable the biodiversity, ecosystem services and create unemployment as well as food insecurity in the district. District authority needs to implement effective strategies with the help of urban planners, environmental engineers, and policymakers to conserve district agricultural wealth and natural resources, which will ensure sustainable, planned, and inclusive urban development in the Rajshahi district.

Rapid urbanization creates a significant impact on urban land use land cover (LULC), increasing the land surface temperature (LST). Remote Sensing is one of the most proficient techniques for identifying the pattern of urban growth and LULC change in a particular region and its impact on urban LST variations. A significant increase in urban LST influenced by rapid LULC change substantially impacts the ecosystem, climate change, and accelerated global warming. Haphazard urban growth and lack of proper planning negatively impact natural resources (vegetation and water bodies) and result in a replacement with non-evaporating surfaces (urban area). This study used multi-temporal Landsat OLI/TM satellite data sets to analyze the impact of LULC changes on LST increase for two decades (1999, 2009, 2019) in Mymensingh city, Bangladesh. After becoming the divisional center, the result suggests that rapid urban growth takes place with a remarkable decrease in vegetation cover and water bodies in the study area. The urban growth almost doubled in the last 20 years, significantly increasing the LST by almost 8˚C. The LST distribution showed a higher concentration in urban areas, followed by bare land, vegetation cover, and water bodies. The study reveals that the decrease in urban green cover/wetlands and expansion in urban areas significantly increased the study area's surface temperature for the last two decades. This study will help urban planners, and environmental engineers quantify LULC change impacts on LST and proposed appropriate policy measures to control it

Water bodies are one of the essential components of the urban community. The pollution of surface and groundwater bodies will significantly hamper the ecosystem and reduce environmental sustainability. Rajshahi city stands beside the river of Padma and one of the primary sources of freshwater in Bangladesh's northwest region. Rapid urban growth of Rajshahi city in the last few decades causes several environmental problems, pollution of surface water, and groundwater. The study examines and maps the ground and surface water quality of Rajshahi city based on community perception using Geographic Information System (GIS). Finally, considering the viewpoints given by the community people and city officials, the study proposed useful strategies to control water pollution and ensure an integrated water resource management (IWRM) plan for Rajshahi city. Based on a structural questionnaire, 300 samples were obtained from 30 wards of with a 95% confidence level and 5% confidence intervals. Along with, two focus group discussions (FGD) and 20 key informants, interviews (KII) were conducted to control water pollution and develop strategies for IWRM plan. The result indicates that groundwater is iron contaminated, and more than 80% of the respondents ensure this water creates skin and hair diseases. Moreover, the supply water treated from the Padma River, which is regularly contaminated with residential and commercial waste materials, household sanitation waste and so on. Proper monitoring systems and awareness in the community level will significantly help to minimize this problem. The city authority, policymakers, and urban planners need to ensure water quality management awareness through the campaign, workshops, education, and regular monitoring. The research will help policymakers to identify effective strategies for ensuring sustainable IWRM plan for this growing city

Pedestrians are the most vulnerable road users and their risky behaviours make the situation worse. This study aimed to assess the crash risk associated with various pedestrian groups and their road crossing behaviours at intersections in Dhaka, Bangladesh. A two-stage, multi-criteria decision-making approach—analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS)—was used for risk assessment. In the first stage, five risk assessment criteria: nearest vehicle speed, nearest vehicle type, vehicle flow, interrupted by vehicle, and minimum gap were identified from the literature, and these criteria were weighted by the AHP. In the second stage, risk associated with various pedestrian groups and their road-crossing behaviours were quantified and ranked by the TOPSIS. The results indicate that male and young pedestrians had higher risk among pedestrian groups. Furthermore, an oblique crossing type, a running crossing pattern, mobile phone usage while crossing, carrying medium-weight baggage, a rolling gap crossing type, high crossing speed, and cross by using conflict zone were found as the riskiest behaviours. Besides, using crosswalk while crossing, single-stage and two-stage crossing types, perpendicular crossing type, and group crossing were found to be the safest behaviours.

Background: Geographic information science (GIS) has established itself as a distinct domain and incredibly useful whenever the research is related to geography, space, and other spatio-temporal dimensions. However, the scientific landscape on the integration of GIS in COVID-related studies is largely unknown. In this systematic review, we assessed the current evidence on the implementation of GIS and other geospatial tools in the COVID-19 pandemic. Methods: We systematically retrieved and reviewed 79 research articles that either directly used GIS or other geospatial tools as part of their analysis. We grouped the identified papers under six broader thematic groups based on the objectives and research questions of the study- environmental, socio-economic, and cultural, public health, spatial transmission, computer-aided modeling, and data mining. Results: The interdisciplinary nature of how geographic and spatial analysis was used in COVID-19 research was notable among the reviewed papers. Although GIS has substantial potential in planning to slow down the spread, surveillance, contact tracing, and identify the trends and hotspots of breakdowns, it was not employed as much as it could have been. This review not only provided an overarching view on how GIS has been used in COVID-19 research so far but also concluded that this geospatial analysis and technologies could be used in future public health emergencies along with statistical and other socio-economic modeling techniques. Our systematic review also provides how both scientific communities and policymakers could leverage GIS to extract useful information to make an informed decision in the future. Conclusions: Despite the limited applications of GIS in identifying the nature and spatio-temporal pattern of this raging pandemic, there are opportunities to utilize these techniques in handling the pandemic. The use of spatial analysis and GIS could significantly improve how we understand the pandemic as well as address the underserviced demographic groups and communities.