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Changes in land use/land cover (LULC) and land surface temperatures (LST) contribute significantly to the formation and intensity of urban heat islands (UHI) effects. The urban thermal field variance index (UTFVI) can effectively describe any city's UHI (thermal characteristics) effect. This study aims to assess and predict the seasonal (summer and winter) UTFVI scenario to evaluate the thermal characteristics of Sylhet city, Bangladesh. Landsat 4–5 TM and 8 OLI images from 1995 to 2020 were used to assess the previous status of LULC and UTFVI and predict the future changes for 2025 and 2030 using cellular automata and artificial neural network machine learning algorithms. Prediction results indicate a substantial increase in urban built-up areas by 42% and 44% in 2025 and 2035, followed by reductions in green cover (21% and 22%), bare land (20% and 21%) and water bodies (1%). The rapid expansion of built-up areas will lead to 13 km2 and 14 km2 stronger UTFVI zones in the predicted years. The study provides effective strategies for mitigating the UTFVI effects by avoiding dense infrastructural development, increasing plantation and water bodies, rooftop gardening and using white colour roofs in construction. The findings of this study will allow the urban planners, policymakers and local government to ensure an eco-friendly, inclusive and sustainable urban development through functional modification and replacement of the LULC distribution depending on the present and future circumstances.

Flood is one of the most devastating climatic disasters around the world. The physical and infrastructural damage of floods is uncontrollable and challenging to recover. Though the mismanagement of the water system is one cause of flood, some countries face a seasonal natural flood, which is impossible to avoid. The history of flood affection in these countries are long, and people don't have any other choice but to adapt to this circumstance every year. The continuous flood event has several climatic impacts that are not broadly documented and remain in the shadows of severe infrastructural damage. This research aims to identify the effect of monsoon floods on vegetation and land surface temperature (LST). The study area is the northeast part of Bangladesh, a highly flood-prone area. The research incorporates Google Earth Engine (GEE) to manage the satellite image data related to this research which are Sentinel-1 SAR imagery and Landsat-8 imagery. Six years of data from 2015 to 2020 have been taken to continuously monitor the flooded area, vegetation, and LST dynamics. Primary results indicate a yearly increase and decrease of the flooded area with 57.3% highest increase rate in 2019. A continuous increase of Enhanced Vegetation Index (EVI) value and decrease of LST has a changing pattern similarity with flooded area fluctuation over the year. Also, the flooded areas have around 50% less mean EVI value than the non-flooded areas, eventually rising average LST in flooded areas. 10,024 grids of 1 km × 1 km have been used to extensively analyze the relationship of flood and EVI through correlation and linear regression. The final result reveals a clear negative correlation value (less than 0.56 for all the years) of EVI with flooded areas, having the highest R-squared value of 0.4325 in 2017.

Flood is one of the most devastating climatic disasters around the world. The physical and infrastructural damage of floods is uncontrollable and challenging to recover. Though the mismanagement of the water system is one cause of flood, some countries face a seasonal natural flood, which is impossible to avoid. The history of flood affection in these countries are long, and people don't have any other choice but to adapt to this circumstance every year. The continuous flood event has several climatic impacts that are not broadly documented and remain in the shadows of severe infrastructural damage. This research aims to identify the effect of monsoon floods on vegetation and land surface temperature (LST). The study area is the northeast part of Bangladesh, a highly flood-prone area. The research incorporates Google Earth Engine (GEE) to manage the satellite image data related to this research which are Sentinel-1 SAR imagery and Landsat-8 imagery. Six years of data from 2015 to 2020 have been taken to continuously monitor the flooded area, vegetation, and LST dynamics. Primary results indicate a yearly increase and decrease of the flooded area with 57.3% highest increase rate in 2019. A continuous increase of Enhanced Vegetation Index (EVI) value and decrease of LST has a changing pattern similarity with flooded area fluctuation over the year. Also, the flooded areas have around 50% less mean EVI value than the non-flooded areas, eventually rising average LST in flooded areas. 10,024 grids of 1 km × 1 km have been used to extensively analyze the relationship of flood and EVI through correlation and linear regression. The final result reveals a clear negative correlation value (less than 0.56 for all the years) of EVI with flooded areas, having the highest R-squared value of 0.4325 in 2017.

This book encourages Children on 3R’s (Reduce, Reuse and Recycle)

Northern Bangladesh, consisting of 16 administrative districts, has suffered from several drought phenomena in the last few decades. This study aims to formulate a drought susceptibility map considering three different types of droughts, which later, was referred to as composite drought, and was derived from three different indices. Standardized Precipitation Index (SPI) along with Vegetation Condition Index (VCI) from MODIS-NDVI, and Standardized Water Index (SWI) were considered for assessing the meteorological, agricultural, and hydrological drought, respectively. Drought condition was estimated for winter and pre-monsoon months (November to June) for 18 years, from 2000 through 2017. Individual drought maps for each month within the specified period were first prepared and then superimposed in the ArcGIS platform to produce an integrated drought map. Afterward, three different drought maps were incorporated to show the composite drought scenario. Then, extreme drought hot spots within the combined map were further identified using local indicators of spatial autocorrelation (LISA) method. Analyses showed that around 65% of the study area faced moderate to severe meteorological drought and mild to moderate agricultural and hydrological drought. The most extreme drought-prone month was March, and among 16 districts, Dinajpur and Naogaon faced the highest level of drought severity. This study is essentially important to unveil the individual drought susceptibility condition for a particular location. Detection of the composite drought hot spots will help in adopting short as well as long term adaptation and mitigation programs. At the same time, the methodology is reproducible for other drought-prone regions around the globe.

This study aims to address the economic, social, and environmental wellbeing issues simultaneously by measuring the carbon intensity of wellbeing (CIWB) of Asian economies employing Prais-Winsten and pooled OLS estimator. The measure of CIWB is made taking into account a ratio of the two indicators—CO2 emissions per capita and life expectancy at birth. There is a paucity of studies that concentrate on human and social wellbeing indicators (i.e., water, sanitation, life expectancy) together applying the Environmental Kuznets Curve (EKC) hypothesis. Therefore, we have also investigated the EKC hypothesis as this theory hypothesizes the link involving human and environmental wellbeing and development. The findings utilizing the two econometric techniques indicate that in both the estimation models urban population access to an improved water source and total population access to improved water source has consistently negative and significant effects on CIWB. The fertility rate and prevalence of HIV pose no threat to CIWB. These findings demonstrate that social and human wellbeing indicators of the Asian economies are sustainable to this moment as they are lowering CIWB which is desirable. Contrary, GDP per capita, exports as a percent of GDP, and urban population have a significant and positive impact on CIWB which poses a challenge for the sustainability issue. We also have found the existence of the EKC hypothesis indicating environmental quality will increase past a turning point. The findings of the paper are well matched with the view of the “Economic and ecological modernization” theory and “human ecology” theory.