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This paper develops a methodology to map the two main rice types in northwest Bangladesh from 1989 to 2016, when Green Revolution technologies and policies resulted in a 300% rice area expansion and localised unsustainable groundwater use. The mapping is performed for the largely irrigated dry season Boro rice (grown from November/December to May/June), and the largely rainfed Aman rice (grown from June/July to October/November). The petabyte archive of Landsat reflectance data available via Google Earth Engine is used to extract monthly time-series of two remotely sensed vegetation indices – the Enhanced Vegetation Index (EVI) and the Global Vegetation Moisture Index (GVMI) – which can capture crop phenological dynamics as they correlate during crop growth stages and can be used to discriminate standing water when EVI is low and GVMI is high. This feature is exploited to detect the effects of flooding before transplanting Boro rice in January and to capture the late phenological phases of Aman rice through harvesting months (October to November). A gap-filled and smoothed EVI and GVMI monthly time-series sequences for both rice cropping months are grouped using machine learning unsupervised K-means clustering into clusters (25 used here), and manually aggregated using crop calendars and local expert knowledge into rice types, other vegetated areas, water, non-permanent water and bare areas. A representative subset of these data are then used to construct a training sample using additional covariates besides the monthly EVI and GVMI values and train a Random Forest (RF) model capable of predicting the land cover types in other years. The resulting RF maps for the two cropping seasons were evaluated against publicly available survey crop statistics (from 1989 to 2016) and published Boro 30 m resolution maps for 2013 to 2016. The maps provide qualitative evidence that unsustainable groundwater extractions can be linked to Boro rice irrigation in some parts of northwest Bangladesh. Further research, using these maps and other hydrological data, is required to quantitatively assess when, where, and how much irrigation contributes to unsustainable groundwater use. The results show that the methodology can accurately (generally within 20% absolute error) capture Boro and Aman rice over the Green Revolution period, and other landscape dynamic characteristics of importance for environmental assessments, such as areas of standing water.

Bangladesh produces only 5% of the cotton she needs to sustain her readymade garments industries. The country has very limited agricultural land and cotton competes with other crops for this scarce land resource. On top of that, Bangladesh is regarded as a country where agriculture is highly vulnerable to the variabilities of weather patterns that result from climate change. Against this backdrop, to better understand the potential for the sustainable expansion of cotton production in Bangladesh, we examine cotton’s agricultural value chain and projected climate risks associated with different phases of the chain. We identified associated stakeholders at different phases of cotton production, engaged with them to understand climatic and non-climatic threats and developed an integrated set of recommendations for climate-risk management through improving the connection of producers to markets, increasing economic returns to small farmers, and improving efficiency along the value chain. We discussed our estimated climate projections with stakeholders to understand the challenges at different stages of production and marketing, and together explored and identified probable solutions. This research offers a new and evolving approach to assess climate change impact on agriculture utilizing a holistic approach, which could be adopted for other crops.

Communities living in the Chittagong Hill Districts (CHD) of Bangladesh recurrently observe landslide disasters during the monsoon season (June–September). CHD is primarily dominated by three distinct groups of hill communities, namely, urbanised hill (Bengali), indigenous tribal and stateless Rohingya refugees. Landslide vulnerability amongst them is complex and varies between physical, social, economic, environmental, institutional and cultural dimensions. This study aims to understand driving forces of landslide disasters in the region by emphasising human factors. Data from the three contrasting communities were collected through participatory workshops, in-depth interviews and fieldwork observation. The participants were local people and landslide experts who were purposefully selected from five case study communities in the CHD. They ranked different socio-economic problems, identified causes of landslides and proposed landslide mitigation action plans. Results suggest that the urbanised Bengali and Rohingya refugee communities are highly vulnerable to landslides. The urbanised hill communities largely deal with poverty, social injustice, lack of planning regulations and illegal hill cutting issues, whereas the Rohingya refugees’ predominant constraints are linked to the ongoing genocide and state-sponsored violence in Myanmar hindering their sustainable repatriation, and their protracted living conditions in Bangladesh. The indigenous tribal communities are comparatively resilient to landslides due to their unique history, traditional knowledge, cultural heritage and lifestyle. Landslides in the CHD should be characterised as socio-natural hazards since the components of landslide disasters are profoundly intertwined with the culture–conflict–corruption nexus.

Though equal and easy accessibility to healthcare facilities are a fundamental right, people of the coastal region often get poor accessibility to healthcare facilities. This research aimed to assess the spatial accessibility to healthcare facilities in the coastal region of Bangladesh. Patuakhali district was selected as the study area. Accessibility to three levels of healthcare facilities: Community Clinic (CC), Upazila Health Complex (UHC) and District Hospital (DH) was measured individually using Geographic Information System (GIS) adopting the simple distance measures. Finally, overall accessibility to healthcare facilities was measured by overlaying accessibility to all three levels of healthcare facilities according to their relative importance. The findings of this study showed that a significant portion (70%) of Patuakhali district had high accessibility to CC; whereas, almost 60% and 40% of the area had poor accessibility to UHC and DH, respectively. Furthermore, 40% and 28% of the area of Patuakhali district had low and high accessibility to overall healthcare facilities, respectively. Furthermore, accessibility to healthcare facilities was found very poor in rural areas, char areas, and seashore. Thereafter, it is recommended to provide an UHC in every upazila and a DH in the southern part of Galachipa upazila to ensure high spatial accessibility of healthcare facilities.

Dhaka, the capital and megacity of the developing country Bangladesh, has experienced a sharp rise in motorcycle users in the last decade, especially after the introduction of ridesharing services. Therefore, the morbidity and mortality rates of motorcycle crash injuries have also increased and become one of the major safety concerns. However, there is scant empirical evidence on motorcycle crash severity in the context of developing countries. Hence, this study was conducted to identify the factors that influenced the severity of motorcycle crashes in Dhaka. A binary logistic regression model was developed using motorcycle crash data of Dhaka over the period of 2006–2015 to identify the contributing factors of motorcycle crash severity. The model output showed that eleven factors significantly increased the probability of fatal motorcycle crashes. These factors were crashes occurring on weekends, during the rainy season, during dawn and night period, at non-intersections, on straight and flat roads, on highways, hit pedestrian type crashes, crashes involving motorcycles with no defect, crashes with heavier vehicles, crashes involving motorcyclists not wearing helmets, and drivers with alcohol suspicion. These findings would help to formulate prevention strategies to reduce the injury severity of motorcycle crashes in the developing countries.

Background: COVID-19 pandemic outbreak is an unprecedented shock throughout the world, which has generated a massive social, human, and economic crisis. Identification of risk factors is crucial to prevent the COVID-19 spread by taking appropriate countermeasures effectively. Therefore, this study aimed to identify the potential risk factors contributing to the COVID-19 incidence rates at the district-level in Bangladesh. Method: Spatial regression methods were applied in this study to fulfill the aim. Data related to 28 demographic, economic, built environment, health, and facilities related factors were collected from secondary sources and analyzed to explain the spatial variability of this disease incidence. Three global (ordinary least squares (OLS), spatial lag model (SLM), and spatial error model (SEM)) and one local (geographically weighted regression (GWR)) regression models were developed in this study. Results: The results of the models identified four factors: percentage of the urban population, monthly consumption, number of health workers, and distance from the capital city, as significant risk factors affecting the COVID-19 incidence rates in Bangladesh. Among the four developed models, the GWR model performed the best in explaining the variation of COVID-19 incidence rates across Bangladesh, with an R2 value of 78.6%. Conclusion: Findings and discussions from this research offer a better insight into the COVID-19 situation, which helped discuss policy implications to negotiate the future epidemic crisis. The primary policy response would be to decentralize the urban population and economic activities from and around the capital city, Dhaka, to create self-sufficient regions throughout the country, especially in the north-western region.

Affordable Housing, the basic human necessity has now become a critical problem in global cities with direct impacts on people's well-being. While a well-functioning housing market may augment the economic efficiency and productivity of a city, it may trigger housing affordability issues leading crucial economic and political crises side by side if not handled properly. In global cities e.g. Singapore and Hong Kong where affordable housing for all has become one of the greatest concerns of the Government, this issue can be tackled capably by the provision of public housing. In Singapore, nearly 90% of the total population lives in public housing including public rental and subsidized ownership, whereas the figure tally only about 45% in Hong Kong. Hence this study is an effort to scrutinizing the key drivers of success in affordable public housing through following a qualitative case study based research methodological approach to present successful experience and insight from different socio-economic and geo-political context. As a major intervention, this research has clinched that, housing affordability should be backed up by demand-side policies aiming to help occupants and proprietors to grow financial capacity e.g. subsidized rental and subsidized ownership can be an integral part of the public housing system to improve housing affordability

Dhaka, the capital and megacity of the developing country Bangladesh, has experienced a sharp rise in motorcycle users in the last decade, especially after the introduction of ridesharing services. Therefore, the morbidity and mortality rates of motorcycle crash injuries have also increased and become one of the major safety concerns. However, there is scant empirical evidence on motorcycle crash severity in the context of developing countries. Hence, this study was conducted to identify the factors that influenced the severity of motorcycle crashes in Dhaka. A binary logistic regression model was developed using motorcycle crash data of Dhaka over the period of 2006–2015 to identify the contributing factors of motorcycle crash severity. The model output showed that eleven factors significantly increased the probability of fatal motorcycle crashes. These factors were crashes occurring on weekends, during the rainy season, during dawn and night period, at non-intersections, on straight and flat roads, on highways, hit pedestrian type crashes, crashes involving motorcycles with no defect, crashes with heavier vehicles, crashes involving motorcyclists not wearing helmets, and drivers with alcohol suspicion. These findings would help to formulate prevention strategies to reduce the injury severity of motorcycle crashes in the developing countries.