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Zoning is an important tool to regulate the use of land and to characterize built form over land, and thus to facilitate urban sustainability. Availability of reliable data is crucial for monitoring land use zoning, which contributes directly to the success of the Sustainable Development Goals (SDGs) in general, and SDG Goal 11 for sustainable cities and communities in particular. However, obtaining this valuable information using traditional survey methods is both costly and time-consuming. Remote sensing technology overcomes these challenges and supports urban policymaking and planning processes. This study unveils a novel approach to developing a cost-effective method for identifying building types using Sentinel-2A, Visible Infrared Imaging Radiometer Suite (VIIRS)–based nighttime light (NTL) data, and TanDEM-X–based Digital Surface Model (DSM) data. A newly developed index for this study, the Normalized Difference Steel Structure Index (NDSSI), is useful for rapidly mapping industrial buildings with steel structures. The implementation status of Dhaka’s existing land use plan was evaluated by analyzing the spatial distribution of different types of building uses. This study classifies residential, commercial, and industrial buildings within Dhaka using building height, and nighttime light emission. The experimental results reveal that about 67% of commercial and 51% of industrial buildings within the Dhaka Metropolitan Area (DMA) do not comply with the land use zoning by the Detailed Area Plan (DAP). It also reveals that approximately 10% of commercial buildings, 9% of industrial buildings, and 6% of residential buildings have encroached upon conservation zones (such as open space, flood-prone zones, water bodies, and proposed areas for future road extension). A major constraint in the study was the low spatial resolution of the nighttime light dataset, which made it difficult to distinguish individual sources of light. Still, the methodological approaches proposed in this study are expected to promote reduced costs and efficacious decision-making in urban transformation and to help achieve SDG 11, especially in developing countries.

Background: National forest inventory and forest monitoring systems are more important than ever considering continued global degradation of trees and forests. These systems are especially important in a country like Bangladesh, which is characterised by a large population density, climate change vulnerability and dependence on natural resources. With the aim of supporting the Government’s actions towards sustainable forest management through reliable information, the Bangladesh Forest Inventory (BFI) was designed and implemented through three components: biophysical inventory, socio-economic survey and remote sensing-based land cover mapping. This article documents the approach undertaken by the Forest Department under the Ministry of Environment, Forests and Climate Change to establish the BFI as a multipurpose, efficient, accurate and replicable national forest assessment. The design, operationalization and some key results of the process are presented. Methods: The BFI takes advantage of the latest and most well-accepted technological and methodological approaches. Importantly, it was designed through a collaborative process which drew from the experience and knowledge of multiple national and international entities. Overall, 1781 field plots were visited, 6400 households were surveyed, and a national land cover map for the year 2015 was produced. Innovative technological enhancements include a semi-automated segmentation approach for developing the wall-to-wall land cover map, an object-based national land characterisation system, consistent estimates between sample-based and mapped land cover areas, use of mobile apps for tree species identification and data collection, and use of differential global positioning system for referencing plot centres. Results: Seven criteria, and multiple associated indicators, were developed for monitoring progress towards sustainable forest management goals, informing management decisions, and national and international reporting needs. A wide range of biophysical and socioeconomic data were collected, and in some cases integrated, for estimating the indicators. Conclusions: The BFI is a new information source tool for helping guide Bangladesh towards a sustainable future. Reliable information on the status of tree and forest resources, as well as land use, empowers evidence-based decision making across multiple stakeholders and at different levels for protecting natural resources. The integrated socioeconomic data collected provides information about the interactions between people and their tree and forest resources, and the valuation of ecosystem services. The BFI is designed to be a permanent assessment of these resources, and future data collection will enable monitoring of trends against the current baseline. However, additional institutional support as well as continuation of collaboration among national partners is crucial for sustaining the BFI process in future.

National forest inventory and forest monitoring systems are more important than ever considering continued global degradation of trees and forests. These systems are especially important in a country like Bangladesh, which is characterised by a large population density, climate change vulnerability and dependence on natural resources. With the aim of supporting the Government’s actions towards sustainable forest management through reliable information, the Bangladesh Forest Inventory (BFI) was designed and implemented through three components: biophysical inventory, socio-economic survey and remote sensing-based land cover mapping. This article documents the approach undertaken by the Forest Department under the Ministry of Environment, Forests and Climate Change to establish the BFI as a multipurpose, efficient, accurate and replicable national forest assessment. The design, operationalization and some key results of the process are presented.

The study focuses on four river basins, Gandaki, Indus, Upper Ganga and Teesta, in the Hindu Kush Himalayan (HKH) region in South Asia. The region is considered one of the more environmentally vulnerable areas in the world due to recurrent natural hazards that can be exacerbated by future climate change. The dependence of the population on natural resources based livelihoods makes the region particularly vulnerable to adverse climate change impacts. Labour migration can help household adaptation, particularly when it incurs significant cash investment. The paper analyses the determinants of household adaptation, including migration, in three sectors, namely, agriculture, livestock, and water. It shows that household adaptation to the negative effects of climate change was very poor in the region, with less than a third of the households undertaking adaptation measures. While labour migration showed a positive influence on household adaptation, it was statistically significant only in agriculture. Nevertheless, migration influenced household adaptation indirectly through livelihood diversification, access to services provide of external stakeholders, and changes in household composition. The study identified location, access to climate information, and services provided by external stakeholders as important factors in household adaptation to climate change.

This book encourages Children about rooftop gardening.

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.