Measuring urban expansion pattern using spatial matrices in Khulna City, Bangladesh.

Khulna, the third-largest metropolitan area in Bangladesh, has become a potential site for the polycentric urbanization for multiple mega-projects. Measurement of urban expansion is essential for regulating haphazard growth and achieving effective management. This study aims to quantify and compare the urban expansion pattern of Khulna City Corporation (KCC) and surrounding areas' development hotspots. Landsat remote sensing images of 1990, 2000, 2010 and 2020 were used to perform supervised classification using Geographic Information System (GIS). To quantify urban expansion, we compute Annual Urban Expansion Rate, Urban Expansion Intensity Index, and Urban Expansion Differentiation Index. Although annual urban expansion in the study area was slow during the first two decades, it accelerated to 6.76% during the last decade. 48% of the total built-up areas have grown during 2010-2020 alone. Even though KCC experienced continuous urban growth over a thirty-year period, after 2010, the rate of urban expansion in peripheral areas exceeded that of KCC. Transboundary and intra-regional transportation and economic corridor development, establishment of economically potential zones (EPZ), urban to rural migration, availability of rich agriculture hinterland, low land price and several direct transportation links between the core and periphery are the major influencing factors of peri-urbanization.

A GIS and remote sensing approach for measuring summer-winter variation of land use and land cover indices and surface temperature in Dhaka district, Bangladesh.

Rapid urbanization has induced land use and land cover change (LULC) that increases land surface temperature (LST). Analyzing seasonal variations of LULC and LST is a precondition for mitigating heat island effects and promoting a sustainable living environment. The objective of this study is to explore the association between the seasonal LST dynamics and LULC indices for the Dhaka district of Bangladesh. The LULC indices are comprised of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Bareness Index (NDBAI), and Modified Normalized Difference Water Index (MNDWI). The results show that the LULC effect on LST in Dhaka is significant, with an increase in summer season LST from 34.58 °C to 37.66 °C and in winter season LST from 24.710C to 26.24 °C. Predictably, the highest and lowest LST values were observed in the built-up and vegetation-covered areas, respectively. Secondly, the correlation values indicate a significant inverse correlation (R2 > 0.50) between NDVI and LST, as well as MNDWI and LST. On the contrary, positive correlations were observed between NDBI and LST, and between NDBAI and LST for both the summer and winter seasons. Finally, subsequent vegetation decline (-69.34%) and increasing built-up area (+11.30%) between 2000 and 2020 in Dhaka district were found to be the most significant factors for the increasing trend and spatial heterogeneity of LST in Dhaka. The methodological approach of this study offers a low-cost efficient technique for monitoring LST hotspots, which can guide land use planners and urban managers for spatial intervention to ensure a livable environment.

COVID-19 Vulnerability Mapping of Asian Countries.

OBJECTIVE: The objective of this study is to map vulnerability of Asian countries to the COVID-19 pandemic. METHOD: According to the Intergovernmental Panel on Climate Change (IPCC) 2007 framework for natural hazards, vulnerability is a function of exposure, sensitivity, and adaptive capacity. From an extensive literature review, we identified 16 socioeconomic, meteorological, environmental, and health factors that influence coronavirus disease 2019 (COVID-19) cases and deaths. The underlying factors of vulnerability were identified using principal component analysis. RESULTS: Our findings indicate that the percentage of the urban population, obesity rate, air connectivity, and the population aged 65 and over, diabetes prevalence, and PM2.5 levels all contributed significantly to COVID-19 sensitivity. Subsequently, governance effectiveness, human development index (HDI), vaccination rate, and life expectancy at birth, and gross domestic product (GDP) all had a positive effect on adaptive capacity. The estimated vulnerability was corroborated by a Pearson correlation of 0.615 between death per million population and vulnerability. CONCLUSION: This study demonstrates the application of universal indicators for assessing pandemic vulnerability for informed policy interventions such as the COVAX vaccine roll-out priority. Despite data limitations and a lack of spatiotemporal analysis, this study's methodological framework allows for ample data incorporation and replication.

Spatial priority for COVID-19 vaccine rollout against limited supply.

The COVID-19 vaccines are limited in supply which requires vaccination by priority. This study proposes a spatial priority-based vaccine rollout strategy for Bangladesh. Demographic, economic and vulnerability, and spatial connectivity - these four types of factors are considered for identifying the spatial priority. The spatial priority is calculated and mapped using a GIS-based analytic hierarchy process. Our findings suggest that both demographic and economic factors are keys to the spatial priority of vaccine rollout. Secondly, spatial connectivity is an essential component for defining spatial priority due to the transmissibility of COVID-19. A total of 12 out of 64 districts were found high-priority followed by 22 medium-priorities for vaccine rollout. The proposed strategy by no means suggests ending mass vaccination by descending age groups but an alternative against limited vaccine supply. The spatial priority of the vaccine rollout strategy proposed in this study might help to curb down COVID-19 transmission and to keep the economy moving. The inclusion of granular data and contextual factors can significantly improve the spatial priority identification which can have wider applications for other infectious and transmittable diseases and beyond.

Common factors of COVID-19 cases and deaths among the most affected 50 countries.

AIMS: The Coronavirus (COVID-19) is a global pandemic requiring global responses. The objective of this paper is to identify the common factors of COVID-19 cases and deaths among the 50 most affected countries. METHODS: We performed Ordinary least squares among a wide range of socio-economic, environmental, climatic and health indicators to explain the number of cases and deaths. RESULTS: The findings are: (i) obesity is the only significant global denominator for the number of COVID-19 cases and deaths; (ii) the percentage of the population over the age of 65 and number of hospital beds per 1000 population inversely correlated to mortality from COVID-19. CONCLUSIONS: Obesity increases vulnerability to COVID-19 infections and mortality. Global awareness of obesity and social investment in health infrastructure are pre-requisite for a pandemic adaptive future. However, the study is limited to cross-sectional data of April 17, 2020.

Soil salinity detection from satellite image analysis: an integrated approach of salinity indices and field data.

This paper attempts to detect soil salinity from satellite image analysis using remote sensing and geographic information system. Salinity intrusion is a common problem for the coastal regions of the world. Traditional salinity detection techniques by field survey and sampling are time-consuming and expensive. Remote sensing and geographic information system offer economic and efficient salinity detection, monitoring, and mapping. To predict soil salinity, an integrated approach of salinity indices and field data was used to develop a multiple regression equation. The correlations between different indices and field data of soil salinity were calculated to find out the highly correlated indices. The best regression model was selected considering the high R (2) value, low P value, and low Akaike's Information Criterion. About 20% variation was observed between the field data and predicted EC from the satellite image analysis. The precision of this salinity detection technique depends on the accuracy and uniform distribution of field data.