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Recent research has analyzed and studied the growing literature on human mobility during quarantine periods using various methodology and techniques. There are several ways to use light pollution to assess mobility. The data from the VIIRS satellite can be used to quantify light pollution and human mobility in the Philippines during quarantine. The data utilized in this study came from NASA's EOSDIS Worldview website. The number of cases and pixels count increases from early April 2020 to late August 2020. However, the cases increased from February to April 2021. This could be attributed to the active human mobility seen between December 2020 and January 2021. Human interactions have been intense since August 2020, causing an increase in COVID cases that peaked between March and April 2021, before dropping in May 2021. Following the conclusion of this study, light pollution VIIRS satellite pictures can be used to identify possible COVID- 19 cases. There are many more factors and variables to consider when writing a comprehensive paper. With the relaxed quarantine time has been achieved beyond June 2021, additional dates may be explored since there may be a direct relationship between light pollution and COVID-19 instances. © 2022 IEEE.
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The Covid-19 pandemic has highlighted the value of strong surveillance systems in supporting our abilities to respond rapidly and effectively in mitigating the impacts of infectious diseases. A cornerstone of such systems is basic subnational scale data on populations and their demographics, which enable the scale of outbreaks to be assessed, risk to specific groups to be determined and appropriate interventions to be designed. Ongoing weaknesses and gaps in such data have however been highlighted by the pandemic. These can include outdated or inaccurate census data and a lack of administrative and registry systems to update numbers, particularly in low and middle income settings. Efforts to design and implement globally consistent geospatial modelling methods for the production of small area demographic data that can be flexibly integrated into health-focussed surveillance and information systems have been made, but these often remain based on outdated population data or uncertain projections. In recent years, efforts have been made to capitalise on advances in computing power, satellite imagery and new forms of digital data to construct methods for estimating small area population distributions across national and regional scales in the absence of full enumeration. These are starting to be used to complement more traditional data collection approaches, especially in the delivery of health interventions, but barriers remain to their widespread adoption and use in disease surveillance and response. Here an overview of these approaches is presented, together with discussion of future directions and needs.
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PurposeThis study aims at understanding the reasons causing the decline in the practice of traditional, regional architectural methods of creating house forms in the Khasia Punji at Jaflong, Sylhet area.Design/methodology/approachTwo main types of traditional and modern house forms were identified and studied in order to document and analyze the aspects of changes in the construction method and material uses, while the interviews together with observational, qualitative and descriptive study formed an insight into the changing socio-cultural dynamics and evolving lifestyle of the tribe. Apart from physical surveys, the primary data on settlement patterns over twenty years' time were reviewed through satellite imaging while the characteristics of local house forms were also collected from tourist photographs through time recorded in Google database.FindingsThe findings of this research have pointed out that in the case of the Khasi tribe, the shift in temporal context, accompanied by a shift in technological, socio-cultural and economic aspects, is fueling the transformation in the formal expression, material and methods of the house building.Research limitations/implicationsLimitations were posed in setting up more constructive and informative interview sessions with the Khasi people due to the coronavirus disease 2019 (COVID-19) situation which limited the survey outcomes in general.Practical implicationsThe scope of this study is to understand the changes and advances in socio-cultural, technological aspects of a society and their impact on the intricate patterns of life and customs that are evidently reflected in the transformation of built environments.Originality/valueThis research attempts to understand the causes behind the transformation of vernacular house forms, taking place in the Khasi village of Jaflong, Sylhet.
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The radiative effects of the large‐scale air traffic slowdown during April and May 2020 due to the international response to the COVID‐19 pandemic are estimated by comparing the coverage (CC), optical properties, and radiative forcing of persistent linear contrails over the conterminous United States and two surrounding oceanic air corridors during the slowdown period and a similar baseline period during 2018 and 2019 when air traffic was unrestricted. The detected CC during the slowdown period decreased by an area‐averaged mean of 41% for the three analysis boxes. The retrieved contrail optical properties were mostly similar for both periods. Total shortwave contrail radiative forcings (CRFs) during the slowdown were 34% and 42% smaller for Terra and Aqua, respectively. The corresponding differences for longwave CRF were 33% for Terra and 40% for Aqua. To account for the impact of any changes in the atmospheric environment between baseline and slowdown periods on detected CC amounts, the contrail formation potential (CFP) was computed from reanalysis data. In addition, a filtered CFP (fCFP) was also developed to account for factors that may affect contrail formation and visibility of persistent contrails in satellite imagery. The CFP and fCFP were combined with air traffic data to create empirical models that estimated CC during the baseline and slowdown periods and were compared to the detected CC. The models confirm that decreases in CC and radiative forcing during the slowdown period were mostly due to the reduction in air traffic, and partly due to environmental changes.Alternate :Plain Language SummaryContrails produced by aircraft flying in cold but humid air both warm the atmosphere by reducing infrared radiation emitted back into space and cool it by increasing reflected sunlight. Due to the decrease in air traffic during the first months of the COVID pandemic, fewer satellite‐detectable contrails were produced compared to pre‐pandemic times, and thus the radiative effects of contrails were also diminished. But changes in the overall temperature and humidity at aircraft cruise altitudes also affect contrail formation and might explain at least some of the observed decrease in contrail coverage during April and May 2020. Analysis of satellite imagery showed that the thickness and ice‐crystal size of the contrails during the COVID period did not change much from pre‐pandemic contrails. The regional contrail coverage was accurately simulated from a combination of the estimated air traffic activity at cruise altitude and the probable frequency of when atmospheric conditions were favorable for contrail formation. This simulation confirms that most of the decrease in contrails and their radiative effects during the COVID‐related slowdown period were due to the reduction in air traffic, and to a lesser extent to changes in temperature and humidity at cruise altitude during April and May 2020.
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Water clarity is a key parameter for assessing changes of aquatic environment. Coastal waters are complex and variable, remote sensing of water clarity for it is often limited by low spatial resolution. The Sentinel-2 Multi-Spectral Instrument (MSI) imagery with a resolution of up to 10 m are employed to solve the problem from 2017 to 2021. Distribution and characteristics of Secchi disk depth (SDD) in Jiaozhou Bay (JZB) are analyzed. Subtle changes in localized small areas are discovered, and main factors affecting the changes are explored. Among natural factors, precipitation and wind play dominant roles in variation in SDD. Human activities have a significant influence on transparency, among which fishery farming has the greatest impact. This is clearly evidenced by the significant improvement of SDD in JZB due to the sharp decrease in human activities caused by coronavirus disease 2019 (COVID-19).Copyright © 2022 The Authors
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This special issue contains 17 papers covering a range of topics related to environmental, geological, and social issues in Bangladesh. The articles use various methodologies, including statistical analysis, satellite imaging, and case studies, to explore issues such as drought, urbanization, healthcare, greenhouse gas emissions, groundwater resources, COVID-19 stigmatization, oil rim reservoir development, coal permeability, seaweed composition, hailstorms, tropical cyclones, heavy metal contamination, flood hazard assessment, and climate change vulnerability. Overall, the articles provide valuable insights and information that can inform policy and decision-making in Bangladesh.
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The lack of accurate data on the rural communities' perspectives of the COVID-19 vaccine impairs the planning, monitoring, and evaluation of vaccine programs. Notably, we do not have adequate data to understand why, although COVID-19 has disproportionately impacted minority populations in these communities, they are reported to be the least likely to be vaccinated. This paper develops a cost-effective community sampling frame based on satellite imagery and machine learning to improve the diversity of data and study the association between household-level visual information and COVID-19 vaccination rates in the Alabama Black Belt. The results provided solid evidence for the hypothesis that high-resolution satellite imagery contains valuable information to understand communities' perspectives on the COVID-19 vaccine. It also generated extra knowledge and implications for community health to help social workers to develop future vaccine promotion strategies in rural America. © 2022 ACM.
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OBJECTIVE: To compare estimates of spatiotemporal variations of surface PM2.5 concentrations in Colombia from 2014 to 2019 derived from two global air quality models, as well as to quantify the avoidable deaths attributable to the long-term exposure to concentrations above the current and projected Colombian standard for PM2.5 annual mean at municipality level. METHODS: We retrieved PM2.5 concentrations at the surface level from the ACAG and CAMSRA global air quality models for all 1,122 municipalities, and compare 28 of them with available concentrations from monitor stations. Annual mortality data 2014-2019 by municipality of residence and pooled effect measures for total, natural and specific causes of mortality were used to calculate the number of annual avoidable deaths and years of potential life lost (YPLL) related to the excess of PM2.5 concentration over the current mean annual national standard of 25 µg/m3 and projected standard of 15 µg/m3. RESULTS: Compared to surface data from 28 municipalities with monitoring stations in 2019, ACAG and CAMSRA models under or overestimated annual mean PM2.5 concentrations. Estimations from ACAG model had a mean bias 1,7 µg/m3 compared to a mean bias of 4,7 µg/m3 from CAMSRA model. Using ACAG model, estimations of total nationally attributable deaths to PM2.5 exposure over 25 and 15 µg/m3 were 142 and 34,341, respectively. Cardiopulmonary diseases accounted for most of the attributable deaths due to PM2.5 excess of exposure (38%). Estimates of YPLL due to all-cause mortality for exceeding the national standard of 25 µg/m3 were 2,381 years. CONCLUSION: Comparison of two global air quality models for estimating surface PM2.5 concentrations during 2014-2019 at municipality scale in Colombia showed important differences. Avoidable deaths estimations represent the total number of deaths that could be avoided if the current and projected national standard for PM2.5 annual mean have been met, and show the health-benefit of the implementation of more restrictive air quality standards.
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Air Pollutants , Air Pollution , Humans , Air Pollutants/adverse effects , Air Pollutants/analysis , Particulate Matter/adverse effects , Particulate Matter/analysis , Colombia/epidemiology , Air Pollution/adverse effects , Air Pollution/analysis , Cities , Environmental Exposure/adverse effects , MortalityABSTRACT
COVID-19 has plagued the world, one of which is Indonesia. During the COVID-19 pandemic, all anthropogenic activities are limited, including activities that cause air pollution, such as transportation and industrial activities. Nitrogen Dioxide (NO2) is one of the parameters of air pollution which has the main source of human activity. Therefore, this study aims to analyze the effect of the COVID-19 pandemic on changes in NO2 gas concentrations in the Yogyakarta Special Province. This study uses Sentinel 5-P satellite imagery data obtained through cloud computing on Google Earth Engine (GEE) to obtain NO2 gas concentration values. The results showed that there was a 3.7% decrease in the concentration of NO2 gas before and after the COVID-19 pandemic. The correlation result between the number of COVID-19 cases and the concentration of NO2 gas is 0.39, which means it has a weak correlation. © 2022 IEEE.
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Significant up gradation of agriculture sector is vital to meet the growing needs of food and other agricultural produce by the rapidly increasing population. Artificial Intelligence with various applications in several fields is making innovative inroads in to farming sector to improve productivity, identify diseases with 98% accuracy, and recognize pest damage. It gives growers a weapon against cereal-hungry bugs. Sensors monitor the fruit ripening, adjusting the light to speed up or slow down the pace of maturation. Farmers can monitor the well-being of their crops or the movement of their animals from their home farm, it can make agriculture less labor intensive, and more efficient. Companies are using high-resolution imagery from drones, planes and satellites to diagnose problems of pests, diseases, moisture stress and nutrient deficiencies in the field. BEEWISE uses artificial intelligence to automate beehive maintenance;and ARMENTA is working on new therapies to treat sick dairy cows. Other firms are targeting trendy sectors like pharmaceutical crops and alternative proteins. This kind of farming requires considerable processing power and is expensive. Artificial Intelligence has to be deployed with discretion by the natural wisdom of users. Machine Learning can just do the repetitive jobs taught and lacks creativity and doesn't get better with experience. The point that use of AI can lead to unemployment is debatable in the face of shortage or non-availability of laborers. This paper attempts to discuss the promises AI holds for farming, problems of its application/implementation, current concerns briefly touching upon the future prospects and Sustainable Agri-Food System Business Models in the COVID-19 Scenario.
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In supply chains where stakeholders belong to the economically disadvantaged segment and form an important part of the supply chain distribution, the complexities grow manifold. Fisheries in developing nations are one such sector where the complexity is not only due to the produce being perishable but also due to the livelihood dependence of others in the coastal regions that belong to the section of economically disadvantaged. This paper explains the contextual challenges of fish supply chain in a developing country and describes how integrating disruptive technologies can address those challenges. Through a positive deviance approach, we show how firms can help unorganized supply chains with economically disadvantaged suppliers by carefully redesigning the supply chain through the integration of satellite imagery and blockchain technology. With COVID-19 in the backdrop, we highlight how such technologies significantly improves the supply chain resilience and at the same time contributes to the income generating opportunities of poor fisherfolks in developing nations. Our study has important implications to both developing markets and food supply chain practitioners as this paper tackles issues such as perishability, demand-supply mismatch, unfair prices, and quality related data transparency in the entire value chain.
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The global expansion of urbanization is posing associated environmental and socioeconomic challenges. The capital city of Ethiopia, Addis Ababa, is also facing similar threats. The development of urban green infrastructures (UGIs) are the forefront mechanisms in mitigating these global challenges. Nevertheless, UGIs in Addis Ababa are degrading and inaccessible to the city residents. Hence, a 56 km long Addis River Side Green Development Project is under development with a total investment of USD 1.253 billion funded by Chinese government aid. In phase one of this grand project, Friendship Square Park (FSP), was established in 2019 with a total cost of about USD 50 million. This paper was initiated to describe the establishment process of FSP and assess its social, economic, and environmental contributions to the city. The establishment process was described in close collaboration with the FSP contractor, China Communications Construction Company, Ltd. (CCCC). The land use changes of FSP’s development were determined by satellite images, while its environmental benefits were assessed through plant selection, planting design, and seedling survival rate. Open and/or close ended questionnaires were designed to assess the socioeconomic values of the park. The green space of the area has highly changed from 2002 (8.6%) to 2019 (56.1%) when the park was completed. More than 74,288 seedlings in 133 species of seedlings were planted in the park. The average survival rate of these seedlings was 93%. On average about 500 people visit the park per day, and 400,000 USD is generated, just from the entrance fee, per annum. Overall, 100% of the visitors were strongly satisfied with the current status of the park and recommended some additional features to be included in it. In general, the park is contributing to the environmental and socioeconomic values of the city residents, and this kind of park should be developed in other sub-cities of the city as well as regional cities of Ethiopia to increase the aesthetic, environmental and socioeconomic values of the country, at large.
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Transmission rates of COVID-19 have been associated with the density of buildings where contact among individuals partially contributes to transmission. The research sought to analyze the spatial distribution of building density derived from satellite images and determine its implications to COVID-19 health risk management using Yogyakarta and its surrounding districts as an example. Fine-scale building distribution obtained through remote sensing data transformation was analyzed with GIS. NDBI was applied to Landsat 8 imagery;then, using multiple linear regression analysis, it was correlated to building density’s training samples generated from high-resolution imagery. The derived percent of building density (PBD) was combined with publicly available records of COVID-19 infection to assess risk. This research found that PBD could explain the uneven COVID-19 diffusion at different stages of its development. Instead of dividing regions into zones based on confirmed cases, government and public health officials should observe new cases in high-PBD districts;then, when the cases are decreasing, their attention should shift to low-PBD districts. Remote sensing data allow for moderate-scale PBD mapping and integrating it with confirmed cases produces spatial health risks, determining target areas for interventions and allowing regionally tailored responses to anticipate or prevent the next wave of infections.
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Appropriate strategies on urban climate mitigation should be formulated by considering the physical morphology of the urban landscape. This study aimed to investigate, analyze, and promote possible strategies to mitigate Jakarta’s urban heat island (UHI) phenomena. Jakarta’s local climate zone (LCZ) was classified into 17 classes using Landsat 8 data and the random forest method. Land surface temperature (LST) characteristic in each LCZ class was analyzed from 2018, 2019 and 2020. The result revealed that most of the local climate zone in Jakarta is dominated by LCZ 6 (open low-rise) and LCZ 3 (compact low-rise), which is the typical residential area in Jakarta. However, the mean LST in 2018, 2019 and 2020 showed that LCZ 3 (compact low-rise) and LCZ 7 (lightweight low-rise) are the areas that were most likely causing high surface temperature with the highest UHI intensity. During the COVID-19 pandemic in 2020, LST in Jakarta decreased drastically in some parts of the area, especially in public facility such as airport. However, the LST value in low-rise areas (LCZ 3 and LCZ 7) remains higher than the other LCZ classes. Materials of the building and land cover play a significant role in raising the land surface temperature. Therefore, mitigation strategies for urban heat islands in Jakarta should be focused on such particular areas mentioned.
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Hundreds of millions of poor families receive some form of targeted social assistance. Many of these antipoverty programs involve some degree of geographic targeting, where aid is prioritized to the poorest regions of the country. However, policy makers in many low-resource settings lack the disaggregated poverty data required to make effective geographic targeting decisions. Using several independent datasets from Nigeria, this paper shows that high-resolution poverty maps, constructed by applying machine learning algorithms to satellite imagery and other nontraditional geospatial data, can improve the targeting of government cash transfers to poor families. Specifically, we find that geographic targeting relying on machine learning-based poverty maps can reduce errors of exclusion and inclusion relative to geographic targeting based on recent nationally representative survey data. This result holds for antipoverty programs that target both the poor and the extreme poor and for initiatives of varying sizes. We also find no evidence that machine learning-based maps increase targeting disparities by demographic groups, such as gender or religion. Based in part on these findings, the Government of Nigeria used this approach to geographically target emergency cash transfers in response to the COVID-19 pandemic.
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Poverty , Social Welfare , Geography , Humans , NigeriaABSTRACT
Timely, accurate, and repeatable crop mapping is vital for food security. Rice is one of the important food crops. Efficient and timely rice mapping would provide critical support for rice yield and production prediction as well as food security. The development of remote sensing (RS) satellite monitoring technology provides an opportunity for agricultural modernization applications and has become an important method to extract rice. This paper evaluated how a semantic segmentation model U-net that used time series Landsat images and Cropland Data Layer (CDL) performed when applied to extractions of paddy rice in Arkansas. Classifiers were trained based on time series images from 2017–2019, then were transferred to corresponding images in 2020 to obtain resultant maps. The extraction outputs were compared to those produced by Random Forest (RF). The results showed that U-net outperformed RF in most scenarios. The best scenario was when the time resolution of the data composite was fourteen day. The band combination including red band, near-infrared band, and Swir-1 band showed notably better performance than the six widely used bands for extracting rice. This study found a relatively high overall accuracy of 0.92 for extracting rice with training samples including five years from 2015 to 2019. Finally, we generated dynamic maps of rice in 2020. Rice could be identified in the heading stage (two months before maturing) with an overall accuracy of 0.86 on July 23. Accuracy gradually increased with the date of the mapping date. On September 17, overall accuracy was 0.92. There was a significant linear relationship (slope = 0.9, r2 = 0.75) between the mapped areas on July 23 and those from the statistical reports. Dynamic mapping is not only essential to assist farms and governments for growth monitoring and production assessment in the growing season, but also to support mitigation and disaster response strategies in the different growth stages of rice.
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The world is under continuous threat of deadly virus Covid-19 and its rapidly evolving and mutating variants. Several efforts are being made by researchers and scientists worldwide to model and predict the behavior of spreading pattern of Covid-19 virus. In this paper, relationship between weather conditions and Covid19 spread rate is modelled with the help of data acquired by satellite using MODIS. To perform all the experiments, three data sets are used: one is taken from NASA related to MODIS and weather satellite images, second data set is of Kaggle related to latest Covid-19 case reports and third data set used is related to weather. Further, Covid-19 data set is also analyzed and modeled using look up learning 2D model. From the experimental results, it is observed that the various weather parameters obtained from satellite can be used to model the impact and spread of Covid-19. The data set once prepared is fed as input to early warning system for Covid-19. It is concluded that the designed system can be an efficient technique for issuing precautionary alert in the society. © 2022 IEEE.
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The COVID-19 pandemic outbreak in early 2020 impacted people’s lives and the working environment and led to a drastic change in human activities. This has opened a window to analyze the people’s response to the safety measures implemented to contain the virus, which can be reflected and monitored using Night-time light (NTL) images. This study aims to demonstrate the use of NTL images captured by satellites to detect changes in human activity in the UAE before and during the pandemic. The study period will include the pandemic year and the previous years (2017-2019) will be used as a control. Raw NTL data was pre-processed to obtain cloud-free radiance images through which the monthly average radiance was calculated. The monthly average radiance is categorized into three classes: residential, commercial, and roads. The radiance levels during the lockdown were compared against the months prior to the restriction imposed. The results revealed that the roads category showed the highest decrease in radiance levels due to the enforcement of the safety measures, followed by the commercial category, whereas the least reduction was observed for the residential category. The results show how NTL radiance could be used in monitoring the changes in human activities.
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This study uses satellite imagery and geospatial data to examine the impact of floods over the main planting areas for double-cropping rice and grain crops in the middle reaches of the Yangtze River. During summer 2020, a long-lasting 62-day heavy rainfall caused record-breaking floods over large areas of China, especially the Yangtze basin. Through close examination of Sentinel-1/2 satellite imagery and Copernicus Global Land Cover, between July and August 2020, the inundation area reached 21,941 and 23,063 km2, and the crop-affected area reached 11,649 and 11,346 km2, respectively. We estimated that approximately 4.66 million metric tons of grain crops were seriously affected in these two months. While the PRC government denied that food security existed, the number of Grains and Feeds imported from the U.S. between January to July 2021 increased by 316%. This study shows that with modern remote sensing techniques, stakeholders can obtain critical estimates of large-scale disaster events much earlier than other indicators, such as disaster field surveys or crop price statistics. Potential use could include but is not limited to monitoring floods and land use coverage changes.
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Over a billion people live in slums, with poor sanitation, education, property rights and working conditions having a direct impact on current residents and future generations. Slum mapping is one of the key problems concerning slums. Policymakers need to delineate slum settlements to make informed decisions about infrastructure development and allocation of aid. A wide variety of machine learning and deep learning methods have been applied to multispectral satellite images to map slums with outstanding performance. Since the physical and visual manifestation of slums significantly varies with geographical region and comprehensive slum maps are rare, it is important to quantify the uncertainty of predictions for reliable and confident application of models to downstream tasks. In this study, we train a U-Net model with Monte Carlo Dropout (MCD) on 13-band Sentinel-2 images, allowing us to calculate pixelwise uncertainty in the predictions. The obtained outcomes show that the proposed model outperforms the previous state-of-the-art model, having both higher AUPRC and lower uncertainty when tested on unseen geographical regions of Mumbai using the regional testing framework introduced in this study. We also use SHapley Additive exPlanations (SHAP) values to investigate how the different features contribute to our model’s predictions which indicate a certain shortwave infrared image band is a powerful feature for determining the locations of slums within images. With our results, we demonstrate the usefulness of including an uncertainty quantification approach in detecting slum area changes over time.