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Abstract: Abstract Geospatial modelling is a useful analytical tool for efficient characterization of inequalities in COVID-19 vaccination coverage for effective management control of the pandemic, which is limited in the literature on a global scale. This study investigated the spatial distribution of disparities in COVID-19 vaccine coverage indicators, namely, total vaccination rate (TVR), population proportion with at least one dose (1 + Dose rate), full vaccination rate (FVR), and booster vaccination rate (BVR) globally by accounting for socio-economic and healthcare accessibility indices, which included the number of vaccine types, COVID-19 containment and health index (CHI), universal healthcare service coverage index (USCI), human development index (HDI), and gross domestic product per capita (GDP/capita), as covariates. The analysis used a global dataset on the four vaccine coverage indicators and covariates. In the multivariate analysis, USCI independently predicted each vaccine coverage rate; CHI independently predicted all coverage rates except BVR; GDP/capita independently predicted only BVR; and number of vaccine types independently predicted FVR and 1 + Dose rate. Spatial autocorrelation tests and cokriging predictions produced spatial maps indicating clustering of countries with low coverage rates (0–29/100 people), mostly in the WHO African region and high clustering of TVR and 1 + Dose rates (101–185 and 49–76 per 100 population, respectively) in other parts, mostly in the developed countries. The study findings highlight the importance of global efforts of improving vaccine diplomacy and dose-sharing to address the inequalities in vaccine coverage. PubDate: 2023-12-01
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Abstract: Abstract The Coronavirus disease 2019 (COVID-19) has influenced the life of all people around the world. This study analyzed the relationship between the weather elements (daily temperature, wind speed and humidity) and daily active, recovered and dead cases of COVID-19 in Rafsanjan, southeast area of Iran. COVID-19 data and meteorological variables were obtained from 29 February 2020 to 20 March 2021 (386 days) from Rafsanjan University of Medical Sciences and Meteorological Organization of Iran, respectively. The results showed that there is a significant inverse association between daily average temperature with the number of daily active cases (r: − 0.293), recovered cases (r: − 0.301) and dead cases (r: − 0.198) of COVID-19 (p < 0.01). With decreasing the average wind speed, the number of daily active cases (r: − 0.224), recovered cases (r: − 0.232) and dead cases (r: − 0.169) of COVID-19 has been increased (p < 0.01). A non-significant positive correlation was observed between daily humidity and active cases (r: 0.033, p = 0.518) and recovered cases (r: 0.044, p = 0.390), and significant positive correlation with the daily dead cases (r: 0.254, p < 0.01). Therefore, temperature and wind speed can be considered as affective factors in COVID-19 transmission as an auxiliary solution. PubDate: 2023-12-01
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Abstract: Abstract Jefferson County in Texas has experienced devastating storms in recent years resulting in billions of dollars in damages. The county has gone through temporal milestones in terms of population growth and industrial development. Many studies have shown that urban development increases the risk of flooding by decreasing the soil infiltration capacity. The current study focuses on estimating the extent to which the urban development in the county has led to increased imperviousness using a combination of historical and current spatial data. Rational runoff coefficients of the County were estimated and compared at three different times over a span of 120 years. A land survey map for 1898, an aerial imagery map for 1966 and a land parcel map for 2019 were obtained from various sources. The three maps available, each in different format, were analyzed to determine the land use and land cover type for the respective years. The runoff coefficient increased by 21% from 1898 to 1966 and remained the same from 1966 till 2019. The estimates are in correlation with industrial and population growth patterns of the county. These preliminary spatial analyses are useful in estimating the contributions of recent major flooding from overall development, or if they may be more impacted from other factors such as changes in weather patterns. PubDate: 2023-12-01
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Abstract: Abstract Local Climate Zone (LCZ) is an effective classification scheme for quantifying and understanding the Urban Heat Island (UHI). There is a need of research that analyze the relationship of LST with LCZ and its seasonal variation in the metropolitan cities of India as they are facing a continuous loss of natural landcover and an increase in impervious land. This study aims to visualize and assess seasonal variations in LST and its LCZ-wise variation. In this paper, LCZ mapping is performed using SAGA GIS according to the guidelines of WUDAPT (World Urban Database and Access Portal Tools). Seasonal LST mapping is also done to get an idea about the average temperature of a particular LCZ class in a specific season of the year. The results confirmed that: (1) Average seasonal LST has difference of 3-4 °C among inter urban LCZs except in New Delhi in winter season. (2) Among all the built-up categories, open mid-rise (LCZ-5) has comparatively higher LST in all seasons. (3) Among non-built-up categories, rock or paved surfaces (LCZ E) have the highest LST, while shrubs or bushes (LCZ-C) have the lowest LST. The findings of this research could be adopted by urban planners for climate-sensitive sustainable development. PubDate: 2023-12-01
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Abstract: Abstract The Sundarbans national park is 10,000 square kilometers tidal mangrove forest in India and Bangladesh and is home to the largest population of Bengal tigers in the world. However, Sundarbans tigers are facing numerous threats such as Habitat loss, land fragmentation, poaching, and prey depletion. For effective conservation efforts, it is crucial to understand the behavior & movements patterns of these tigers within their habitat. In this study on the Sundarbans region, telemetry data analysis is done which is obtained from WII (Wildlife Institute of India) for four tigers with 4000 GPS locations along with prey distribution dataset. Using Machine Learning algorithms like Ridge regression, KNN (K-Nearest Neighbor), Decision tree, SVM (Support Vector Machine) and MLP (Multi-Layer Perceptron), the proposed model predicts each predator’s next location based on predator- prey locations & neighboring predator interaction with different combinations of predator-prey categories before comparing results among all algorithms. The results showcased that among all ML algorithms, Decision Tree algorithm produced best results with highest accuracy rate of predicted predator location compared to actual location. This analysis provides input that can be used to develop more effective conservation strategies to combat poaching. PubDate: 2023-12-01
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Abstract: Abstract This article explores two different methods, vector re-classification and raster fuzzification, for choosing the best railway routes between Shiraz and Yazd in Iran using spatial information systems. The right use of these systems can have significant economic benefits for railways, allowing for better decision-making and more efficient resource allocation. Topography analysis in spatial information systems, including calculating the shortest and optimal route, is a practical and valuable application. The study used satellite images and Google Earth Engine to extract vector or raster maps for the cost-path algorithm. The results of the raster fuzzy approach suggested alternative A (111 km, slope 2%) and alternative B (115 km, slope 25%), while the vector reclassification approach showed alternative C (131 km, slope 28%). After employing semi and non-structure problem-solving, as well as profile and rout topographic analysis, path A was selected as the optimal route. The study concluded that the raster fuzzy approach was more effective in finding railway paths and could provide better results for decision-makers in similar projects. PubDate: 2023-12-01
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Abstract: Abstract Coconut palm tree plantation, monitoring and management in tropical countries is vital to improving exports, domestic food use and the economy. Remote sensing and GIS technologies are widely used to maintain and monitor the coconut palm’s health and production using very high-resolution satellite data. Deep learning algorithms will help identify and monitor the growth and health of coconut palms automatically using satellite data. In this study, a deep learning model has been developed to identify the coconut trees using Worldview-2 satellite data. ArcGIS Pro software is used to develop the deep learning model. Single Shot Detector (SSD) algorithm is adopted for developing the model to detect the coconut trees. The model is based on the object detection technique. The generated model accurately identified the individual trees for the study area using the Worldview-2 satellite data. Produced maps will be used to calculate the population of coconut trees. PubDate: 2023-12-01
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Abstract: Abstract The atmospheric aerosols and air pollutants affect the earth's atmosphere, human health and climate system. Human-induced aerosols and air pollutants are the major causes of the deterioration of air quality. The COVID-19 lockdown restricted the movement of people and vehicles, stopped industrial and agricultural activities and may have impacts on the aerosols in the atmosphere. Spatio-temporal map of MODIS Terra AOD_550 nm, OMI Aura UVAI, Ozone, NO2, SO2 and AIRS CO during the lockdown illustrates the significant reduction in their concentration. During the lockdown, the North India shows a record reduction of over 20% in Aerosol Optical Depth and Aerosol Index values. A substantial decrease in AOD and AI was also observed in Eastern and Western parts of India. The average AOD value were reduced from 1.36 (2016–2019) to 1.09 (2020) over India during the lockdown. The satellite-retrieved aerosol variables over India recorded lowest AOD values on 29th March, 2020 (0.2566) and 21st April 2020 (0.2591). Similarly, air pollutants CO, NO2 and SO2 also significantly reduced in India. Despite all variables showing a reduction in concentration, Ozone recorded an increase in value during lockdown primarily over North and North-eastern parts of India. Western India recorded a substantial reduction in SO2 (47%) followed by Central India (31%). As pan India is considered, CO was reduced by 1%, NO2 reduced by 15.29% and SO2 was reduced by 26.82% during the lockdown period. This abrupt reduction in aerosol and air pollutants concentration over India was mainly due to the lockdown of COVID-19. PubDate: 2023-12-01
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Abstract: Abstract Spatial planning often requires a scientific understanding of the spatial variation of environmental variables. This is accomplished by spatial prediction of point observations from geographic locations, transforming point data into seamless raster interpolations for the region of interest. The most widely used geostatistical interpolation technique is kriging that minimises errors and produces unbiased predictions. Machine learning (ML) and Deep Learning based spatial estimation approaches have recently received a lot of attention. A significant amount of research has gone into creating new methods of data-driven, computationally efficient spatial prediction of variables with increased prediction accuracy. Using Citation Network Analysis of journal papers published over the past 30 years, we investigated the development, evolution and significant milestones in the spatial prediction techniques and their applications. The main path analysis of research evolution in a citation network was carried out to understand the development trajectory and direction of future of research in this field. PubDate: 2023-12-01
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Abstract: Abstract A notable shortcoming in contemporary digital navigation systems is their failure to incorporate live weather data. This study investigates the possibility of improving urban navigation experiences by integrating real-time weather data. We developed a navigation tool that utilizes weather data from the OpenWeather API, providing users with real-time insights into weather conditions such as temperature, wind speed and direction, precipitation, and atmospheric pressure. This tool enables users to make informed decisions about their routes and travel plans by providing updated temperature information for selected routes and assessing the risk of road sections based on weather conditions. Incorporating weather data into navigation systems has the potential to enhance driving safety, minimize travel durations, and alleviate weather-related disturbances in urban settings. PubDate: 2023-11-29
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Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Predicting the meteorological factors of the climate in the medium and long term is a significant challenge with socio-economic and environmental implications, given its complex and chaotic nature. The current short-term weather predictions the Iraqi meteorological organization offers are less valuable. As a result, this study introduces four machine-learning methods—artificial neural network, support vector machine, random forest (RF), and K nearest neighbors—to forecast six meteorological factors: total precipitation (TPRE), minimum temperature (MINT), maximum temperature (MAXT), relative humidity (RHUM), top-of-atmosphere radiation (TOAR), and wind speed (WIND) up to 1, 3, 6, and 12 months ahead in four Iraqi governorates. Data on these factors from 1981 to 2021 were extracted from the Modern-ERA Retrospective Analysis for Research and Applications version 2 dataset. The findings indicate that the RF algorithm outperformed other algorithms regarding prediction accuracy, while the SVR algorithm exhibited the least accuracy. Moreover, TPRE had the lowest performance with an average root-mean-square error (RMSE) of 19.002; conversely, RHUM and WIND showed much better performance with average RMSE values of 7.259 and 0.192 respectively. The highest performance was observed for MINT (MAXT and TOAR prediction with average-RMSE values of 2.346, 2.244, and 5.314, respectively). The present study’s findings will bring significant advantages in safeguarding human lives and property and promoting health, security, and economic prosperity. PubDate: 2023-11-22
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Abstract: Abstract The global impact of coal mining and associated activities on land use/land cover (LULC) changes is significant. This study used Landsat satellite images from 1990 to 2020 to assess LULC changes and their impact on land surface temperature (LST) in four districts of Chhattisgarh state, India. Over three decades, Korba and Raigarh districts saw expansion in coal mines, built-up areas, and water bodies, while forest areas diminished by 711.3 km2 and 212.87 km2, respectively. Koriya district saw coal mine expansion of 5.68 km2 (1990–2010), later declining to 2.85 km2, alongside growth in built-up regions, and forest cover reduction by 251.31 km2 in 2020. Surguja district experienced coal mine and built-up area expansion (1990–2020), with initial forest decline of 160.21 km2 in 2010 followed by recovery in 2020. LST was determined using the Mono-window algorithm. LST increased during winter and summer, with the most significant rise in summer. Vegetation-rich regions had lower LST, while coal mines had the highest temperatures. There was a positive relationship between mining land patch size and patch temperatures. This study underscores the need for vegetation restoration in mining areas, particularly abandoned sites, and sustainable mining practices to mitigate coal mining's warming effects. PubDate: 2023-11-18
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Abstract: Abstract Inherent limitations of Landsat images restrict the accuracy of land categorization efforts for a better comprehension involved in transforming raw image data into relevant land cover information. Managing mixed pixels and complex spectrum responses, the introduction of advanced algorithmic approaches is essential. Hybrid classification methods that include spectral, spatial, and contextual data can increase the precision of assigning class labels to pixels with confusing features. This paper demonstrate the construction of automatic images segmentation based on deep convolution neural networks with object-oriented integration. Harnessing machine learning approaches in remote sensing images, an exper imental phase showed the best fit model that can be implemented further into larger areas with a Kappa validation value of 99.466% and errors of 0.015 on average. The model used to classify land use to see land degrada tion in Air Bengkulu watershed, the main source of annual flood disaster in Bengkulu area. We found that the area has been reduced by 20%, 1.9%, 48%, and 7.9% for forest, bare land, plantations, and rice fields, respectively, from its initial area of year 2000. Furthermore, increase value of palm oil plantations (7.6%) over the area showed indirectly reason for replacement of agriculture allocation with correlation value of 97.12%. PubDate: 2023-11-15
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Abstract: Abstract The flood occurrence frequency has increased over the years due to climate change, and various state-of-the-art methods have been proposed for flood mapping using Synthetic Aperture Radar (SAR) data. However, whenever there are similarities in the radar backscatter values of permanent water bodies and sand areas, the riverine floods are generally ignored due to high computational complexity. This paper proposes a multi-source data fusion-based model for mapping the Kosi river floodplain areas in the Supaul district of Bihar, India, using both VV and VH bands of Sentinel-1 SAR imagery. The proposed model involves image pre-processing, classification, and post-processing of results to obtain the flood map. The combination of Otsu automatic threshold detection and change detection methods is used for reducing the overestimation of flooded pixels while identifying flood-prone areas. The post-processing involves the identification of high and low-confidence flood regions, riverine floods, generation of flood maps, and estimation of flooded areas. The impact of the flood on the nearby area is captured using multi-temporal images of the Supaul district. The pre-processing, visualizing, processing, and analysis of the results are carried out in Google Earth Engine. The proposed method is suitable for identifying flooding in both non-permanent and permanently low backscattering areas. Kindly check and confirm the inserted city name is correct for affiliation 3. All author's details are updated through e-proofing; kindly assign *Aditya Raj, *Tanupriya Choudhury, *Greetta Pinheiro and *Jung-Sup Um as Corresponding author(s). PubDate: 2023-11-05
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Abstract: Abstract Climate change is a genuine issue threatening the presence of species and livelihoods, consequently causing the vulnerability. Agricultural vulnerability is seriously undermined by regular disasters, caused by climate change. Therefore, researchers and policymakers are showing their interest in the effect of climate change on agricultural vulnerability through publishing research works. This study aims to perform a bibliometric analysis of climate change-induced agricultural vulnerability using the Web of Science and Scopus databases, and PRISMA method. The study highlights the current trend, hot-spot area, and their development through a literature dataset taken into consideration from 2010 to 2021. The outcome showed that the USA, UK, Australia, and China are the countries with high publications potentials. Consequently, there has been a significant increase (R2 = 0.90) in highlighted research area (2010–2021). The findings revealed that climate change and agricultural vulnerability research expanded gradually into different subject categories. The most frequent keywords were ‘climate change,’ ‘vulnerability,’ ‘adaptation,’ and ‘agriculture.’ The result showed that five clusters displayed the co-occurrences of term map. With the help of each clustering group, development of the respective research field can be smartly analyzed. Based on the findings, several research gaps are identified and offer opportunities for further studies. PubDate: 2023-11-03
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Abstract: Abstract The frequency of tropical cyclones has increased across the globe due to climate change in recent years. The eastern coastal plains of India have witnessed significant rise in frequency and severity of tropical cyclones during past decades, making it essential to do a comprehensive vulnerability assessment and implement effective risk reduction measures. Therefore, this study seeks to analyse the spatial vulnerability of tropical cyclones in coastal Odisha using geospatial techniques and fuzzy analytical hierarchy process. Seventeen spatial criteria within physical, social, and mitigation aspects has been used to assess the vulnerability to tropical cyclones. Result shows that Baleswar and parts of Bhadrak and Kendrapara districts are the most vulnerable regions to tropical cyclones. In terms of physical and social vulnerabilities, about 40% area of Odisha falls under high and very highly vulnerable zones to tropical cyclones. Overall, about 41% of the area comes under high and very high vulnerability without mitigation capacity, but integration of mitigation capacity may reduce it to 21%, which emphasize the significance of mitigation measures in reducing vulnerability to cyclones. The results may be helpful in spatial planning for effective cyclone risk management and implementing mitigation measures to improve cyclone resilience in the region. PubDate: 2023-10-31
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Abstract: Abstract In the realm of survey research, establishing connections within large datasets remains a challenge. This study aims to unveil underlying connections within extensive survey data, emphasizing the need for a more integrated approach to decipher intricate relationships among survey elements. Utilizing computational semantics, machine learning, and advanced spatiotemporal models, we developed an all-encompassing database. This novel database is adept at extracting and characterizing features from a multitude of survey studies, spotlighting relationships among metadata elements such as terms, variables, and topics. The derived relationships are systematically stored as connectivity matrices. These matrices not only quantify the degree of interconnectedness among features but also provide insights into their complex interplay. As a result, our system functions akin to a digital geographical data librarian. Beyond merely serving as a storage tool, this system facilitates interdisciplinary research. It equips researchers with the capability to discern connections between survey elements, enabling them to identify the most influential paths among features based on diverse criteria. Such a tool fosters cross-disciplinary integration and unveils potential ties between seemingly unrelated survey attributes, paving the way for breakthroughs in understanding and application. PubDate: 2023-10-28
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Abstract: Abstract There are major gaps remaining in understanding of species distribution and how relationships between biodiversity, environment and scales change over space and time. This review explores the significance, challenges, future directions, and the potential contribution of Earth Observations based Essential Biodiversity Variables (EBVs) to enhance our understanding of biodiversity. Integrating EBVs with Remote Sensing of Earth Observations (RS-EO) is found to be an effective approach to quantify and monitor changes in biodiversity over space and time. Species serves as the fundamental taxonomic units of biodiversity and are the focal points of conservation policies. Prioritizing the utilization of species-level metrics and their seamless integration into the EBV framework is crucial. The current study has contributed 11 potential EBVs to the existing knowledge base. Integrating multiple data sources and methodologies is essential for overcoming the constraints and obtaining a more comprehensive understanding of biodiversity patterns. This synergy offers a holistic approach for monitoring, assessing, and managing biodiversity, to contribute significantly to global conservation efforts and sustainable development goals. PubDate: 2023-10-21
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Abstract: Abstract This study conducted a comprehensive analysis of erosion and accretion rates in the Kakinada coastal zones of Andhra Pradesh using multitemporal satellite images spanning six time periods from 1972 to 2022. Two methods, End Point Rate (EPR) and Linear Regression Rate (LRR), were employed to assess erosion and accretion, revealing maximum erosion rates of -28.64 and − 31.27 m/year and maximum accretion rates of 20.11 and 22.74 m/year in the study area. These changes were attributed to natural disasters like tsunamis and cyclones and anthropogenic activities such as harbour construction, beach sand excavation, industrialization of garbage dump sites, urbanization, and domestic sewage discharge. Further analysis, using the Kalman filter model, forecasted shoreline changes, predicting erosion of -842 and − 891 m and accretion of 700 and 679 m in 2032 and 2042, respectively. Specific erosion zones were identified near Uppada, Gadimoga, and Korangi, while accretion was predominant in Kakinada, Nadakuduru, Kovvadda, and Panasapadu regions. The use of multitemporal satellite images and analytical methods highlights the crucial role of spatial information science in tracking long-term coastal changes and assessing localized erosion and accretion dynamics. These findings provide the tools for effective decision-making in safeguarding vulnerable shorelines amidst evolving environmental and human-induced challenges. PubDate: 2023-10-20
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