Environmental Systems Research Research Articles

Impact of cross-border transportation corridors on changes of land use and landscape pattern: A case study of the China-Laos railway

Regional integration initiatives have triggered impacts on land use changes (LUC) and landscape patterns through geo-economic cooperation like cross-border infrastructure, which are often presented, but comprehensive studies on the extent and degree remains insufficient. The China-Laos Railway (CLR), a cross-border transportation corridor connecting Kunming in China with the Laotian capital, Vientiane, which was launched in December 2016 and operation started in December 2021, has recently witnessed rapid and notable changes in landscape and land use. This case study provides a distinctive opportunity to evaluate the relative significance of political and socioeconomic factors on LUC and landscape patterns. In this study, we integrate 10-m land-use data products (2017–2022) provided by Environmental Systems Research Institute, Inc (ESRI) with geospatial analysis to quantify and compare the impacts of cross-border transportation corridors on LUC within a 10 km buffer area along the CLR. The results showed that since 2017, about 3 % deforestation caused by the expansion of cropland (8 %) and construction land (38 %) along the CLR. The comprehensive dynamic degree along the CLR displays two peaks, appearing within the 2–4 km and 8–10 km buffer zones. Interestingly, the fragmentation of land patches within the buffer zone decreases as the distance from the CLR increases. Moreover, the construction of the CLR has a greater impact on the Laotian side, while its completion and operation triggered more significant changes on the Chinese side. The study quantitatively assesses the extent, type, and intensity of the impacts of cross-border transportation corridors on LUC and landscape patterns.

Exploring Bangladesh's Soil Moisture Dynamics via Multispectral Remote Sensing Satellite Image

Accurate monitoring and mapping of soil moisture are essential for sustainable agricultural practices, water resource management, and climate studies. This study aims to explore the mapping of soil moisture in Bangladesh using multispectral remote-sensing satellite images. The purpose of this study is to prepare a map of soil moisture aiming to help government authorities in developing agricultural activities to accelerate the sustainable development of the rural economy in Bangladesh. A total of 14 Landsat scenes of paths 135-139 and rows 42-46 covers the entire Bangladesh. Thus, a set of Landsat imagery (a total of 14 scenes) for the year 2022 was used in this study to map the soil moisture of Bangladesh through the application of Geographical Information System (GIS) and Remote Sensing. Satellite Image preprocessing, correction, and analysis were done with ENVI software (version 5.1, developed by Research Systems, Inc., USA) and the ArcGIS software (version 10.6, developed by Environmental Systems Research Institute, USA). For the study of the long-term variation of soil moisture over Bangladesh and its seasonal characteristics, a soil moisture map can be used. In addition, to improve the climate model over Bangladesh, an up-to-date soil moisture map will be very helpful. The objective of this study is to provide accurate and detailed up-to-date spatial soil moisture information at reduced cost and time which is essential for environment modeling, risk assessment, decision-making for different government agencies and development partners, and help toward socio-economic development. In this study, the map shows soil moisture as very wet, wet, dry, and very dry soils of Bangladesh. The overall land cover classification accuracy was 92.56%, with a Kappa value 0.90 for Random Forest and the overall soil classification accuracy was 87.27%, with a Kappa value 0.858 for maximum likelihood classification indicating good consistency.

“The spatial distributions of degraded craters and valley networks on Mars as a function of elevation: Their implications for Noachian climate”

Cold and dry models of Noachian Mars postulate that the Noachian epoch characterized a hyperarid and frigid world dominated by low-latitude cold-locked icefields. This ice caps supposedly protected underlying substrates and topography of the Southern Highlands from erosional degradation. I test the Late Noachian Icy Highlands (LNIH) hypothesis by mapping small distribution of both degraded and fresh [2–4] km diameter craters, crater depth and infilling of the smallest [2–5] km craters, valley network (VN) and inverted channels within an area of interest (AOI) representing over 1.1 × 107 km2 of the Noachian Southern Highlands utilizing Environmental Systems Research Institute (ESRI) ArcMap, clipped exclusively to Noachian terrain. Crater degradation results from this study do not show preferential preservation of fresh craters at elevation. Also, crater rim – crater floor (km) depth values show greatest erosion at elevations >1 km purported LNIH Equilibrium Line Altitude (ELA). VN and inverted channels likewise give evidence for net precipitation and erosion at elevation and net deposition and water transport below the global dichotomy at 0 km and extinction at −2.78 km. VN distribution patterns also requires an intertropical convergence zone (ITCZ). I propose that my results are most consistent with emerging PMGCMs incorporating atmospheric collision-induced-absorption (CIA) of CO2 and H2, bypassing the Faint Young Sun Paradox. I propose that statistical evidence derived from geomorphological features on Mars should be used as boundary conditions for future Mars global climate models.

Association between community-level political affiliation and peripartum vaccination

Political affiliation has been associated with vaccine uptake, but whether this association holds in pregnancy, when individuals are recommended to receive multiple vaccinations, remains to be studied. This study aimed to examine the association between community-level political affiliation and vaccinations for tetanus, diphtheria, and pertussis; influenza; and COVID-19 in pregnant and postpartum individuals. A survey was conducted about tetanus, diphtheria, and pertussis and influenza vaccinations in early 2021, with a follow-up survey of COVID-19 vaccination among the same individuals at a tertiary care academic medical center in the Midwest. Geocoded residential addresses were linked at the census tract to the Environmental Systems Research Institute 2021 Market Potential Index, which ranks a community in comparison to the US national average. The exposure for this analysis was community-level political affiliation, defined by the Market Potential Index as very conservative, somewhat conservative, centrist, somewhat liberal, and very liberal (reference). The outcomes were self-reported vaccinations for tetanus, diphtheria, and pertussis; influenza; and COVID-19 in the peripartum period. Modified Poisson regression was used and adjusted for age, employment, trimester at assessment, and medical comorbidities. Of 438 assessed individuals, 37% lived in a community characterized by very liberal political affiliation, 11% as somewhat liberal, 18% as centrist, 12% as somewhat conservative, and 21% as very conservative. Overall, 72% and 58% of individuals reported receiving tetanus, diphtheria, and pertussis and influenza vaccinations, respectively. Of the 279 individuals who responded to the follow-up survey, 53% reported receiving COVID-19 vaccination. Individuals living in a community characterized by very conservative political affiliation were less likely to report vaccinations for tetanus, diphtheria, and pertussis (64% vs 72%; adjusted risk ratio, 0.83; 95% confidence interval, 0.69-0.99); influenza (49% vs 58%; adjusted risk ratio, 0.79; 95% confidence interval, 0.62-1.00); and COVID-19 (35% vs 53%; adjusted risk ratio, 0.65; 95% confidence interval, 0.44-0.96) than those in a community characterized by very liberal political affiliation. Individuals living in a community characterized by centrist political affiliation were less likely to report vaccinations for tetanus, diphtheria, and pertussis (63% vs 72%; adjusted risk ratio, 0.82; 95% confidence interval, 0.68-0.99) and influenza (44% vs 58%; adjusted risk ratio, 0.70; 95% confidence interval, 0.54-0.92) than those in a community characterized by very liberal political affiliation. Compared with pregnant and postpartum individuals living in communities characterized by very liberal political beliefs, those living in communities characterized by very conservative political beliefs were less likely to report vaccinations for tetanus, diphtheria, and pertussis; influenza; and COVID-19, and those in communities characterized by centrist political beliefs were less likely to report vaccinations for tetanus, diphtheria, and pertussis and influenza. Increasing vaccine uptake in the peripartum period may need to consider engaging an individual's broader sociopolitical milieu.

Comparing global and local land cover maps for ecosystem management in the Himalayas

As a breakthrough in geographical data science, two high-resolution (10m) global land cover products were published by the Environmental Systems Research Institute (ESRI) and European Space Agency (ESA). Yet, the accuracy and applicability of those maps have not been diagnosed in the Himalayas which is characterized by multifunctional and heterogenous landscapes. We compared the ESA and ESRI land cover products in terms of classification performance, overall accuracy, spatial agreement, and applicability in the Hindu-Kush Himalayan region by taking a case of multifunctional landscapes of Nepal. In addition, we generated a local land cover map in the Google Earth Engine (GEE) environment based on Sentinel-2 imagery for 2020 and compared it with the ESRI and ESA land cover products. The overall accuracy of ESRI product was higher (87.3%) than ESA (71.2%) and GEE-generated (73%) map. Shrubland, grassland and forestland were found to be the most common source of confusion in land cover mapping for both products. The spatial agreement (pixel match) of ESA and ESRI products was found to be about 62% of the landscape area, indicating substantial agreement between both products for land cover classification. The ESRI product is more reliable in mapping the Himalayan region. However, we suggest that the global land cover products must be flexible to further improvement by incorporating local topography, vegetation phenotype, spatial heterogeneity, and temporal dynamics of landscape and ecosystem services, particularly in the Himalayas.

Analysis of the Spatial-Temporal Evolution of Urbanization Quality in Zhejiang Province, China

Taking cities in Zhejiang Province of China from 2011 to 2020 as the research object, a multi-dimensional urbanization quality evaluation index system was constructed using the comprehensive analysis method, and the urbanization quality of 11 cities in Zhejiang Province was quantitatively measured using the entropy weight method. The system classification and time-space evolution analysis were carried out using ArcGIS software (Environmental Systems Research Institute, Inc., RedLands, CA, USA) to comprehensively study the evolution characteristics and influencing factors of the urbanization quality of cities in Zhejiang Province. This study provides a reference for local governments to formulate feasible urbanization development strategies and policies to promote the high-quality development of urbanization and for the construction of new urbanization in other provinces and cities.

Seasonal and spatial variations in soil biochemical properties in areas with different degrees of mining subsidence in Central China

Land subsidence due to underground mining results in soil nutrient loss and reduced soil fertility. Land reclamation, which refers to the improvement of soil nutrients through targeted measures, requires spatial and temporal distribution information on the soil properties. However, this has not yet been studied. Therefore, the spatial autocorrelation of soil chemical and biological properties was examined in the Pingdingshan coal mining area during four seasons in China from 80 collected and analyzed soil samples. The spatial autocorrelation was determined by the global and local Moran’s indices. Spatial clustering and variability were analyzed with ArcGIS 10.2 (Environmental Systems Research Institute, ESRI, USA) and GeoDa 7.0 software (Center for Spatial Data Science in University of Chicago, USA). Soil pH and the soil total carbon (TC) content, soil total nitrogen (TN) content, soil available phosphorus (AP) content, and soil alkali-hydrolyzable nitrogen (AN) content were measured as soil chemical properties. The soil biological properties included the quantities of bacteria, actinomycetes, and fungi. The soil chemical and biological properties showed a strong spatial autocorrelation during the four seasons. “High-high” and “low-low” were the dominant autocorrelation types that were distributed in serious and light subsidence areas. The soil properties decreased as the mining subsidence degree increased. Both the soil TC and soil AP contents were highest in spring, the soil TN content was highest in autumn, and the soil AN content was highest in summer. These results provide a reliable theoretical reference for land reclamation and ecological restoration in mining areas.

Coronavirus disease 2019 risk assessment of adults and geospatial mapping in selected communities of Southwest Nigeria.

Coronavirus disease 2019 (COVID-19) remained a worldwide public health problem. Risk assessment and mapping can be deployed to assist in the control and management of disease outbreaks. The aim of this study was to conduct COVID-19 risk assessment and mapping in selected communities of Southwest Nigeria. This was a cross-sectional study of adults, 18 years and above, involving the use of multi-stage sampling. Data collection was done with a pre-tested, structured, interviewer-administered questionnaire. The Statistical Package for the Social Sciences version 23 and Environmental Systems Research Institute ArcGIS desktop version 10.5 were used for data analysis and spatial mapping, respectively. The threshold for statistical significance was set at P < 0.05. The respondents' mean age was 40.6 ± 14.5 years. Self-reported vulnerability factors identified included hypertension, diabetes mellitus, working in hospital facility, cigarette smoking and age ≥60 years amongst others. About a quarter (20.2%) had a high risk of COVID-19 following risk quantification. The risk cuts across geographical locations and socio-economic status. Education was significantly associated with COVID-19 risk. The spatial interpolation map revealed that the farther a community was from the high-burden area, the lower the risk of COVID-19. There was a high prevalence of self-reported COVID-19 risk. Identified communities with COVID-19 high-risk burden in the risk mapping and those with stratified proximity to these areas need to be targeted by the government for a public health awareness campaign.

Intelligent Analysis Cloud Platform for Soil Moisture-Nutrients-Salinity Content Based on Quantitative Remote Sensing

Quickly obtaining accurate soil quality information is the premise for accurate agricultural production and increased crop yield. With the development of the digital information industry, smart agriculture has become a new trend in agricultural development and there is increasing demand for efficiently and intelligently acquiring good soil quality information. Scientists worldwide have developed many remote sensing quantitative inversion models, which need to be systematized and intelligent for agricultural personnel to enjoy the dividends of information technology such as 3S (remote sensing, geographic information system, and global navigation satellite system) techniques. Accordingly, to meet the need of farmers, agricultural managers, and agricultural researchers to acquire timely information on regional soil quality, in this paper, we designed a cloud platform for inversion analysis of moisture, nutrient, salinity, and other important soil quality indicators. The platform was developed using ArcGIS (The software is produced by the Environmental Systems Research Institute, Inc. of America in Redlands, CL, USA) and GeoScene (The software is produced by GeoScene Information Technology Co.,Ltd., Beijing, China) software, with Java and JavaScript as programing languages and SQL Server as the database management system with a PC client, a web client, and a mobile app. On the basis of the existing quantitative remote sensing models, the platform realizes mapping functions, intelligent inversion of soil moisture–nutrient–salinity (SMNS) content, data analysis mining, soil knowledge base, platform management, and so on. It can help different users acquire, manage, and analyze data and make decisions based on the data. In addition, the platform can customize model parameters according to regional characteristics, improving analysis accuracy and expanding the application area. Overall, the platform employs 3S techniques, Internet technology, and mobile communication technology synthetically and realizes intelligent inversion and decision analysis of significant soil quality information, such as moisture–nutrient–salinity content. This platform has been applied to the analysis of soil indicators in several areas and has produced good operational results and benefits. This study will enable rapid data analysis and provide technical support for regional agriculture production, contributing to the development of smart agriculture.

Geospatial analysis of patients' social determinants of health for health systems science and disparity research.

Geospatial analysis of patients' social determinants of health for health systems science and disparity research.

Assessment of Implementing Land Use/Land Cover LULC 2020-ESRI Global Maps in 2D Flood Modeling Application

Floods are one of the most dangerous water-related risks. Numerous sources of uncertainty affect flood modeling. High-resolution land-cover maps along with appropriate Manning’s roughness values are the most significant parameters for building an accurate 2D flood model. Two land-cover datasets are available: the National Land Cover Database (NLCD 2019) and the Land Use/Land Cover for Environmental Systems Research Institute (LULC 2020-ESRI). The NLCD 2019 dataset has national coverage but includes references to Manning’s roughness values for each class obtained from earlier studies, in contrast to the LULC 2020-ESRI dataset, which has global coverage but without an identified reference to Manning’s roughness values yet. The main objectives of this study are to assess the accuracy of using the LULC 2020-ESRI dataset compared with the NLCD 2019 dataset and propose a standard reference to Manning’s roughness values for the classes in the LULC 2020-ESRI dataset. To achieve the research objectives, a confusion matrix using 548,117 test points in the conterminous United States was prepared to assess the accuracy by quantifying the cross-correspondence between the two datasets. Then statistical analyses were applied to the global maps to detect the appropriate Manning’s roughness values associated with the LULC 2020-ESRI map. Compared to the NLCD 2019 dataset, the proposed Manning’s roughness values for the LULC 2020-ESRI dataset were calibrated and validated using 2D flood modeling software (HEC-RAS V6.2) on nine randomly chosen catchments in the conterminous United States. This research’s main results show that the LULC 2020-ESRI dataset achieves an overall accuracy of 72% compared to the NLCD 2019 dataset. The findings demonstrate that, when determining the appropriate Manning’s roughness values for the LULC 2020-ESRI dataset, the weighted average technique performs better than the average method. The calibration and validation results of the proposed Manning’s roughness values show that the overall Root Mean Square Error (RMSE) in depth was 2.7 cm, and the Mean Absolute Error (MAE) in depth was 5.32 cm. The accuracy of the computed peak flow value using LULC 2020-ESRI was with an average error of 5.22% (2.0% min. to 8.8% max.) compared to the computed peak flow values using the NLCD 2019 dataset. Finally, a reference to Manning’s roughness values for the LULC 2020-ESRI dataset was developed to help use the globally available land-use/land-cover dataset to build 2D flood models with an acceptable accuracy worldwide.

Performance evaluation of pansharpening Sentinel 2A imagery for informal settlement identification by spectral-textural features

The diversity of informal settlement morphologies across locales makes their mapping inherently challenging in heterogeneous urban landscapes. The aim of this study was to evaluate the potential of pansharpening techniques on Sentinel 2A data, and textural features, in enhancing informal settlement identification accuracy in a fragmented urban environment. Brovey transform, intensity, hue and saturation transform, Environmental Systems Research Institute (ESRI), simple mean, and Gram–Schmidt techniques were employed to pansharpen multispectral bands of Sentinel 2A, bands 5, 6, and 7 in the first group, and bands 8A, 11 and 12 in another, using an average of bands 4 and 8 as the panchromatic band. The main objective was to investigate the efficacy of pansharpening Sentinel 2A imagery and texture analysis in automated mapping of morphologically varied informal settlements. An evaluation of the quality of fused images was undertaken through computation of the correlation between the spectral values of the original multispectral and pansharpened image. Grey-level-co-occurrence matrix texture features were extracted from the pansharpened images, and subsequently incorporated in the classification process, using a support vector machine classifier. Our results confirm that the Gram–Schmidt fusion technique yielded the highest informal settlement identification accuracy (F-score 95.2%; overall accuracy 91.8%). The experimental results demonstrated the potential of pansharpening Sentinel 2A, and the added value of image texture for a more nuanced characterisation of informal settlements.

Designing, generating, and translating deep-ocean observations for and with international policy makers

Abstract Deep-ocean observing is essential for informing policy making in the arenas of climate, biodiversity, fisheries, energy and minerals extraction, pollution, hazards, and genetic resources. The Deep Ocean Observing Strategy (DOOS), a UN Ocean Decade endorsed programme, is meeting with representatives from relevant international bodies and agreements to strengthen their interface with the deep-ocean science community, ensure that deep observing is responsive to societal needs, identify points of entry for science in policy making, and to develop relevant products for broad use. DOOS collaboration with the Environmental Systems Research Institute (Esri) facilitates this co-design. A DOOS policy liaison team is being formed to link the contacts, voices, and messaging of multiple deep-ocean networks and organizations in reaching international policy makers. The UN Ocean Decade will help to gain the ear of target communities, scale communication channels appropriately, minimize duplicative efforts, maximize limited resources, and organize inclusive and equitable public and private partners in deep-ocean science and policy.

Evaluating Effects of Post-Fire Climate and Burn Severity on the Early-Term Regeneration of Forest and Shrub Communities in the San Gabriel Mountains of California from Sentinel-2(MSI) Images

Studying the early changes in post-fire vegetation communities may improve the overall resilience of forests. The necessity for doing so was demonstrated by the Bobcat Fire, which seriously threatened the central San Gabriel Mountains and the Angeles National Forest in California. This study aimed to monitor and quantify the effects of climatological and topographic conditions along with burn severity on early (within 1 year) post-fire forests and shrubs community regeneration. In this study, we used Sentinel-2(MSI) intensive time-series imagery (July 2020–October 2021) to make a confusion matrix combined with 389 vegetation sample points on Google Earth Pro. The overall accuracy (OA) and the Kappa coefficient, calculated from the confusion matrix, were used as evaluation parameters to validate the classification results. With multiple linear regression models and Environmental Systems Research Institute (ESRI) historical images, we analyzed the effects of climate and slope aspects on the regeneration of post-fire forest and shrub communities. We also quantitatively analyzed the regeneration rates based on five burn severity types. The results show that the normalized burning rate (NBR) was the most accurate vegetation classification indicator in this study (OA: 92.3–99.5%, Kappa: 0.88–0.98). The vegetation classification accuracy based on SVM is about 6.6% higher than K-Means. The overall accuracy of the burn area is 94.87%. Post-fire climate factors had a significant impact on the regeneration of the two vegetation communities (R2: 0.42–0.88); the optimal regeneration slope was 15–35°; and the fire severity changed the original competition relationship and regeneration rate. The results provide four main insights into the regeneration of post-fire vegetation communities: (1) climate factors in the first regenerating season have important impacts on the regeneration of forest and shrub communities; (2) daytime duration and rainfall are the most significant factors for forests and shrubs regeneration; (3) tolerable low burn severity promotes forests regeneration; and (4) forests have a certain ability to resist fires, while shrubs can better tolerate high-intensity fire ecology. This study could support the implementation of strategies for regionalized forest management and the targeted enhancement of post-fire vegetation community resilience.

The Effects of Lifestyle on COVID-19 Vaccine Hesitancy in the United States: An Analysis of Market Segmentation.

The aim of this study is to correlate lifestyle characteristics to COVID-19 vaccination rates at the U.S. County level and provide where and when COVID-19 vaccination impacted different households. We grouped counties by their dominant LifeMode, and the mean vaccination rates per LifeMode are calculated. A 95% confidence interval for both the mean and median vaccination rate for each LifeMode is generated. The limits of this interval were compared to the nationwide statistics to determine whether each LifeMode’s vaccine uptake differs significantly from the nationwide average. We used Environmental Systems Research Institute Inc. (ESRI) Tapestry LifeModes data that are collected at the U.S. household level through geodemographic segmentation typically used for marketing purposes. High risk Lifestyle segments and their locations are clearly the areas in the U.S. where the public might benefit from a COVID-19 vaccine. We then used logistic regression analysis to predict vaccination rates using ESRI’s tapestry segmentation and other demographic variables. Our findings demonstrate that vaccine uptake appears to be highest in the urban corridors of the Northeast and the West Coast and in the retirement communities of Arizona and Florida and lowest in the rural areas of the Great Plains and Southeast. Looking closely at other parts of the West such as the Dakotas and Montana, counties that contain Native American reservations have higher vaccination rates. Racial/ethnic minorities also adopt the vaccine at higher rates. The most effective predictor of vaccination hesitancy was Republican voting habits, with Republican counties less likely to take the vaccine. The other predictors in order of importance were college education, minority race/ethnicity, median income, and median age. Our approach correlating lifestyle characteristics to COVID-19 vaccination rate at the U.S. County level provided unique insights into where and when COVID-19 vaccination impacted different households. The results suggest that prevention and control policies can be implemented to those specific households.

COVID-19 Variant Surveillance and Social Determinants in Central Massachusetts: Development Study.

BackgroundPublic health scientists have used spatial tools such as web-based Geographical Information System (GIS) applications to monitor and forecast the progression of the COVID-19 pandemic and track the impact of their interventions. The ability to track SARS-CoV-2 variants and incorporate the social determinants of health with street-level granularity can facilitate the identification of local outbreaks, highlight variant-specific geospatial epidemiology, and inform effective interventions. We developed a novel dashboard, the University of Massachusetts’ Graphical user interface for Geographic Information (MAGGI) variant tracking system that combines GIS, health-associated sociodemographic data, and viral genomic data to visualize the spatiotemporal incidence of SARS-CoV-2 variants with street-level resolution while safeguarding protected health information. The specificity and richness of the dashboard enhance the local understanding of variant introductions and transmissions so that appropriate public health strategies can be devised and evaluated.ObjectiveWe developed a web-based dashboard that simultaneously visualizes the geographic distribution of SARS-CoV-2 variants in Central Massachusetts, the social determinants of health, and vaccination data to support public health efforts to locally mitigate the impact of the COVID-19 pandemic.MethodsMAGGI uses a server-client model–based system, enabling users to access data and visualizations via an encrypted web browser, thus securing patient health information. We integrated data from electronic medical records, SARS-CoV-2 genomic analysis, and public health resources. We developed the following functionalities into MAGGI: spatial and temporal selection capability by zip codes of interest, the detection of variant clusters, and a tool to display variant distribution by the social determinants of health. MAGGI was built on the Environmental Systems Research Institute ecosystem and is readily adaptable to monitor other infectious diseases and their variants in real-time.ResultsWe created a geo-referenced database and added sociodemographic and viral genomic data to the ArcGIS dashboard that interactively displays Central Massachusetts’ spatiotemporal variants distribution. Genomic epidemiologists and public health officials use MAGGI to show the occurrence of SARS-CoV-2 genomic variants at high geographic resolution and refine the display by selecting a combination of data features such as variant subtype, subject zip codes, or date of COVID-19–positive sample collection. Furthermore, they use it to scale time and space to visualize association patterns between socioeconomics, social vulnerability based on the Centers for Disease Control and Prevention’s social vulnerability index, and vaccination rates. We launched the system at the University of Massachusetts Chan Medical School to support internal research projects starting in March 2021.ConclusionsWe developed a COVID-19 variant surveillance dashboard to advance our geospatial technologies to study SARS-CoV-2 variants transmission dynamics. This real-time, GIS-based tool exemplifies how spatial informatics can support public health officials, genomics epidemiologists, infectious disease specialists, and other researchers to track and study the spread patterns of SARS-CoV-2 variants in our communities.

Accuracy assessment of land use land cover LULC 2020 (ESRI) data in Con Dao island, Ba Ria – Vung Tau province, Vietnam

Managing land use in an efficient way results in food security and economic development. Recently, the Environmental Systems Research Institute (ESRI) announced a new product, LULC 2020 (ESRI), that has been produced by the classification with Artificial Intelligence (AI) technique of satellite data at 10-meter resolution. This data has global coverage and is freely available on the internet, but the questions are how good the data is and how to apply it to local practice and land use system. This study aims to answer those questions by emphasizing the accuracy assessment of the LULC 2020 (ESRI) data. The systematic samples of 281 test sites and the Geographic Information Systems (GIS) analysis have been conducted by using the Confusion matrix method in comparison with the visualization of high-resolution data from Google Earth images and field survey data. Topographic information data such as Digital Elevation Model (DEM) and slope data have been integrated for achieving better data accuracy, with overall accuracy increasing from 79.72% (Overall Kappa Statistic 0.421) to 84.34% (Overall Kappa Statistic 0.589). The improved land use types are hilly non-forest, other agricultural, and flat unused types that are typical for the local land use classification system. The study provides a new data processing method to apply LULC 2020 (ESRI) for land use mapping in Con Dao island, Ba Ria - Vung Tau province in the East Sea of Vietnam.

Comparison of Three Ten Meter Land Cover Products in a Drought Region: A Case Study in Northwestern China

The ecological and natural conditions in drought regions are harsh. Water shortages and land desertification are prominent features of these regions. The land cover in these regions has a large impact on global climate change, as well as on ecological protection and construction. To make rational and sustainable use of land resources, it is crucial to quickly grasp the accuracy and spatial distribution differences of multi-source remote sensing land cover products in drought regions. Therefore, taking northwestern China as the study area, in this study, the accuracy and spatial pattern distribution differences of three high-resolution (10 m) land cover products, namely, the Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC), European Space Agency (ESA), and Environmental Systems Research Institute (ESRI) products, were compared and analyzed via area composition similarity, spatial pattern consistency, and absolute accuracy assessment for three validation samples. The results show that the FROM-GLC product had the highest overall accuracy, ranging from 53.81% to 73.45%. The ESRI product had the lowest overall accuracy, ranging from 35.90% to 64.16%. The spatial consistencies of the three products were low, accounting for 46.26% of the total area, and they were mostly distributed in a single area (mainly bare land and forest). The low accuracy for grassland, bare land, shrubland, and other vegetation types was the primary reason for the large differences between the three products. Future research should focus on improving the mapping accuracy for these vegetation types. Accuracies for water and cropland of the three products were consistent, and, thus, the FROM-GLC, ESA, and ESRI products can be used as auxiliary data in research related to water resources and cropland resources in drought regions.

Trends in Environmental Management Systems Research. A Content Analysis

Abstract Environmental Management Systems (EMS) represent a solid opportunity for companies that voluntarily undertake a path towards the adoption of environmental protection policies. Many studies investigated the link between EMS and improvement of environmental performance, demonstrating how the adoption of an international standard as ISO 14000 or EMAS guarantees the company an improvement in its performance and an increase in transparency towards stakeholders. However, scientific research in this field reflects the strong variability due to the breadth and diversity of sectors in which the two international standards can be adopted. The aim of this work is to analyse, through textual statistics and text mining methods, the trends that characterize scientific production, highlighting most debated topics useful for developing new research perspectives in this research field.

Digital environmental impact assessment: An exploration of emerging digital approaches for non-technical reports

Environmental Impact Assessment (EIA) is a tool for providing systematic assessment to identify environmental, social, and economic impacts of significant developments. Through a series of technical assessments, public-facing reports are produced, including non-technical summaries of larger reports- these documents communicate assessment outcomes to non-technical public stakeholders during consultation. Consultation approaches have stagnated and achieve minimal feedback with a notable age-bias based on low turn-outs at consultation events. Digital technology is being promoted to create a widely accessible, engaging, information source to aid public knowledge and awareness of EIA outcomes. In recent years, progressive digital reporting approaches such as Environmental Systems Research Institute (ESRI) StoryMaps, purpose-built web-solutions and bespoke-built websites, have been adopted by environmental and engineering consultancies to digitalise EIA public-facing reports.The 1998 Aarhus Convention sets out fundamental requirements for public participation within environmental decision-making, requiring inclusivity, accessibility and transparency. Consultation phases are increasingly utilising digitalised reports for their non-technical stakeholder audience, and therefore need to fulfil Aarhus requirements. This study explored the usability of digitalised public-facing documents. Interactive digital reports intended for a non-technical audience, using the interface formats ESRI StoryMaps, purpose-built web solutions and bespoke websites, were focused on due to their increasing use within public EIS reporting in engineering and environmental consultancies operating globally. Three research approaches were employed: a usability test of a purpose-built interactive digital document, interviews with digital EIA practitioners and a comparative cartographic and interface feature review. We found evidence of a digital divide between age groups and ability to effectively utilise a precedent digital report- with older users (aged 66+) less able to access information and avoid errors. Digital literacy on its own was not a reliable indicator of a user's ability to navigate a digital environment but is likely compounded by other factors such as cognitive function among older users. Practitioners saw opportunities in digitalised reports but recognised limitations in their value to some users. Current digital reporting practice presents initial usability issues, indicating direction for essential development towards a receptor-based, personalised, report.