Assessment of COVID-19 lockdown impact on surface water quality using remote sensing techniques in Raipur, Chhattisgarh, India

Due to improper management, industrialization and urbanization resulted in poorer surface and river water quality flowing through the city. Still, complete lockdown in the country resulted in improved surface water quality. Hence, a study has been performed to analyze these changes held during COVID-19 lockdown using a combination of different parameters derived from spatial data. The study includes analyses of significant water bodies, surface water bodies through out the city;the survey has proven that the lockdown situation that occurred due to the pandemic has resulted in improved water quality which has been determined based on water bodies analysis done for 12 major water bodies, and by the study performed it has been observed that the area of the nonturbid water has increased by 0.148 sq. km after the lockdown situation occurred. The study will be helpful to assess the impacts of lockdown on water bodies to take the sustainable measures which can be taken shortly for the improved regulation of pollutants and other contaminants based on positive effects on the surface water quality. © 2023 Elsevier Inc. All rights reserved.

Measures of Greenspace Exposure and Their Association to Health-Related Outcomes for the Periods before and during the 2020 Lockdown: A Cross-Sectional Study in the West of England

Greenspaces are argued to be one of the important features in the urban environment that impact the health of the population. Previous research suggested either positive, negative, or no associations between greenspaces and health-related outcomes. This paper takes a step backward to, first, explore different quantitative spatial measures of evaluating greenspace exposure, before attempting to investigate the relationship between those measures and health-related outcomes. The study uses self-reported health data from an online cross-sectional survey conducted for residents in the West of England. This yielded data of greenspace use, physical activity, wellbeing (ICECAP-A score), and connectedness to nature for 617 participants, divided into two sets: health outcomes for the period before versus during the 2020 lockdown. The study uses the participants' postcodes (provided in the survey) to calculate eleven spatial measures of greenspace exposure using the software ArcGIS Pro 2.9.5. A total of 88 multivariate regression models were run while controlling for eleven confounders of the participants' characteristics. Results inferred 57 significant associations such that six spatial measures of greenspace exposure (NDVI R200m, NDVI R300m, NDVI R500m, Network Distance to nearest greenspace access, Euclidean Distance to nearest greenspace access, and Euclidean Distance to nearest 0.5 ha doorstep greenspace access) have significant association to at least one of the four health-related outcomes, suggesting a positive impact on population health when living in greener areas or being closer to greenspaces. Moreover, there are further significant associations between the frequency of use of greenspaces and increasing physical activity or feeling more connected to nature. Still, the residents' patterns of using greenspaces significantly changed during versus before lockdown and has impacted the relationships between health outcomes and the greenspace exposure measures.

SURFACE URBAN HEAT ISLAND IN MOSCOW DURING THE COVID-19 PANDEMIC LOCKDOWN IN 2020

The influence of the COronaVIrus Disease 2019 (COVID-19) pandemic lockdown (the period of strict quarantine measures) in the spring of 2020 on the ‘Surface Urban Heat Island' (SUHI) geographical phenomenon in Moscow has been studied. For this purpose, we used the measurements of the surface temperature TS made by Moderate Resolution Imaging Spectroradiometer (MODIS) radiometer installed on Terra and Aqua satellites. As a result, TS during the 2020 lockdown, both in the city and surrounding rural zone, was found lower than at the same calendar time in the previous 20 years due to the relatively cold spring. The SUHI intensity as the difference between TS inside Moscow and the surrounding rural zone around it during the lockdown was also lower than usual (on average in the previous 20 years), but this decrease is relatively small and nonsignificant. The Normalized Difference Vegetation Index (NDVI) in Moscow and Moscow region during the lockdown was close to its usual values, but the leaf area index (LAI) was significantly lower than its average values in the previous 20 years. Thus, the weakening of the SUHI during the lockdown in 2020 was caused mostly by lower heat loss due to transpiration in the rural zone. This was associated with the slowdown in vegetation development as a result of the cold spring. Besides, an additional possible reason was the reduction of human activity due to the collapse of many anthropogenic heat sources in the city. According to long-term MODIS data, the SUHI intensity in Moscow and the surface temperature in Moscow region, as well as the NDVI and LAI values, do not demonstrate statistically significant long-term trends in the spring season over the past 21 years, despite climate changes. In spring, during faster snow melting in cities, when it still persists in the rural zone, the SUHI intensity can be record high (up to 8 ºC). © 2022, Russian Geographical Society. All rights reserved.

COVID-19 lockdown effect on land surface temperature and normalized difference vegetation index

Coronavirus disease (COVID-19) has changed the human lifestyle just like a disaster in 2020. Many people died throughout the world due to its severe attack. Lockdown is the most common term used in today's life to prevent the adverse effect of COVID-19. However, during the lockdown period, a significant improvement in the urban environment was noticed in almost every part of the world. During the lockdown period, the decrease in the number of running vehicles and moving people on the road lowers the pollution level and it has a direct positive impact on the urban environment. The study examines the changes found in land surface temperature (LST) and normalized difference vegetation index (NDVI) during the lockdown period in Raipur city, India with the earlier periods (2013-19) to compare the environmental status. The results indicate that the LST is reduced and NDVI is increased significantly during the lockdown period, and the negativity of the LST-NDVI correlation is increased remarkably. The study also shows a better ecological status of the city during the lockdown period. The study is useful for environmental strategists and urban planners.

Natural land cover positively correlates with COVID-19 health outcomes.

BACKGROUND: The coronavirus disease 2019 (COVID-19) poses special challenges for societies, as the disease causes millions of deaths. Although the direct prevention measures affect the prevalence and mortality the most, the other indirect factors, including natural environments and economics, could not be neglected. Evaluating the effect of natural land cover on COVID-19 health outcomes is an urgent and crucial public health topic. METHODS: Here, we examine the relationships between natural land cover and the prevalence and mortality of COVID-19 in the United States. To probe the effects of long-term living with natural land cover, we extract county-level land cover data from 2001 to 2019. Based on statistically spatial tests, we employ the Spatial Simultaneous Autoregressive (SAC) Model to estimate natural land cover's impact and monetary values on COVID-19 health outcomes. To examine the short-term effects of natural environments, we build a seasonal panel data set about the greenery index and COVID-19 health outcomes. The panel SAC model is used to detect the relationship between the greenery index and seasonal COVID-19 health outcomes. RESULTS: A 1% increase in open water or deciduous forest is associated with a 0.004-death and 0.163-conformed-case, or 0.006-death and 0.099-confirmed-case decrease in every 1,000 people. Converting them into monetary value, for the mortality, a 1% increase in open water, deciduous forest, or evergreen forest in a county is equivalent to a 212-, 313-, or 219-USD increase in household income in the long term. Moreover, for the prevalence, a 1% change in open water, deciduous forest, or mixed forest is worth a 382-, 230-, or 650-USD increase in household income. Furthermore, a rational development intensity is also critical to reduce the risk of the COVID-19 pandemic. More greenery in the short term is also linked to lower prevalence and mortality. CONCLUSIONS: Our study underscores the importance of incorporating natural land cover as a means of mitigating the risks and negative consequences of future pandemics like COVID-19 and promoting overall public health.

Impact of COVID lockdowns on spatio-temporal variability in land surface temperature and vegetation index.

In urban areas, industrial and human activities are the prime cause that exacerbates the heating effects, also called the urban heat island (UHI) effect. The land surface temperature (LST), normalized difference vegetation index (NDVI), and the proportion of vegetation (Pv) are indicators of measurement of the heating/urbanization effects. In the present work, we investigated the impact of the COVID-19 lockdown, i.e., restricted human activities. We used Landsat-8 OLI/TIRS (level 1) data to investigate spatial and temporal heterogeneity changes in these urbanization indicators during full and partial lockdown periods in 2020 and 2021, with 2019 as the base year. We have selected three cities in India's eastern coal mining belt, Bokaro, Dhanbad, and Ranchi, for the study. Results showed a significant decrease in LST values over all sites, with a maximum reduction over mining sites, i.e., Bokaro and Dhanbad. The LST value decreased by about 13-19% during the lockdown period. Vegetation indices (i.e., NDVI and Pv) showed a substantial increase of about 15% overall sites. With decreased LST values and increased NDVI values, these quantities' correlations became more negative during the lockdown period. More positive changes are noticed over mining sites than non/less mining sites. This indirectly indicates the reduction in the heat-absorbing particles in the environment and surface of these sites, a possible cause for the reduction in LST values substantially. Reduction in coal particles at the land and vegetation surface likely contributed to decreased LST and enhanced vegetation indices. To check the statistical significance of changes in the UHI indicators in the lockdown period, statistical tests (ANOVA and Tukey's test) are performed. Results indicate that most of the case changes have been significant. The study's finding suggests the lockdown's positive impact on the heating/UHI effects. It emphasizes the need for planned lockdowns as city mitigation strategies to overcome pollution and environmental issues.

Association of greenness with COVID-19 deaths in India: An ecological study at district level.

BACKGROUND: The world has witnessed a colossal death toll due to the novel coronavirus disease-2019 (COVID-19). A few environmental epidemiology studies have identified association of environmental factors (air pollution, greenness, temperature, etc.) with COVID-19 incidence and mortality, particularly in developed countries. India, being one of the most severely affected countries by the pandemic, still has a dearth of research exploring the linkages of environment and COVID-19 pandemic. OBJECTIVES: We evaluate whether district-level greenness exposure is associated with a reduced risk of COVID-19 deaths in India. METHODS: We used average normalized difference vegetation index (NDVI) from January to March 2019, derived by Oceansat-2 satellite, to represent district-level greenness exposure. COVID-19 death counts were obtained through May 1, 2021 (around the peak of the second wave) from an open portal: covid19india.org. We used hierarchical generalized negative binomial regressions to check the associations of greenness with COVID-19 death counts. Analyses were adjusted for air pollution (PM2.5), temperature, rainfall, population density, proportion of older adults (50 years and above), sex ratio over age 50, proportions of rural population, household overcrowding, materially deprived households, health facilities, and secondary school education. RESULTS: Our analyses found a significant association between greenness and reduced risk of COVID-19 deaths. Compared to the districts with the lowest NDVI (quintile 1), districts within quintiles 3, 4, and 5 have respectively, around 32% [MRR = 0.68 (95% CI: 0.51, 0.88)], 39% [MRR = 0.61 (95% CI: 0.46, 0.80)], and 47% [MRR = 0.53 (95% CI: 0.40, 0.71)] reduced risk of COVID-19 deaths. The association remains consistent for analyses restricted to districts with a rather good overall death registration (>80%). CONCLUSION: Though cause-of-death statistics are limited, we confirm that exposure to greenness was associated with reduced district-level COVID-19 deaths in India. However, material deprivation and air pollution modify this association.

Mapping Changes in Particulate Pollution and Plant Health in the Indian Sub-continent during the COVID-19 Pandemic

Particulate matter (PM) air pollution is ranked the 13th leading cause of mortality across the globe. Lockdowns during the COVID-19 pandemic provided a unique opportunity to assess the potential beneficial impact on air quality and possibly biologic outcomes. The main objectives of this project were to utilize NASA satellite-derived data and: 1) Observe changes in PM2.5 across the four countries before the outbreak of COVID-19 and through the observed case peak months in 2020 and 2021;2) Examine changes in normalized difference vegetation index (NDVI) during, around, and subsequent to COVID-19 peak months;3) Evaluate changes in precipitation and land surface temperature as other potential contributors to changes in plant health. Remote sensing datasets included "Aerosol Optical Thickness” to measure air pollution, and "Normalized Difference Vegetation Index” to examine vegetation health. We found that PM2.5 concentration substantially decreased in some areas of the sub-continent, during the peak months, while NDVI improved. While accompanying precipitation and land surface temperature may account for some of the changes in NDVI, they alone cannot explain the improvement in plant health during shutdowns. Thus, at least some of the decreased plant stress may be attributed to lower emission of atmospheric pollutants, including PM2.5. © 2023 by authors, all rights reserved.

COVID-19 infection rate but not severity is associated with availability of greenness in the United States.

Human exposure to greenness is associated with COVID-19 prevalence and severity, but most relevant research has focused on the relationships between greenness and COVID-19 infection rates. In contrast, relatively little is known about the associations between greenness and COVID-19 hospitalizations and deaths, which are important for risk assessment, resource allocation, and intervention strategies. Moreover, it is unclear whether greenness could help reduce health inequities by offering more benefits to disadvantaged populations. Here, we estimated the associations between availability of greenness (expressed as population-density-weighted normalized difference vegetation index) and COVID-19 outcomes across the urban-rural continuum gradient in the United States using generalized additive models with a negative binomial distribution. We aggregated individual COVID-19 records at the county level, which includes 3,040 counties for COVID-19 case infection rates, 1,397 counties for case hospitalization rates, and 1,305 counties for case fatality rates. Our area-level ecological study suggests that although availability of greenness shows null relationships with COVID-19 case hospitalization and fatality rates, COVID-19 infection rate is statistically significant and negatively associated with more greenness availability. When performing stratified analyses by different sociodemographic groups, availability of greenness shows stronger negative associations for men than for women, and for adults than for the elderly. This indicates that greenness might have greater health benefits for the former than the latter, and thus has limited effects for ameliorating COVID-19 related inequity. The revealed greenness-COVID-19 links across different space, time and sociodemographic groups provide working hypotheses for the targeted design of nature-based interventions and greening policies to benefit human well-being and reduce health inequity. This has important implications for the post-pandemic recovery and future public health crises.

How agricultural economists are using big data: a review

Purpose>The purpose of this paper is to describe the main ways in which large amounts of information have been integrated to provide new measures of food consumption and agricultural production, and new methods for gathering and analyzing internet-based data.Design/methodology/approach>This study reviews some of the recent developments and applications of big data, which is becoming increasingly popular in agricultural economics research. In particular, this study focuses on applications of new types of data such as text and graphics in consumers' online reviews emerging from e-commerce transactions and Normalized Difference Vegetation Index (NDVI) data as well as other producer data that are gaining popularity in precision agriculture. This study then reviews data gathering techniques such as web scraping and data analytics tools such as textual analysis and machine learning.Findings>This study provides a comprehensive review of applications of big data in agricultural economics and discusses some potential future uses of big data.Originality/value>This study documents some new types of data that are being utilized in agricultural economics, sources and methods to gather and store such data, existing applications of these new types of data and techniques to analyze these new data.

Monitoring of Black Pepper Growth at Different Elevation using Ground Data and NDVI Time Series

Implementation of remote sensing in agriculture helps to enhance crop growth monitoring especially during the Covid-19 pandemic. To enhance black pepper growth condition, a study was conducted at two study sites in Bintulu, Sarawak. Hence, this study aims (i) to construct a black pepper growth monitoring at different levels of elevation in Suka Farm (SF) and Taime Farm (TF);and (ii) to integrate limited ground data and NDVI time series from Landsat 8OLI for black pepper growth monitoring. Elevation maps were generated using Natural Neighbor (NN) based on the ground data analysed using ArcGIS 10.4 Software. Three elevation levels were classified into the lower, middle, and upper levels. Observational ground data and NDVI time series of Landsat 8 OLI were calculated using SAS 9.4 software. All parameters then correlating with the elevation levels using Pearson Correlation Coefficient. Optimum growth of black pepper growth in SF and TF was identified at an elevation range between 39m–50m. The NDVI time series also indicated equivalent results as the ground data. This study proposed that the elevation of an area gives a significant impact on black pepper growth. Besides, the NDVI time series of Landsat 8 OLI was feasible for monitoring black pepper growth.

Spatial effect of anti-COVID measures on land surface temperature (LST) in urban areas: A case study of a medium-sized city

This case study investigates the magnitude and nature of the spatial effect generated by the anti-COVID measures on land surface temperature (LST) in the city of Targu Mures (Marosvasarhely), Romania. The measures were taken by the Romanian government during the state of emergency (March 16 - May 14, 2020) due to the SARS-CoV-2 coronavirus pandemic. The study shows that - contrary to previous studies carried out on cities in China and India in most of the urban areas of Marosvasarhely LST has increased in the period of health emergency in 2020 concerning the large average of the years 2000-2019. Remote sensing data from the MODIS and the Landsat satellites show. that MODIS data, having a moderate spatial (approximately 1 km) but good temporal resolution (daily measurements), show a temperature increase of +0.78 degrees C, while Landsat data, having better spatial (30 m) but lower temporal resolution, show an even greater increase, +2.36 degrees C in the built-up areas. The difference in temperature increase is mainly due to the spatial resolution difference between the two TIR band sensors. The LST anomaly analysis performed with MODIS data also shows a positive anomaly increase of 1 degrees C. However, despite this increase, with the help of the hotspot-coldspot analysis of the Getis-Ord Gi* statistic we were able to identify 46 significant coldspots that showed a 1- 2 degrees C decrease of LST in April 2020 compared to the average of the previous years in April. Most of these coldspots correspond to factory areas, public transport epicenters, shopping centers, industrial polygons. and non-residential areas. This shows that anti-COVID measures in the medium-sized city of Marosvasarhely had many effects on LST in particular areas that have links to the local economy, trade. and transport. Paired sample t-test for areas identified with LST decrease shows that there is a statistically significant difference in the average LST observed before and after anti-COVID measures were applied. MODIS-based LST is satisfactory for recognizing patterns and trends at large or moderate geographical scales. However, for a hotspot-coldspot analysis of the urban heat islands, it is more suitable to use Landsat data.

Trends in the Environmental Conditions, Climate Change and Human Health in the Southern Region of Ukraine

The Kherson, Mykolaiv, Odesa, and Zaporizhzhia oblasts, being adjusted to the coasts of the Black and Azov Seas, are located in the steppe zone and constitute the southern region of Ukraine. The environmental parameters and health indicators of the population of the region are sensitive to the impact of natural (e.g., climate change) and anthropogenic processes. An analysis of satellite remote sensing data (NOAA NDVI time series) for the assessment of vegetation condition demonstrates an increase in frequency and duration of drought events in the region during the last few decades. It may have a relation to climate change processes. Data analysis of local meteorological observations over the past 100 years proved alterations of some bioclimatic indexes. The Equivalent Effective Temperature (IEET) increases in winter and summer (due to the increasing repeatability of high anomalous temperatures) and remains stable in spring and autumn seasons. The increasing number and variability of climate anomalies can provoke an increase in cardiovascular and some other diseases in the local population. At the same time, an analysis of the statistical data of health indicators of the population (such as morbidity of digestion, breathing, and the endocrine and circulatory systems) shows a tendency to decrease morbidity (contrary to the indicators of the mountain regions’ population, which have higher values of life expectancy). Interrelations between environmental, climate change, and population health indicators in the Black Sea region are being discussed.

The impact of COVID-19 pandemic on wheat yield in El Sharkia Governorate, Egypt & nbsp;

The Corona pandemic limits human activities at a time when the world is facing challenges in food safety. Wheat is foremost cereal crop grown healthy in Egypt, especially in El Sharkia Governorate. The target of this work is to monitor wheat cultivation in El Sharkia Governorate during COVID-19 pandemic. The Normalized Difference Vegetation Index (NDVI) estimates were made by band 4 and band 5 of Landsat-8 images of 2018, 2019 and 2020. At the start of May 2020 a field visit was made to 50 sites cultivated with wheat to find out their yield and collecting soil samples. The Yield with NDVI was shown to have a strong relationship (R-2 = 0.84). The NDVI maps of 2018, 2019 and 2020 were produced using ENVI 5.3 software. The changes in wheat cultivation during 2018-2020 were analyzed and discussed in detail. Decrement in wheat yield was noticed in 2020 due to the lack of production requirements owing to the pandemic. (C) 2022 National Authority of Remote Sensing & Space Science. Published by Elsevier B.V.& nbsp;& nbsp;

Understanding the influence of COVID-19 induced lockdown on urban thermal environment of Ranchi city, India

The objective of the study is to understand the pattern of land surface temperature (LST) and normalized difference vegetation index (NDVI) developed in Ranchi city during Covid-19-induced lockdown (2020) and its comparison with previous years. The study incorporated Landsat 8 (Operational land imager) data from United States Geological Survey and air temperature and relative humidity data from power.larc.nasa.gov for the years 2017, 2019 and 2020. The results exposed a drastic change in the LST and NDVI pattern of the city. The mean LST of the city during April has declined from 39.80°C in 2017 to 32.38°C in 2020. Similarly, the mean LST of May also declined from 38.41°C in 2017 to 34.84°C in 2020. On the contrary, the city experienced an ascending growth of NDVI from 0.24 to 0.26 in April and May 2017 to 0.349 and 0.37 in 2020, respectively. Additionally, the city portrays declining air temperature with enhanced relative humidity. Ranchi city also exhibited relatively maximum area under ecologically excellent category in the year 2020 and reduced area under ecologically the worst category based on urban thermal field variance index. Thus, reduced temperature with augmented humidity and NDVI developed a healthy urban environment. [ FROM AUTHOR] Copyright of Geografisk Tidsskrift is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Aventinus Minor Project: Remote Sensing for Archaeological Research in Rome (Italy)

This paper presents the results of a preliminary survey in a central urban area of Rome, Italy. The results were obtained from both desktop and remote sensing surveys. The Aventinus Minor Project (AMP) is a community archaeological excavation project focusing on an understudied area in Rome with limited modern archaeological excavation: the Aventinus Minor or Little Aventine. The remote sensing (RS) anomalies revealed by the survey illustrate that this area is potentially rich in buried structures potentially correlated with ancient visible remains (i.e., the Servian Walls and Santa Balbina church). The application of RS approaches (such as NDVI, VARI, and GPR) and the creation of a GIS platform lays the foundations for a correct and georeferenced reporting of all collected data, providing a nuanced understanding of the urban archaeology in the dense topography of Rome.

Analysis of the relationships between land surface temperature and spectral indices pre-and during pandemic using Landsat-8 images (case study: Gerbangkertosusila)

Gerbangkertosusila (Gresik-Bangkalan-Mojokerto-Surabaya-Sidoarjo-Lamongan) is one of the biggest metropolitan areas in Indonesia impacted hardest by COVID-19 after social restriction. High temperature conditions are an issue in the Gerbangkertosusila area. Reduced mobility and industrial activity lead to decrease in surface temperature. The research was carried out using the Statistical Mono Windows (SMW) algorithm in separate periods of time (July 2019, July 2020, October 2020, May 2021) to represent the changes between social restriction policy and the weather. This research goal is to examine the relationship between land surface temperature with changes of spectral indices, such as NDVI (Normalized Difference Vegetation Index) and NDBI (Normalized Difference Built-up Index) data. These three parameters are correlated with a simple linear regression equation to calculate how much influence occurs in each different period, then the qualitative analysis is carried out to explain the variations between the distribution of hotspot and annual temperature chart to the real conditions. The result shows strong positive correlation coefficient between changes of NDBI pixel and the LST in each period of time such as 0.62;0.80;0.70;and 0.80. Meanwhile the NDVI-LST correlation coefficient shows negative results such as-0.57;-0.43;-0.38;-0.41. This research also concludes that in the social restriction period, the Land Surface Temperature doesn't affect the variability of NDVI © 2021 SPIE.

City-level greenness exposure is associated with COVID-19 incidence in China.

Accumulating studies have suggested an important role of environmental factors (e.g. air pollutants) on the occurrence and development of coronavirus disease 2019 (COVID-19). Evidence concerning the relationship of greenness on COVID-19 is still limited. This study aimed to assess the association between greenness and COVID-19 incidence in 266 Chinese cities. A total of 12,377 confirmed COVID-19 cases were identified through February 29th, 2020. We used the average normalized difference vegetation index (NDVI) during January and February 2020 from MOD13A2 product, to represent the city-level greenness exposure. A generalized linear mixed-effects model was used to estimate the association between NDVI exposure and COVID-19 incidence using COVID-19 cases as the outcome. We evaluated whether the association was modified by population density, GDP per capita, and urbanization rate, and was mediated by air pollutants. We also performed a series of sensitivity analyses to discuss the robustness of our results. Per 0.1 unit increment in NDVI was negatively associated with COVID-19 incidence (IRR: 0.921, 95% CI: 0.898, 0.944) after adjustment for confounders. Associations with COVID-19 incidence were stronger in cities with lower population density, lower GDP per capita, and lower urbanization rate. We failed to detect any mediation effect of air pollutants on the association between NDVI and COVID-19 incidence. Sensitivity analyses also indicated consistent estimates. In conclusion, our study suggested a beneficial association between city-level greenness and COVID-19 incidence. We could not establish which mechanisms may explain this relationship.

Academic Incentives Should Not Promote the "Extinction of Nature Experience"

Evidence suggests that a decline in people's exposure to nature corresponds to decreasing support for nature-a phenomenon we call extinction of nature experience. Here, we evaluate three current trends in conservation research and consider if they contribute to a decrease in exposure to nature. We suggest that while using sensors, algorithms, technocentric thinking, conducting meta-analyses, and taking more lab-based approaches all have significant potential to advance conservation goals, they lead to researchers spending less time in the field and an extinction of nature experience. A reduction of researcher field time will mean fewer local field assistants are hired and trained;lower engagement of researchers with ground realities;and a rift in conservation research, planning, and implementation. We suggest that the field of conservation science should balance how it allocates time and rewards to field versus non-field components. If we are not careful, we will select researchers that are distant from the biodiversity itself and the communities that are affecting it locally. Since the pandemic began many researchers were unable to go to their field sites and if care is not taken, the pressures that promote the extinction of nature experience may be promoted by institutions in a post-COVID-19 world.

A protocol of comprehensive assessment of exposure to greenspace in school children.

Evidence on a comprehensive greenspace exposure assessment on primary school children is scarce yet. Therefore, we aimed to assess a comprehensive greenspace exposure on primary school children and their behavioral function. We assessed different aspects of exposure to greenspace as well as behavioral function in 704 primary school children in Sabzevar, Iran, during the COVID-19 pandemic (i.e., 22 September 2020 to 10 March 2021). The greenspace indicators were including Normalized Difference Vegetation Index (NDVI) in 100, 300 and 500m buffers around children's homes based on Landsat 8 images with 30 × 30 m resolution, residential proximity to green space based on the Euclidean distance of the geocoded residential address to (i) the nearest green space of any area and (ii) the nearest green space with an area of at least 5000m2 (i.e., major green space) based on land use map of the study area, time spent in public and private green spaces, number of plant pots at home and visual access to greenspace based on a prepared questionnaire. The behavioral development of primary school children was assessed using a Persian online validated version of the Strengths and Difficulties Questionnaire (SDQ) filled by parents.