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There is evidence that sacred places across the world are launching augmented reality (AR) applications. This application of AR is somehow prompted by the most recent Covid-19 pandemic where in-person experiences are altered by the virtual. AR, as an innovative technology, augments the physical environment with digitally generated imagery that can generate privileges for tourists in sacred places and become the reason to trigger cultural conflicts and religious controversy. This in-depth interview-based research aims to explore the tourists' views and ideas of applying AR in the Mosque City of Bagerhat of Bangladesh, a UNESCO World Heritage Site in terms of possibilities, cultural conflicts, and religious controversy. Findings show that the application of AR in a sacred place can support tourists in many useful ways, can offer them positive experiences, and help in sustainability concerns of the site. However, the application of AR in a sacred place can be an element of conflicting interests between the religious and general tourists. Adequate attention is thus required from the parties involved in terms of applying AR in the Mosque City of Bagerhat of Bangladesh, a sacred religious site.
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Insights on the indirect effects of the COVID-19 pandemic are critical for designing and implementing policies to alleviate the food security burden it may have caused, and for bolstering rural communities against similar macroeconomic shocks in the future. Yet estimating the causal effects of the pandemic is difficult due to its ubiquitous nature and entanglement with other shocks. In this descriptive study, we combine high-resolution satellite imagery to control for plot-level rainfall with household socio-economic panel data from 2014, 2016, 2019 and 2020, to differentiate the effect of the pandemic from climatic shocks on food security in Morogoro, Tanzania. We find evidence of decreased incomes, increased prices of staple foods, and increased food insecurity in 2020 relative to previous years, and link these changes to the pandemic by asking households about their perceptions of COVID-19. Respondents overwhelmingly attribute economic hardships to the pandemic, with perceived impacts differing by asset level.
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The emergence of COVID-19 pandemic has forced many countries implement social restrictions, including Indonesia. There has been a growing interest in understanding the impact of the pandemic on air quality. This research analyses the air pollution before and after the COVID-19 pandemic in Jakarta and Banjarmasin, Indonesia, with a detailed analysis. It compared the results with previous years to determine the significant improvement in air quality and related weather factors obtained from Landsat 8 and 9 imagery. OMI and MERRA-2 were analysed for PM2.5, NO2, SO2, O3 and WRF-Chem model result especially for PM2.5 against the COVID-19 pandemic. As a result, there was a decrease in PM2.5 during the pandemic year in Jakarta, although it was not as good as in 2016 conditions. In Jakarta and Banjarmasin, PM2.5, NO2 and SO2 decreased in 2021 from 2020, which were in line with the high incidence of COVID-19 in 2021. This shows that more air quality increased in the form of healthy days in DKI Jakarta in 2020 than in 2019. In other words, there was an increase in air quality during the implementation Large-Scale Social Restriction (PSBB) policy in 2020 compared to 2019 before the COVID-19 pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Long-term spatiotemporal Land Use and Land Cover (LULC) analysis is an objective tool for assessing patterns of sustainable development (SD). The basic purpose of this research is to define the Driving Mechanisms (DM) and assess the trend of SD in the Burabay district (Kazakhstan), which includes a city, an agro-industrial complex, and a national natural park, based on the integrated use of spatiotemporal data (STD), economic, environmental, and social (EES) indicators. The research was performed on the GEE platform using Landsat and Random Forest. The DM were studied by Multiple Linear Regression and Principal Component Analysis. SD trend was assessed through sequential transformations, aggregations, and integrations of 36 original STD and EES indicators. The overall classification accuracy was 0.85–0.97. Over the past 23 years, pasture area has changed the most (−16.69%), followed by arable land (+14.72%), forest area increased slightly (+1.81%), and built-up land—only +0.16%. The DM of development of the AOI are mainly economic components. There has been a noticeable drop in the development growth of the study area in 2021, which is apparently a consequence of the COVID-19. The upshots of the research can serve as a foundation for evaluating SD and LULC policy.
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The effects of the COVID-19 pandemic on urban environments are addressed in many recent studies. However, limited research has been conducted to examine the impact of the pandemic on anthropogenic emissions over urban land use types, and their relation to socioeconomic characteristics. Anthropogenic heat, as the main contributor to the urban temperature, is changed by the sudden halt imposed by COVID-19 lockdowns. This study thus focuses on previously under-explored urban thermal environments by quantifying the impact of COVID-19 on urban thermal environments across different land-use types and related socioeconomic drivers in Edmonton, Canada. Using Landsat images, we quantified and mapped the spatial pattern of land surface temperature (LST) for business, industrial, and residential land use areas during both the pandemic lockdown and pre-pandemic periods in the study area. Results show that temperature declined in business and industrial areas and increased in residential areas during the pandemic lockdown. Canadian census and housing price data were then used to identify the potential drivers behind the LST anomaly of residential land use. The most important variables that affected LST during the lockdown were found to be median housing price, visible minority population, postsecondary degree, and median income. This study adds to the expanding body of literature about the impact of the COVID-19 pandemic by providing unique insights into the effect of lockdown on a city's thermal environments across different land use types and highlights critical issues of socioeconomic inequalities, which is useful for future heat mitigating and health equity-informed responses.
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Land use land cover change (LULCC) is among the major factors affecting the natural environment worldwide. Studying LULCC is essential as it contributes to natural resource management, biodiversity conservation, and land use planning, especially during pandemics such as COVID-19. This study aimed at assessing the trend (1995-2021) and magnitude of LULCC in the Burunge WMA ecosystem before (2015-2018) and during COVID-19 (2018-2021). The data on LULCC were collected from the satellite imagery on the USGS website, whereas the data on perceptions of local communities on LULCC from Mwada, Kakoi and Maweni villages were collected through a household questionnaire survey (HQS) of 445 randomly sampled households, focused group discussions (FGDs) and key informant interviews (KIIs). Quantitative data were analyzed using MS Excel 2019, R software (2022.02.0 + 443) and ArcGIS (Version 10.8). Qualitative data were analyzed using content analysis techniques. The findings indicated a fluctuation in agriculture, forest, and water coverage. For instance, agriculture and settlements increased significantly by 23.91% in 2015-2021 and 5.71% in 1995-2005 respectively, whereas forested land showed a maximum increase of 7.33% in 1995-2005. However, there was a pronounced increase in agricultural lands (3.99%) during the COVID-19 phase as compared to the same time frame before the pandemic. Local communities pointed to agriculture and settlements as the major activities contributing to LULCC. The findings show significant LULCC in Burunge WMA which calls for special attention from responsible authorities and other stakeholders for the achievement of biodiversity conservation and the development of livelihoods in the area.
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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.
Subject(s)
COVID-19 , Hot Temperature , Humans , Temperature , Cities , Environmental Monitoring/methods , COVID-19/epidemiology , Communicable Disease Control , UrbanizationABSTRACT
Presence of suspended particulate matter (SPM) in a waterbody or a river can be caused by multiple parameters such as other pollutants by the discharge of poorly maintained sewage, siltation, sedimentation, flood and even bacteria. In this study, remote sensing techniques were used to understand the effects of pandemic-induced lockdown on the SPM concentration in the lower Tapi reservoir or Ukai reservoir. The estimation was done using Landsat-8 OLI (Operational Land Imager) having radiometric resolution (12-bit) and a spatial resolution of 30 m. The Google Earth Engine (GEE) cloud computing platform was used in this study to generate the products. The GEE is a semi-automated workflow system using a robust approach designed for scientific analysis and visualization of geospatial datasets. An algorithm was deployed, and a time-series (2013-2020) analysis was done for the study area. It was found that the average mean value of SPM in Tapi River during 2020 is lowest than the last seven years at the same time.
Subject(s)
COVID-19 , Particulate Matter , Humans , Particulate Matter/analysis , Cloud Computing , Search Engine , Communicable Disease ControlABSTRACT
Cover crops are planted to reduce soil erosion, increase soil fertility, and improve watershed management. In the Delmarva Peninsula of the eastern United States, winter cover crops are essential for reducing nutrient and sediment losses from farmland. Cost-share programs have been created to incentivize cover crops to achieve conservation objectives. This program required that cover crops be planted and terminated within a specified time window. Usually, farmers report cover crop termination dates for each enrolled field (∼28,000 per year), and conservation district staff confirm the report with field visits within two weeks of termination. This verification process is labor-intensive and time-consuming and became restricted in 2020–2021 due to the COVID-19 pandemic. This study used Harmonized Landsat and Sentinel-2 (HLS, version 2.0) time-series data and the within-season termination (WIST) algorithm to detect cover crop termination dates over Maryland and the Delmarva Peninsula. The estimated remote sensing termination dates were compared to roadside surveys and to farmer-reported termination dates from the Maryland Department of Agriculture database for the 2020–2021 cover crop season. The results show that the WIST algorithm using HLS detected 94% of terminations (statuses) for the enrolled fields (n = 28,190). Among the detected terminations, about 49%, 72%, 84%, and 90% of remote sensing detected termination dates were within one, two, three, and four weeks of agreement to farmer-reported dates, respectively. A real-time simulation showed that the termination dates could be detected one week after termination operation using routinely available HLS data, and termination dates detected after mid-May are more reliable than those from early spring when the Normalized Difference Vegetation Index (NDVI) was low. We conclude that HLS imagery and the WIST algorithm provide a fast and consistent approach for generating near-real-time cover crop termination maps over large areas, which can be used to support cost-share program verification.
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Not many efforts have been made so far to understand the effects of both the 2015-2016 drought and the 2020 lockdown measures on the agricultural production of smallholder vis-a-vis commercial farmers in Kwazulu-Natal. Google Earth Engine, and random forest algorithm, are used to generate a dataset that help to investigate this question. A regression is performed on double differenced data to investigate the effects of interest. A k-mean cluster analysis, is also used to determine whether the distribution patterns of crop production changed with drought and disruption of agricultural production input. Results show that: (1) droughts affected the agricultural production of both areas similarly. Crop cover declined in both areas for one season after droughts were broken. Then recovery was driven by greener, more productive crops rather than the expansion of crop area. (2) The response of both areas to the COVID-19 lockdown was also similar. Both smallholder and commercial areas' Normalised Difference Vegetation Index - a proxy for crop vitality - improved in response to regulations favourable to the sector and improved rainfall. No significant adjustments in crop cover were observed. Production therefore changed primarily at the intensive margin (improved productivity of existing croplands) rather than the extensive (changing the extent of land under cultivation). (3) Cluster analysis allows for a more granular view, showing that the positive impact of lockdowns on agriculture were concentrated in areas with high rainfall and close proximity to metropolitan markets. Both smallholder and commercial farmers therefore are reliant on market access together with favourable environmental conditions for improved production.
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Since the shale Oil/Gas revolution, gas flaring and venting in the United States has garnered increasing attention. There is a pressing need to understand the spatial–temporal characteristics of gas flaring and track the associated greenhouse gas emissions. In this context, we use a thermal anomaly index (TAI) incorporating the Google Earth Engine (GEE) cloud computation and local batch processing for monitoring gas flaring and characterizing its spatial–temporal dynamics. We then apply a quantitative analysis of satellite-based carbon dioxide (CO2) and methane (CH4) in the gas flaring region. Here, we generate a gas flaring sites inventory in Texas from 2013 to 2022 based on > 83,500 multi-source moderate-resolution images (including 74,627 Sentinel-2 Multispectral Instrument [MSI] images and 8,969 Landsat-8 Operational Land Imager [OLI] images). Validations and comparisons demonstrate that our method is reliable for MSI and OLI images, with an overall accuracy of > 95 % and a low commission rate and omission rate. We detected 217,034 gas flares from 9,296 flaring sites in Texas, and the majority (>92 %) were found in the central and western regions of the Permian Basin and the Eagle Ford Shale. The number of detected gas flaring sites vastly outnumbered the existing Visible Infrared Imaging Radiometer Suite (VIIRS) fire products, with an upward trend from 2013 to 2019 and a downward trend from 2020 to 2022. Notably, the gas flaring sites dropped significantly at the beginning of the COVID-19 pandemic (from December 2019 to May 2020), with the lowest average monthly growth rate of −14.38 %, and fell to the level of mid-2017. Application of gas flaring data identifies the localized greenhouse gas (GHG) emission hotspots in Texas and demonstrates that the increased effect of CH4 released from gas flaring regions was significantly stronger than that of CO2. These findings can provide references for monitoring similar small industrial sources in the future, can be used as an essential supplement to low-resolution fire products, and improve our understanding of CO2 and CH4 emissions from gas flaring at fine spatial scales.
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Transmission rates of COVID-19 have been associated with the density of buildings where contact among individuals partially contributes to transmission. The research sought to analyze the spatial distribution of building density derived from satellite images and determine its implications to COVID-19 health risk management using Yogyakarta and its surrounding districts as an example. Fine-scale building distribution obtained through remote sensing data transformation was analyzed with GIS. NDBI was applied to Landsat 8 imagery;then, using multiple linear regression analysis, it was correlated to building density’s training samples generated from high-resolution imagery. The derived percent of building density (PBD) was combined with publicly available records of COVID-19 infection to assess risk. This research found that PBD could explain the uneven COVID-19 diffusion at different stages of its development. Instead of dividing regions into zones based on confirmed cases, government and public health officials should observe new cases in high-PBD districts;then, when the cases are decreasing, their attention should shift to low-PBD districts. Remote sensing data allow for moderate-scale PBD mapping and integrating it with confirmed cases produces spatial health risks, determining target areas for interventions and allowing regionally tailored responses to anticipate or prevent the next wave of infections.
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Appropriate strategies on urban climate mitigation should be formulated by considering the physical morphology of the urban landscape. This study aimed to investigate, analyze, and promote possible strategies to mitigate Jakarta’s urban heat island (UHI) phenomena. Jakarta’s local climate zone (LCZ) was classified into 17 classes using Landsat 8 data and the random forest method. Land surface temperature (LST) characteristic in each LCZ class was analyzed from 2018, 2019 and 2020. The result revealed that most of the local climate zone in Jakarta is dominated by LCZ 6 (open low-rise) and LCZ 3 (compact low-rise), which is the typical residential area in Jakarta. However, the mean LST in 2018, 2019 and 2020 showed that LCZ 3 (compact low-rise) and LCZ 7 (lightweight low-rise) are the areas that were most likely causing high surface temperature with the highest UHI intensity. During the COVID-19 pandemic in 2020, LST in Jakarta decreased drastically in some parts of the area, especially in public facility such as airport. However, the LST value in low-rise areas (LCZ 3 and LCZ 7) remains higher than the other LCZ classes. Materials of the building and land cover play a significant role in raising the land surface temperature. Therefore, mitigation strategies for urban heat islands in Jakarta should be focused on such particular areas mentioned.
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Ongoing urbanization has led to the continuous expansion of built-up areas;as a result, open space is under great threat. Despite the wealth of studies conducted on open spaces, there is still a further need to further investigate the morphology of open space, particularly in an effort to understand the trends and drivers of open space morphological transformation that remain under-researched. Besides, although the previous literature has highlighted several factors influencing urban space morphology, it remains unclear how those key drivers interact. In this article, the PRISMA methodology was used to conduct a systematic literature review, screening and selecting articles from three primary databases (Web of Science, Elsevier, and Scopus). In total, 47 journal articles covering the years 2000 to 2022 were selected for the final review to identify key factors that influence open space morphology, including natural geographical factors, socioeconomic factors, and government policy factors. The results indicate that, as cities developed, the size of green spaces decreased, their structure fragmented, and their distribution became progressively less connected. Meanwhile, socioeconomic determinants played a greater role in influencing changes in green spaces than natural geographical factors and policy management factors. In addition, carrying out the present study confirmed that Landsat remote-sensing data with landscape metrics is a powerful research method for studying green space change. A research framework is offered in this paper to illustrate an understanding of which factors influence the dynamics of green spaces, identify the interaction mechanisms, and provide an optimization strategy of urban open space for urban planners or policymakers.
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• MOD16 products indicated significant underestimations in all paddy rice ET observations. • R n estimation in overcast conditions and LAI reconstruction were two key causes. • Daily R n estimations under all-sky conditions by a global cloudy index algorithm were improved by 40.6%. • Daily LAI dynamics estimated by the LTDG_PhenoS algorithm were improved by 818.7%. • Daily ET estimations were improved by 68.7%. Reliable estimations in evapotranspiration (ET) of paddy rice ecosystems by satellite products are critical because of their important roles in regional hydrological processes and climate change. However, the NASA MODIS ET products (MOD16A2) and its derivatives do not have good correlations with all global paddy rice ET observations. In this research, MOD16 model sensitivity analyses and parameter optimization strategies were conducted in order to solve the problem. Results suggested that underestimation of daily net radiation (R n) in overcast conditions and less satisfactory reconstruction of field-scale leaf area index (LAI) growth trajectory from the start date of field flooding and transplanting (FFTD) to the end of growing seasons by MODIS coarse vegetation index were identified as two major causes. A Light and Temperature-Driven Growth model and a Phenology-based LAI temporal Smoothing method fusion algorithm (LTDG_PhenoS) and an improved R n estimation method were introducted and evaluated in paddy rice fields in South Korea, Japan, China, Philippines, India, Spain, Italy, and the USA from 2002 to 2019. The LTDG_PhenoS algorithm considers Landsat and MODIS EVI observations and meteorological data as input variables and 30-m LAI daily time series as outcomes. Introducing the global cloudy index algorithm resulted in improved estimations of daily R n under all-sky conditions, with a significant decrease of root mean square error (RMSE) from 1.87 to 1.11 MJ m−2 day−1. The LTDG_PhenoS algorithm well reconstructed crop LAI growth dynamics from the FFTD to the end of rice growing seasons, with a substantial decline of RMSE from 1.49 to 0.27 m2/m−2. The FFTD estimations by the LTDG_PhenoS algorithm had an R2 of 0.97 and a small RMSE of less than 12-days. Daily ET rates estimated by novel algorithms had a substantial decline in RMSE from 2.88 to 0.90 mm day−1. [ FROM AUTHOR] Copyright of Journal of Hydrology is the property of Elsevier B.V. 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.)
ABSTRACT
Timely, accurate, and repeatable crop mapping is vital for food security. Rice is one of the important food crops. Efficient and timely rice mapping would provide critical support for rice yield and production prediction as well as food security. The development of remote sensing (RS) satellite monitoring technology provides an opportunity for agricultural modernization applications and has become an important method to extract rice. This paper evaluated how a semantic segmentation model U-net that used time series Landsat images and Cropland Data Layer (CDL) performed when applied to extractions of paddy rice in Arkansas. Classifiers were trained based on time series images from 2017–2019, then were transferred to corresponding images in 2020 to obtain resultant maps. The extraction outputs were compared to those produced by Random Forest (RF). The results showed that U-net outperformed RF in most scenarios. The best scenario was when the time resolution of the data composite was fourteen day. The band combination including red band, near-infrared band, and Swir-1 band showed notably better performance than the six widely used bands for extracting rice. This study found a relatively high overall accuracy of 0.92 for extracting rice with training samples including five years from 2015 to 2019. Finally, we generated dynamic maps of rice in 2020. Rice could be identified in the heading stage (two months before maturing) with an overall accuracy of 0.86 on July 23. Accuracy gradually increased with the date of the mapping date. On September 17, overall accuracy was 0.92. There was a significant linear relationship (slope = 0.9, r2 = 0.75) between the mapped areas on July 23 and those from the statistical reports. Dynamic mapping is not only essential to assist farms and governments for growth monitoring and production assessment in the growing season, but also to support mitigation and disaster response strategies in the different growth stages of rice.
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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.
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Since the last decade, prior to the emergence of COVID-19, the incidence of pneumonia, pneumonia in Indonesia has steadily increased (Minister of Health RI, 2020) along with deforestation phenomenon (Adhyaksa et al, 2019) and global warming (Mirsaeidi et al, 2016). Forest recovery ecosystem is a must to negate this disease. This research was conducted on determining the economic value of the ecosystem service to compensate reforestation program. This research was conducted in Lampung Province started from May to October 2021, by utilizing of Landsat imagery series of 2009, 2012, 2015, 2018, and 2019 for detecting forest covers. The effect on the incidence of pneumonia was determined using multiple linear regression models and to make some simulations work for estimating the reforestation costs. The results prove that the increasement air temperature, and the changes area of state forests, people's forests, bare land, plantations, and urban areas affect the incidence of pneumonia significantly. The determination of the value of environmental services for public costs is required at IDR 942,227,915,- from the maintenance cost of IDR 249,216,000, the cost of reforestation at the state forest area of 5,907,792 Ha and the people's forest of 6,040,689 Ha in case the air temperature increase up to 2 degrees C as the way to mitigate the global warming.
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The proper functioning of the river ecosystem has been symbolised by healthy aquatic life. The river Ganga has shown signs of rejuvenation due to lockdown. In this study, an attempt has been made to analyse the change in river water quality using Sentinel-2 and Landsat-8 imageries. The quantitative analysis has been performed for temperature and normalised difference turbidity index (NDTI). The qualitative analysis has been performed for pH, dissolved oxygen (DO) and total suspended solids (TSSs). Ghazipur, Varanasi and Mirzapur stretches have been selected for this study. In the Ghazipur stretch, the river temperature decreased by 7.14% in May 2020 (lockdown period) as compared to May 2019 (1 year before lockdown). Similarly, in the Varanasi stretch, this decrease has been by 8.62%, and in the Mirzapur stretch, this decrease has been by 12.06% in May 2020 compared to May 2019. For the same period, NDTI in the Ghazipur, Varanasi and Mirzapur stretch has been decreased by 0.22, 0.26 and 0.24, respectively. The pH and DO of the river increased, and TSS decreased for the considered time period. The lockdown during the second wave of the coronavirus disease 2019 was not helpful for river rejuvenation. This study elicited how the behaviour of the parameters changed during the lockdown. Research highlights: River Ganga becomes much cleaner in the lockdown period (May 2020) compared to the pre-lockdown time. In the Mirzapur stretch, the temperature decreased most in May 2020 as compared to May 2019. In the Varanasi stretch, there is a maximum variation in the NDTI value in May 2020 in comparison with that of May 2019. The most significant task will be to maintain river conditions during post-lockdown similar to that prevailed during lockdown. In the second wave COVID-19 lockdown the river again became polluted like the pre-COVID times. [ FROM AUTHOR] Copyright of Journal of Earth System Science is the property of Springer Nature 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.)
ABSTRACT
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;