Investigating the Effect of Lockdown During COVID-19 on Land Surface Temperature: Study of Dehradun City, India

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 (P_v) 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 P_v) 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.

Co-occurrence of urban heat and the COVID-19: Impacts, drivers, methods, and implications for the post-pandemic era

Cities, the main place of human settlements, are under various mega challenges such as climate change, population increase, economic growth, urbanization, and pandemic diseases, and such challenges are mostly interlinked. Urban heat, due to heatwaves and heat islands, is the combined effect of climate change and urbanization. The COVID-19 is found to be a critical intervention of urban heat. However, the interrelationship between COVID-19 and urban heat has not been fully understood, constraining urban planning and design actions for improving the resilience to the dual impacts of heat and the pandemic. To close this research gap, this paper conducted a review on the co-occurrence of urban heat and the COVID-19 pandemic for a better understanding of their synergies, conflicts or trade-offs. The research involves a systematic review of urban temperature anomalies, variations in air pollutant concentrations, unbalanced energy development, and thermal health risks during the pandemic lockdown. In addition, this paper further explored data sources and analytical methods adopted to screen and identify the interventions of COVID-19 to urban heat. Overall, this paper is of significance for understanding the impact of COVID-19 on urban heat and provides a reference for coping with urban heat and the pandemic simultaneously. The world is witnessing the co-existence of heat and the pandemic, even in the post-pandemic era. This study can enlighten city managers, planners, the public, and researchers to collaborate for constructing a robust and resilient urban system for dealing with more than one challenges.

Assessing variations in land cover-land use and surface temperature dynamics for Dehradun, India, using multi-time and multi-sensor landsat data

Rapid urbanisation and industrialisation coupled with overpopulation have altered land cover/land use (LCLU) and surface temperature (ST) patterns in Dehradun. Monitoring these changes through satellite-based remote sensing is required to ensure the sustained development of this ecologically fragile region. Here, LU and ST dynamics of the Dehradun municipal area have been estimated using Landsat-5 datasets for 1991, 1998, and 2008 and Landsat-8 dataset for 2018. LU maps have been extracted using a Gaussian Maximum Likelihood classifier with an overall accuracy of over 88% and Kappa coefficients above 0.85. Results reveal that the urban region expanded by 80.6% in the 27 years while dense vegetation and dry river bed classes have declined sharply. Sparse vegetation has risen by 3 km², whereas bare ground has decreased by about 4.3 km². Mean ST has increased above 9 °C from 1991 to 2018 in every season. A seasonal influence is evident on the mean ST per LU class's trend, which rose between 8 °C and 12 °C for every LU class, indicating significant warming across each LU class. ST probably has non-linear relationships with its causal factors represented by spectral indices, elevation, and population density. Urban heat island (UHI) formation is thus evinced, promulgating the administration's urgent action to save the environment and redrawing policies for ambitious projects like smart cities.

Evaluation of seasonal ecological vulnerability using LULC and thermal state dynamics using Landsat and MODIS data: a case study of Prayagraj City, India (1987-2018)

Prayagraj city (India) has been selected as a smart city by the Ministry of Housing and Urban Affairs, Government of India in 2015. However, long-term spatiotemporal seasonal Land Use/Land Cover (LULC) dynamics and Land Surface Temperature (LST) interactions with ecological vulnerability for different seasons are lacking. Accordingly, this research has been carried out to study the seasonal (summer and winter) LULC and its change pattern, thermal dynamics, and their role in exploring the ecological state over Prayagraj city and its surroundings using multi-temporal Landsat (1987-2018) and MODIS Terra data (2007-2018) at both diurnal and nocturnal scenarios. The LULC classification was carried out using Maximum Likelihood Classifier (MLC) by adopting the Anderson classification scheme with more than 85% of overall accuracy. The Landsat data-based LST has been estimated using Mono-Window Algorithm (MWA) for diurnal scenario whereas MODIS-based LST was calculated for nocturnal scenario. Ecological vulnerability state has been evaluated both in day-time and night-time using Urban Thermal Field Variance Index (UTFVI) in summer and winter during 1987-2018 and 2007-2018, respectively. Overall, built-up land increased the most by 18.25% which was responsible for massive urbanization during 1987-2018. In contrast, forest land decreased by 2.22% during 1987-2018. The most vulnerable class was agriculture land followed by forest land irrespective of seasons. Thermal state was intensified by mean LST by 1.25 ℃ in summer and 0.58 ℃ in winter in day-time. However, in night-time, the mean LST intensified by 6.64 ℃ in summer and 1.86 ℃ in winter. The excellent ecological class having no SUHI effects declined in summer during 1988-2018 by 1.59% but surged in winter by 12.33% during 1987-2018 in north-west regions at day-time, whereas in night-time the excellent ecological class having no SUHI effects severely declined in summer as well as in winter during 2007-2018 by 11.1% and 1.32%, respectively. However, the worst ecological class having strongest SUHI effects severely spread in night-times compared to day-time which mainly concentrated in central core part of the city during 2007-2018 by 5.33%. The present study has generated a comprehensive long-term geospatial database which can be used for urban planning to achieve sustainable development to make Prayagraj city a truly smart city in future.

A GIS and remote sensing approach for measuring summer-winter variation of land use and land cover indices and surface temperature in Dhaka district, Bangladesh

Rapid urbanization has induced land use and land cover change (LULC) that increases land surface temperature (LST). Analyzing seasonal variations of LULC and LST is a precondition for mitigating heat island effects and promoting a sustainable living environment. The objective of this study is to explore the association between the seasonal LST dynamics and LULC indices for the Dhaka district of Bangladesh. The LULC indices are comprised of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Bareness Index (NDBAI), and Modified Normalized Difference Water Index (MNDWI). The results show that the LULC effect on LST in Dhaka is significant, with an increase in summer season LST from 34.58 °C to 37.66 °C and in winter season LST from 24.710C to 26.24 °C. Predictably, the highest and lowest LST values were observed in the built-up and vegetation-covered areas, respectively. Secondly, the correlation values indicate a significant inverse correlation (R² > 0.50) between NDVI and LST, as well as MNDWI and LST. On the contrary, positive correlations were observed between NDBI and LST, and between NDBAI and LST for both the summer and winter seasons. Finally, subsequent vegetation decline (-69.34%) and increasing built-up area (+11.30%) between 2000 and 2020 in Dhaka district were found to be the most significant factors for the increasing trend and spatial heterogeneity of LST in Dhaka. The methodological approach of this study offers a low-cost efficient technique for monitoring LST hotspots, which can guide land use planners and urban managers for spatial intervention to ensure a livable environment.

Spatiotemporal pattern of Covid-19 outbreak in Turkey

The earliest case of Covid-19 was documented in Wuhan city of China and since then the virus has been spreading throughout the globe. The aim of this study is to evaluate the clusters of Covid-19 among the provinces in Turkey and to examine whether the clustering pattern has changed after the country's lockdown strategy. The spatial dependence of Covid-19 in 81 provinces of Turkey was examined by spatial analysis between February 8 and June 28, 2021. Global and Local Moran's I and Gi* were employed to measure the global and local spatial autocorrelation degrees. The geographical distribution of Covid-19 in the provinces of Turkey showed a strong spatial autocorrelation while the spatial structure of the clusters varied by weeks. The findings of the study show that the complete lockdown carried out in Turkey has been quite effective in mitigating Covid-19. The importance of spatial relations in preventing the spread of the disease in Turkey has also been demonstrated in this context.

Ambient temperature and Covid-19 transmission: An evidence from a region of Iran based on weather station and satellite data

BACKGROUND

The SARS-CoV-2 virus pandemic is primarily transmitted by direct contact between infected and uninfected people, though, there are still many unknown factors influencing the survival and transmission of the virus. Air temperature is one of the main susceptible factors. This study aimed to explore the impact of air and land surface temperatures on Covid-19 transmission in a region of Iran.

METHOD

Daily Land Surface Temperature (LST) measured by satellite and Air Temperature measured by weather station were used as the predictors of Covid-19 transmission. The data were obtained from February 2020 to April 2021. Spatio-temporal kriging was used in order to predict LST in some days in which no image was recorded by the satellite. The validity of the predicted values was assessed by Bland-Altman technique. The impact of the predictors was analyzed by Distributed Lag Non-linear Model (DLNM). In addition to main effect of temperature, its linear as well as non-linear interaction effect with relative humidity were considered using Generalized Additive Model (GAM) and a bivariate response surface model. Sensitivity analyses were done to select models' parameters, autocorrelation model and function of associations.

RESULTS

The dose-response curve revealed that the impact of both predictors was not obvious, though, the risk of transmission tended to be positive due to low values of temperatures. Although the linear interaction effect was not statistically significant, but joint patterns showed that the impact of both LST and AT tended to be different when humidity values were changed.

CONCLUSION

However the findings suggested that both LST and AT were not statistically important predictors, but they tended to predict the Covid-19 transmission in some lags. Because of local based evidence, the wide confidence intervals and then non-significant values should be cautiously interpreted.

Urban green space mediates spatiotemporal variation in land surface temperature: a case study of an urbanized city, Bangladesh

Rapid urbanization with an increasing rate of urban built-up area is decreasing urban green space resulting in changing urban microclimate conditions showing increasing land surface temperature. A better understanding of these effects is important to formulate effective strategies in addressing the impact of increasing built-up area. Land surface temperature patterns in an urbanized city in Bangladesh (Mymensingh district) were investigated using Landsat satellite sensor data from 1988 to 2016. A total of nineteen Landsat satellite images were used to retrieve land surface temperature (LST), normalized difference vegetation index (NDVI), and normalized difference built-up index (NDBI). The radiative transfer equation (RTE) model was applied to derive LST for the years 1988, 1992, 1999, 2004, 2008, 2012, and 2016. Further, the Landsat-derived LST results were compared with MODIS Terra satellite outputs (MOD11A1) for the validation of our study results. Our results showed NDVI higher in 2008 and lower in 2004, LST maximum in 1988 and minimum in 2008, and NDBI higher in 2004 and lower in 2012. Seasonally, summer was characterized by higher LST and winter by lower LST, while NDVI was higher in autumn and lower in winter, however, NDBI was higher in winter and lower in autumn. Spatially, a relatively higher LST and NDBI was observed in the southwest, followed by central, and northern regions, whereas the trend was opposite for NDVI. Using Pearson's correlation, results showed a strong significant negative correlation between LST and NDVI and a positive significant correlation between LST and NDBI. Further, simple linear regression analysis revealed that LST decreased with increasing NDVI most quickly in 2012, followed by the years 2016, 2008, 1992, 1988, 1999, and 2004. On the other hand, LST increased with increasing NDBI most quickly in 1999, followed by the years 2016, 1988, 1992, 2012, 2004, and 2008. Thus, long-term observation suggested that urbanization had driven a decrease in green space while simultaneously increasing the land surface temperature within an urbanized area. This study has concluded that the protection of urban green spaces is needed as an effective step toward addressing adverse effects of regional climate change and desertification.

Long-Term Impacts of COVID-19 Lockdown on the NO2 Concentrations and Urban Thermal Environment: Evidence from the Five Largest Urban Agglomerations in China

Under the threat of COVID-19, many regions around the world implemented lockdown policies to control the spread of the virus. This restriction on both social and economic activities has improved the quality of the environment in certain aspects. However, most previous studies have only focused on the short-term impact of lockdown policies on the urban environment. The long-term effects of lockdown require a more focused exploration and analysis. Thus, five major urban agglomerations in China were selected as the research area; changes in the numerical and spatial distribution of NO2 concentration and surface temperature during four different lockdown stages in 2019, 2020, and 2021 were investigated to analyze the long-term effects of lockdown policies on the urban environment. The results indicated that the impact of shorter lockdowns was short-term and unsustainable; the NO2 concentrations increased again with the resumption of production. Compared with air pollutants, thermal environmental problems are more complex. The effect of the lockdown policy was not reflected in the decrease in the area proportions of the high- and sub-high-temperature regions but rather in the spatial distribution of the high-temperature area, which was manifested as a fragmentation and dispersion of heat source patches. In addition to the severity of the lockdown, the impact of the lockdown policy was also closely related to the level of development and industrial structure of each city. Among the urban environments of the five agglomerations, the most affected were the Yangtze River Delta and Yangtze River Middle-Reach urban agglomerations, which had the largest decline in NO2 concentrations and the most notable fragmentation of heat source patches.

Impacts of the COVID-19 confinement on air quality, the Land Surface Temperature and the urban heat island in eight cities of Andalusia (Spain)

The COVID-19 outbreak and ensuing global lockdown situation have generated a very negative impact on the world economy, but they have also lent us a unique opportunity to research and better grasp the impacts of human activity on environmental pollution and urban climates. Such studies will be of vital importance for decision-making on measures needed to mitigate the effects of climate change in urban areas, in order to turn them into resilient environments. This study looks at eight cities in the region of Andalusia (southern Spain) to comprehensively assess their environmental quality with parameters (Pm10, So2, No2, Co and O3) obtained from meteorological stations. The aim was to determine how these parameters affect the Land Surface Temperature (LST) and the Surface Urban Heat Island (SUHI), on the basis of Sentinel 3 satellite thermal images. Knowing to what extent improved air quality can reduce the LST and SUHI of cities will be essential in the context of future environmental studies on which to base sustainable decisions. The geographic situation of cities in the Mediterranean Sea basin, highly vulnerable to climate change, and the high pollution rates and high daily temperature variations of these urban areas make them particularly attractive for analyses of this sort. During the confinement period, average reductions of some environmental pollutants were achieved: So2 (-33.5%), Pm10 (-38.3%), No2 (-44.0%) and Co (-26.5%). However, the environmental variable O3 underwent an average growth of 5.9%. The LST showed an average reduction of -4.6 °C (-19.3%), while the SUHI decreased by 1.02 °C (-59.8%). These values exhibit high spatio-temporal variations between day and night, and between inland and coastal cities.

Study of the Urban Heat Island (UHI) Using Remote Sensing Data/Techniques: A Systematic Review

Urban Heat Islands (UHI) consist of the occurrence of higher temperatures in urbanized areas when compared to rural areas. During the warmer seasons, this effect can lead to thermal discomfort, higher energy consumption, and aggravated pollution effects. The application of Remote Sensing (RS) data/techniques using thermal sensors onboard satellites, drones, or aircraft, allow for the estimation of Land Surface Temperature (LST). This article presents a systematic review of publications in Scopus and Web of Science (WOS) on UHI analysis using RS data/techniques and LST, from 2000 to 2020. The selection of articles considered keywords, title, abstract, and when deemed necessary, the full text. The process was conducted by two independent researchers and 579 articles, published in English, were selected. Qualitative and quantitative analyses were performed. Cfa climate areas are the most represented, as the Northern Hemisphere concentrates the most studied areas, especially in Asia (69.94%); Landsat products were the most applied to estimates LST (68.39%) and LULC (55.96%); ArcGIS (30.74%) was most used software for data treatment, and correlation (38.69%) was the most applied statistic technique. There is an increasing number of publications, especially from 2016, and the transversality of UHI studies corroborates the relevance of this topic.

Thermal and ecological assessment based on land surface temperature and quantifying multivariate controlling factors in Bogura, Bangladesh

In recent years, the world has shown considerable concerns about environmental degradation accompanied by urban expansion. In terms of size, Bogura is equivalent to most of the major cities in Bangladesh, yet no thermal and ecological assessment has ever been conducted here. This study uses multitemporal Landsat satellite images between 2001 and 2020 to investigate the thermal and ecological conditions of Bogura Sadar (sub-district). Land surface temperature (LST) is obtained from Landsat images using the widely used radiative transfer equation. The thermal and ecological conditions are evaluated by computing urban heat island (UHI) and urban thermal field variance index (UTFVI) from LST data. The influence of vegetation, built-area, water-body, and bare soil on LST are examined using land cover indices through pixel-level multivariate linear regression analysis. According to the findings of this sub-district-scale (urban and rural areas) study, the mean LST has increased by 0.62 °C in the last 20 years. As per local administrative-wise findings, LST has increased in most areas, regardless of their urban or rural function. The difference between the urban area and the rest of the surroundings was significant (1.74 °C) in 2020. In 2001, UHI affected area was 5.65 km², which expanded to 8.84 km² in 2020. Thermal and ecological conditions are worse in urban areas than its surrounding areas. The regression models of the LST and land cover indices could explain more than half (R²: 0.66 to 0.73) of LST variation over the years. Land cover could explain the LST in 2020 to the least extent implying that anthropogenic activities have greater influence than earlier. Land cover could explain less than half of the LST variation in the urban area.

COVID-19 and environmental concerns: A rapid review

COVID-19 has slowed global economic growth and consequently impacted the environment as well. Parallelly, the environment also influences the transmission of this novel coronavirus through various factors. Every nation deals with varied population density and size; air quality and pollutants; the nature of land and water, which significantly impact the transmission of coronavirus. The WHO (Ziaeepour et al., 2008) [1] has recommended rapid reviews to provide timely evidence to the policymakers to respond to the emergency. The present study follows a rapid review along with a brief bibliometric analysis of 328 research papers, which synthesizes the evidence regarding the environmental concerns of COVID-19. The novel contribution of this rapid review is threefold. One, we take stock of the diverse findings as regards the transmission of the novel coronavirus in different types of environments for providing conclusive directions to the ongoing debate regarding the transmission of the virus. Two, our findings provide topical insights as well as methodological guidance for future researchers in the field. Three, we inform the policymakers on the efficacy of environmental measures for controlling the spread of COVID-19.

Global assessment of tropospheric and ground air pollutants and its correlation with COVID-19

The declaration of COVID-19 pandemic by the WHO initiated a series of lockdowns globally that varied in stringency and duration; however, the spatiotemporal effects of these lockdowns on air quality remain understudied. This study evaluates the global impact of lockdowns on air pollutants using tropospheric and ground-level indicators over a five-month period. Moreover, the relationship between air pollution and COVID-19 cases and mortalities was examined. Changes in the global tropospheric (NO2, aerosols, and O3) and ground-level (PM2.5, PM10, NO2, and O3) pollutants were observed, and the maximum air quality improvement was observed immediately after lockdown. Except for a few countries, a decline in air pollutants correlated with a reduction in Land Surface Temperature (LST). Notably, regions with higher tropospheric NO2 and aerosol concentrations were also COVID-19 hotspots. Our analysis showed moderate positive correlation for NO2 with COVID-19 cases (R2 = 0.33; r = 0.57, P = 0.006) and mortalities (R2 = 0.40; r = 0.63, P = 0.015), while O3 showed a weak-moderate positive correlation with COVID-19 cases (R2 = 0.22; r = 0.47, P = 0.003) and mortalities (R2 = 0.12; r = 0.35, P = 0.012). However, PM2.5, and PM10 showed no significant correlation with either COVID-19 cases or mortality. This study reveals that humans living under adverse air pollution conditions are at higher risk of COVID-19 infection and mortality.