Gis And Remote Sensing: A Review Of Applications To The Study Of The Covid-19 Pandemic

Abstract

The spread of the 2019 novel coronavirus disease (COVID-19) has engulfed the world with a rapid, unexpected, and far-reaching global crisis. In the study of COVID-19, Geographic Information Systems (GIS) and Remote Sensing (RS) have played an important role in many aspects, especially in the fight against COVID-19. This review summarises 102 scientific papers on applications of GIS and RS on studies of the COVID-19 pandemic. In this study, two themes of GIS and RS-related applications are grouped into the six categories of studies of the COVID-19 including spatio-temporal changes, WebGISbased mapping, the correlation between the COVID-19 and natural, socio-economic factors, and the environmental impacts. The findings of this study provide insight into how to apply new techniques (GIS and RS) to better understand, better manage the evolution of the COVID-19 pandemic and effectively assess its impacts.

Full text

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GIS AND REMOTE SENSING: A REVIEW OF APPLICATIONS
TO THE STUDY OF THE COVID19 PANDEMIC
REVIEW PAPER
Quoc-lap Kieu1, Tien-thanh Nguyen2*, Anh-huy Hoang3
1Faculty of Natural Resources and Environment, Thainguyen University of Sciences, Tan Thinh Ward,Thainguyen,
250000, Vietnam
2Faculty of Surveying, Mapping and Geographic Information, Hanoi University of Natural Resources and
Environment, No. 41A, Phu Dien Road, North-Tu Liem District, Hanoi,100000, Vietnam
3Faculty of Environment, Hanoi University of Natural Resources and Environment, No. 41A, Phu Dien Road, North-Tu
Liem District, Hanoi, 100000, Vietnam
*Corresponding author: tdgis_ntthanh@163.com; ntthanh@hunre.edu.vn
Received: May 16th, 2021 / Accepted: November 9th, 2021 / Published: December 31st, 2021
https://doi.org/10.24057/2071-9388-2021-054
ABSTRACT. The spread of the 2019 novel coronavirus disease (COVID-19) has engulfed the world with a rapid, unexpected,
and far-reaching global crisis. In the study of COVID-19, Geographic Information Systems (GIS) and Remote Sensing (RS) have
played an important role in many aspects, especially in the fight against COVID-19. This review summarises 102 scientific
papers on applications of GIS and RS on studies of the COVID-19 pandemic. In this study, two themes of GIS and RS-related
applications are grouped into the six categories of studies of the COVID-19 including spatio-temporal changes, WebGIS-
based mapping, the correlation between the COVID-19 and natural, socio-economic factors, and the environmental impacts.
The findings of this study provide insight into how to apply new techniques (GIS and RS) to better understand, better manage
the evolution of the COVID-19 pandemic and effectively assess its impacts.
KEYWORDS: GIS; remote sensing; applications; COVID-19; viral infection; impacts; environment
CITATION: Quoc-lap Kieu, Tien-thanh Nguyen, Anh-huy Hoang (2021). Gis And Remote Sensing: A Review Of Applications To
The Study Of The Covid-19 Pandemic.
Geography, Environment, Sustainability, Vol.14, No 4, p. 117-124
https://doi.org/10.24057/2071-9388-2021-054
ACKNOWLEDGEMENTS: We wish to thank the editors and anonymous reviewers for their valuable and constructive comments
and suggestions on this paper that have helped us to greatly improve the quality of the paper.
Conflict of interests: The authors reported no potential conflict of interest.
INTRODUCTION
The COVID-19 pandemic has been a global health
concern due to the rapid spread of the disease (WHO
2020) since a deadly new coronavirus strain, the SARS-
CoV-2 virus was initially discovered in Wuhan city, PR
China. The COVID-19 pandemic has been described as
asocial, human, and economic crisis (United Nations 2020).
Globally, as of October 22, 2021, there were more than
242.3 million conrmed cases of the COVID-19, including
more than 4.9 million deaths reported to WHO (WHO
2021). Geography is considered a key part of ghting the
COVID-19 Coronavirus outbreak (Shepherd 2020). Later,
Rose-Redwood et al. (2020) highlight that the COVID-19
pandemic is thoroughly spatial in nature. It is, therefore,
the assessment of the scale of the COVID-19 pandemic
from a geographical perspective that can oer a better
understanding of the spatial distribution, better manage
the COVID-19 infection, and eectively study its impacts.
A Geographic Information System (GIS) is an essential tool
to examine the spatial distribution of spatial objects. Many
COVID-19-related data such as the locations of (visited)
COVID-19 cases can be considered a type of spatial object
which has a spatial dimension and can be mapped by a
GIS. When assessing potential geographical accessibility
for health utilization studies, Graves (2012) showed the
importance of GIS in analyzing epidemiological data,
revealing trends and interrelationships that would be
dicult to discover in tabular format and the visualization
of problems in relation to existing health and social services
and the natural environment and so more eectively target
resources. Later, the use of GIS in geospatial health has
been also rmly established as a useful tool for collating,
exploring, visualizing, and analyzing health data in a
graphic manner (Cicalò and Valentino 2019). Along with
GIS, remote sensing allows acquiring information about
theEarth’ssurface without actually being in contact with
it. The applications of remote sensingdata to studies of
human health, especially in infectious disease research
have been reviewed by Viana et al. (2017). It is, therefore,
GIS and remote sensing data are fundamental to keep
infectious diseases and their geographical distribution
under control (Cicalò and Valentino 2019). In this study, to
understand the spatial patterns, eects, and consequences
of COVID-19 in the context of geography, this study aims
to review the applications of GIS and remote sensing in
the study of the COVID-19 pandemic. In this study, we rst
review the applications of GIS to detect the spatio-temporal
changes, Web-GIS-based mapping of the COVID-19,
and the identication of the correlation between the

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COVID-19 and natural, social-economical variables. It will
then go on to discuss the applications of remote sensing
techniques on the environmental impacts of the COVID.
The environmental impacts of the COVID-9 will mainly
focus on studies of its impacts on water and air quality.
APPLICATIONS OF GIS IN THE STUDY OF THE COVID-19
PANDEMIC
Spatio-temporal changes
GIS can help to study the COVID-19 epidemic spread
at the country or state scale (Kodge 2021; Saeed et al.
2021), at the regional scale (Amdaoud et al. 2021; Onafeso
et al. 2021), and at the global scale (Bisanzio et al. 2020;
Gelfand et al. 2021; Meng 2021). When the SARS-CoV-2
virus was initially discovered at the end of 2019 in Wuhan
city, Hubei province, central China, and quickly spread
throughout China, application studies of GIS on the ght of
the COVID-19 pandemic in China have been conducted by
many authors. One of the most cited studies was carried out
by Guan et al. (2020). With the help of GIS, Guan et al. (2020)
extracted data regarding 1099 patients with laboratory-
conrmed Covid-19 from 552 hospitals in 30 provinces,
autonomous regions, and municipalities in mainland
China through January 29, 2020. Later, by analyzing the
spatial distribution of COVID-19 cases in the early stages
of the epidemic and determining their correlation with the
population migration from Wuhan city and Hubei province
using ArcGIS software and the Bayesian space-time model,
Chen et al. (2020) concluded that the population that
emigrated from Wuhan was the main infection source in
other cities and provinces. This is of great importance for
strictly implementing isolation or social distancing rules
and early warning and prevention of future outbreaks.
After COVID-19 rapidly spread across China and the rest
of the world, many studies make use of GIS to detect the
spatio-temporal changes in many countries, especially in
the worst-aected countries such as the USA (Feng et al.
2020; Rui et al. 2021; Wang et al. 2021), Italy (Giuliani et al.
2020; Gross et al. 2020; He et al. 2020), England (Elson et al.
2021; Sartorius et al. 2021), South Korea (He et al. 2020; Kim
and Castro 2020; Lee et al. 2020), Iran (He et al. 2020), Brazil
(Castro et al. 2021), Russia (Kuznetsov et al. 2020a) and
most recently in India (Bag et al. 2020; Bhunia et al. 2021).
When characterizing the dynamics and quantifying the
trends of the COVID-19 epidemic in the United States using
spatial and space-time scan statistics and the Joinpoint
analysis, Wang et al. (2021) indicate that higher risks of
clustering and incidence of COVID-19 were consistently
observed in metropolitan versus rural counties, counties
closest to core airports, the most populous counties, and
counties with the highest proportion of racial/ethnic
minorities. Feng et al. (2020) revealed that GIS can help
to eectively characterize spatio-temporal transmission
of COVID-19 and its mitigation strategies. Most recently,
when analyzing the spread of COVID-19 in the USA, Rui
et al. (2021) found that the spatio-temporal multivariate
time-series model is especially suitable for envisioning
the virus transmission tendency across a geographic area
over time. Using an endemic-epidemic multivariate time-
series mixed-eects generalized linear model for areal
disease counts, Giuliani et al. (2020) successfully modeled
and predicted the spatio-temporal spread of COVID-19
in Italy. In the early stages of the COVID-19 pandemic
between January and June 2020 in England, using spatial
and spatio-temporal kernel estimates developed by
Davies and Lawson (2019), Elson et al. (2021) discovered
the spatio-temporal distribution of COVID-19 infection. In
Brazil, Castro et al. (2021) successfully used daily data on
reported cases and deaths to understand, measure, and
compare the spatio-temporal pattern of the spread across
municipalities. In South Korea, to understand the COVID-19
clustering across districts in South Korea and how the
spatial pattern of COVID-19 changes, Kim and Castro
(2020) successfully applied the global Moran’s I statistic
and the retrospective space-time scan statistic to analyze
spatio-temporal clusters of COVID-19. Most recently, when
identifying spatial patterns of COVID-19 disease clustering
in India using another global spatial autocorrelation statistic,
the Getis-Ord G*
i statistic, Bhunia et al. (2021) discovered
that this statistic can help public health professionals to
eectively identify risk areas for disease and take decisions
in real-time to control this viral disease.
WebGIS-based mapping
When considering the usage of Web-based (or Web-
GIS-based) mapping during the COVID-19 pandemic,
Mooney and Juhász (2020) concluded that Web-GIS maps
have been widely used for delivering public information
on this fast-moving, epidemiologically complex, and
geographically unbounded process. Similar to those
reported by Mooney and Juhász (2020), Franch-Pardo
et al. (2020) also showed the importance of WebGIS-
based mapping in the dissemination and provision of
(ocial) information on COVID-19, especially for the
spatial representation of the pandemic and its evolution.
When conducting a study on geographical tracking and
mapping of coronavirus disease COVID-19/SARS-CoV-2
epidemic and associated events around the world, Boulos
and Geraghty (2020) successfully employed dierent types
of WebGIS-based mapping such as practical online/mobile
GIS and mapping dashboards for tracking the 2019/2020
coronavirus epidemic. According to Franch-Pardo et al.
(2020), the most international information compiled,
the most widely referenced viewer, and the rst to go
online out is an interactive WebGIS-based dashboard to
track COVID-19 in real-time developed by John Hopkins
University (see Dong et al. (2020) for more detail). At the
same time, Cicalò and Valentino (2019) successfully used
the web-based for the study of epidemics and design of
the web maps on COVID-19. Since then, more WebGIS-
based applications for mapping COVID-9 have been
created for each country such as Russia (Kuznetsov et
al. 2020a; Momynaliev et al. 2021), the USA (Cicalò and
Valentino 2019; Gao et al. 2020), China (Xu et al. 2020), UK
(Mooney and Juhász 2020), Israel (Rossman et al. 2020) and
Italy (Mooney and Juhász 2020).
Typically, to obtain a real-time nationwide view of
symptoms across the entire population in Israel, an online
questionnaire was employed in a study by Rossman et al.
(2020) to identify geographic clusters in which the virus
is spreading. This study is potential for the detection of
COVID-19 outbreaks. And recently, to help increase risk
awareness of the public, support data-driven public health
and governmental decision-making, and help enhance
community responses to the COVID-19 pandemic, Gao et
al. (2020) successfully used daily updated human mobility
statistical patterns derived from large-scale anonymized and
aggregated smartphone location big data at the county-
level in the United States to provide timely quantitative
information on how people in dierent counties and states
reacted to the social distancing guidelines.
In Russia, a number of authors have successfully
applied WebGIS to mapping the COVID-19 epidemic.
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Kuznetsov et al. (2020b) focused on the investigation and
design of the methodology and software prototype for
GIS-based support of medical administration and planning
on a city scale when accounting and controlling infectious
diseases. Later, with the aim of evaluating the usefulness
of Internet queries related to the smell to assess the
eectiveness of anti-epidemic measures of preventing the
spread of COVID-19 in some regions of Russia, Momynaliev
et al. (2021) concluded that the rise in the sudden interest
in smell among Internet users can be seen as a valuable
minimally invasive indicator of the spread of the coronavirus
in the population, as well as to assess the eectiveness of
anti-epidemic measures against COVID-19. WebGIS for
mapping the COVID-19 pandemic has been also proved
eectiveby other scholars (Bachilo et al. 2020; Nekliudov et
al. 2020).
Identication of correlations between COVID-19 and
natural, socio-economic factors
To analyze the correlation between conrmed cases
of COVID-19 and several geographic, meteorological,
and socio-economic variables at the province level in
Spain, Oto-Peralías (2020) points out that the arrival of
the summer heat may play in limiting the spread of the
virus. From a dierent angle, using GIS-based approaches
such as spatial lag and spatial error models to investigate
spatial dependence and multiscale geographically
weighted regression models to locally examine spatial
non-stationarity, Mollalo et al. (2020) point out the eects
of signicant explanatory variables (income inequality,
median household income, the proportion of black females,
and the proportion of nurse practitioners) on a relatively
high spatial variability of COVID-19 incidence rates in the
continental United States. Also in the United States, when
investigating the correlation between the geographic
spread of COVID-19 and the structure of social networks
as measured by Facebook, Kuchler et al. (2020) concluded
that a social connectivity index can help epidemiologists
predict the spread of communicable diseases.
In the rst COVID-19 wave, Russia has the third-
highest number of conrmed cases after the United States
and Brazil (Pramanik et al. 2020). Many contributions to
apply GIS in the ght against the second COVID-19 wave
have been made by scholars in Russia. Typically, using
the Random Forest algorithm, Pramanik et al. (2020)
successfully investigated the relationship between climatic
factors (air temperature, relative humidity, wind speed,
sunshine, diurnal temperature change, and temperature
seasonality) and the rise of COVID-19 intensity. Later, when
investigating the meteorological controls on the spread of
SARS-CoV-2 in Russia using correlation analysis and factor
analysis, Shankar et al. (2021) indicated that the increase
of temperature increases the spreading and the decreased
humidity with increase death rates. Spatial dynamics and
diusion factors across Russian regions were analyzed
in a study by Zemtsov and Baburin (2020). In that study,
Zemtsov and Baburin (2020) have revealed that the SARS-
CoV-2 has spread faster in regions where the population
has a higher susceptibility to diseases.
When India experienced the third wave of COVID-19,
much eort has been put into the identication of
correlations between COVID-19 and natural, socio-
economic factors. Using GIS-based proximity analysis
and census data of Jaipur city and socio-economic
parameters (population, population density, percentage
of main workers, and percentages of literates), Kanga
et al. (2021) researched the risk of COVID-19 infection
utilizing integrated hazard and vulnerability components
associated with this pandemic for eective risk mitigation.
Also in India, using a GIS-based geostatistical approach,
risk analysis of COVID-19 infections in Kolkata Metropolitan
city was carried out by Nath et al. (2021). With the help
of GIS-based approaches in combination with related
socio-economic variables, extensive studies on the risk
assessment to COVID-19 infection have been conducted
in many badly-aected countries by COVID-19 such as
the United States (Ali et al. 2021b; DuClos et al. 2021), Italy
(Tiboni et al. 2020), England (Sartorius et al. 2021), Brazil
(Gomes et al. 2020; Martines et al. 2021), Peru (Badillo-
Rivera et al. 2020), India (Kanga et al. 2021; Nath et al. 2021)
and Bangladesh (Masrur et al. 2020; Rahman et al. 2021a).
APPLICATIONS OF REMOTE SENSING IN THE STUDY OF
THE COVID-19 PANDEMIC
Studies of impacts on water quality
When the SARS-CoV-2 virus was initially identied in
December 2019 in Wuhan city, China, and quickly spread
throughout China Sivakumar (2021) point out that the
spread of COVID-19 will increase the water demand and
worsen the water quality, leading to additional challenges
in water. It is, therefore, the impacts of the COVID-19
pandemic on water quality employing remote sensing
techniques have been studied by many authors in China.
One of the rst studies on the impacts of the COVID-19
on water quality in China was carried out by Avtar et al.
(2020) to quantitatively estimate the chlorophyll-a (Chl-a)
concentrations in dierent lake bodies of Wuhan, China.
In that study, Avtar et al. (2020) concluded that there was
an elevated concentration of Chl-a during the COVID-19
lockdown. Also in Wuhan city, Sun et al. (2021) employed
multi-sensor satellite images (Landsat-8/OLI, Sentinel-2/
MSI, and HY-1C/CZI) to estimate the turbidity of lakes.
It was found that the mean turbidity showed a 24.9%
decline from 33.4 NTU to 25.1 NTU after the lockdown in
Wuhan, which dropped 16.0% compared to that in the
previous year (Sun et al. 2021). Later, when investigating
the lockdown eects of the COVID-19 on total suspended
solids (TSS) concentrations in the Lower Min River (China)
during COVID-19 using dierent multi-temporal optical
remotely sensed images acquired from November 2019
to April 2020 such as Landsat-5 Thematic Mapper (TM),
Landsat-8 Operational Land Imager (OLI), and China’s
GaoFen-1 (GF-1) Wide Field of View (WFV) images, Xu et al.
(2021) indicated that the lockdown measures have resulted
in a 48% fall in TSS concentrations in February 2020. Xu et
al. (2021) also concluded that industrial production, social
and economic activities, and river shipping appear to be
the main factors contributing to the river’s TSS decline in
the lockdown period. These ndings were consistent with
those reported in a most recent study of Liu et al. (2022)
that COVID-19 lockdown improved river water quality in
China.
After the SARS-CoV-2 virus quickly spread across the
globe, this problem has received much attention from
several authors in the worst-aected countries such
as India, Spain, and Italy. To understand the eects of
COVID-19 lockdown, Wagh et al. (2021) assessed the
indicative lake water quality for the Lake Hussain Sagar
(India) using Landsat-8 sensor Operational Land Imager
(OLI). This study results have shown that there were a
reduction in Chlorophyll-a (Chl-a) and Colored Dissolved
Organic Matter (CDOM) concentrations and a signicant
reduction in lake water pollution (Wagh et al. 2021). From

120
the above discussion, it can be concluded that there has
been an improvement in the water quality during the
COVID-19 lockdown. These ndings were consistent with
those reported by many studies (Arif et al. 2020; Najah et
al. 2021; Yunus et al. 2020). In this study, Adwibowo (2020)
investigated the eects of social distancing on water
quality in the Jakarta coast based on remote sensing data
captured by Copernicus Sentinel-3B Ocean and Land Color
Instrument in January and February of 2020, Adwibowo
(2020) gured out that there were reductions of levels
and areas of chlorophyll-a in the coast as a function of
social distancing and activity restrictions. Weeks later, with
the help of Sentinel-2A images and the ArcGIS software,
Parra Boronat (2020) conducted a study on the eects of
before (since February 3rd, 2020) and during (until June
22nd, 2020) the quarantine caused by COVID-19 on the
Alboran Sea (Spain). The study results of Parra Boronat
(2020) indicated that seawater quality has been improved
after the quarantine caused by COVID-19. Later, to study
the impacts of the 2020 COVID-19 lockdown and the
2019 extreme ood in the Venice lagoon (northeast Italy),
Niroumand-Jadidi et al. (2020) employed Planet Scope
imagery to retrieve water quality. The results of Niroumand-
Jadidi et al. (2020) have shown that a remarkable reduction
of the turbidity during the lockdown, due to the COVID-19
pandemic and capture the high values of total suspended
matter (TSM) during the ood condition. Using Sentinel-2A,
-2B, and optical satellite data, Tripathi et al. (2020) showed
that the Ganga River’s water quality has been improved
during COVID-19 lockdowns in India (24th March to 18th
May 2020) while comparing with the normal days.
Studies of impacts on air quality
In the early stages of the COVID-19 pandemic initially
discovered in Wuhan city, PR China, nitrogen dioxide (NO2)
concentrations estimated from remotely sensed images
had been proved to fall by as much as 30% across China
and by as much as 50% across areas of central Europe
(NASA 2020). Later, one of the rst studies on the impacts
of the COVID pandemic on air quality was carried out by
Talukdar et al. (2020). When modeling the global air quality
conditions in the perspective of COVID-19 stimulated
lockdown periods using MERRA-2and AIRS data, Talukdar
et al. (2020) concluded that amid lockdown aerosol optical
depth (AOD), sulfur dioxide (SO2), ozone, carbon monoxide
(CO), particulate matter (PM2.5), and black carbon (BC)
concentration level have been signicantly reduced in fully
lockdown countries. Later, studies on specic countries
and regions through remotely sensed images were
gradually reported. One of the Google Scholar rankings
ofmost highly cited studies on spatio-temporal patterns of
COVID-19 impact on human activities and environment in
mainland China using nighttime light and air quality data
was carried out by Liu et al. (2020). In that study, Liu et al.
(2020) discovered a signicant decreasing trend in the daily
average Air Quality Index for mainland China from January
to March 2020, with cleaner air in most provinces during
February and March, compared to January 2020. With the
help of satellite data, Zheng et al. (2020) estimated the
decline and rebound in China’s CO2emissions during the
COVID-19 pandemic. Similar to those reported in a study
by Zheng et al. (2020), Chen et al. (2021) also revealed the
driving force of China’s CO₂ emissions fell by more than
40% compared with the same period in 2019 when the
city was closed from the end of January to the beginning
of 2020.
Using the ground-based remote sensing techniques,
Ionov et al. (2021) found that there was a decrease in CO2
emission obtained during the COVID-19 lockdown period
in 2020 and the same period of 2019 in the city of St.
Petersburg, Russia. Singh et al. (2020) explored the dynamics
of dierent air pollutants and qualitatively highlight
potential links with COVID-19 pressures during dierent
phases of the pandemic in Russia using Sentinel-5P based
datasets. It was found that regional concentrations of NO2
and O3 increased signicantly, in some cases by more than
50% during the “lockdown” in Russia. Employing OMI and
AIRS data to estimate the extent of the reduction of major
pollutants such as carbon monoxide, nitrogen dioxide,
and sulfur dioxide in the south-east Asian regions from
January to April 2020, Metya et al. (2020) discovered air
quality improved in India and China during the COVID-19
outbreak in which NO2was reduced the most; CO to some
extent and SO2 experienced a nominal reduction. Similar
to those reported by Metya et al. (2020), there were also
a decrease in NO2 in the Beijing-Tianjin-Hebei region and
most of Northeast and Central China during COVID-19
(Nichol et al. 2020) and drastic reductions in NO2 (up to
-54.3%) in the urban area during partial lockdown (Nakada
and Urban 2020). Using low spatial resolution images,
Das et al. (2020) have shown that most of the countries,
for example, Italy, Spain, Germany, the UK, the USA, Russia,
India, Mexico, China, Australia, Brazil show a decreasing
trend of NO2 during COVID-19 lockdown in March 2020
when comparing with those obtained from the previous
year. Most recently, when investigating the COVID-19
transmission change under dierent lockdown scenarios
in Dhaka city, Bangladesh, study results of Rahman et al.
(2021b) showed that overall, 26, 20.4, 17.5, 9.7, and 8.8%
declined in PM2.5, NO2, SO2, O3, and CO concentrations,
respectively, in Dhaka City during the partial and full
lockdown compared to the period before the lockdown.
Late work usingdierent types of remotely sensed images
also conrmed the air quality improvement during the
COVID-19 lockdown, quarantine, and social distancing,
with studies from badly aected countries such as England
(Wyche et al. 2021), Italy (Filippini et al. 2020; Sannino et
al. 2020), Brazil (Brito et al. 2020), and most recently India
(Naqvi et al. 2021; Sathe et al. 2021).
Studies of other impacts on the environment
Apart from the main impacts on water and air quality
as reviewed above, using remotely sensed images, many
studies have been conducted on the other environmental
impacts of the COVID-19 pandemic such as urban heat
islands (Ali et al. 2021a; Alqasemi et al. 2021; Teufel et al.
2021) and ecology (Firozjaei et al. 2021). In the study
of urban heat islands, when conducting a study on
the remote sensing-based assessment of changes in
urban heat island eect associated with the lockdown
implementations to retard the spread of the COVID-19
virus in Pakistan, Ali et al. (2021a) had come to a conclusion
that restrictions on transportation in the cities resulted in
an evident drop in the surface urban heat island eect,
particularly in megacities. This nding is consistent with
those recently reported in studies of Alqasemi et al. (2021)
using the Level 2 Sentinel 5P data in the United Arab
Emirates and of Teufel et al. (2021) using MODIS images in
Montreal (Canada). Most recently, in the study of ecological
status, the impact of the COVID-19 lockdowns on urban
surface ecological status in Milan and Wuhan cities was
assessed in the research of Firozjaei et al. (2021). It was
found that, due to the COVID-19 lockdowns, built-up, bare
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soil, and green spaces for Milan and Wuhan dramatically
decreased (Firozjaei et al. 2021). To investigate the eect of
lockdown during COVID-19 on land surface temperature
using the TIRS sensor data acquired during the COVID-19
lockdown and post-lockdown in Dehradun city, India,
Maithani et al. (2020) discovered that there was an increase
in a number of hot spots accompanied by a decrease in
thermal comfort level post-lockdown. In Russia, one of
the most aected country by COVID-19, when studying
the reduction of surface radiative forcing observed from
remote sensing data during global COVID-19 lockdown in
April 2020, Mazhar et al. (2021) revealed that aerosol optical
depth and NO2 shows a signicant increase in some part
of Russia. Also in Russia, when comparing environmental
noise measurements in urban conditions before and
during the COVID-19 period, Vasilyev (2021) indicated that
transport noise level is reduced, but the industrial noise
level is almost the same, especially in low-frequency range.
These ndings were also consistent with those reported in
the worst-aected countries such as China (Fan et al. 2020;
Filonchyk et al. 2020), the United States (Acharya et al. 2021;
Wei et al. 2020), and European countries (Li et al. 2020).
CONCLUSIONS
This study is an addition to studies of the applications
of GIS and remote sensing on the COVID-19 pandemic. Two
themes of GIS and RS-related applications are grouped into
six categories of the COVID-19-related studies including
spatio-temporal changes, WebGIS-based mapping, the
identication of the correlation between the COVID-19
pandemic and natural, socio-economical variables
using GIS, and the use of remote sensing to assess the
environmental impacts of the COVID-19. GIS methods play
an important role in COVID-19 related-decision-making,
more importantly, social mobilization and community
responses. COVID-19 studies with remote sensing can
be an eective tool in the assessment of the impacts
of COVID-19. This study provides insight and a better
understanding of the applications of GIS and RS on studies
of the COVID-19 pandemic. It can be concluded from this
review that both GIS and RS have played an important role
in many aspects of COVID-19 studies, especially in the ght
against COVID-19.
AUTHOR CONTRIBUTIONS
Quoc-lap Kieu conceived, designed, and prepared
the research. Tien-thanh Nguyen carried out the formal
analysis, wrote and edited the manuscript. Anh-huy
Hoang supervised the research and provided conceptual
advice. All authors discussed the results, implications and
commented on the manuscript at all stages.
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