Exploring the effect of COVID-19 pandemic lockdowns on urban cooling: A tale of three cities.

COVID-19 pandemic has had a major impact on our society, environment and public health, in both positive and negative ways. The main aim of this study is to monitor the effect of COVID-19 pandemic lockdowns on urban cooling. To do so, satellite images of Landsat 8 for Milan and Rome in Italy, and Wuhan in China were used to look at pre-lockdown and during the lockdown. First, the surface biophysical characteristics for the pre-lockdown and within-lockdown dates of COVID-19 were calculated. Then, the land surface temperature (LST) retrieved from Landsat thermal data was normalized based on cold pixels LST and statistical parameters of normalized LST (NLST) were calculated. Thereafter, the correlation coefficient (r) between the NLST and index-based built-up index (IBI) was estimated. Finally, the surface urban heat island intensity (SUHII) of different cities on the lockdown and pre-lockdown periods was compared with each other. The mean NLST of built-up lands in Milan (from 7.71 °C to 2.32 °C), Rome (from 5.05 °C to 3.54 °C) and Wuhan (from 3.57 °C to 1.77 °C) decreased during the lockdown dates compared to pre-lockdown dates. The r (absolute value) between NLST and IBI for Milan, Rome and Wuhan decreased from 0.43, 0.41 and 0.16 in the pre-lockdown dates to 0.25, 0.24, and 0.12 during lockdown dates respectively, which shows a large decrease for all cities. Analysis of SUHI for these cities showed that SUHII during the lockdown dates compared to pre-lockdown dates decreased by 0.89 °C, 1.78 °C, and 1.07 °C respectively. The results indicated a high and substantial impact of anthropogenic activities and anthropogenic heat flux (AHF) on the SUHI due to the substantial reduction of huge anthropogenic pressure in cities. Our conclusions draw attention to the contribution of COVID-19 lockdowns (reducing the anthropogenic activities) to creating cooler cities.

The Geography of the Covid-19 Pandemic: A Data-Driven Approach to Exploring Geographical Driving Forces.

The Covid-19 pandemic emerged and evolved so quickly that societies were not able to respond quickly enough, mainly due to the nature of the Covid-19 virus' rate of spread and also the largely open societies that we live in. While we have been willingly moving towards open societies and reducing movement barriers, there is a need to be prepared for minimizing the openness of society on occasions such as large pandemics, which are low probability events with massive impacts. Certainly, similar to many phenomena, the Covid-19 pandemic has shown us its own geography presenting its emergence and evolving patterns as well as taking advantage of our geographical settings for escalating its spread. Hence, this study aims at presenting a data-driven approach for exploring the spatio-temporal patterns of the pandemic over a regional scale, i.e., Europe and a country scale, i.e., Denmark, and also what geographical variables potentially contribute to expediting its spread. We used official regional infection rates, points of interest, temperature and air pollution data for monitoring the pandemic's spread across Europe and also applied geospatial methods such as spatial autocorrelation and space-time autocorrelation to extract relevant indicators that could explain the dynamics of the pandemic. Furthermore, we applied statistical methods, e.g., ordinary least squares, geographically weighted regression, as well as machine learning methods, e.g., random forest for exploring the potential correlation between the chosen underlying factors and the pandemic spread. Our findings indicate that population density, amenities such as cafes and bars, and pollution levels are the most influential explanatory variables while pollution levels can be explicitly used to monitor lockdown measures and infection rates at country level. The choice of data and methods used in this study along with the achieved results and presented discussions can empower health authorities and decision makers with an interactive decision support tool, which can be useful for imposing geographically varying lockdowns and protectives measures using historical data.

Characterizing, monitoring, and simulating land cover dynamics using GlobeLand30: A case study from 2000 to 2030.

Land cover maps provide us with a unique opportunity to monitor our environmental and anthropogenic resources over space and time. Temporal land cover maps increase the efficiency of land monitoring process by providing a set of observations so that any changes in the landscape can be tracked. So far, many land change monitoring efforts have been limited to small regions and analysing local processes, because there has been a lack of fine resolution global land cover maps for regional and global studies. Recently, the availability of bi-temporal GlobeLand30 product for years 2000 and 2010 has strengthened our land monitoring observations through a 10-year window at 30-meter resolution. The main objective of this study is to explore the land cover changes within 2000-2010 in one of the most dynamic regions in the Middle East. To do so, GlobeLand30 datasets were used for characterizing the regional landscapes and the distribution of the existing land cover types. Moreover, the study aims at predicting the future land cover patterns by 2030 based on the historical changes. The quantitative analysis reveals a high degree of changes from the majority of land types to barelands with the highest contribution from grasslands and shrublands. The vibrant spots of change are located on central Iran towards its eastern border while the persistent spots are located on its north and northwestern parts. The predictive measures using Markov chain analysis message forthcoming changes in favor of increasing barelands being taken from grasslands, cultivated lands, and shrublands. The findings are beneficial to a wide range of end users including academics, land managers, and environmentalists on a) which resources were available in the first decade of 21st century, and b) how they were treated within a decade, and c) how the resources will evolve within the following two decades. Further discussions on the achieved results and future directions of research are presented in the conclusions.