The present study focused on rapid urbanization due to the change in the existing landforms which has caused substantial adverse impacts on Urban Thermal Environment. In the present study, we have acquired the Landsat data (TM and OLI) for the years 1990, 2000, 2010, and 2020 to observe the land use changes (vegetation cover, built up land, barren land, and water) in Lahore using the supervised image classification method. Later, the impact of urbanization has been examined with Land Surface Temperature (LST) and eventually the Surface Urban Heat Island (SUHI) has been calculated. Accuracy of the classified images revealed an overall accuracy (Kappa co-efficient) of 95.3% (0.929%), 92.05% (0.870%), 89.7% (0.891%), and 85.8% (0.915%) for the years 1990, 2000, 2010, and 2020, respectively. It was found that vegetation cover decreased from 60.5% in 1990 to 47.7% in 2020 at the cost of urbanization. The overall built-up land increased by 23.52% from 1990 to 2020. Urbanization has influenced the LST, and it was examined that maximum LST consistently increased with increase in built-up land. The difference between urban and rural buffer reveals that SUHI has also been increasing over the years. SUHI has been raised from 1.72 C in 1990 to 2.41 C in 2020, and about 0.69 C relative change has been observed. It has also been observed that the Normalized Difference Vegetation Index (NDVI) and LST have an inverse relationship. The research outcomes of this study are useful for urban climatologists, urban planners, architects, and policymakers to devise climate resilient policies, structure, and decisions to balance the urban green spaces for a healthy urban environment.

About 41% of the earth is drought-affected, which has impacted nearly 2 billion people, and it is expected that more than 90% of terrestrial areas will be degraded by 2050. To evade and mitigate the harmful impacts of drought, it is necessary to study the rainfall variability and assess the drought trend at a global and regional level. This study utilized 70 meteorological stations in South Korea to evaluate the rainfall variability, drought, and its trend during the past five decades using the standardized precipitation evapotranspiration index (SPEI) and the standardized precipitation index (SPI). Rainfall data normality was assessed with mean, standard deviation, skewness, and kurtosis. The highest amount of rainfall was observed in the months of June, July, and August. The SPI and SPEI 12-month results revealed that 1982, 1988, 2008, 2015, and 2017 were dry years throughout the country, while from 2013 to 2017 mixed drought events were observed for the 6-month time series. The Mann-Kendall trend test was applied to the 1- and 12-month time series, and the results revealed that the months of January, March, April, May, June, and August had a significant negative trend, which means drought is increasing in these months, while the months of September, October, and December had a significant positive trend, which means wetter conditions prevailed in these months during the study period. It was observed in the 12-month time series that only two met stations had a significant negative trend, while only one had a significant positive trend. It was found that January and March were the driest months, and October was the wettest month. The detected drought events in this research are consistent with ENSO events. We have observed differences in drought characteristics (duration and frequency) for both indices. Climatic data revealed that South Korea has faced drought conditions (rainfall deficit) due to a shortened monsoon season. This study can provide guidance on water management strategies under the changing pattern of drought in South Korea.

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Coronavirus pandemic disease (COVID-19) has spread globally. Presently, there is insufficient data regarding clinical studies and its epidemiological features. However, it is comprehended that most of the COVID-19 infected patients show mild to moderate symptoms which improve without any medical assistance attributing to enhanced immune system by generating antibodies against the viral antigens. In this comparative study, the active cases, recovered cases, deaths, and total confirmed cases from January 2020 to 23rd August 2021 have been analyzed using a geospatial technique inverse distance weighting (IDW). Until latter, the total number of COVID-19 cases reported in Italy were 4,168,699 including 128,715 deceased, 3,904,429 recovered and 135,555 cases were still active carriers. Out of total cases 20.76% were reported in Lombardia region with a death rate of 26.26%. This mortality rate was found higher in comparison with rate followed by Emilia-Romagna (10.35%), Piemonte (9.10%), and Vento (9.06%). While percentage of recovery was found variable i.e. in Lombardia 20.98%, followed by Veneto 10.89%, Campania 10.88% and Emilia-Romagna 9.72%. COVID-19 evolution in Italy has majorly affected the urban area i.e., Rome, Milan, Naples, Bologna, and Florence. Geospatial technology played a vital statistical role by tracking infected patients, active cases, and the recovered cases. Thus, it is acknowledged that geospatial techniques are an important tool in statistical evaluation of disease spread and their control among populations