The aim of this paper is to compare the two largest forest fires that occurred in Greece in July 2018 using metrics for burned area and burn severity mapping, derived only from free satellite data. Sentinel-2 satellite images of the European Space Agency (ESA) within the Copernicus program provide a spatial resolution of 10 m, which facilitates more accurate monitoring of environmental phenomena such as forest fires. The processing of the satellite images and the calculation of the metrics was performed using SNAP software, which is an open-source software developed by ESA. The mapping of the obtained results was performed in the QGIS software, which is also an open-source software. The delimitation of the burned area and the classification of the severity of both wildfires was performed using the Relativized Burn Ratio (RBR) satellite index. These results were contrasted with the Copernicus Emergency Management Service (EMS) maps related to these two events. Our results obtained in relation to the size of the burned area show smaller affected areas than the Copernicus Emergency Management Service maps. This is explained by the different methods used in the delimitation of the burned areas. In the case of Mati's wildfire the EMS has created the thematic layer by means of visual interpretation using post-event satellite image and in the case of Kineta's wildfire was applied a semi-automatic approach. Moreover, in this study is proposed and evaluated a new burn severity metric, the burned vegetation index (BVI) which shows where the most significant changes in healthy vegetation occurred. This new index was compared with RBR, dNDVI and dNBR using statistical correlation. The results indicate that BVI shows better the burned vegetation and its statistical correlation with RBR is significant (R 2 = 0.92).
This case study investigates the magnitude and nature of the spatial effect generated by the anti-COVID measures on land surface temperature (LST) in the city of Târgu Mureș (Marosvásárhely), Romania. The measures were taken by the Romanian government during the state of emergency (March 16 – May 14, 2020) due to the SARS-CoV-2 coronavirus pandemic. The study shows that – contrary to previous studies carried out on cities in China and India – in most of the urban areas of Marosvásárhely LST has increased in the period of health emergency in 2020 concerning the large average of the years 2000–2019. Remote sensing data from the MODIS and the Landsat satellites show, that MODIS data, having a moderate spatial (approximately 1 km) but good temporal resolution (daily measurements), show a temperature increase of +0.78 °C, while Landsat data, having better spatial (30 m) but lower temporal resolution, show an even greater increase, +2.36 °C in the built-up areas. The difference in temperature increase is mainly due to the spatial resolution difference between the two TIR band sensors. The LST anomaly analysis performed with MODIS data also shows a positive anomaly increase of 1 °C. However, despite this increase, with the help of the hotspot-coldspot analysis of the Getis-Ord Gi* statistic we were able to identify 46 significant coldspots that showed a 1–2 °C decrease of LST in April 2020 compared to the average of the previous years in April. Most of these coldspots correspond to factory areas, public transport epicenters, shopping centers, industrial polygons, and non-residential areas. This shows that anti-COVID measures in the medium-sized city of Marosvásárhely had many effects on LST in particular areas that have links to the local economy, trade, and transport. Paired sample t-test for areas identified with LST decrease shows that there is a statistically significant difference in the average LST observed before and after anti-COVID measures were applied. MODIS-based LST is satisfactory for recognizing patterns and trends at large or moderate geographical scales. However, for a hotspot-coldspot analysis of the urban heat islands, it is more suitable to use Landsat data.
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