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EN
Land Surface Temperature (LST) is an important variable within global cli mate change. With the appearance of remote sensing techniques and advanced GIS software, it is now possible to estimate LST. In this study, the effect of lock-down during COVID-19 on the LST was assessed using Landsat 8 Imagery. LST dynamic was investigated for three different periods: Before, during and after the COVID-19 lockdown. The study was conducted in Casablanca City. The results showed that during the emergence of COVID-19 with lock down policy applied, the LST decreases remarkably compared to the previous 4-years’ average LST. After the easing of restrictions, the LST increased to exceed the previous 4-year mean LST. Furthermore, throughout all studied periods, the LST recorded its higher values in industrial zones and areas with high urban density and urban transportation, which indicates the conspicuous impact of anthropogenic activities on the LST variation. These findings indicate an ability to assess the feasibility of planned lockdowns intended as a potential preventive mechanism to reduce LST peaks and the loss of air quality in metropolitan environments in the future.
EN
We investigate the structural properties of a spatio-temporal network of earthquake events that incorporates magnitude information between the connected events. The network creates temporally directed links from an origin event towards a later event if it breaks the record closest distance from the origin among all the events in the catalog so far. Additionally, the links are conditionally classified based on the magnitude difference between connected events: “up” (“down”) connections point from a weaker (stronger) to a stronger (weaker) event. Using earthquake records from the Philippines from 1973 to 2012 and southern California from 1982 to 2012, we observe that the out-degree distributions show slight deviations from the corresponding Poisson distribution of the same mean. The space and time separations of connected earthquakes both show power-law regimes, suggesting spatio-temporal (self)organization. More importantly, the conditional distributions of “up” and “down” connections in space, time, and network structure point to a higher likelihood of a stronger event triggering a nearby weaker event for the first few connections, as in the case of aftershocks. The results are captured by a sandpile-based model where a small but finite probability of preferentially targeting the most susceptible grid site is introduced. Our analysis, coupled with the discrete model analog, provides a quantitative picture of the spatio-temporal and magnitude organization of seismicity beyond just the successive events. The technique may be extended to further characterize similar long-period earthquake records to yield a more complete picture of the underlying processes involved in seismicity.
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