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Landslides are catastrophic natural phenomena occurring as a consequence of climatic, tectonic, and human activities, sometimes combined among them. Mostly due to climate change effects, the frequency of occurrence of these events has quickly grown in recent years, with a consequent increase in related damage, both in terms of loss of human life and effects on the involved infrastructures. Therefore, implementing properly actions to mitigate consequences from slope instability is fundamental to reduce their impact on society. Satellite systems, thanks to the advantages offered by their global view and sampling repetition capability, have proven to be valid tools to be used for these activities in addition to traditional techniques based on in situ measurements. In this work, we propose an advanced multitemporal technique aimed at identifying and mapping landslides using satellite-derived land cover information. Data acquired by the Multispectral Instrument (MSI) sensor aboard the Copernicus Sentinel-2 platforms were used to investigate a landslide affecting Pomarico city (southern Italy) in January 2019. Results achieved indicate the capability of the proposed methodology in identifying, with a good trade-off between reliability and sensitivity, the area affected by the landslide not just immediately after the event, but also a few months later. The technique was implemented within the Google Earth Engine Platform, so that it is completely automatic and could be applied everywhere. Therefore, its potential for supporting mitigation activities of landslide risks is evident. [ABSTRACT FROM AUTHOR]

Climate-induced drought events are responsible for forest decline and mortality in different areas of the world. Forest response to drought stress periods may be different, in time and space, depending on vegetation type and local factors. Stress analysis may be carried out by using field methods, but the use of remote sensing may be needed to highlight the effects of climate-change-induced phenomena at a larger spatial and temporal scale. In this context, satellite-based analyses are presented in this work to evaluate the drought effects during the 2000s and the possible climatological forcing over oak forests in Southern Italy. To this aim, two approaches based on the well-known Normalized Difference Vegetation Index (NDVI) were used: one based on NDVI values, averaged over selected decaying and non-decaying forests; another based on the Robust Satellite Techniques (RST). The analysis of the first approach mainly gave us overall information about 1984–2011 rising NDVI trends, despite a general decrease around the 2000s. The second, more refined approach was able to highlight a different drought stress impact over decaying and non-decaying forests. The combined use of the RST-based approach, Landsat satellite data, and Google Earth Engine (GEE) platform allowed us to identify in space domain and monitor over time significant oak forest changes and climate-driven effects (e.g., in 2001) from the local to the Basilicata region scale. By this way, the decaying status of the Gorgoglione forest was highlighted two years before the first visual field evidence (e.g., dryness of apical branches, bark detachment, root rot disease). The RST exportability to different satellite sensors and vegetation types, the availability of suitable satellite data, and the potential of GEE suggest the possibility of long-term monitoring of forest health, from the local to the global scale, to provide useful information to different end-user classes. [ABSTRACT FROM AUTHOR]

Different parameters (chemical, physical, biological, etc.) have been associated to the complex process of preparation of earthquakes. Among these, also the fluctuations of Earth's thermally emitted radiation, as measured by sensors on board of satellite system operating in the Thermal Infra-Red (TIR) spectral range, and anomalous variations of ionospheric electron content (TEC) and ion densities have been proposed, since long time, as possible indicators of an impending earthquake.Since 2001, the general change detection approach RST (Robust Satellite Techniques; Tramutoli, 1998; 2005; 2007) has been used to discriminate anomalous thermal signals, possibly associated to seismic activity, from normal fluctuations of Earth's thermal emission related to other causes (e.g. meteorological) independent on the earthquake occurrence. Being based on a statistical definition of anomalies and on a suitable method for their identification even in very different local (e.g. related to atmosphere and/or surface) and observational (e.g. related to the time/season or satellite view angles) conditions, and thanks to its full exportability on different satellite packages, the RST methodologies could be applicable also to TEC measurements recorded by the GPS satellite constellation, in order to discriminate anomalous signals from normal fluctuations of the signal itself.In this work, RST approach has been reformulated in order to be applicable to the TEC measurements. To this aim, more than six years (since 2003 up to 2009) of TEC measurements collected over Italy have been analyzed to study the preparatory phases of the L'Aquila earthquake (6 April 2009 Mw 6.3). Achieved results will be discussed and compared with TIR anomalies detected in the same period by using independent RST analyses carried out over Italian peninsula by Genzano et al., 2009 (using MSG/SEVIRI TIR data), Lisi et al., 2010 (using NOAA/AVHRR TIR data) and Pergola et al., 2010 (using EOS/MODIS TIR data), in order to evaluate the impact of the joint use of both parameters (TIR and TEC) in the framework of a multi-parametric approach to time-Dependent Assessment of Seismic Hazard (t-DASH). [ABSTRACT FROM AUTHOR]