Anomalous thermal infrared (TIR) emissions have widely been detected by satellite sensors before the major earthquakes. A recent processing technique for geostationary thermal data, developed for the case of the 2009 April 6, magnitude 6.3 L'Aquila earthquake, makes it possible to identify areas of enhanced TIR emissions around the epicentral region at a mean distance of less than 50 km but inside a radius of about 100 km. The index, called Night Thermal Gradient (NTG), derived from 4-D time-series data (two spatial and two temporal coordinates), identifies TIR anomalies by following the temperature trend during night, when the surface of the Earth is expected to cool. Leading up to the L'Aquila earthquake, an anomalous warming trend was observed. In this study, the anomalous NTG pattern is compared to the expected normal trend, taking into account the seismogenic faults, the overall tectonic setting, lithological spatial features, the orography and world stress map near the epicentral region. Main results are that a certain lithological selectivity can be recognized and that the known main stress field and seismogenic faults seem to be less important than certain tectonic lineaments, which are classified as non-seismogenic. The strong correlation between the topography and the TIR anomalies is in agreement with proposed physical mechanism for the generation of TIR anomalies. This relation is, in turn, present mainly in correspondence to two tectonic lineaments which in particular are thrusts: therefore, strong compressive states seem to be a positive condition for the generation of TIR anomalies. The temporary modification of these stress fields have triggered the Paganica Fault to its normal rupture mechanism. It is important to note that the distances, over which the TIR anomalies occurred, are an order of magnitude larger than the estimated length of the main fault rupture. Pixel-by-pixel time-series comparisons between the maximum TIR anomaly area and the epicentre of the main shock show that the increase in radiative emission occurred in the areas of maximum TIR anomalies and did not start by spreading outward from the epicentral region.

Relations between ground surface warming-up and earthquakes are presented by means of the analysis of thermal data detected by Meteosat satellite. The analysis has been carried out on the basis of Land Surface Temperature (LST) data, by the application of algorithms for two purposes: the reduction of the effect of cloud coverage and daily weather variability and the improvement in resolution of thermal maps. The main case study has been L'Aquila earthquake on 6th April 2009 with two different observation timeframes: one in absence of significant seismic activity (October 2008), one right before and straight after the same earthquake (January-May 2009). The detected thermal anomalies reveal the possibility to associate surface thermal phenomena to the imminent manifestation of seismic events.

Applications of non-invasive sensing techniques to investigate the internal structure and surface of precious and delicate objects represent a very important and consolidated research field in the scientific domain of cultural heritage knowledge and conservation. The present article is the first of three reviews focused on contact and non-contact imaging techniques applied to surveying cultural heritage at micro- (i.e., manufacts), meso- (sites) and macro-scales (landscapes). The capability to infer variations in geometrical and physical properties across the inspected surfaces or volumes is the unifying factor of these techniques, allowing scientists to discover new historical sites or to image their spatial extent and material features at different scales, from landscape to artifact. This first part concentrates on the micro-scale, i.e., inspection, study and characterization of small objects (ancient papers, paintings, statues, archaeological findings, architectural elements, etc.) from surface to internal properties.

Mediterranean mountainous areas of shallow soil often display a mosaic of tree clumps surrounded by grass. The combined role and dynamics of water extracted from the underlying rock, and the competition between adjacent patches of trees and grass, has not been investigated. We quantified the role rock water plays in the seasonal dynamics of evapotranspiration (ET), over a patchy landscape in the context of current and past seasonal climate changes, and land‐cover change strategies. Soil water budget suggests deep water uptake by roots of trees (0.8–0.9 mm/d), penetrating into the fractured basalt, subsidized grass transpiration in spring through hydraulic redistribution. However, in summer trees used all the rock water absorbed (0.79 mm/d). A 15‐year data set shows that, with increasing seasonal drought‐severity (potential ET/precipitation) to >1.04, the vertical water flux through the bottom of the thin soil layer transitions from drainage to uptake in support of ET. A hypothetical grass‐covered landscape, with no access to deep water, would require 0.68–0.85 mm/d more than is available, forcing shortened growing season and/or reduced leaf area. Long‐term decreasing winter precipitation and increasing spring potential ET suggest drying climate, so far with stable vegetation mosaic but progressively earlier peak of grass leaf area. Intervention policies to increase water yield by reducing tree cover will curtail grass access to rock moisture, while attempting to increase tree‐related products (including carbon sequestration) by increasing forest cover will limit water availability per tree leaf area. Both changes may further reduce ecosystem stability.

Stranded beach litter is a ubiquitous issue. Manual monitoring and retrieval can be cost and labour intensive. Therefore, automatic litter monitoring and retrieval is an essential mitigation strategy. In this paper, we present important foundational blocks that can be expanded into an autonomous monitoring-and-retrieval pipeline based on drone surveys and object detection using deep learning. Drone footage collected on the islands of Malta and Gozo in Sicily (Italy) and the Red Sea coast was combined with publicly available litter datasets and used to train an object detection algorithm (YOLOv5) to detect litter objects in footage recorded during drone surveys. Across all classes of litter objects, the 50%–95% mean average precision (mAP50-95) was 0.252, with the performance on single well-represented classes reaching up to 0.674. We also present an approach to geolocate objects detected by the algorithm, assigning latitude and longitude coordinates to each detection. In combination with beach morphology information derived from digital elevation models (DEMs) for path finding and identifying inaccessible areas for an autonomous litter retrieval robot, this research provides important building blocks for an automated monitoring-and-retrieval pipeline.