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Decision-making plays a key role in reducing landslide risk and preventing natural disasters. Land management, recovery of degraded lands, urban planning, and environmental protection in general are fundamental for mitigating landslide hazard and risk. Here, we present a GIS-based multi-scale approach to highlight where and when a country is affected by a high probability of landslide occurrence. In the first step, a landslide human exposure equation is developed considering the landslide susceptibility triggered by rain as hazard, and the population density as exposed factor. The output, from this overview analysis, is a global GIS layer expressing the number of potentially affected people by month, where the monthly rain is used to weight the landslide hazard. As following step, Logistic Regression (LR) analysis was implemented at a national and local level. The Receiver Operating Characteristic indicator is used to understand the goodness of a LR model. The LR models are defined by a dependent variable, presence–absence of landslide points, versus a set of independent environmental variables. The results demonstrate the relevance of a multi-scale approach, at national level the biophysical variables are able to detect landslide hotspot areas, while at sub-regional level geomorphological aspects, like land cover, topographic wetness, and local climatic condition have greater explanatory power. [ABSTRACT FROM AUTHOR]

After a natural disaster or humanitarian crisis, rescue forces and relief organisations are dependent on fast, area-wide and accurate information on the damage caused to infrastructure and the situation on the ground. This study focuses on the assessment of building damage levels on optical satellite imagery with a two-step ensemble model performing building segmentation and damage classification trained on a public dataset. We provide an extensive generalization study on pre- and post-disaster data from the passage of the cyclone Idai over Beira, Mozambique, in 2019 and the explosion in Beirut, Lebanon, in 2020. Critical challenges are addressed, including the detection of clustered buildings with uncommon visual appearances, the classification of damage levels by both humans and deep learning models, and the impact of varying imagery acquisition conditions. We show promising building damage assessment results and highlight the strong performance impact of data pre-processing on the generalization capability of deep convolutional models. [ABSTRACT FROM AUTHOR]

This study was aimed to investigate the role of two exotic bivalves ( Ruditapes philippinarum and Musculista senhousia ) on the trophic enrichment and the nutritional quality of sediments from the Sacca di Goro (northern Adriatic Sea) through manipulative experiments. The concentrations of sedimentary total organic matter, biopolymeric carbon, carbohydrates, proteins, lipids, and chlorophyll- a were determined through a 64 days-long period. The experimental setup consisted in three replicated treatments ( R. philippinarum , M. senhousia and simply sand), with treatments and time as fixed factors. All the biochemical classes showed wide fluctuation, and significant differences among treatments were detected during the experimental period. Both bivalve species played a role in sediment enrichment process. At the end of the experimental period, proteins represented the main biochemical class of organic compounds in the treatments with bivalves (48.5 and 44.5% of the biopolymeric fraction in the M. senhousia and R. philippinarum treatments, respectively), while in the control treatment they were only 32.7%. The accumulation of proteic nitrogen increased the nutritional quality of the sedimentary organic matter. Moreover, the presence of M. senhousia also enhanced the accumulation of microphytobenthic carbon. [ABSTRACT FROM AUTHOR]

To assess the recovery of the lagoon in the Valle di Gorino, Italy, the macrobenthic assemblages were studied throughout a two year period, i.e. before and after the opening of a floodgate. Three stations were located along a gradient of confinement, and sampled for macrofauna, water and sedimentary parameters. Analyses were performed through univariate and multivariate techniques. The macrobenthic assemblages were characterized by a small total number of species, strong dominance in number by a few of these species, and low diversity, but changes in community structure and composition were clearly recognizable. Significant negative correlations were found between macroalgae and macrobenthic community attributes. The altered hydrological regime due to the opening of the floodgate disrupted macroalgal cycles and altered the related macrofaunal successional dynamics: species linked to the macroalgal degradation phase became dominant throughout the Valle. After the intervention, all areas exhibited similar species composition and abundances, and appeared to be in a permanently disturbed condition. A rescaling of environmental conditions repositioned species roles in the community: those species which were typical of a certain phase of the former successional progression, were successively dominating the whole lagoon, irrespective of time and the estuarine gradient. After the intervention, the spatial and temporal distribution of macrobenthic taxa was under the control of factors linked to organic enrichment of the sediments. [ABSTRACT FROM PUBLISHER]