Abstract
In the last decade, real-time access to data and the use of high-resolution spatial information have provided scientists and engineers with valuable information to help them understand risk. At the same time, there has been a rapid growth of novel and cutting-edge information and communication technologies for the collection, analysis and dissemination of data, re-inventing the way in which risk management is carried out throughout its cycle (risk identification and reduction, preparedness, disaster relief and recovery). The applications of those geospatial technologies are expected to enable better mitigation of, and adaptation to, the disastrous impact of natural hazards. The description of risks may particularly benefit from the integrated use of new algorithms and monitoring techniques. The ability of new tools to carry out intensive analyses over huge datasets makes it possible to perform future risk assessments, keeping abreast of temporal and spatial changes in hazard, exposure, and vulnerability. The present special issue aims to describe the state-of-the-art of natural risk assessment, management, and communication using new geospatial models and Earth Observation (EO)architecture. More specifically, we have collected a number of contributions dealing with: (1) applications of EO data and machine learning techniques for hazard, vulnerability and risk mapping; (2) natural hazards monitoring and forecasting geospatial systems; (3) modeling of spatiotemporal resource optimization for emergency management in the post-disaster phase; and (4) development of tools and platforms for risk projection assessment and communication of inherent uncertainties.
Highlights
Time and again, natural hazards heavily impact land and society throughout the world [1,2]
Taking advantage of the day, night, and cloud-penetrating capacity and high spatial resolution of Synthetic Aperture Radar (SAR), and exploiting the short revisit time of SAR images provided by the Cosmo-SkyMed constellation of four satellites, the study demonstrated that the combination of direct observation of flooding from SAR remote sources and flood inundation models can archive rapid and accurate flood predictions in what are usually complex topographies of flat terrain, such as urbanized areas located at a river delta
The results showed that the Multi-Agent System (MAS) was more accurate than the contract net protocol (CNP) model in terms of urban search and rescue (USAR) operational time and the number of human fatalities and, confirms MAS as a promising approach to support decision-making in natural hazard emergency management
Summary
Natural hazards (e.g., floods, landslides, earthquakes, and so on) heavily impact land and society throughout the world [1,2]. Recent studies have offered a number of innovative strategies based on bigdata, EO infrastructures and (geospatial) modeling to improve knowledge about natural hazard phenomena through new remote sensing algorithms, such as landslide detection and quantification [11,12] and delineation of flood-prone areas over large territories and/or with a growing level of detail [13] These strategies are aimed at quantitatively assessing risk projections and weighing a portfolio of future scenarios [14], and supporting risk and emergency management into the broader context of related governance (multi-institutional) arrangements [15,16].
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