Special Issue on Mapping, Monitoring and Assessing Disasters

Mapping, monitoring, and assessing technologies and related studies and applications play a significant role in disaster management and disaster risk mitigation. In recent years, synergies of modern and innovative methodologies have augmented the efficiency of disaster mapping shortly after their advent and made it possible for involved scientists and researchers to acquire and analyze related data and to disseminate critical information to first responders during emergencies, authorities involved in disaster management and recovery processes, the affected population, and the general public.

This present Special Issue comprises 10 research papers addressing various issues of mapping and monitoring disasters and assessing their impact. They highlight recent advances in the field and are valuable for understanding the complexity of the generated phenomena. The phenomena and their effects included in this Special Issue can be divided into: (i) earthquakes and related ground deformation [1,2,3], (ii) earthquake-triggered landslides (ETL) [4,5,6], (iii) landslides triggered by hydrometeorological hazards comprising medicanes [6,7], (iv) tsunamis [8], and (v) wildfires [9,10].

Sakkas et al. [1] present the results of monitoring seismicity and ground deformation in the Ionian Islands (western Greece) during a period of intense seismic activity (2014–2018) with destructive earthquakes. Joint pre-, co-, and post-seismic ground deformation and seismological analysis is performed based on geodetic data from the commercial and institutional continuous Global Navigation Satellite System (GNSS) networks in the area, as well as seismological data from the Hellenic Unified Seismic Network (HUSN), respectively.

Kaviris et al. [2] monitored the 2020 to 2021 Thiva (Central Greece) earthquake sequence. They utilized double-difference relocation to assemble a high-resolution earthquake catalogue and examine, in detail, the distribution of hypocenters and the spatiotemporal evolution of the sequence. By applying instrumental and imaging geodesy, they delineated the local deformation and identified long-term trends that could have contributed to stress loading.

Vassilakis et al. [3] monitored the 27 September 2021, Mw = 6.0 Arkalochori (Crete, southern Greece) earthquake. They conducted interdisciplinary research comprising geological mapping, tectonic analysis, fault photorealistic model creation by unmanned aerial system (UAS) data processing, as well as post-seismic surface deformation analysis by differential interferometry synthetic aperture radar (DInSAR) image interpretation coupled with accurately relocated epicenters recorded by locally established seismographs.

Mavroulis et al. [4] applied UAS-aided photogrammetry and terrestrial laser scanning (TLS) to high-visit coastal areas in the western part of Lefkada Island (western Greece), often affected by ETL. This application aims to explore how the capabilities of these cutting-edge methodologies contribute to the improvement of our understanding on and monitoring of the structural integrity of slopes. This approach allows the initial identification of high-risk zones and the subsequent prioritization of measures and strategies for risk-mitigation-driven development.

Mavroulis et al. [5] studied ETL from historical times to present in Cephalonia Island (western Greece). Based on scientific publications and numerous contemporary sources, they compiled an inventory of sites affected by ETL, several of which caused human losses and injuries. The study further examines the ETL susceptibility, exploiting 10 landslide causal factors in the frame of a geographic information system (GIS)-based analytic hierarchy process (AHP). The comparison of the ETL inventory and the landslide susceptibility index (LSI) map highlights the high to critically high susceptible zones and reveals that the majority of ETL was generated within the highlighted susceptible zones.

Nikolakopoulos et al. [6] focused on developing a UAS photogrammetric survey guidance for accurate landslide mapping and monitoring in steep terrains. They conducted four identical tests within landslide areas with different characteristics. High-resolution orthophotos and digital surface models (DSMs) emerge from the UAS imagery processing through structure-from-motion (SfM) photogrammetry. Accuracy assessment is carried out using quantitative and qualitative comparative approaches, and a strong relation is revealed between UAS acquisition geometry and landslide characteristics.

Valkaniotis et al. [7] identified the landslides triggered by the 2020 Ianos medicane by using early remote sensing data and conducting a series of post-event field surveys for verification. The rapid landslide recording is then compared with new methods of automated landslide mapping through the detection of changes in satellite imagery. All applied methods captured large events in mountainous areas and landslides with significant dimensions and/or long outflow distance. In terms of comparing the compiled inventory with past events, they concluded that the Ianos landslides were triggered along roughly the same locations of historical occurrences, revealing a relationship with long-term climatic and lithological/geomorphological conditions.

Sischka et al. [8] modeled the 1917 Samoa tsunamigenic earthquake from its origin to produce outputs of tsunami inundation extent and depth at spatially flexible grid resolution, which are validated using available run up observations and tide gauge records. Then, they combined the inundation model with digital distributions of buildings to produce exposure metrics for evaluating the likely impacts on present-day coastal assets and populations if a similar tsunami were to occur. They provided the first detailed 1917 tsunami inundation model, supporting an appreciation of the regional risk to local tsunamis.

Falaras et al. [9] examined the effects of the 2021 wildfires in the Attica region (Greece) based on Earth observation and GIS-based techniques for the development of a web app that includes the derived knowledge. The effects of wildfires are estimated with the use of Sentinel-2 satellite imagery concerning burned area extent and burn severity using a normalized burn ratio (NBR)-based method. In addition, the erosion risk is modeled on a pre-fire and post-fire basis with the revised universal soil loss equation (RUSLE). This study highlights the importance of assessing the effects of wildfires with a holistic approach to produce useful knowledge tools in post-fire impact assessment and restoration.

Azócar de la Cruz et al. [10] used several factors, such as human activity, geographic, topographic, and land cover variables to develop a model of ignition risk. The study area corresponds to Maule region (Chile), a large zone with a Mediterranean climate, affected by a megafire in 2017. Wildland fire management requires integrating this information into decision-making processes if we consider that the impact of climate change persists.