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. [PERSON] 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. [PERSON] 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. [PERSON] 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. [PERSON] 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. [PERSON] 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. We congratulate all authors for their valuable contributions to this Special Issue and to all reviewers for their valuable time and constructive comments that helped improve the overall merit of this Special Issue. We also thank the Editorial Team of \"Applied Sciences\" for the collaboration, and we express our appreciation to the Section Managing Editor for the excellent cooperation and continuous support throughout the preparation of the Special Issue.