Forest fires can result in loss of life, damage to infrastructure, and adverse environmental impacts. This study showcases an integrated approach for conducting high-detail fire risk assessment and supporting strategic planning and management of fire events in peri-urban areas that are susceptible to forest fires. The presented methodology encompasses fire hazard modeling, vulnerability and exposure assessment, and in situ observations. Numerous fire hazard scenarios were tested, simulating the spatiotemporal spread of fire events under different wind characteristics. The vulnerability of the studied areas was assessed by combining population data (density and age) and building characteristics, while the exposure parameter employed land value (EUR/m²) as an indicator for qualitatively estimating potential economic effects in the study area. Field campaigns facilitated the identification and recording of critical areas and points, including high-risk buildings and population gathering areas, which subsequently informed the mitigation and fire management planning suggestions. Moreover, field recordings acted as an iterative process for validating and updating the fire risk maps. This research work utilizes state-of-the-art techniques to achieve an analysis of fire risk at a building-block level. Overall, the study presents an applied and end-to-end methodology for effectively addressing forest fire risk in peri-urban areas.

Floods are disastrous events as they induce fatalities, damages to the environment, properties and infrastructure, at a global level. The Region of Attica, that hosts Athens (capital of Greece), has suffered various floods, including the severe flood in Mandra (2017) with 24 fatalities. In 2021, a Programming Agreement was signed between the Prefecture of Attica and the National Observatory of Athens to conduct the research study «Earthquake, fire and flood risk assessment in the Region of Attica» (Part A) in selected and most vulnerable areas. In the framework of this research and technical work, state-ofthe-art methodologies were developed and implemented that support multi-parameter flood risk assessment and management planning at high spatial resolution (building block level). This work integrates different data sources, including remote sensing, in-situ measurements, field visits, and simulations, and is characterized by considerable added value, as it supports the civil protection authorities as well as all the other relevant stakeholders in the decision-making, first response and management of floods. Furthermore, this innovative approach is transferable, adaptable and scalable, and it is now being implemented in the Garyllis river basin in Cyprus, in the framework of the «EXCELSIOR» H2020 Widespread Teaming project.

Floods affect many regions of the world every year and are the most deadly natural hazard. The increasing pressures of a growing global population, widespread ecosystem degradation, and the compounding effects of climate variability and change are significantly increasing flood risks worldwide. Hydrodynamic models, combined with Earth Observations (EO), play an increasingly important role in the comprehensive analysis and characterization of floods, providing a deeper understanding of their dynamics in past, present, and future scenarios.Under the “Copernicus Emergency Management Service (CEMS) Risk and Recovery Mapping (RRM)” framework, this on-call study (i.e., CEMS activation “ΕMSN: Retrospective flood temporal analysis of floods in Saarland, Germany”) focused on the mid-May 2024 (16/05/2024-22/05/2024) flood in Saarland, Germany, which resulted in extensive damage across the Saarland state capital Saarbrücken and several districts in Saarland. Leveraging advancements in Earth observation (EO), this study integrated multi-source remote sensing data into a 2D hydraulic modeling framework to enhance the understanding of flood dynamics in the region through a comprehensive temporal analysis.Using the HEC-RAS hydraulic modeling open-source software of the United States Army Corps of Engineers (USACE), a rain-on-grid approach was employed to simulate direct rainfall runoff to supplement fluvial model simulation of flood propagation over a 7-day period. Model calibration was based on observed water depth data from Gauging stations’ recordings, with adjustments made to improve accuracy. Validation was conducted using EO-derived flood delineations from multitemporal post-event imagery, spanning multi-Platform Satellite products including SAR (Sentinel-1A, RadarSat-2, COSMO-SkyMed and TerraSAR-X) and Optical (Planet Scope) imagery. Therefore, the outputs of the study including the water depth and the flood persistence were derived from the combination of the hydraulic modeling and remote sensing methodologies.Despite the relatively lower flood thematic accuracy of EO-derived flood outlines in urban and forested areas given the inherent limitations of the SAR analysis techniques, the availability of multitemporal EO imagery was decisive in validating the hydraulic modelling accuracy and robustness. The study findings emphasize the emerging potential of EO data for validating hydraulic models and therefore enhancing flood mapping and monitoring capabilities. In this context, the availability of multitemporal EO datasets further enhanced the flood modelling performance in providing a better insight into the flood propagation and dynamics over the whole period of impact.Acknowledgment: The service took place under the Framework Service Contract 945236–IPR–2023 “Copernicus Emergency Management Service (CEMS) Risk and Recovery Mapping (RRM) Tailor-Made Products. We would like to acknowledge the great support of the JRC CEMS team memebrs, namely Guido Di Carlo, Cristina Rosales Sanchez, and Emanuele Sapino, for the completion of this service contract.

The retailers’ profitability and the consumers’ satisfaction depend on finding the optimal location for a retail store. When considering the stores’ spatial distribution, business potential can be understood and a squandering planning of resources can be avoided. In this paper we identify the spatial patterns of retail stores located in the traditional commercial centers of twelve large -and medium-sized Greek cities, aiming to explain why such patterns exist. The type of retail activities was determined using the image of the ground-floor stores provided by the Google Street View (GSV) service and thus 7322 stores were recorded in a geodatabase as point features. The results reveal that the retail stores’ distribution has a clustered and random spatial pattern at least in one city, where the high population density and the increase in rental prices of premises for professional activities constitute the factors that form these spatial patterns respectively.

When there is an absence of disaster prevention measures, natural hazards can lead to disasters. An essential part of disaster risk management is the geospatial modeling of devastating hazards, where data sharing is of paramount importance in the context of early-warning systems. This research points out the usefulness of Spatial Data Infrastructures (SDIs) for disaster risk reduction through a literature review, focusing on the necessity of data unification and disposal. Initially, the principles of SDIs are presented, given the fact that this framework contributes significantly to the fulfilment of specific targets and priorities of the Sendai Framework for Disaster Risk Reduction 2015–2030. Thereafter, the challenges of SDIs are investigated in order to underline the main drawbacks stakeholders in emergency management have to come up against, namely the semantic misalignment that impedes efficient data retrieval, malfunctions in the interoperability of datasets and web services, the non-availability of the data in spite of their existence, and a lack of quality data, while also highlighting the obstacles of real case studies on national NSDIs. Thus, diachronic observations on disasters will not be made, despite these comprising a meaningful dataset in disaster mitigation. Consequently, the harmonization of national SDIs with international schemes, such as the Group on Earth Observations (GEO) and European Union’s space program Copernicus, and the usefulness of Artificial Intelligence (AI) and Machine Learning (ML) for disaster mitigation through the prediction of natural hazards are demonstrated. In this paper, for the purpose of disaster preparedness, real-world implementation barriers that preclude SDIs to be completed or deter their functionality are presented, culminating in the proposed future research directions and topics for the SDIs that need further investigation. SDIs constitute an ongoing collaborative effort intending to offer valuable operational tools for decision-making under the threat of a devastating event. Despite the operational potential of SDIs, the complexity of data standardization and coordination remains a core challenge.

From the perspective of a team of engineers that studies an area in order to estimate flood risk, a highly studied area presents both benefits and challenges. On the one hand, stands the benefit of accessibility to data and existing knowledge, while on the other the challenge of optimal analysis and utilization of such material.The region of Attica, Greece, fits exactly the description of a highly studied area. It hosts the capital of the country, Athens, and close to half of the national population. For this reason, flood risk has been studied throughout the 20th and 21st century in various spatial scales, and using different methods and tools. In a contemporary study of flood risk with modern digital computational tools, we undertook the tasks of compiling, analyzing and mapping important information from those past studies.In this work, we present the relevant challenges faced during the project “Earthquake, fire and flood risk assessment in the Region of Attica” as well as inferences for future studies of similar type. Following the previous step of our comprehensive methodology for the estimation and mapping of flood risk in Attica Region, we received as an input a set of existing studies for our study area. In the process of abstracting the spatial data from those studies, we grouped such data in three categories: spatial information in text, in CAD files and in GIS files. Each type of information presented its own type of challenges. Information in text was time-consuming to process, both because it necessitated reading the whole studies to be pinpointed, but also due to the requirement for manual extraction and conversion to GIS format. Spatial information found in CAD and GIS files presented mostly software-related challenges in managing to achieve a common representation scale and geospatial reference of the plans. Lastly, a challenge that was common for all types of data was the identification of whether the hydraulic works or terrain conditions that were presented in each study were actually present as such today. This required cross-methodological communication including both with the previous methodological steps of contact with the institutions that provided the studies, and with the next methodological steps of site visits for the confirmation of the recorded information.Overall, we identify the following as crucial priorities for efficient abstracting and synthesizing the spatial data on flood risk in a highly studied area. Firstly, clarity in defining the types of data being searched for, secondly, the utilization of CAD and GIS software with interoperability functions and easy scaling and georeference functionalities, thirdly, the direct communication with the institutions that created or utilize the studies, and lastly, the realization of site visits to verify the recorded information.