Natural Hazard Assessment Research Articles

Assessment of Natural Radioactivity and Radiation Hazard in Soil and Rock Samples from Mining Sites within North-Eastern Nigeria

Assessment of Natural Radioactivity and Radiation Hazard in Soil and Rock Samples from Mining Sites within North-Eastern Nigeria

Spatiotemporal variation and teleconnections of extreme precipitation in the Upper Indus Basin: insights for natural hazard assessment

ABSTRACT The study investigates trends and teleconnections of extreme precipitation events in the Upper Indus Basin (UIB) within the Indian region, part of the crucial geo-ecological Hindu Kush Himalayan Mountain system. Utilizing high-resolution (10 km × 10 km) HAR data from 2002 to 2013, we analyzed 11 indices established by the Expert Team on Climate Change Detection and Indices (ETCCDI) to observe variations in extreme precipitation at the monthly, seasonal, and annual time scale. The result shows an increase in dry and wet extreme precipitation frequency and intensity, increasing monsoon precipitation and a shift of winter precipitation, increasing continuous dry days. Using the APHRODITE daily (0.25° × 0.25°) data from 1952 to 2014, continuous wavelet transform and wavelet coherence analysis indicate significant periodicities and correlations with large-scale climate anomalies such as the El Niño-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO). Wavelet coherence analysis reveals that monthly extreme precipitation events significantly correlate with ENSO at a 3–5 years periodicity, while annual extremes show significant coherence with NAO at 8–10 years and over 16 years periodicities. The study highlights the impact of global climate change on regional precipitation and the need for adaptive water management policies to mitigate flood risks during the rainy season and address water scarcity in the dry season.

Natural radionuclide profiles and radiological risks in soils and rocks of the Koytash-Ugam Range, Uzbekistan.

This investigation quantifies the activity concentrations of natural radionuclides (226Ra, 232Th, and 40K) in the soils and certain rocks of the Koytash-Ugam Range, Uzbekistan, and assesses their radiological risks. Gamma-spectrometric analysis of soil and rock samples revealed activity concentrations ranging from 456.2 ± 56.0 to 813.9 ± 76.0Bqkg-1 for 40K, 18.2 ± 6.3 to 70.0 ± 12.0Bqkg-1 for 226Ra, and 30.1 ± 2.9 to 57.9 ± 10Bqkg-1 for 232Th. This data indicates a heterogeneous distribution of radionuclides, informing radiation safety and health risk assessments on a global scale. The calculation of radiological hazard indices, including the alpha-index (ranging from 0.09 to 0.35), gamma-index (ranging from 0.40 to 0.73), and both internal (ranging from 0.40 to 0.54) and external (ranging from 0.36 to 0.54) hazard indices, was undertaken to ascertain potential health risks. The radium equivalent activity ranged from 108.4 to 199.3Bqkg-1, and the absorbed dose rates were 51.0-93.3 nGy h-1 indoors and 96.6-178.2 nGy h-1 outdoors. These metrics underlie the estimated annual effective dose of 536.5-988.5 × 10-3mSv y-1, highlighting the variability in radiation exposure. Additionally, the potential lifetime cancer risk was projected at 1770.4 to 3262.0 per million, with an annual gonadal dose equivalent of 361.9 to 655.5 μSv y-1, reflecting natural background radiation influence. The results underscore the importance of safe material use in construction and the necessity for routine natural radioactivity monitoring. Radon flux density (RFD) values within acceptable construction limits (26-176mBqm-2s-1) suggest the area's suitability for development, considering recommended safety guidelines. This study not only aids local environmental and public health frameworks but also enriches the international knowledge base, facilitating comparative studies for the advancement of global radiation protection standards. Through a detailed examination of radionuclide distribution in an under-researched area, our research highlights the critical need for integrated international approaches to natural radiological hazard assessment.

A Novel Attention-Based Generalized Efficient Layer Aggregation Network for Landslide Detection from Satellite Data in the Higher Himalayas, Nepal

Landslide event detection poses a significant challenge in the remote sensing community, especially with the advancements in computer vision technology. As computational capabilities continue to grow, the traditional manual and partially automated methods of landslide recognition from remote sensing data are transitioning towards automatic approaches using deep learning algorithms. Moreover, attention models, encouraged by the human visual system, have emerged as crucial modules in diverse applications including natural hazard assessment. Therefore, we suggest a novel and intelligent generalized efficient layer aggregation network (GELAN) based on two prevalent attention modules, efficient channel attention (ECA) and convolutional block attention module (CBAM), to enrich landslide detection techniques from satellite images. CBAM and ECA are separately integrated into GELAN at different locations. The experiments are conducted using satellite images of the Nepal Himalayan region. Standard metrics such as precision, recall, F-score, and mAP (mean average precision) are considered for quantitative evaluation. GELANc+CBAM (F-score = 81.5%) demonstrates the best performance. This study underscores the suitability of the proposed approach in up-to-date inventory creation and accurate landslide mapping for disaster recovery and response efforts. Moreover, it contributes to developing early prediction models for landslide hazards.

Geomorphology, morphodynamic, and surficial material of the Orosi Valley, Costa Rica

The Orosi Valley in Costa Rica holds significant geomorphological importance due to its diverse landscapes shaped by tectonic activity, river systems, erosion processes, and rich biodiversity. Moreover, this valley is important for agriculture, tourism, water reservoirs, and hydroelectricity production. This study presents a comprehensive geomorphological mapping of the Orosi Valley in Costa Rica. Six distinct geomorphological units have been identified and classified through a combination of field surveys, topographic data analysis, and remote sensing techniques. These units include hillslopes, alluvial fans, terraces at the valley margins, volcanic landforms, colluvial deposits, landslide scars, and fluvial landforms. The results provide valuable insights into the geomorphological processes and landform evolution within the Orosi Valley, contributing to scientific knowledge, land use planning, natural hazard assessment, and environmental management in the region. This study holds global importance by deepening the understanding of how geomorphology, disaster risk, and socio-economic development are interconnected. It informs worldwide initiatives to create resilient communities in response to evolving environmental hazards.

Evaluating the contribution of geophysics to the assessment of natural hazards

This paper discussed the role of geophysics in the evaluation of natural hazards with the view to enhancing familiarity with earth shakes, ground characteristics, landslide likelihood, and volcanic deliveries. Semantic and geophysical data were collected from seismic surveys, measurement of groundwater, electrical resistivity of the subsoil and gas emission. Seismic data analysis revealed diverse activity across sites, with magnitudes ranging from 3.234 to 6.456 Mw, emphasizing the importance of geophysical monitoring in identifying high-risk areas. Groundwater measurements indicated spatial variations in water table depth from 1.234 to 6.789 m, essential for effective resource management. Soil resistivity values ranged from 75.123 to 145.901 Ohm-m, providing insights into soil properties relevant for geotechnical and environmental studies. Landslide susceptibility assessment used the factor of slope angle, vegetation cover, and rainfall intensity; these were 10. 901° to 28. 345°, 55. 234% to 88. 456%, and 6. 789 to 20. 901 mm/hr respectively. The gas emission of SO2 ranged between 0. 123–0. 901 kg/s, that of CO2, 0. 456–1. 234 kg/s, while H2S ranged between 0. 789–1. 567 kg/s based on the emission of the several sites suitable for eruption prediction models. A statistical approach was used in which Histogram, scatter plot and radar chart were used to explain the data collected. The study conclude that the application of various geophysical methods is strikingly useful in natural hazard and risk assessment and planning and therefore beneficial in reducing disaster risks. These results provide compelling evidence about the importance of geophysics in propagating awareness in the Earth processes and improvement of hazards.

Multiparametric stations for real-time monitoring and long-term assessment of natural hazards

The present work would like to illustrate a new concept of multiparametric stations to characterize the crustal fluids-tectonic interaction in specific geological contexts. The dynamics of crustal fluids in relation to tectonics is a complex and sometimes intricate issue. Several factors act and mutually influence themselves, so that in each tectonic and geological context they follow a specific behavior, and a comprehensive cause-effect rule is hard to find. Changes in water chemistry and levels and in soil flux regimes (e.g., CO2, CH4, radon) are just a few examples well documented in the literature as being pre-, co- and post-seismic modifications as well as being markers of the local tectonic stress acting in the crust. A regional study combined with a long-lasting multiparametric monitoring is needed to prepare to a seismic sequence in a given place. The field infrastructure was set up starting from the end of 2021, and multiparametric stations have been installed in correspondence of active seismogenic sources initially located in Northern Italy. Data are transmitted in real-time and archived in an ad hoc developed relational database. Monitoring is mainly focused on groundwater parameters (water level, temperature, and electrical conductivity) of aquifers showing distinct degrees of confinement and lithologies. Sites are also equipped of meteorological sensors (pressure, temperature, rain, humidity, wind speed and direction), radon sensors and surface and borehole seismic stations providing accelerometric and velocimetric data. A mud volcano field is also monitored and holds the installation of a permanent CO2 soil flux station. A statistical analysis working flow is also proposed for a preliminary evaluation of the acquired time-series. In particular, a couple of tools to detect, and thus filter, anthropogenic and meteorological effects on a groundwater level series is described. We wish to provide a model of approach to analogous study cases in other potentially seismic areas.

Advancing computational models for wave dynamics applications with quartic trigonometric tension b-spline techniques

This paper presents a computational model using Quartic Trigonometric Tension B-spline (QT-TB) function with collocation approach for nonlinear dispersive wave equation. Shallow water wave modeling plays a crucial role in various fields, including coastal engineering, oceanography, and natural hazard assessment. The QT-TB function is employed for spatial derivatives, integrating the nonlinear term through the linearization technique. While maintaining most characteristics of traditional polynomial B-splines, this method improves numerical solutions, enhancing accuracy in modeling. The performance of the method is rigorously assessed across three benchmark problems, with results compared to those of prior studies employing identical parameters. Detailed numerical illustrations are presented. Graphical representations are utilized to illustrate the single solitary wave motion, dynamics of coupled solitary waves and dynamics of triplet solitary waves in this study. Numerical experiments validate the method's accuracy and efficiency in capturing RLW-driven wave dynamics, establishing it as a reliable tool for various applications. The presented work includes an analysis of the stability of the proposed scheme, employing the Fourier method and shows that its unconditional stable.

Optimizing Residential Construction Site Selection in Mountainous Regions Using Geospatial Data and eXplainable AI

The rapid urbanization of Abha and its surrounding cities in Saudi Arabia’s mountainous regions poses challenges for sustainable and secure development. This study aimed to identify suitable sites for eco-friendly and safe building complexes amidst complex geophysical, geoecological, and socio-economic factors, integrating natural hazards assessment and risk management. Employing the Fuzzy Analytic Hierarchy Process (Fuzzy-AHP), the study constructed a suitability model incorporating sixteen parameters. Additionally, a Deep Neural Network (DNN) based on eXplainable Artificial Intelligence (XAI) conducted sensitivity analyses to assess the parameters’ influence on optimal location decision making. The results reveal slope as the most crucial parameter (22.90%), followed by altitude and land use/land cover (13.24%), emphasizing topography and environmental considerations. Drainage density (11.36%) and rainfall patterns (9.15%) are also significant for flood defense and water management. Only 12.21% of the study area is deemed “highly suitable”, with “no-build zones” designated for safety and environmental protection. DNN-based XAI demonstrates the positive impact of variables like the NDVI and municipal solid waste generation on site selection, informing waste management and ecological preservation strategies. This integrated methodology provides actionable insights for sustainable and safe residential development in Abha, aiding informed decision making and balancing urban expansion with environmental conservation and hazard risk reduction.

Comparison of debris flow observations, including fine-sediment grain size and composition and runout model results, at Illgraben, Swiss Alps

Abstract. Debris flows are important processes for the assessment of natural hazards due to their damage potential. To assess the impact of a potential debris flow, parameters such as the flow velocity, flow depth, maximum discharge, and volume are of great importance. This study uses data from the Illgraben observation station in the central Alps of Switzerland to explore the relationships between these flow parameters and the debris flow dynamics. To this end, we simulated previous debris flow events with the RAMMS::Debrisflow (Rapid Mass Movement Simulation::Debrisflow) runout model, which is based on a numerical solution of the shallow water equations for granular flows using the Voellmy friction relation. Here, the events were modelled in an effort to explore possible controls on the friction parameters μ and ξ, which describe the Coulomb friction and the turbulent friction, respectively, in the model. Additionally, sediment samples from levee deposits were analysed for their grain size distributions (14 events) and their mineralogical properties (4 events) to explore if the properties of the fine-grained matrix have an influence on the debris flow dynamics. Finally, field data from various debris flows such as the flow velocities and depths were statistically compared with the grain size distributions, the mineralogical properties, and the simulation results to identify the key variables controlling the kinematics of these flows. The simulation results point to several ideal solutions, which depend on the Coulomb and turbulent friction parameters (μ and ξ, respectively). In addition, the modelling results show that the Coulomb and turbulent frictions of a flow are related to the Froude number if the flow velocity is < 6–7 m s−1. It is also shown that the fine-sediment grain size or clay-particle mineralogy of a flow neither correlates with the flow's velocity and depth, nor can it be used to quantify the friction in the Voellmy friction relation. This suggests that the frictional behaviour of a flow may be controlled by other properties such as the friction generated by the partially fluidised coarse granular sediment. Yet, the flow properties are well-correlated with the flow volume, from which most other parameters can be derived, which is consistent with common engineering practice.

Flood Susceptibility Assessment of Chandpur Sadar Upazila using Analytical Hierarchy Process and Frequency Ratio Model

Flood susceptibility is a critical aspect of natural hazard assessment and disaster risk reduction. The objective of this study find out flood risk zone of chandpur sadar upazila using the Flood frequency ratio (FFR) and Analytical hierarchy process (AHP). The article explore the complex idea of flood susceptibility, focusing on analytical techniques used. The integration of twelve spatial data layers, such as Sediment Transport Index (STI), elevation, Topographic Wetness Index (TWI), slope, aspect, Land Use Land Cover (LULC), soil type, rainfall patterns, Normalize Difference Vegetation Index (NDVI), drainage density and distance from the river, is required for the assessment of flood vulnerability. A flood inventory map was buildup and using these methods, two vulnerability map was created, it was discovered that 60% & 48% of Chandpur's territory is categorized by very high to high risk zone. Flood has dangerous effect on livelihood. I was hoped that it will be a helpful study for researcher and people of this upazila will be benefited.

Data for critical infrastructure network modelling of natural hazard impacts: Needs and influence on model characteristics

Natural hazards impact interdependent infrastructure networks that keep modern society functional. While a variety of modelling approaches are available to represent critical infrastructure networks (CINs) on different scales and analyse the impacts of natural hazards, a recurring challenge for all modelling approaches is the availability and accessibility of sufficiently high-quality input and validation data. The resulting data gaps often require modellers to assume specific technical parameters, functional relationships, and system behaviours. In other cases, expert knowledge from one sector is extrapolated to other sectoral structures or even cross-sectorally applied to fill data gaps. The uncertainties introduced by these assumptions and extrapolations and their influence on the quality of modelling outcomes are often poorly understood and difficult to capture, thereby eroding the reliability of these models to guide resilience enhancements. Additionally, ways of overcoming the data availability challenges in CIN modelling, with respect to each modelling purpose, remain an open question. To address these challenges, a generic modelling workflow is derived from existing modelling approaches to examine model definition and validations, as well as the six CIN modelling stages, including mapping of infrastructure assets, quantification of dependencies, assessment of natural hazard impacts, response & recovery, quantification of CI services, and adaptation measures. The data requirements of each stage were systematically defined, and the literature on potential sources was reviewed to enhance data collection and raise awareness of potential pitfalls. The application of the derived workflow funnels into a framework to assess data availability challenges. This is shown through three case studies, taking into account their different modelling purposes: hazard hotspot assessments, hazard risk management, and sectoral adaptation. Based on the three model purpose types provided, a framework is suggested to explore the implications of data scarcity for certain data types, as well as their reasons and consequences for CIN model reliability. Finally, a discussion on overcoming the challenges of data scarcity is presented.

Multi-source deep-learning approach for automatic geomorphological mapping: the case of glacial moraines

ABSTRACT Landform mapping is the initial step of many geomorphological analyses (e.g. assessment of natural hazards and natural resources) and requires vast resources to be applied to wide areas at high-resolution. Among geomorphological objects, we focus on glacial moraine mapping, since it is a task relevant to many fields (e.g. paleoclimate and glacial geomorphology). Here we proposed to exploit the potential of Deep Learning-based approaches to map moraine landforms by exploiting multi-source remote sensing imagery. To this end, we propose the first Deep Learning model to map glacial moraines, namely MorNet. As multi-source remote sensing information, we combine together three different sources: Topographic (Pleiades-derived DSM), Multispectral (Sentinel-2), and SAR (Sentinel-1) data. To cope with such heterogeneous information, the proposed model has a dedicated branch for each input source and, a late fusion mechanism is leveraged to combine them with the aim to provide the final mapping. The performance of the MorNet model is evaluated on several glacier valleys in China in the Himalayan range. This area contains minimally eroded moraines, so they are well-defined and of varied morphology. The behavior of the proposed method is compared to models using individual mono-source models in order to highlight the benefit to simultaneously leverage multi-source information. The use of multi-source data allows MorNet to exploit the complementarity of the three input sources and improve its performance from an f1-score of about 41.6 using a single source to 52.8 using three sources. MorNet provides a first-order moraine map through its ability to identify well-defined moraines. Consequently, MorNet can identify areas likely to contain moraines and intends to be used as a tool by experts to facilitate and support large-scale mapping.

Resilient critical infrastructures: An innovative methodological perspective for critical infrastructure (CI) integrated assessment models by inducing digital technologies during multi-hazard incidents

Over the last decade, the notion of community resilience, which encompasses planning for, opposing, absorbing, and quickly recovering from disruptive occurrences, has gained momentum across the world. Critical Infrastructures (CI) are seen as critical to attaining success in today's densely populated countries. Such infrastructures must be robust in the face of multi-hazard catastrophes by implementing appropriate disaster management and recovery plans. Given these facts, it is critical to establish a new methodological perspective with an integrated system for effective disaster management of CI, as well as an intelligent application that will aid in the construction of more resilient and sustainable cities and communities. This perspective proposes a holistic gaming scenario application for assessing the vulnerability and accessibility of critical infrastructures during multi-hazard events, with a primary focus on conducting an integrated assessment for critical infrastructures and their assets. Mainly, the perspective includes a holistic gaming scenario application that will aid in accurately quantifying geographical spatial information and integrating big data into predictive and prescriptive management tools using virtual reality.•Conducting Integrated Assessment Models for evaluating vulnerability of Critical Infrastructures.•Inducing Digital Technologies during Multi-Hazard Incidents for improving Natural hazard assessment models.•Developing an open-world gaming scenario that is considered with high visual motion pictures and scenes.

An evaluation of the General Bathymetric Chart of the Ocean in shoreline-crossing geomorphometric investigations of volcanic islands

Digital elevation models (DEMs) are crucial in natural hazard assessments, as they often present the only comprehensive information. While satellites deliver remote sensing information of the land surface of up to 2m resolution, only 25% of the seafloor is mapped with a minimum resolution of 400m. The acquisition of high-resolution bathymetry requires hydroacoustic surveys by research vessels or autonomous vehicles, which is time-consuming and expensive. Predicted bathymetry from satellite altimetry, on the other hand, is widely available but has a significantly lower spatial resolution and high uncertainties in elevation, especially in shallow waters. The research on volcanic islands as a source of both volcanic as well as marine hazards such as tsunamis, is greatly limited by the lack of high-resolution bathymetry. Here we compare 24 geomorphometric parameters of 47 volcanic islands derived from a) the comprehensive bathymetric data of the General Bathymetric Chart of the Ocean (GEBCO) and b) high-resolution (< 250m), ship-based bathymetry. Out of 24 parameters tested, 20 show < ± 2.5% median deviation, and quartiles < ± 10%. Parameters describing the size of a volcanic island are the most robust and slope parameters show the greatest deviations. With this benchmark, we will be able to increase geomorphometric investigations to volcanic islands where little or no high-resolution bathymetry data is available.

Design and performance of the NVE snow scales

Continuous information on snow water equivalent (SWE) is of great importance in the natural hazard assessment (i.e. snow melt floods), in power production planning, water supply forecasting and more. In 2009 NVE designed and developed a new electronic snow sensor: The M2525. The main goal was to develop a sensor that was better than a snow pillow with respect to measurement errors as well as being more robust and less prone to defects. This study investigates the performance of the M2525 and the two smaller snow scales, the M604 and M910, at six study sites from 2009 to 2023. The performance of the scales is validated against manual snow surveys. I addition we have used exploratory analysis of the time series with the purpose to detect and discover typical patterns of measurement error.The largest snow scale, the M2525, seems to overestimate SWE by a ratio of +0,14 compared to manual surveys. However, M2525 does not seem to have trouble with pressure relief of the same magnitude as a snow pillow. Data analysis from the smallest scales showed both over- and underestimation, as well as small signs of measurement errors indicating that further observations and analyses are required.

Geomorphic Process Chains in High‐Mountain Regions—A Review and Classification Approach for Natural Hazards Assessment

AbstractPopulations and infrastructure in high mountain regions are exposed to a wide range of natural hazards, the frequency, magnitude, and location of which are extremely sensitive to climate change. In cases where several hazards can occur simultaneously or where the occurrence of one event will change the disposition of another, assessments need to account for complex process chains. While process chains are widely recognized as a major threat, no systematic analysis has hitherto been undertaken. We therefore establish new understanding on the factors that directly trigger or alter the disposition for subsequent events in the chain and derive a novel classification scheme and parameters to aid natural hazard assessment. Process chains in high mountains are commonly associated with glacier retreat or permafrost degradation. Regional differences exist in the nature and rate of sequencing—some process chains are almost instantaneous, while other linkages are delayed. Process chains involving rapid sequences are difficult to predict, and impacts are often devastating. We demonstrate that process chains are triggered most frequently by progressive failures, being the result of gradual landscape weakening and not due to the occurrence of a distinct process. If fluvial processes are part of the process chain the reach (or mobility) of process chains is increased. Increased mobility can also occur if sediment deposition areas along river channels are activated. As climate changes causes glacial environments to transform into sediment‐rich paraglacial and fluvial landscapes, it is expected that the mobility of process chains will increase in the future.

GIS-based landslide susceptibility modeling using data mining techniques

Introduction: Landslide is one of the most widespread geohazards around the world. Therefore, it is necessary and meaningful to map regional landslide susceptibility for landslide mitigation. In this research, landslide susceptibility maps were produced by four models, namely, certainty factors (CF), naive Bayes (NB), J48 decision tree (J48), and multilayer perceptron (MLP) models.Methods: In the first step, 328 landslides were identified via historical data, interpretation of remote sensing images, and field investigation, and they were divided into two subsets that were assigned different uses: 70% subset for training and 30% subset for validating. Then, twelve conditioning factors were employed, namely, altitude, slope angle, slope aspect, plan curvature, profile curvature, TWI, NDVI, distance to rivers, distance to roads, land use, soil, and lithology. Later, the importance of each conditioning factor was analyzed by average merit (AM) values, and the relationship between landslide occurrence and various factors was evaluated using the certainty factor (CF) approach. In the next step, the landslide susceptibility maps were produced based on four models, and the effect of the four models were quantitatively compared by receiver operating characteristic (ROC) curves, area under curve (AUC) values, and non-parametric tests.Results: The results demonstrated that all the four models can reasonably assess landslide susceptibility. Of these four models, the CF model has the best predictive performance for the training (AUC=0.901) and validating data (AUC=0.892).Discussion: The proposed approach is an innovative method that may also help other scientists to develop landslide susceptibility maps in other areas and that could be used for geo-environmental problems besides natural hazard assessments.

Іmproving the methodology of natural fire hazard assessment for forested lands based on a risk-oriented approach

An analysis of the combustibility of the forests of «Zhovtneve» State Enterprise was carried out, with the identification of the most fire-prone forests. The distribution of the forest fund plots of each of the researched forestry by classes of natural fire hazard was analyzed. The spatial distribution of the fire density including forest fires as well as wildfires in the 500- meter buffer zone around forest was studied. The article describes a methodology that allows improving the existing methodology for assessing the natural fire hazard of forest lands based on a risk-oriented approach. According to the Kernel density algorithm with the use of desktop GIS systems, a map of the probability of fire occurrence was built. According to the results of the Kernel Density algorithm, the main flammability centers and the density of fires were identified and the territory of the enterprise was divided into classes according to the probability of fires. According to the proposed classification of the fire probability occurrence: low (up to 10.6 cases is the arithmetic average for the entire map); medium (from 10.7 to 28 — 1.01–1.5σ); high (over 28 — over 1.5σ). The integration of the probability of fire occurrence into the scale of natural fire hazard was carried out through the technique of «risk matrices». Thus, the combination of the classes of natural fire hazard classes with the classes of the fire probability occurrence made it possible to obtain an integrated FRS indicator (Fire Risk Score), which includes both: the level of natural fire hazard and the probability of fire occurrence. In order to verify the results, calculations of actual flammability (fire density and flammability by burnt area) were carried out for each class of the national scale of fire hazard and for the FRS classes of each plot. It was found that significant levels of flammability are characteristic only for the first two classes of natural fire hazard (fire density 83.64 cases per 1000 hectares of forests), the flammability of forests with higher natural hazard classes is practically absent, which indicates the need to differentiate the tree stands that are belongs to these two classes. On the other hand, classifying forests with the assignment of points according to FRS revealed an increase in the indicators of forest combustibility and fire density as the FRS indicator increases (at FRS values of 2–3 there are no fires, at FRS values from 4 to 8, combustibility is moderate (from 1.25 to 3.7 cases per 1000 ha), at values above 9 points, the flammability increases sharply and reaches its peak at the FRS value of 13 points (224.5 cases per 1000 ha). Thus, the improved scale based on the risk-oriented approach (fire risk score) more fully reflects the level of danger in the forest based not only on the natural features of the area and fuel complexes characteristics, but also taking into account the spatial location of fire sources and the frequency of fires in each location.

Introduction to a Thematic Set of Papers on Remote Sensing for Natural Hazards Assessment and Control

Remote sensing is currently showing high potential to provide valuable information at various spatial and temporal scales concerning natural hazards and their associated risks. Recent advances in technology and processing methods have strongly contributed to the development of disaster risk reduction research. In this Special Issue titled “Remote Sensing for Natural Hazards Assessment and Control”, we propose state-of-the-art research that specifically addresses multiple aspects of the use of remote sensing for natural hazards. The aim was to collect innovative methodologies, expertise, and capabilities to detect, assess monitor, and model natural hazards. In this regard, 18 open-access papers showcase scientific studies based on the exploitation of a broad range of remote sensing data and techniques, as well as focusing on a well-assorted sample of natural hazard types.