Aug 2026· Remote Sensing· Vol 18, pp. 2770· 0 citations· 72 references
Abstract
Typhoons often cause severe casualties, property losses, and infrastructure damage, and high-resolution spatial risk assessment is an important basis for developing effective disaster prevention and mitigation strategies. However, most existing typhoon risk assessments are conducted at relatively coarse spatial scales and provide limited representation of intra-urban differences in mitigation capacity. To address this gap, this study takes Haikou, China, as the study area and develops a spatially detailed mitigation-capacity indicator system. Mitigation capacity is incorporated as a key dimension into the conventional hazard–exposure–vulnerability framework. Based on multi-source geospatial data, data mining, and spatial analysis, a risk assessment system comprising 23 indicators was established. Indicator weights were determined using the analytic hierarchy process, and all indicator layers were harmonized to generate a typhoon risk map on a 30 m analytical grid. The results show that the high-resolution risk maps can effectively characterize the spatial extent and level differentiation of typhoon risk while revealing significant spatial heterogeneity in risk at the fine grid scale. In Haikou, 18.99% of the area is classified as being at high and very high risk levels, mainly distributed along the coastal zones of Shishan Town, Xixiu Town, Changliu Town, Lingshan Town, and Yanfeng Town. A preliminary plausibility check was conducted using eight georeferenced typhoon-related fatality locations recorded from 2015 to 2024. Five were located in high- or very-high-risk zones. Given the limited sample size, this comparison does not constitute formal statistical validation, but the observed spatial correspondence provides preliminary support for the plausibility of the assessment results. This study provides spatially explicit decision support for identifying intra-urban variations in typhoon risk, delineating priority areas for disaster mitigation, and optimizing the allocation of mitigation resources.
Multi-hazard assessment plays a crucial role in understanding the spatial concurrence and cumulative effects of various environmental hazards. However, limited availability of integrated hazard information and the lack of spatially explicit multi-hazard assessments constrain urban risk governance and landscape-scale nature-based solution planning. This study addresses this gap by enhancing scientific understanding of the spatial distribution of multiple environmental hazards in Kabul region, where data limitations and informal growth hinder effective risk management. The study pursues two objectives: (1) to assess and map the spatial distribution of individual environmental hazards, and (2) to identify multi-hazard zones and their specific hazard combinations. This study assesses five key hazards—flood (FH), heat (HH), drought (DH), soil erosion (SEH), and groundwater stress (GWS)—using Spatial Multi-Criteria Decision Analysis within a GIS framework. The study rates and weights nine parameters through hazard-specific methods. It then integrates the binary hazard layers using two approaches: (1) raster calculator to quantify the spatial overlap of hazard-prone areas, and (2) weighted multiplication of binary layers to interpret unique hazard combinations. The results show that approximately 80% of Kabul experiences at least three hazards. Single-hazard zones account for 1.8% of the study area, while two-, three-, four-, and five-hazard zones represent 18.6%, 44.3%, 32.9%, and 2.8%, respectively. FH emerged as the most spatially extensive individual hazard (8.5%), with frequent combinations including FH–GWS (8.47%), GWS–HH (17.79%), SEH–HH (8.54%), SEH–GWS–HH (18.81%), and DH–GWS–HH (8.16%). The findings enhance understanding of hazard combination and enable more targeted mitigation planning. Develops an integrative geospatial framework to map multiple environmental hazards in a data-scarce urban environment in the Global South. More than 80% of the study area is exposed to three or more concurrent hazards. Provides a hazard baseline to support urban risk governance and landscape-scale Nature-based Solutions (NBS) planning. Develops an integrative geospatial framework to map multiple environmental hazards in a data-scarce urban environment in the Global South. More than 80% of the study area is exposed to three or more concurrent hazards. Provides a hazard baseline to support urban risk governance and landscape-scale Nature-based Solutions (NBS) planning.
Maisam Rafiee, Mohammad Reza Ansari, Daniela Kempa et al.· Environmental Management· 0 citations
This study provides a comprehensive geospatial assessment of flood susceptibility in the Lower Magat River Basin, focusing on the municipalities of Solano, Bayombong, Quezon, and Bagabag in Nueva Vizcaya. The study addresses the escalating threat of inundation by utilizing the Analytic Hierarchy Process (AHP) to evaluate and weight six critical parameters: distance to rivers, precipitation, elevation, slope, soil type, and land use/land cover (LULC). Results indicate that Distance to Rivers (36.1%) and Precipitation (25.4%) are the primary drivers of flood hazard in the region. The integration of these factors through weighted overlay analysis reveals a heterogeneous distribution of risk, with Solano exhibiting the most pervasive vulnerability, as 71.72% (48.04 sq.km) of its land area is classified as high susceptibility. In contrast, Bayombong and Quezon harbor the most extreme localized risks, with Barangays Casat, Darubba, and Runruno emerging as critical "Very High" hazard hotspots. The findings demonstrate that high-risk zones are concentrated within a 600-meter buffer of the Magat River, particularly where low-gradient slopes (0–5.8%) and heavy clay soils impede drainage. The study achieved a high consistency ratio (CR < 0.10), validating the model's reliability for land-use planning. It is recommended that local government units integrate these susceptibility maps into their Comprehensive Land Use Plans (CLUP), enforce strict river easement zones, and implement nature-based solutions like reforestation to mitigate runoff. This research serves as a critical baseline for disaster risk reduction, providing a data-driven framework for enhancing community resilience against future flooding events.
Mynchilyn A. Pale· International journal of res...· 0 citations
This study aims to deepen the understanding of the spatiotemporal evolution of the Extreme Precipitation Index in the YRD, evaluating the comprehensive flood disaster risk across the region. Existing studies have rarely incorporated the frequency, duration, and intensity of extreme precipitation events as indicators of the hazard of causative factors into comprehensive flood risk assessments. Using daily precipitation records from 107 national meteorological stations spanning 1960–2024, this study employs four extreme precipitation indices recommended by the ETCCDI (PREPTOT, CWD, R95p, and Rx1day) to examine the spatiotemporal characteristics of extreme precipitation in the YRD, one of China’s most densely populated and economically significant regions. Furthermore, a comprehensive flood risk assessment framework encompassing the hazard of causative factors, the sensitivity of disaster-forming environments, the vulnerability of disaster-affected entities, and disaster prevention capabilities is constructed. Based on this framework, an integrated Analytic Hierarchy Process–Entropy Weight method is adopted to evaluate Flood Risks in the YRD. The results showed that: (1) during the period 1960—2024, only the Extreme Precipitation Index (R95p) exhibited a significant upward trend, increasing at a rate of 1.7 mm/10a, whereas PREPTOT, CWD, and Rx1day showed slight declining trends. Nevertheless, all four indices displayed pronounced oscillatory characteristics, characterized by recurring “decrease—increase” cycles over time. (2) In terms of spatial distribution, PREPTOT and R95p exhibited a clear south-to-north gradient pattern, while CWD and Rx1day demonstrated a multicentric distribution. This pattern highlights the combined influence of typhoon landfall frequency and topographic conditions on extreme precipitation across the southern YRD. (3) The Flood Risks in the YRD exhibited a distinct spatial pattern characterized by higher risk levels in the east than in the west and in the south than in the north. Areas classified as moderate-to-high risk accounted for 48.6% of the total study area, with high-risk zones primarily concentrated in the Shanghai—Hangzhou—Ningbo corridor. These findings suggest that Flood Risks in the YRD are not driven by a single factor; rather, they result from the complex interactions among extreme precipitation, topographic and geomorphological conditions, levels of social exposure, and regional buffering capacities. Consequently, under the increasingly frequent occurrence of extreme precipitation events, flood management strategies that rely predominantly on static engineering measures are becoming insufficient. Greater emphasis should therefore be placed on enhancing the resilience of urban lifeline infrastructure, improving high-resolution forecasting and early-warning capabilities for extreme precipitation, and establishing dynamic warning-release mechanisms based on risk thresholds. Such measures are essential for effectively mitigating regional flood risks.
Flood susceptibility mapping is crucial for understanding flood-prone areas and mitigating the associated risks in vulnerable regions like the Sebeya Catchment. This study adopted a GIS-based Analytical Hierarchy Process (GIS-AHP) integrated with local community knowledge to evaluate flood susceptibility using 10 conditioning factors: Topographic Wetness Index (TWI), Elevation, Rainfall, Slope, Land use/Land cover (LULC), Soil types, Normalized Difference Vegetative Index (NDVI), Distance to roads, Distance to rivers, and drainage density. These factors were selected based on their established influence on flood susceptibility as identified through literature review, expert consultation, and local community experience in the flood-affected zones. Spatial datasets were gathered from remote sensing platforms, Digital Elevation Models, Meteorological records, and existing geospatial databases, and were processed within a GIS environment. The pairwise comparison matrix of the AHP was used to derive weighting coefficients representing the relative contribution of each factor in inducing flood, with Rainfall (0.23), Slope (0.15), Distance to river (0.12), drainage density (0.12), and Elevation (0.11) as the most influential criteria. The findings revealed that 88.4% of the study area falls within a moderate flood-susceptible zone, whereas 6.4% and 5.2% fall within high and low susceptible zones, respectively. The current study indicates that damage to infrastructure, loss of livelihoods, displacement of communities, and increased costs of disaster response are key consequences observed in affected regions. A confusion matrix approach was employed to validate the flood susceptibility map, and the results indicate 0.97 as an overall accuracy, confirming strong model performance and reliability. The proposed adaptive strategies for enhancing flood resilience include improvement in land use planning, use of early warning systems, and sustainable catchment management.
Assiel Mugabe, T. Kabera, Félicien Majoro et al.· GeoHazards· 0 citations
Flood susceptibility mapping is vital for disaster risk reduction in Bangladesh’s northeastern region, where recurrent floods severely affect livelihoods and ecosystems. The study focuses on the Sunamganj district in Bangladesh, one of the most flood-prone areas in the country because of its low-lying terrain, proximity to the Meghalaya hills and its extensive network of rivers. Despite numerous flood assessments, few studies have systematically integrated multi-criteria decision analysis with high-resolution geospatial data to produce reliable flood risk maps. Therefore, this research aims to develop a robust flood susceptibility map using an integrated Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) framework. Nine flood-conditioning factors- Slope, Elevation, Topographic Wetness Index (TWI), drainage density, soil texture, rainfall, land use/land cover (LULC), and distances from rivers and roads- were assessed using Sentinel-2 imagery, SRTM DEM, BARC soil data, CHRS rainfall, and OpenStreetMap databases. Factor weights were assigned using an AHP-based pairwise comparison matrix, with a consistency ratio of 0.08, confirming the reliability of the expert judgments. Results revealed that slope (26.12%), elevation (19.63%), and TWI (15.58%) were the most influential parameters. Spatial analysis showed that 17% of the study area falls within very high and 20% within high susceptibility zones. The obtained AUC value of 0.844 indicates very good predictive performance of the model. This means the model has an 84.4% probability of correctly distinguishing between flood-prone and non-flood-prone areas. The findings provide spatially explicit insights to guide land-use planning, infrastructure design, and climate-resilient adaptation strategies. The integrated AHP-GIS approach used in this study demonstrates significant potential for regional-scale flood susceptibility assessment in monsoon-dominated haor environments.
Regional land subsidence risk assessment is often constrained by low-resolution contour-based continuous data and subjective empirical grading, which may weaken the objectivity and spatial detail of evaluation results. To address this limitation, this study establishes an enhanced methodological framework for objective regional land subsidence risk assessment by integrating high-resolution remote-sensing raster data, an enhanced raster information quantity method, AHP-EWM combined weighting, natural-break risk zoning, and sensitivity analysis. The framework organizes 11 indicators into hazard, vulnerability, and exposure components. The raster information quantity method assigns objective class-level risk scores based on observed raster deformation intensity, whereas AHP-EWM weighting combines literature-based process understanding with data-driven variability. This enhanced methodological framework supports a complete evaluation process, from index construction and class-level scoring to integrated weighting, risk zoning, and stability verification. The results indicate that deep groundwater exploitation and land subsidence rate are the dominant hazard-related factors, whereas road proximity, population density, gross domestic product, and land use strongly influence exposure-related risk. The comprehensive risk pattern shows clear spatial heterogeneity, with higher-risk zones mainly concentrated where strong subsidence hazards, high vulnerability, and dense socioeconomic exposure overlap. Sensitivity analysis indicates that most areas remain unchanged or change by only one risk class under alternative classification schemes, demonstrating good stability and robustness. This study provides a reproducible methodological reference for high-resolution and objective assessment of regional land subsidence risk. Proposes a robust index system for regional land subsidence risk assessment. Formulates an enhanced raster information quantity method in individual index scoring of land subsidence risk assessment. Assesses zoning stability through sensitivity analysis and relates higher-risk zones to infrastructure exposure. Provides transferable methodology for global geohazard-prone regions
Bo Liu, Jiaxu Wang, Shinong Li et al.· Environmental Earth Sciences· 0 citations
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