Articles | Volume 18, issue 2
https://doi.org/10.5194/nhess-18-583-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/nhess-18-583-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
23 Feb 2018
Research article |
| 23 Feb 2018
Slope stability and rockfall assessment of volcanic tuffs using RPAS with 2-D FEM slope modelling
Department of Engineering Geology and Geotechnics, Budapest University of Technology and Economics, 1111 Budapest, Hungary
Árpád Barsi
Department of Photogrammetry and Geoinformatics, Budapest University of Technology and Economics, 1111 Budapest, Hungary
Gyula Bögöly
Department of Engineering Geology and Geotechnics, Budapest University of Technology and Economics, 1111 Budapest, Hungary
Tamás Lovas
Department of Photogrammetry and Geoinformatics, Budapest University of Technology and Economics, 1111 Budapest, Hungary
Árpád Somogyi
Department of Photogrammetry and Geoinformatics, Budapest University of Technology and Economics, 1111 Budapest, Hungary
Péter Görög
Department of Engineering Geology and Geotechnics, Budapest University of Technology and Economics, 1111 Budapest, Hungary
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ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-3-2024, 1–11, https://doi.org/10.5194/isprs-annals-X-3-2024-1-2024, https://doi.org/10.5194/isprs-annals-X-3-2024-1-2024, 2024
J. M. Lógó and A. Barsi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 835–840, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-835-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-835-2023, 2023
V. Horváth and A. Barsi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 895–900, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-895-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-895-2023, 2023
M. Fawzy, G. Szabó, and A. Barsi
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 57–64, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-57-2023, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-57-2023, 2023
J. M. Lógó and A. Barsi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 383–388, https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-383-2022, https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-383-2022, 2022
M. Barsi and A. Barsi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 343–348, https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-343-2022, https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-343-2022, 2022
M. Barsi and A. Barsi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2021, 9–14, https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-9-2021, https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-9-2021, 2021
J. M. Lógó, N. Krausz, V. Potó, and A. Barsi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2021, 389–394, https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-389-2021, https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-389-2021, 2021
T. Lovas, K. Hadzijanisz, V. Papp, and A. J. Somogyi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2020, 251–257, https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-251-2020, https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-251-2020, 2020
A. Barsi, A. Csepinszky, N. Krausz, H. Neuberger, V. Poto, and V. Tihanyi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W16, 41–45, https://doi.org/10.5194/isprs-archives-XLII-2-W16-41-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W16-41-2019, 2019
Z. Kugler, G. Szabó, H. M. Abdulmuttalib, C. Batini, H. Shen, A. Barsi, and G. Huang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 315–320, https://doi.org/10.5194/isprs-archives-XLII-4-315-2018, https://doi.org/10.5194/isprs-archives-XLII-4-315-2018, 2018
V. Potó, A. Csepinszky, and Á. Barsi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 917–921, https://doi.org/10.5194/isprs-archives-XLII-2-917-2018, https://doi.org/10.5194/isprs-archives-XLII-2-917-2018, 2018
F. Albrecht, T. Blaschke, S. Lang, H. M. Abdulmutalib, G. Szabó, Á. Barsi, C. Batini, A. Bartsch, Zs. Kugler, D. Tiede, and G. Huang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 15–22, https://doi.org/10.5194/isprs-archives-XLII-3-15-2018, https://doi.org/10.5194/isprs-archives-XLII-3-15-2018, 2018
Á. Barsi, Zs. Kugler, I. László, Gy. Szabó, and H. M. Abdulmutalib
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 61–67, https://doi.org/10.5194/isprs-archives-XLII-3-61-2018, https://doi.org/10.5194/isprs-archives-XLII-3-61-2018, 2018
N. Rehany, A. Barsi, and T. Lovas
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W8, 213–220, https://doi.org/10.5194/isprs-archives-XLII-2-W8-213-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W8-213-2017, 2017
C. Batini, T. Blaschke, S. Lang, F. Albrecht, H. M. Abdulmutalib, Á. Barsi, G. Szabó, and Zs. Kugler
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 447–453, https://doi.org/10.5194/isprs-archives-XLII-2-W7-447-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-447-2017, 2017
A. Somogyi, A. Barsi, B. Molnar, and T. Lovas
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 587–590, https://doi.org/10.5194/isprs-archives-XLI-B5-587-2016, https://doi.org/10.5194/isprs-archives-XLI-B5-587-2016, 2016
A. Barsi, T. Lovas, B. Molnar, A. Somogyi, and Z. Igazvolgyi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 465–468, https://doi.org/10.5194/isprs-archives-XLI-B3-465-2016, https://doi.org/10.5194/isprs-archives-XLI-B3-465-2016, 2016
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Orlando García-Feal, José González-Cao, Diego Fernández-Nóvoa, Gonzalo Astray Dopazo, and Moncho Gómez-Gesteira
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Seok Bum Hong, Hong Sik Yun, Sang Guk Yum, Seung Yeop Ryu, In Seong Jeong, and Jisung Kim
Nat. Hazards Earth Syst. Sci., 22, 3435–3459, https://doi.org/10.5194/nhess-22-3435-2022, https://doi.org/10.5194/nhess-22-3435-2022, 2022
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This study advances previous models through machine learning and multi-sensor-verified results. Using spatial and meteorological data from the study area (Suncheon–Wanju Highway in Gurye-gun), the amount and location of black ice were modelled based on system dynamics to predict black ice and then simulated with the geographic information system (m2). Based on the model results, multiple sensors were buried at four selected points in the study area, and the model was compared with sensor data.
Edward E. Salakpi, Peter D. Hurley, James M. Muthoka, Adam B. Barrett, Andrew Bowell, Seb Oliver, and Pedram Rowhani
Nat. Hazards Earth Syst. Sci., 22, 2703–2723, https://doi.org/10.5194/nhess-22-2703-2022, https://doi.org/10.5194/nhess-22-2703-2022, 2022
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Edward E. Salakpi, Peter D. Hurley, James M. Muthoka, Andrew Bowell, Seb Oliver, and Pedram Rowhani
Nat. Hazards Earth Syst. Sci., 22, 2725–2749, https://doi.org/10.5194/nhess-22-2725-2022, https://doi.org/10.5194/nhess-22-2725-2022, 2022
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agro-ecological zones.
Weijie Zou, Yi Zhou, Shixin Wang, Futao Wang, Litao Wang, Qing Zhao, Wenliang Liu, Jinfeng Zhu, Yibing Xiong, Zhenqing Wang, and Gang Qin
Nat. Hazards Earth Syst. Sci., 22, 2081–2097, https://doi.org/10.5194/nhess-22-2081-2022, https://doi.org/10.5194/nhess-22-2081-2022, 2022
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Landslide dams are secondary disasters caused by landslides, which can cause great damage to mountains. We have proposed a procedure to calculate the key parameters of these dams that uses only a single remote-sensing image and a pre-landslide DEM combined with landslide theory. The core of this study is a modeling problem. We have found the bridge between the theory of landslide dams and the requirements of disaster relief.
C. Scott Watson, John R. Elliott, Susanna K. Ebmeier, María Antonieta Vásquez, Camilo Zapata, Santiago Bonilla-Bedoya, Paulina Cubillo, Diego Francisco Orbe, Marco Córdova, Jonathan Menoscal, and Elisa Sevilla
Nat. Hazards Earth Syst. Sci., 22, 1699–1721, https://doi.org/10.5194/nhess-22-1699-2022, https://doi.org/10.5194/nhess-22-1699-2022, 2022
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We assess how greenspaces could guide risk-informed planning and reduce disaster risk for the urbanising city of Quito, Ecuador, which experiences earthquake, volcano, landslide, and flood hazards. We use satellite data to evaluate the use of greenspaces as safe spaces following an earthquake. We find disparities regarding access to and availability of greenspaces. The availability of greenspaces that could contribute to community resilience is high; however, many require official designation.
Seth Bryant, Heather McGrath, and Mathieu Boudreault
Nat. Hazards Earth Syst. Sci., 22, 1437–1450, https://doi.org/10.5194/nhess-22-1437-2022, https://doi.org/10.5194/nhess-22-1437-2022, 2022
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The advent of new satellite technologies improves our ability to study floods. While the depth of water at flooded buildings is generally the most important variable for flood researchers, extracting this accurately from satellite data is challenging. The software tool presented here accomplishes this, and tests show the tool is more accurate than competing tools. This achievement unlocks more detailed studies of past floods and improves our ability to plan for and mitigate disasters.
Tadas Nikonovas, Allan Spessa, Stefan H. Doerr, Gareth D. Clay, and Symon Mezbahuddin
Nat. Hazards Earth Syst. Sci., 22, 303–322, https://doi.org/10.5194/nhess-22-303-2022, https://doi.org/10.5194/nhess-22-303-2022, 2022
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Extreme fire episodes in Indonesia emit large amounts of greenhouse gasses and have negative effects on human health in the region. In this study we show that such burning events can be predicted several months in advance in large parts of Indonesia using existing seasonal climate forecasts and forest cover change datasets. A reliable early fire warning system would enable local agencies to prepare and mitigate the worst of the effects.
Yahong Liu and Jin Zhang
Nat. Hazards Earth Syst. Sci., 22, 227–244, https://doi.org/10.5194/nhess-22-227-2022, https://doi.org/10.5194/nhess-22-227-2022, 2022
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Through a comprehensive analysis of the current remote sensing technology resources, this paper establishes the database to realize the unified management of heterogeneous sensor resources and proposes a capability evaluation method of remote sensing cooperative technology in geohazard emergencies, providing a decision-making basis for the establishment of remote sensing cooperative observations in geohazard emergencies.
Diego Guenzi, Danilo Godone, Paolo Allasia, Nunzio Luciano Fazio, Michele Perrotti, and Piernicola Lollino
Nat. Hazards Earth Syst. Sci., 22, 207–212, https://doi.org/10.5194/nhess-22-207-2022, https://doi.org/10.5194/nhess-22-207-2022, 2022
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In the Apulia region (southeastern Italy) we are monitoring a soft-rock coastal cliff using webcams and strain sensors. In this urban and touristic area, coastal recession is extremely rapid and rockfalls are very frequent. In our work we are using low-cost and open-source hardware and software, trying to correlate both meteorological information with measures obtained from crack meters and webcams, aiming to recognize potential precursor signals that could be triggered by instability phenomena.
Natalie Brožová, Tommaso Baggio, Vincenzo D'Agostino, Yves Bühler, and Peter Bebi
Nat. Hazards Earth Syst. Sci., 21, 3539–3562, https://doi.org/10.5194/nhess-21-3539-2021, https://doi.org/10.5194/nhess-21-3539-2021, 2021
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Surface roughness plays a great role in natural hazard processes but is not always well implemented in natural hazard modelling. The results of our study show how surface roughness can be useful in representing vegetation and ground structures, which are currently underrated. By including surface roughness in natural hazard modelling, we could better illustrate the processes and thus improve hazard mapping, which is crucial for infrastructure and settlement planning in mountainous areas.
Hugues Brenot, Nicolas Theys, Lieven Clarisse, Jeroen van Gent, Daniel R. Hurtmans, Sophie Vandenbussche, Nikolaos Papagiannopoulos, Lucia Mona, Timo Virtanen, Andreas Uppstu, Mikhail Sofiev, Luca Bugliaro, Margarita Vázquez-Navarro, Pascal Hedelt, Michelle Maree Parks, Sara Barsotti, Mauro Coltelli, William Moreland, Simona Scollo, Giuseppe Salerno, Delia Arnold-Arias, Marcus Hirtl, Tuomas Peltonen, Juhani Lahtinen, Klaus Sievers, Florian Lipok, Rolf Rüfenacht, Alexander Haefele, Maxime Hervo, Saskia Wagenaar, Wim Som de Cerff, Jos de Laat, Arnoud Apituley, Piet Stammes, Quentin Laffineur, Andy Delcloo, Robertson Lennart, Carl-Herbert Rokitansky, Arturo Vargas, Markus Kerschbaum, Christian Resch, Raimund Zopp, Matthieu Plu, Vincent-Henri Peuch, Michel Van Roozendael, and Gerhard Wotawa
Nat. Hazards Earth Syst. Sci., 21, 3367–3405, https://doi.org/10.5194/nhess-21-3367-2021, https://doi.org/10.5194/nhess-21-3367-2021, 2021
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The purpose of the EUNADICS-AV (European Natural Airborne Disaster Information and Coordination System for Aviation) prototype early warning system (EWS) is to develop the combined use of harmonised data products from satellite, ground-based and in situ instruments to produce alerts of airborne hazards (volcanic, dust, smoke and radionuclide clouds), satisfying the requirement of aviation air traffic management (ATM) stakeholders (https://cordis.europa.eu/project/id/723986).
Johnny Douvinet, Anna Serra-Llobet, Esteban Bopp, and G. Mathias Kondolf
Nat. Hazards Earth Syst. Sci., 21, 2899–2920, https://doi.org/10.5194/nhess-21-2899-2021, https://doi.org/10.5194/nhess-21-2899-2021, 2021
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This study proposes to combine results of research regarding the spatial inequalities due to the siren coverage, the political dilemma of siren activation, and the social problem of siren awareness and trust for people in France. Surveys were conducted using a range of complementary methods (GIS analysis, statistical analysis, questionnaires, interviews) through different scales. Results show that siren coverage in France is often determined by population density but not risks or disasters.
Fabio Brighenti, Francesco Carnemolla, Danilo Messina, and Giorgio De Guidi
Nat. Hazards Earth Syst. Sci., 21, 2881–2898, https://doi.org/10.5194/nhess-21-2881-2021, https://doi.org/10.5194/nhess-21-2881-2021, 2021
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In this paper we propose a methodology to mitigate hazard in a natural environment in an urbanized context. The deformation of the ground is a precursor of paroxysms in mud volcanoes. Therefore, through the analysis of the deformation supported by a statistical approach, this methodology was tested to reduce the hazard around the mud volcano. In the future, the goal is that this dangerous area will become both a naturalistic heritage and a source of development for the community of the area.
Doris Hermle, Markus Keuschnig, Ingo Hartmeyer, Robert Delleske, and Michael Krautblatter
Nat. Hazards Earth Syst. Sci., 21, 2753–2772, https://doi.org/10.5194/nhess-21-2753-2021, https://doi.org/10.5194/nhess-21-2753-2021, 2021
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Multispectral remote sensing imagery enables landslide detection and monitoring, but its applicability to time-critical early warning is rarely studied. We present a concept to operationalise its use for landslide early warning, aiming to extend lead time. We tested PlanetScope and unmanned aerial system images on a complex mass movement and compared processing times to historic benchmarks. Acquired data are within the forecasting window, indicating the feasibility for landslide early warning.
Michal Bíl, Pavel Raška, Lukáš Dolák, and Jan Kubeček
Nat. Hazards Earth Syst. Sci., 21, 2581–2596, https://doi.org/10.5194/nhess-21-2581-2021, https://doi.org/10.5194/nhess-21-2581-2021, 2021
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The online landslide database CHILDA (Czech Historical Landslide Database) summarises information about landslides which occurred in the area of Czechia (the Czech Republic). The database is freely accessible via the https://childa.cz/ website. It includes 699 records (spanning the period of 1132–1989). Overall, 55 % of all recorded landslide events occurred only within 15 years of the extreme landslide incidence.
Anna Kruspe, Jens Kersten, and Friederike Klan
Nat. Hazards Earth Syst. Sci., 21, 1825–1845, https://doi.org/10.5194/nhess-21-1825-2021, https://doi.org/10.5194/nhess-21-1825-2021, 2021
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Messages on social media can be an important source of information during crisis situations. This article reviews approaches for the reliable detection of informative messages in a flood of data. We demonstrate the varying goals of these approaches and present existing data sets. We then compare approaches based (1) on keyword and location filtering, (2) on crowdsourcing, and (3) on machine learning. We also point out challenges and suggest future research.
Enrique Guillermo Cordaro, Patricio Venegas-Aravena, and David Laroze
Nat. Hazards Earth Syst. Sci., 21, 1785–1806, https://doi.org/10.5194/nhess-21-1785-2021, https://doi.org/10.5194/nhess-21-1785-2021, 2021
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We developed a methodology that generates free externally disturbed magnetic variations in ground magnetometers close to the Chilean convergent margin. Spectral analysis (~ mHz) and magnetic anomalies increased prior to large Chilean earthquakes (Maule 2010, Mw 8.8; Iquique 2014, Mw 8.2; Illapel 2015, Mw 8.3). These findings relate to microcracks within the lithosphere due to stress state changes. This physical evidence should be thought of as a last stage of the earthquake preparation process.
Corey M. Scheip and Karl W. Wegmann
Nat. Hazards Earth Syst. Sci., 21, 1495–1511, https://doi.org/10.5194/nhess-21-1495-2021, https://doi.org/10.5194/nhess-21-1495-2021, 2021
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For many decades, natural disasters have been monitored by trained analysts using multiple satellite images to observe landscape change. This approach is incredibly useful, but our new tool, HazMapper, offers researchers and the scientifically curious public a web-accessible
cloud-based tool to perform similar analysis. We intend for the tool to both be used in scientific research and provide rapid response to global natural disasters like landslides, wildfires, and volcanic eruptions.
Matti Wiegmann, Jens Kersten, Hansi Senaratne, Martin Potthast, Friederike Klan, and Benno Stein
Nat. Hazards Earth Syst. Sci., 21, 1431–1444, https://doi.org/10.5194/nhess-21-1431-2021, https://doi.org/10.5194/nhess-21-1431-2021, 2021
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In this paper, we study when social media is an adequate source to find metadata about incidents that cannot be acquired by traditional means. We identify six major use cases: impact assessment and verification of model predictions, narrative generation, recruiting citizen volunteers, supporting weakly institutionalized areas, narrowing surveillance areas, and reporting triggers for periodical surveillance.
Hui Liu, Ya Hao, Wenhao Zhang, Hanyue Zhang, Fei Gao, and Jinping Tong
Nat. Hazards Earth Syst. Sci., 21, 1179–1194, https://doi.org/10.5194/nhess-21-1179-2021, https://doi.org/10.5194/nhess-21-1179-2021, 2021
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We trained a recurrent neural network model to classify microblogging posts related to urban waterlogging and establish an online monitoring system of urban waterlogging caused by flood disasters. We manually curated more than 4400 waterlogging posts to train the RNN model so that it can precisely identify waterlogging-related posts of Sina Weibo to timely determine urban waterlogging.
Roope Tervo, Ilona Láng, Alexander Jung, and Antti Mäkelä
Nat. Hazards Earth Syst. Sci., 21, 607–627, https://doi.org/10.5194/nhess-21-607-2021, https://doi.org/10.5194/nhess-21-607-2021, 2021
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Predicting the number of power outages caused by extratropical storms is a key challenge for power grid operators. We introduce a novel method to predict the storm severity for the power grid employing ERA5 reanalysis data combined with a forest inventory. The storms are first identified from the data and then classified using several machine-learning methods. While there is plenty of room to improve, the results are already usable, with support vector classifier providing the best performance.
Michaela Wenner, Clément Hibert, Alec van Herwijnen, Lorenz Meier, and Fabian Walter
Nat. Hazards Earth Syst. Sci., 21, 339–361, https://doi.org/10.5194/nhess-21-339-2021, https://doi.org/10.5194/nhess-21-339-2021, 2021
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Mass movements constitute a risk to property and human life. In this study we use machine learning to automatically detect and classify slope failure events using ground vibrations. We explore the influence of non-ideal though commonly encountered conditions: poor network coverage, small number of events, and low signal-to-noise ratios. Our approach enables us to detect the occurrence of rare events of high interest in a large data set of more than a million windowed seismic signals.
Luiz Felipe Galizia, Thomas Curt, Renaud Barbero, and Marcos Rodrigues
Nat. Hazards Earth Syst. Sci., 21, 73–86, https://doi.org/10.5194/nhess-21-73-2021, https://doi.org/10.5194/nhess-21-73-2021, 2021
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This paper aims to provide a quantitative evaluation of three remotely sensed fire datasets which have recently emerged as an important resource to improve our understanding of fire regimes. Our findings suggest that remotely sensed fire datasets can be used to proxy variations in fire activity on monthly and annual timescales; however, caution is advised when drawing information from smaller fires (< 100 ha) across the Mediterranean region.
Philippe Weyrich, Anna Scolobig, Florian Walther, and Anthony Patt
Nat. Hazards Earth Syst. Sci., 20, 2811–2821, https://doi.org/10.5194/nhess-20-2811-2020, https://doi.org/10.5194/nhess-20-2811-2020, 2020
Patric Kellermann, Kai Schröter, Annegret H. Thieken, Sören-Nils Haubrock, and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 20, 2503–2519, https://doi.org/10.5194/nhess-20-2503-2020, https://doi.org/10.5194/nhess-20-2503-2020, 2020
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The flood damage database HOWAS 21 contains object-specific flood damage data resulting from fluvial, pluvial and groundwater flooding. The datasets incorporate various variables of flood hazard, exposure, vulnerability and direct tangible damage at properties from several economic sectors. This paper presents HOWAS 21 and highlights exemplary analyses to demonstrate the use of HOWAS 21 flood damage data.
Giuseppe Esposito, Ivan Marchesini, Alessandro Cesare Mondini, Paola Reichenbach, Mauro Rossi, and Simone Sterlacchini
Nat. Hazards Earth Syst. Sci., 20, 2379–2395, https://doi.org/10.5194/nhess-20-2379-2020, https://doi.org/10.5194/nhess-20-2379-2020, 2020
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In this article, we present an automatic processing chain aimed to support the detection of landslides that induce sharp land cover changes. The chain exploits free software and spaceborne SAR data, allowing the systematic monitoring of wide mountainous regions exposed to mass movements. In the test site, we verified a general accordance between the spatial distribution of seismically induced landslides and the detected land cover changes, demonstrating its potential use in emergency management.
Mohammad Malakootian and Majid Nozari
Nat. Hazards Earth Syst. Sci., 20, 2351–2363, https://doi.org/10.5194/nhess-20-2351-2020, https://doi.org/10.5194/nhess-20-2351-2020, 2020
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The present study estimated the Kerman–Baghin aquifer vulnerability using DRASTIC and composite DRASTIC (CDRASTIC) indices with the aid of geographic information system (GIS) techniques. The aquifer vulnerability maps indicated very similar results, identifying the north-west parts of the aquifer as areas with high to very high vulnerability. According to the results, parts of the studied aquifer have a high vulnerability and require protective measures.
Diana Contreras, Alondra Chamorro, and Sean Wilkinson
Nat. Hazards Earth Syst. Sci., 20, 1663–1687, https://doi.org/10.5194/nhess-20-1663-2020, https://doi.org/10.5194/nhess-20-1663-2020, 2020
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The socio-economic condition of the population determines their vulnerability to earthquakes, tsunamis, volcanic eruptions, landslides, soil erosion and land degradation. This condition is estimated mainly from population censuses. The lack to access to basic services, proximity to hazard zones, poverty and population density highly influence the vulnerability of communities. Mapping the location of this vulnerable population makes it possible to prevent and mitigate their risk.
Simona Colombelli, Francesco Carotenuto, Luca Elia, and Aldo Zollo
Nat. Hazards Earth Syst. Sci., 20, 921–931, https://doi.org/10.5194/nhess-20-921-2020, https://doi.org/10.5194/nhess-20-921-2020, 2020
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We developed a mobile app for Android devices which receives the alerts generated by a network-based early warning system, predicts the expected ground-shaking intensity and the available lead time at the user position, and provides customized messages to inform the user about the proper reaction to the alert. The app represents a powerful tool for informing in real time a wide audience of end users and stakeholders about the potential damaging shaking in the occurrence of an earthquake.
Richard Styron, Julio García-Pelaez, and Marco Pagani
Nat. Hazards Earth Syst. Sci., 20, 831–857, https://doi.org/10.5194/nhess-20-831-2020, https://doi.org/10.5194/nhess-20-831-2020, 2020
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The Caribbean and Central American region is both tectonically active and densely populated, leading to a large population that is exposed to earthquake hazards. Until now, no comprehensive fault data covering the region have been available. We present a new public fault database for Central America and the Caribbean that synthesizes published studies with new mapping from remote sensing to provide fault sources for the CCARA seismic hazard and risk analysis project and to aid future research.
María del Pilar Jiménez-Donaire, Ana Tarquis, and Juan Vicente Giráldez
Nat. Hazards Earth Syst. Sci., 20, 21–33, https://doi.org/10.5194/nhess-20-21-2020, https://doi.org/10.5194/nhess-20-21-2020, 2020
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A new combined drought indicator (CDI) is proposed that integrates rainfall, soil moisture and vegetation dynamics. The performance of this indicator was evaluated against crop damage data from agricultural insurance schemes in five different areas in SW Spain. Results show that this indicator was able to predict important droughts in 2004–2005 and 2011–2012, marked by crop damage of between 70 % and 95 % of the total insured area. This opens important applications for improving insurance schemes.
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Short summary
The present study demonstrates the application of drones and terrestrial laser scanner in stability assessment of steep, hardly accessible rock slopes that can endanger human lives. These technologies can be deployed very quickly, but data processing requires time. For reliable hazard evaluation, besides these techniques, engineering geological field work, laboratory tests of the mechanical properties of rocks and computer simulations are also necessary.
The present study demonstrates the application of drones and terrestrial laser scanner in...
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