Articles | Volume 15, issue 9
https://doi.org/10.5194/nhess-15-2111-2015
© Author(s) 2015. 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-15-2111-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
22 Sep 2015
Research article |
| 22 Sep 2015
An approach to reduce mapping errors in the production of landslide inventory maps
M. Santangelo
CORRESPONDING AUTHOR
CNR IRPI, Via della Madonna Alta 126, 06128 Perugia, Italy
I. Marchesini
CNR IRPI, Via della Madonna Alta 126, 06128 Perugia, Italy
F. Bucci
CNR IRPI, Via della Madonna Alta 126, 06128 Perugia, Italy
M. Cardinali
CNR IRPI, Via della Madonna Alta 126, 06128 Perugia, Italy
F. Fiorucci
CNR IRPI, Via della Madonna Alta 126, 06128 Perugia, Italy
F. Guzzetti
CNR IRPI, Via della Madonna Alta 126, 06128 Perugia, Italy
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The paper discusses the use of rockfall modelling software and photogrammetry applied to images acquired by RPAS to provide support to civil protection agencies during emergency response. The paper focuses on a procedure that was applied to define the residual rockfall risk for a road that was hit by an earthquake-triggered rockfall that occurred during the seismic sequence that hit central Italy on 24 August 2016. Road reopening conditions were decided based on the results of this study.
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M. Mergili, I. Marchesini, M. Alvioli, M. Metz, B. Schneider-Muntau, M. Rossi, and F. Guzzetti
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P. Salvati, C. Bianchi, F. Fiorucci, P. Giostrella, I. Marchesini, and F. Guzzetti
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G. Vessia, M. Parise, M. T. Brunetti, S. Peruccacci, M. Rossi, C. Vennari, and F. Guzzetti
Nat. Hazards Earth Syst. Sci., 14, 2399–2408, https://doi.org/10.5194/nhess-14-2399-2014, https://doi.org/10.5194/nhess-14-2399-2014, 2014
I. Marchesini, F. Ardizzone, M. Alvioli, M. Rossi, and F. Guzzetti
Nat. Hazards Earth Syst. Sci., 14, 2215–2231, https://doi.org/10.5194/nhess-14-2215-2014, https://doi.org/10.5194/nhess-14-2215-2014, 2014
B. Merz, J. Aerts, K. Arnbjerg-Nielsen, M. Baldi, A. Becker, A. Bichet, G. Blöschl, L. M. Bouwer, A. Brauer, F. Cioffi, J. M. Delgado, M. Gocht, F. Guzzetti, S. Harrigan, K. Hirschboeck, C. Kilsby, W. Kron, H.-H. Kwon, U. Lall, R. Merz, K. Nissen, P. Salvatti, T. Swierczynski, U. Ulbrich, A. Viglione, P. J. Ward, M. Weiler, B. Wilhelm, and M. Nied
Nat. Hazards Earth Syst. Sci., 14, 1921–1942, https://doi.org/10.5194/nhess-14-1921-2014, https://doi.org/10.5194/nhess-14-1921-2014, 2014
A. Manconi, F. Casu, F. Ardizzone, M. Bonano, M. Cardinali, C. De Luca, E. Gueguen, I. Marchesini, M. Parise, C. Vennari, R. Lanari, and F. Guzzetti
Nat. Hazards Earth Syst. Sci., 14, 1835–1841, https://doi.org/10.5194/nhess-14-1835-2014, https://doi.org/10.5194/nhess-14-1835-2014, 2014
A. C. Mondini, A. Viero, M. Cavalli, L. Marchi, G. Herrera, and F. Guzzetti
Nat. Hazards Earth Syst. Sci., 14, 1749–1759, https://doi.org/10.5194/nhess-14-1749-2014, https://doi.org/10.5194/nhess-14-1749-2014, 2014
S. Raia, M. Alvioli, M. Rossi, R. L. Baum, J. W. Godt, and F. Guzzetti
Geosci. Model Dev., 7, 495–514, https://doi.org/10.5194/gmd-7-495-2014, https://doi.org/10.5194/gmd-7-495-2014, 2014
C. Vennari, S. L. Gariano, L. Antronico, M. T. Brunetti, G. Iovine, S. Peruccacci, O. Terranova, and F. Guzzetti
Nat. Hazards Earth Syst. Sci., 14, 317–330, https://doi.org/10.5194/nhess-14-317-2014, https://doi.org/10.5194/nhess-14-317-2014, 2014
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Snow depth variations caused by wind are an important factor in avalanche danger, but detailed and up-to-date information is rarely available. We propose a monitoring system, using lidar and optical sensors, to measure the snow depth distribution at high spatial and temporal resolution. First results show that we can quantify snow depth changes with an accuracy on the low decimeter level, or better, and can identify events such as avalanches or displacement of snow during periods of strong winds.
Thuy T. Nguyen, Shahbaz Mushtaq, Jarrod Kath, Thong Nguyen-Huy, and Louis Reymondin
Nat. Hazards Earth Syst. Sci., 25, 913–927, https://doi.org/10.5194/nhess-25-913-2025, https://doi.org/10.5194/nhess-25-913-2025, 2025
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We reviewed the use of satellite-based data in designing agricultural index-based insurance (IBI) products, an effective tool for managing climate risk and promoting sustainable development. Despite the increasing number of studies since 2010 to present, the review revealed a gap in applying the approach to perennial crops in developing countries. We also highlighted the growing importance of satellite data for index insurance, employing high-resolution datasets to reduce basis risk.
Harriet E. Thompson, Joel C. Gill, Robert Šakić Trogrlić, Faith E. Taylor, and Bruce D. Malamud
Nat. Hazards Earth Syst. Sci., 25, 353–381, https://doi.org/10.5194/nhess-25-353-2025, https://doi.org/10.5194/nhess-25-353-2025, 2025
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We present a methodology to compile single hazards and multi-hazard interrelationships in data-scarce urban settings, which we apply to the Kathmandu Valley, Nepal. Using blended sources, we collate evidence of 21 single natural hazard types and 83 multi-hazard interrelationships that could impact the Kathmandu Valley. We supplement these exemplars with multi-hazard scenarios developed by practitioner stakeholders, emphasising the need for inclusive disaster preparedness and response approaches.
Aydoğan Avcıoğlu, Ogün Demir, and Tolga Görüm
EGUsphere, https://doi.org/10.5194/egusphere-2025-7, https://doi.org/10.5194/egusphere-2025-7, 2025
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Here we demonstrate an approach for the development of inventories from internet sources to geolocalized geohazard incidents. We created a tool that autonomously gets news, processes it using NLP and machine learning, and maps using Open Street Map. Consequently, we present spatiotemporal inventories for geohazards resulting in a total of 13940 incidents between 1997 and 2023 in Türkiye. Our alternative and easy-to-implement development inventory method aids geohazard management and resilience.
Sadhana Nirandjan, Elco E. Koks, Mengqi Ye, Raghav Pant, Kees C. H. Van Ginkel, Jeroen C. J. H. Aerts, and Philip J. Ward
Nat. Hazards Earth Syst. Sci., 24, 4341–4368, https://doi.org/10.5194/nhess-24-4341-2024, https://doi.org/10.5194/nhess-24-4341-2024, 2024
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Critical infrastructures (CIs) are exposed to natural hazards, which may result in significant damage and burden society. Vulnerability is a key determinant for reducing these risks, yet crucial information is scattered in the literature. Our study reviews over 1510 fragility and vulnerability curves for CI assets, creating a unique publicly available physical vulnerability database that can be directly used for hazard risk assessments, including floods, earthquakes, windstorms, and landslides.
Fabio Brill, Pedro Henrique Lima Alencar, Huihui Zhang, Friedrich Boeing, Silke Hüttel, and Tobia Lakes
Nat. Hazards Earth Syst. Sci., 24, 4237–4265, https://doi.org/10.5194/nhess-24-4237-2024, https://doi.org/10.5194/nhess-24-4237-2024, 2024
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Droughts are a threat to agricultural crops, but different factors influence how much damage occurs. This is important to know to create meaningful risk maps and to evaluate adaptation options. We investigate the years 2013–2022 in Brandenburg, Germany, and find in particular the soil quality and meteorological drought in June to be statistically related to the observed damage. Measurement of crop health from satellites is also related to soil quality and not necessarily to anomalous yields.
Maria R. P. Sudibyo, Eva P. S. Eibl, Sebastian Hainzl, and Matthias Ohrnberger
Nat. Hazards Earth Syst. Sci., 24, 4075–4089, https://doi.org/10.5194/nhess-24-4075-2024, https://doi.org/10.5194/nhess-24-4075-2024, 2024
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We assessed the performance of permutation entropy (PE), phase permutation entropy (PPE), and instantaneous frequency (IF), which are estimated from a single seismic station, to detect changes before, during, and after the 2014–2015 Holuhraun eruption in Iceland. We show that these three parameters are sensitive to the pre-eruptive and eruptive processes. Finally, we discuss their potential and limitations in eruption monitoring.
Enrique A. Navarro, Jorge A. Portí, Alfonso Salinas, Sergio Toledo-Redondo, Jaume Segura-García, Aida Castilla, Víctor Montagud-Camps, and Inmaculada Albert
Nat. Hazards Earth Syst. Sci., 24, 3925–3943, https://doi.org/10.5194/nhess-24-3925-2024, https://doi.org/10.5194/nhess-24-3925-2024, 2024
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The World Wide Lightning Location Network (WWLLN) operates a globally distributed network of stations that detect lightning signals at a planetary scale. A detection efficiency of 29 % with a location accuracy of between 2 and 3 km is obtained for the area of Spain by comparing WWLLN data with those of the Spanish State Meteorological Agency. The network's capability to resolve convective-storm cells generated in a cutoff low-pressure system is also demonstrated in the west Mediterranean Sea.
Lakshmi Amani Chimata, Suresh Babu Anuvala Setty Venkata, Shashi Vardhan Reddy Patlolla, Durga Rao Korada Hari Venkata, Sreenivas Kandrika, and Prakash Chauhan
EGUsphere, https://doi.org/10.5194/egusphere-2024-2278, https://doi.org/10.5194/egusphere-2024-2278, 2024
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Fast flood assessments are important for providing effective help during emergencies and planning for future floods. This study presents a new automated way to quickly measure flood depth. By using satellite images and digital elevation models, this method makes it easier to get real-time flood information. We applied this new method in several flood-prone areas in India and found that it provides more accurate results than existing flood measurement tools.
Tengfei Wang, Fucheng Lu, Jintao Qin, Taosheng Huang, Hui Kong, and Ping Shen
Nat. Hazards Earth Syst. Sci., 24, 3075–3094, https://doi.org/10.5194/nhess-24-3075-2024, https://doi.org/10.5194/nhess-24-3075-2024, 2024
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Harsh environments limit the use of drone, satellite, and simultaneous localization and mapping technology to obtain precise channel morphology data. We propose AscDAMs, which includes a deviation correction algorithm to reduce errors, a point cloud smoothing algorithm to diminish noise, and a cross-section extraction algorithm to quantitatively assess the morphology data. AscDAMs solves the problems and provides researchers with more reliable channel morphology data for further analysis.
Vikas Ghadamode, Aruna Kumari Kondarathi, Anand K. Pandey, and Kirti Srivastava
Nat. Hazards Earth Syst. Sci., 24, 3013–3033, https://doi.org/10.5194/nhess-24-3013-2024, https://doi.org/10.5194/nhess-24-3013-2024, 2024
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In 2004-tsunami-affected South Andaman, tsunami wave propagation, arrival times, and run-up heights at 13 locations are computed to analyse pre- and post-tsunami shoreline and land use–land cover changes to understand the evolving hazard scenario. The LULC changes and dynamic shoreline changes are observed in zones 3, 4, and 5 owing to dynamic population changes, infrastructural growth, and gross state domestic product growth. Economic losses would increase 5-fold for a similar tsunami.
Wiebke S. Jäger, Marleen C. de Ruiter, Timothy Tiggeloven, and Philip J. Ward
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-134, https://doi.org/10.5194/nhess-2024-134, 2024
Revised manuscript under review for NHESS
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Multiple hazards, occurring at the same time or shortly after one another, can have more extreme impacts than single hazards. We examined the disaster records in the global emergency events database EM-DAT to better understand this phenomenon. We developed a method to identify such multi-hazards and analyzed their reported impacts using statistics. Multi-hazards have accounted for a disproportionate amount of the overall impacts, but there are different patterns in which the impacts compound.
Christian Werthmann, Marta Sapena, Marlene Kühnl, John Singer, Carolina Garcia, Tamara Breuninger, Moritz Gamperl, Bettina Menschik, Heike Schäfer, Sebastian Schröck, Lisa Seiler, Kurosch Thuro, and Hannes Taubenböck
Nat. Hazards Earth Syst. Sci., 24, 1843–1870, https://doi.org/10.5194/nhess-24-1843-2024, https://doi.org/10.5194/nhess-24-1843-2024, 2024
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Early warning systems (EWSs) promise to decrease the vulnerability of self-constructed (informal) settlements. A living lab developed a partially functional prototype of an EWS for landslides in a Medellín neighborhood. The first findings indicate that technical aspects can be manageable, unlike social and political dynamics. A resilient EWS for informal settlements has to achieve sufficient social and technical redundancy to maintain basic functionality in a reduced-support scenario.
Johnny Douvinet, Noé Carles, Pierre Foulquier, and Matthieu Peroche
Nat. Hazards Earth Syst. Sci., 24, 715–735, https://doi.org/10.5194/nhess-24-715-2024, https://doi.org/10.5194/nhess-24-715-2024, 2024
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This study provided an opportunity to assess both the perception of the tsunami hazard and the knowledge of alerts in five municipalities located along the French Mediterranean coastlines. The age and location of the respondents explain several differences between the five municipalities surveyed – more so than gender or residence status. This study may help local authorities to develop future tsunami awareness actions and to identify more appropriate strategies to be applied in the short term.
Nathalie Rombeek, Jussi Leinonen, and Ulrich Hamann
Nat. Hazards Earth Syst. Sci., 24, 133–144, https://doi.org/10.5194/nhess-24-133-2024, https://doi.org/10.5194/nhess-24-133-2024, 2024
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Severe weather such as hail, lightning, and heavy rainfall can be hazardous to humans and property. Dual-polarization weather radars provide crucial information to forecast these events by detecting precipitation types. This study analyses the importance of dual-polarization data for predicting severe weather for 60 min using an existing deep learning model. The results indicate that including these variables improves the accuracy of predicting heavy rainfall and lightning.
Adriaan L. van Natijne, Thom A. Bogaard, Thomas Zieher, Jan Pfeiffer, and Roderik C. Lindenbergh
Nat. Hazards Earth Syst. Sci., 23, 3723–3745, https://doi.org/10.5194/nhess-23-3723-2023, https://doi.org/10.5194/nhess-23-3723-2023, 2023
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Landslides are one of the major weather-related geohazards. To assess their potential impact and design mitigation solutions, a detailed understanding of the slope is required. We tested if the use of machine learning, combined with satellite remote sensing data, would allow us to forecast deformation. Our results on the Vögelsberg landslide, a deep-seated landslide near Innsbruck, Austria, show that the formulation of such a machine learning system is not as straightforward as often hoped for.
Xabier Blanch, Marta Guinau, Anette Eltner, and Antonio Abellan
Nat. Hazards Earth Syst. Sci., 23, 3285–3303, https://doi.org/10.5194/nhess-23-3285-2023, https://doi.org/10.5194/nhess-23-3285-2023, 2023
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We present cost-effective photogrammetric systems for high-resolution rockfall monitoring. The paper outlines the components, assembly, and programming codes required. The systems utilize prime cameras to generate 3D models and offer comparable performance to lidar for change detection monitoring. Real-world applications highlight their potential in geohazard monitoring which enables accurate detection of pre-failure deformation and rockfalls with a high temporal resolution.
Vipasana Sharma, Sushil Kumar, and Rama Sushil
Nat. Hazards Earth Syst. Sci., 23, 2523–2530, https://doi.org/10.5194/nhess-23-2523-2023, https://doi.org/10.5194/nhess-23-2523-2023, 2023
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Snow avalanches are a natural hazard that can cause danger to human lives. This threat can be reduced by accurate prediction of the danger levels. The development of mathematical models based on past data and present conditions can help to improve the accuracy of prediction. This research aims to develop a neural-network-based model for correlating complex relationships between the meteorological variables and the profile variables.
Guillermo Tamburini-Beliveau, Sebastián Balbarani, and Oriol Monserrat
Nat. Hazards Earth Syst. Sci., 23, 1987–1999, https://doi.org/10.5194/nhess-23-1987-2023, https://doi.org/10.5194/nhess-23-1987-2023, 2023
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Landslides and ground deformation associated with the construction of a hydropower mega dam in the Santa Cruz River in Argentine Patagonia have been monitored using radar and optical satellite data, together with the analysis of technical reports. This allowed us to assess the integrity of the construction, providing a new and independent dataset. We have been able to identify ground deformation trends that put the construction works at risk.
Adrian Wicki, Peter Lehmann, Christian Hauck, and Manfred Stähli
Nat. Hazards Earth Syst. Sci., 23, 1059–1077, https://doi.org/10.5194/nhess-23-1059-2023, https://doi.org/10.5194/nhess-23-1059-2023, 2023
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Soil wetness measurements are used for shallow landslide prediction; however, existing sites are often located in flat terrain. Here, we assessed the ability of monitoring sites at flat locations to detect critically saturated conditions compared to if they were situated at a landslide-prone location. We found that differences exist but that both sites could equally well distinguish critical from non-critical conditions for shallow landslide triggering if relative changes are considered.
Anirudh Rao, Jungkyo Jung, Vitor Silva, Giuseppe Molinario, and Sang-Ho Yun
Nat. Hazards Earth Syst. Sci., 23, 789–807, https://doi.org/10.5194/nhess-23-789-2023, https://doi.org/10.5194/nhess-23-789-2023, 2023
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This article presents a framework for semi-automated building damage assessment due to earthquakes from remote-sensing data and other supplementary datasets including high-resolution building inventories, while also leveraging recent advances in machine-learning algorithms. For three out of the four recent earthquakes studied, the machine-learning framework is able to identify over 50 % or nearly half of the damaged buildings successfully.
Jan Freihardt and Othmar Frey
Nat. Hazards Earth Syst. Sci., 23, 751–770, https://doi.org/10.5194/nhess-23-751-2023, https://doi.org/10.5194/nhess-23-751-2023, 2023
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In Bangladesh, riverbank erosion occurs every year during the monsoon and affects thousands of households. Information on locations and extent of past erosion can help anticipate where erosion might occur in the upcoming monsoon season and to take preventive measures. In our study, we show how time series of radar satellite imagery can be used to retrieve information on past erosion events shortly after the monsoon season using a novel interactive online tool based on the Google Earth Engine.
Jiale Qian, Yunyan Du, Jiawei Yi, Fuyuan Liang, Nan Wang, Ting Ma, and Tao Pei
Nat. Hazards Earth Syst. Sci., 23, 317–328, https://doi.org/10.5194/nhess-23-317-2023, https://doi.org/10.5194/nhess-23-317-2023, 2023
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Human activities across China show a similar trend in response to rains. However, urban resilience varies significantly by region. The northwestern arid region and the central underdeveloped areas are very fragile, and even low-intensity rains can trigger significant human activity anomalies. By contrast, even high-intensity rains might not affect residents in the southeast.
Orlando García-Feal, José González-Cao, Diego Fernández-Nóvoa, Gonzalo Astray Dopazo, and Moncho Gómez-Gesteira
Nat. Hazards Earth Syst. Sci., 22, 3859–3874, https://doi.org/10.5194/nhess-22-3859-2022, https://doi.org/10.5194/nhess-22-3859-2022, 2022
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Extreme events have increased in the last few decades; having a good estimation of the outflow of a reservoir can be an advantage for water management or early warning systems. This study analyzes the efficiency of different machine learning techniques to predict reservoir outflow. The results obtained showed that the proposed models provided a good estimation of the outflow of the reservoirs, improving the results obtained with classical approaches.
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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The devastating effects of recurring drought conditions are mostly felt by pastoralists that rely on grass and shrubs as fodder for their animals. Using historical information from precipitation, soil moisture, and vegetation health data, we developed a model that can forecast vegetation condition and the probability of drought occurrence up till a 10-week lead time with an accuracy of 74 %. Our model can be adopted by policymakers and relief agencies for drought early warning and early action.
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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The impact of drought may vary in a given region depending on whether it is dominated by trees, grasslands, or croplands. The differences in impact can also be the agro-ecological zones within the region. This paper proposes a hierarchical Bayesian model (HBM) for forecasting vegetation condition in spatially diverse areas. Compared to a non-hierarchical model, the HBM proved to be a more natural method for forecasting drought in areas with different land covers and
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.
Cited articles
Antonini, G., Cardinali, M., Guzzetti, F., Reichenbach, P., and Sorrentino, A.: Carta Inventario dei Fenomeni Franosi della Regione Marche ed aree limitrofe, Publication no. 580, 2 sheets, 2816, 2817, 2836, scale 1 : 100 000, CNR Gruppo Nazionale per la Difesa dalle Catastrofi Idrogeologiche, Rome, 1993.
Antonini, G., Ardizzone, F., Cacciano, M., Cardinali, M., Castellani, M., Galli, M., Guzzetti, F., Reichenbach, P., and Salvati, P.: Rapporto Conclusivo Protocollo d'Intesa fra la Regione dell'Umbria, Direzione Politiche Territoriali Ambiente e Infrastrutture, ed il CNR IRPI di Perugia per l'acquisizione di nuove informazioni sui fenomeni franosi nella regione dell'Umbria, la realizzazione di una nuova carta inventario dei movimenti franosi e dei siti colpiti da dissesto, l'individuazione e la perimetrazione delle aree a rischio da frana di particolare rilevanza, e l'aggiornamento delle stime sull'incidenza dei fenomeni di dissesto sul tessuto insediativo, infrastrutturale e produttivo regionale, Unpublished report, May 2002, 140 pp., 2002a.
Antonini, G., Ardizzone, F., Cardinali, M., Galli, M., Guzzetti, F., and Reichenbach, P.: Surface deposits and landslide inventory map of the area affected by the 1997 Umbria-Mare earthquakes, B. Soc. Geol. Ital., 121, 843–853, 2002b.
APAT: Servizio Geologico d'Italia, Dipartimento Difesa del Suolo, Carta Geologica d'Italia alla scala 1 : 50 000, Foglio 613 Taormina, S.EL.CA., Firenze, available at: http://www.isprambiente.gov.it/Media/carg/613_TAORMINA/Foglio.html (last access: 30 June 2015), 2008.
Ardizzone, F., Cardinali, M., Carrara, A., Guzzetti, F., and Reichenbach, P.: Impact of mapping errors on the reliability of landslide hazard maps, Nat. Hazards Earth Syst. Sci., 2, 3–14, https://doi.org/10.5194/nhess-2-3-2002, 2002.
Ardizzone, F., Cardinali, M., Galli, M., Guzzetti, F., and Reichenbach, P.: Identification and mapping of recent rainfall-induced landslides using elevation data collected by airborne Lidar, Nat. Hazards Earth Syst. Sci., 7, 637–650, https://doi.org/10.5194/nhess-7-637-2007, 2007.
Ardizzone, F., Basile, G., Cardinali, M., Casagli, N., Del Conte, S., Del Ventisette, C., Fiorucci, F., Garfagnoli, F., Gigli, G., Guzzetti, F., Iovine, G. G. R., Mondini, A. C., Moretti, S., Panebianco, M., Raspini, F., Reichenbach, P., Rossi, M., Tanteri, L., and Terranova, O. G.: Landslide inventory map for the Briga and the Giampilieri catchments, NE Sicily, Italy, J. Maps, 8, 176–180, https://doi.org/10.1080/17445647.2012.694271, 2012.
Ardizzone, F., Fiorucci, F., Santangelo, M., Cardinali, M., Mondini, A. C., Rossi, M., Reichenbach, P., and Guzzetti, F.: Use of very-high resolution stereoscopic satellite images for landslide mapping, in: Landslide Science and Practice, edited by: Margottini, C., Canuti, P., and Sassa, K., Springer, Berlin, Heidelberg, 95–101, 2013.
Bernstein, R.: Image geometry and rectification, in: Manual of Remote Sensing, edited by: Colwell, R. N., American Society of Photogrammetry, Falls Church, VA, 1983.
Borghuis, A. M., Chang, K., and Lee, H. Y.: Comparison between automated and manual mapping of typhoon-triggered landslides from SPOT-5 imagery, Int. J. Remote Sens., 28, 1843–1856, 2007.
Brabb, E. E. and Pampeyan, E. H.: Preliminary map of landslide deposits in San Mateo County, California, US Geological Survey Miscellaneous Field Studies Map, MF-344, US Geological Survey, 1972.
Brabb, E. E., Wieczorek, G. F., and Harp, E. L.: Map showing 1983 landslides in Utah, U.S. Geological Survey Miscellaneous Field Studies Map, MF-1867, US Geological Survey, 1989.
Brunsden, D.: Mass movements; the research frontier and beyond: a geomorphological approach, Geomorphology, 7, 85–128, 1993.
Cardinali, M., Guzzetti, F., and Brabb, E. E.: Preliminary map showing landslide deposits and related features in New Mexico, US Geological Survey Open File Report 90/293, 4 sheets, scale 1 : 500 000, US Geological Survey, Washington, 1990.
Cardinali, M., Antonini, G., Reichenbach, P., and Guzzetti, F.: Photo geological and landslide inventory map for the Upper Tiber River basin, Publication n. 2116, scale 1 : 100 000, GNDCI INternal Report, CNR, Gruppo Nazionale per la Difesa dalle Catastrofi Idrogeologiche, Rome, 2001.
Cardinali, M., Reichenbach, P., Guzzetti, F., Ardizzone, F., Antonini, G., Galli, M., Cacciano, M., Castellani, M., and Salvati, P.: A geomorphological approach to the estimation of landslide hazards and risks in Umbria, Central Italy, Nat. Hazards Earth Syst. Sci., 2, 57–72, https://doi.org/10.5194/nhess-2-57-2002, 2002.
Carrara, A., Cardinali, M., and Guzzetti, F.: Uncertainty in assessing landslide hazard and risk, ITC Journal, 2, 172–183, 1992.
Cheng, K. S., Wei, C., and Chang, S. C.: Locating landslides using multi-temporal satellite images, Adv. Space Res., 33, 96–301, 2004.
Congalton, R. G.: A review of assessing the accuracy of classifications of remotely sensed data, Remote Sens. Environ., 37, 35–46, 1991.
Cruden, D. M. and Varnes, D. J.: Landslide types and processes, in: Landslides, Investigation and Mitigation, edited by: Turner, A. K. and Schuster, R. L., Transportation Research Board Special Report 247, Washington, D.C., 36–75, 1996.
Delaunay, J.: Carte de France des zones vulnèrables a des glissements, écroulements, affaissements et effrondrements de terrain, 81 SGN 567 GEG, Bureau de Recherches Géologiques et Minières, 23 pp., 1981.
Di Maggio, C., Madonia, G., and Vattano, M.: Deep-seated gravitational slope deformations in western Sicily: controlling factors, triggering mechanisms, and morphoevolutionary models, Geomorphology, 129, 173–189, https://doi.org/10.1016/j.geomorph.2013.11.023, 2014.
Duman, T. Y., Çan, T., Emre, Ö., Keçer, M., Doğan, A., Şerafettin, A., and Serap, D.: Landslide inventory of northwestern Anatolia, Turkey, Eng. Geol., 77, 99–114, 2005.
Fiorucci, F., Cardinali, M., Carlà, R., Rossi, M., Mondini, A. C., Santurri, L., Ardizzone, F., and Guzzetti, F.: Seasonal landslides mapping and estimation of landslide mobilization rates using aerial and satellite images, Geomorphology, 129, 59–70, https://doi.org/10.1016/j.geomorph.2011.01.013, 2011.
Galli, M., Ardizzone, F., Cardinali, M., Guzzetti, F., and Reichenbach, P.: Comparing landslide inventory maps, Geomorphology, 94, 268–289, https://doi.org/10.1016/j.geomorph.2006.09.023, 2008.
Gao, J. and Maroa, J.: Topographic controls on evolution of shallow landslides in pastoral Wairarapa, New Zealand, 1979–2003, Geomorphology, 114, 373–381, https://doi.org/10.1016/j.geomorph.2009.08.002, 2010.
Giordan, D., Allasia, P., Manconi, A., Baldo, M., Santangelo, M., Cardinali, M., Corazza, A., Albanese, V., Lollino, G., and Guzzetti, F.: Morphological and kinematic evolution of a large earthflow: the Montaguto landslide, southern Italy, Geomorphology, 187, 61–79, 2013.
GRASS Development Team: Geographic Resources Analysis Support System (GRASS) Software, Version 6.4.0, Open Source Geospatial Foundation, available at: http://grass.osgeo.org (last access: 30 June 2015), 2012.
Grelle, G., Revellino, P., Donnarumma, A., and Guadagno, F. M.: Bedding control on landslides: a methodological approach for computer-aided mapping analysis, Nat. Hazards Earth Syst. Sci., 11, 1395–1409, https://doi.org/10.5194/nhess-11-1395-2011, 2011.
Guzzetti, F. and Cardinali, M.: Carta Inventario dei Fenomeni Franosi della Regione dell'Umbria ed aree limitrofe, Publication n. 204, 2 sheets, scale 1 : 100 000, CNR, Gruppo Nazionale per la Difesa dalle Catastrofi Idrogeologiche, Rome, 1989.
Guzzetti, F. and Cardinali, M.: Landslide inventory map of the Umbria region, Central Italy, in: Proceedings ALPS 90 6th International Conference and Field Workshop on Landslides, edited by: Cancelli, A., Milan, Italy, 273–284, 1990.
Guzzetti, F., Malamud, B. D., Turcotte, D. L., and Reichenbach, P.: Power-law correlations of landslide areas in central Italy, Earth Planet. Sc. Lett., 195, 169–183, 2002.
Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., and Ardizzone, F.: Probabilistic landslide hazard assessment at the basin scale, Geomorphology, 72, 272–299, https://doi.org/10.1016/j.geomorph.2005.06.002, 2005.
Guzzetti, F., Galli, M., Reichenbach, P., Ardizzone, F., and Cardinali, M.: Landslide hazard assessment in the Collazzone area, Umbria, Central Italy, Nat. Hazards Earth Syst. Sci., 6, 115–131, https://doi.org/10.5194/nhess-6-115-2006, 2006.
Guzzetti, F., Ardizzone, F., Cardinali, M., Galli, M., and Reichenbach, P.: Distribution of landslides in the Upper Tiber River basin, central Italy, Geomorphology, 96, 105–122, https://doi.org/10.1016/j.geomorph.2007.07.015, 2008.
Guzzetti, F., Ardizzone, F., Cardinali, M., Rossi, M., and Valigi, D.: Landslide volumes and landslide mobilization rates in Umbria, central Italy, Earth Planet. Sc. Lett., 279, 222–229, https://doi.org/10.1016/j.epsl.2009.01.005, 2009.
Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., and Chang, K. T.: Landslide inventory maps: new tools for and old problem, Earth-Sci. Rev., 112, 42–66, https://doi.org/10.1016/j.earscirev.2012.02.001, 2012.
Haneberg, W. C., Cole, W. F., and Kasali, G.: High-resolution lidar-based landslide hazard mapping and modeling, UCSF Parnassus Campus; San Francisco, USA, B. Eng. Geol. Environ., 68, 263–276, https://doi.org/10.1007/s10064-009-0204-3, 2009.
Jackson Jr., L. E., Bobrowsky, P. T., and Bichler, A.: Identification, Maps and Mapping – Canadian Technical Guidelines and Best Practices Related to Landslides: A National Initiative for Loss Reduction, Geological Survey of Canada, Open File 7059, https://doi.org/10.4095/292122, 2012.
Lu, P., Stumpf, A., Kerle, N., and Casagli, N.: Object-oriented change detection for landslide rapid mapping, IEEE T. Geosci. Remote, 8, 701–705, 2011.
Malamud, B. D., Turcotte, D. L., Guzzetti, F., and Reichenbach, P.: Landslide inventories and their statistical properties, Earth Surf. Proc. Land., 29, 687–711, 2004a.
Malamud, B. D., Turcotte, D. L., Guzzetti, F., and Reichenbach, P.: Landslides, earthquakes, and erosion, Earth Planet. Sci. Lett., 229, 45–59, https://doi.org/10.1016/j.epsl.2004.10.018, 2004b.
Marcelino, E. V., Formaggio, A. R., and Maeda, E. E.: Landslide inventory using image fusion techniques in Brazil, Int. J. Appl. Earth Obs., 11, 181–191, 2009.
Marchesini, I., Santangelo, M., Fiorucci, F., Cardinali, M., Rossi, M., and Guzzetti, F.: A GIS method for obtaining geologic bedding attitude, in: Landslide Science and Practice, edited by: Margottini, C., Canuti, P., and Sassa, K., Springer, Berlin, Heidelberg, 243–247, 2013.
Martha, T. R., Kerle, N., Jetten, V., van Westen, C., and Vinod Kumar, K.: Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods, Geomorphology, 116, 24–36, 2010.
McDonald, H. C. and Grubbs, R. C.: Landsat imagery analysis: an aid for predicting landslide prone areas for highway construction, Vol. 1-b, NASA Earth Resource Survey Sym., Houston, Texas, 769–778, 1975.
Mondini, A. C. and Chang, K.-T.: Combining spectral and geoenvironmental information for probabilistic event landslide mapping, Geomorphology, 213, 183–189, https://doi.org/10.1016/j.geomorph.2014.01.007, 2014.
Mondini, A. C., Chang, K.-T., and Yin, H.-Y.: Combining multiple change detection indices for mapping landslides triggered by typhoons, Geomorphology, 134, 440–451, https://doi.org/10.1016/j.geomorph.2011.07.021, 2011a.
Mondini, A. C., Guzzetti, F., Reichenbach, P., Rossi, M., Cardinali, M., and Ardizzone, F.: Semi-automatic recognition and mapping of rainfall induced shallow landslides using satellite optical images, Remote Sens. Environ., 115, 1743–1757, https://doi.org/10.1016/j.rse.2011.03.006, 2011b.
Mondini, A. C., Marchesini, I., Rossi, M., Chang, K.-T., Pasquariello, G., and Guzzetti, F.: Bayesian framework for mapping and classifying shallow landslides exploiting remote sensing and topographic data, Geomorphology, 201, 135–147, https://doi.org/10.1016/j.geomorph.2013.06.015, 2013.
Mondini, A. C., Viero, A., Cavalli, M., Marchi, L., Herrera, G., and Guzzetti, F.: Comparison of event landslide inventories: the Pogliaschina catchment test case, Italy, Nat. Hazards Earth Syst. Sci., 14, 1749–1759, https://doi.org/10.5194/nhess-14-1749-2014, 2014.
Murillo-García, F. G., Alcántara-Ayala, I., Ardizzone, F., Cardinali, M., Fiourucci, F., and Guzzetti, F.: Satellite stereoscopic pair images of very high resolution: a step forward for the development of landslide inventories, Landslides, 12, 277–291, https://doi.org/10.1007/s10346-014-0473-1, 2015.
Nichol, E. J., Shaker, A., and Wong, M. S: Application of high-resolution stereo satellite images to detailed landslide hazard assessment, Geomorphology, 76, 68–75, 2006.
Pike, R. J.: The geometric signature: quantifying landslide-terrain types from digital elevation models, Math. Geol., 20, 491–511, https://doi.org/10.1007/BF00890333, 1988.
Radbruch-Hall, D. H., Colton, R. B., Davies, W. E., Lucchitta, I., Skipp, B. A., and Varnes, D. J.: Landslide overview map of the conterminous United States, US Geological Survey Professional Paper 1183, US Geological Survey, Washington, 25 pp., 1982.
Razak, K. A., Straatsma, M. W., van Westen, C. J., Malet, J.-P., and de Jong, S. M.: Airborne laser scanning of forested landslides characterization: terrain model quality and visualization, Geomorphology, 126, 186–200, https://doi.org/10.1016/j.geomorph.2010.11.003, 2011.
Razak, K. A., Santangelo, M., Van Westen, C. J., Straatsma, M. W., and de Jong, S. M.: Generating an optimal DTM from airborne laser scanning data for landslide mapping in a tropical forest environment, Geomorphology, 190, 112–125, 2013.
Reichenbach, P., Guzzetti, F., and Cardinali., M.: Map of sites historically affected by landslides and floods, The AVI Project, 2nd Edn., CNR GNDCI Publication Number 1782, map at 1 : 1 200 000 scale, CNR, Rome, 1998.
Reichenbach, P., Galli, M., Cardinali, M., Guzzetti, F., and Ardizzone, F.: Geomorphological mapping to assess landslide risk: concepts, methods and applications in the Umbria region 25 of Central Italy, in: Landslide Hazard and Risk, edited by: Glade, T., Anderson, M. G., and Crozier, M. J., John Wiley & Sons, Ltd., Chippenham, 429–468, 2004.
Rocchini, D., Metz, M., Frigeri, A., Delucchi, L., Marcantonio, M., and Neteler, M.: Robust rectification of aerial photographs in an open source environment, Computers Geosci., 39, 145–151, https://doi.org/10.1016/j.cageo.2011.06.002, 2011.
Rosin, P. L. and Hervás, J.: Remote sensing image thresholding methods for determining landslide activity, Int. J. Remote Sens., 26, 1075–1092, 2005.
Rossi, M., Guzzetti, F., Reichenbach, P., Mondini, A. C., and Peruccacci, S.: Optimal landslide susceptibility zonation based on multiple forecasts, Geomorphology, 114, 129–142, https://doi.org/10.1016/j.geomorph.2009.06.020, 2009.
Salvati, P., Guzzetti, F., Reichenbach, P., Cardinali, M., and Stark, C. P.: Map of landslides and floods with human consequences in Italy, CNR Gruppo Nazionale per la Difesa dalle Catastrofi Idrogeologiche Publication n. 2822, scale 1 : 1 200 000, CNR, Rome, 2003.
Santangelo, M., Cardinali, M., Rossi, M., Mondini, A. C., and Guzzetti, F.: Remote landslide mapping using a laser rangefinder binocular and GPS, Nat. Hazards Earth Syst. Sci., 10, 2539–2546, https://doi.org/10.5194/nhess-10-2539-2010, 2010.
Santangelo, M., Maresini, I., Bucci, F., Fiorucci, F., Cardinali, M., and Guzzetti, F.: Landslide mapping: improving accuracy and efficiency, in: Proceedings of the 7th EUREGEO Congress, 12–15 June 2012, Bologna, 788–789, 2012.
Santangelo, M., Gioia, D., Cardinali, M., Guzzetti, F., and Schiattarella, M.: Interplay between mass movement and fluvial network organization: an example from southern Apennines, Italy, Geomorphology, 188, 54–67, https://doi.org/10.1016/j.geomorph.2012.12.008, 2013.
Santangelo, M., Gioia, D., Cardinali, M., Guzzetti, F., and Schiattarella, M.: Landslide inventory map for the upper Sinni River valley, Southern Italy, J. Maps, 11, 444–453, https://doi.org/10.1080/17445647.2014.949313, 2015a.
Santangelo, M., Marchesini, I., Cardinali, M., Fiorucci, F., Rossi, M., Bucci, F., and Guzzetti, F.: A method for the assessment of the influence of bedding on landslide abundance and types, Landslides, 12, 295–309, https://doi.org/10.1007/s10346-014-0485-x, 2015b.
Sauchyn, D. J. and Trench, N. R.: Landsat applied to landslide mapping, Photogramm. Eng. Rem. S., 44, 735–741, 1978.
Stark, C. P. and Guzzetti, F.: Landslide rupture and the probability distribution of mobilized debris volumes, J. Geophys. Res., 114, F00A02, https://doi.org/10.1029/2008JF001008, 2009.
Stark, C. P. and Hovius, N.: The characterization of landslide size distributions, Geophys. Res. Lett., 28, 1091–1094, https://doi.org/10.1029/2000GL008527, 2001.
Stumpf, A. and Kerle, N.: Object-oriented mapping of landslides using Random Forest, Remote Sens. Environ., 115, 2564–2577, https://doi.org/10.1016/j.rse.2011.05.013, 2011.
Thiery, Y., Malet, J.-P., Sterlacchini, S., Puissant, A., and Maquaire, O.: Landslide susceptibility assessment by bivariate methods at large scales: application to a complex mountainous environment, Geomorphology, 92, 38–59, 2007.
Trigila, A., Iadanza, C., and Spizzichino, D.: Quality assessment of the Italian Landslide Inventory using GIS processing, Landslides, 7, 455–470, https://doi.org/10.1007/s10346-010-0213-0, 2010.
Van Den Eeckhaut, M., Poesen, J., Verstraeten, G., Vanacker, V., Moeyersons, J., Nyssen, J., van Beek, L. P. H., and Vandekerckhove, L.: Use of LIDAR-derived images for mapping old landslides under forest, Earth Surf. Proc. Land., 32, 754–769, https://doi.org/10.1002/esp.1417, 2007.
Van Den Eeckhaut, M., Kerle, N., Poesen, J., and Hervás, J.: Object-oriented identification of forested landslides with derivatives of single pulse LiDAR data. Geomorphology, 173–174, 30–42, https://doi.org/10.1016/j.geomorph.2012.05.024, 2012.
Yang, X. and Chen, L.: Using multi-temporal remote sensor imagery to detect earthquake-triggered landslides, Int. J. Appl. Earth Obs., 12, 487–495, 2010.
Short summary
In this work, we present a new semi-automatic procedure to prepare landslide inventory maps that uses GIS applications and tools for the digitization of photo-interpreted data. Results show that the new semi-automatic procedure proves more efficient for the production of landslide inventories and results in the production of more accurate maps, compared to the manual procedure. The presented work has potential consequences for multiple applications of landslide studies.
In this work, we present a new semi-automatic procedure to prepare landslide inventory maps that...
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