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227 results for “landslides”
Landslides from Space - Big Sur Landslide, USA (20th May 2017)
<p>One of the biggest, recent landslides in California burried the famous Pacific Coast Highway and added approx. 5 hectar of new land<br> <br> The pre-event acquisition is from 17th May 2017 (Sentinel-2) and the post-event acquisition is from 27th May 2017 (Sentinel-2). A false colour composite with near-infrared, red and green band is visualised as RGB image.<br> <br> <em>Contains modified Copernicus Sentinel data (2017)</em></p>
Landslides from Space - Qamea Island Landslides, Fiji (18th December 2016)
<p>A cyclone crossed the islands of Fiji in December 2016. It caused floods and landslide all over the country.<br> <br> The pre-event acquisition is from 15th November 2016 (Sentinel-2) and the post-event acquisition is from 14th January 2017 (Sentinel-2). A false colour composite with near-infrared, red and green band is visualised as RGB image.<br> <br> <em>Contains modified Copernicus Sentinel data (2016-2017)</em></p>
Landslides from Space - Kaikoura Earthquake-triggered Landslides, New Zealand (14th November 2016)
<p>On 14th November 2016 an earthquake with a maginitude of 7.8 triggered multiple landslides. Especially the village of Kaikoura was affected as it was cut off from the rest of New Zealand when its main road, State Highway 1. was blocked through multiple rockfalls and landslides.<br> <br> The pre-event acquisition is from 14th September 2016 (Sentinel-2) and the post-event acquisition is from 15th December 2016 (Sentinel-2).<br> <br> <em>Contains modified Copernicus Sentinel data (2016)</em></p>
Landslides from Space - Volcan Landslide (10th January 2017)
<p>On 10th January 2017 a landslide hit the village Volcan. Two people died in this event and the famous Dakar Rally was disrupted.<br> <br> The pre-event acquisition is from 17th December 2016 (Sentinel-2) and the post-event acquisition is from 15th February 2017 (Sentinel-2).<br> <br> <em>Contains modified Copernicus Sentinel data (2016-2017)</em></p>
Landslides from Space - Aranayake Landslide, Sri Lanka (17th May 2016)
<p>On 17th May 2016 a landslide close to Aranayake was triggered by exceptional rainfall. About 140 people lost their life in this disaster.<br> <br> The pre-event acquisition is from 26th April 2016 (Sentinel-2) and the post-event acquisition is from 25th June 2016 (Sentinel-2). A false colour composite with near-infrared, red and green band is visualised as RGB image.<br> <br> <em>Contains modified Copernicus Sentinel data (2016)</em></p>
Landslides from Space - Dzongu Landslide, India (2nd August 2016)
<p>On 2nd of August 2016 an impressive landslide appeared in Northern India. A mountainside collapsed and blocked a river.<br> <br> The pre-event acquisition is from 9th July 2016 (Sentinel-2) and the post-event acquisition is from 27th October 2016 (Sentinel-2). A false colour composite with near-infrared, red and green band is visualised as RGB image.<br> <br> <em>Contains modified Copernicus Sentinel data (2016)</em></p>
Landslides from Space - Sucun Landslide, China (28th October 2016)
<p>In October 2016, Typhoon Megi passed over China. Through the heavy rainfall a mass movement was accelerated and about 350'000 cubic metres of debris slided down the hill. This landslide killed 27 people.<br> <br> The pre-event acquisition is from 25th March 2016 (Sentinel-2) and the post-event acquisition is from 30th December 2016 (Sentinel-2). A false colour composite with near-infrared, red and green band is visualised as RGB image.<br> <br> <em>Contains modified Copernicus Sentinel data (2016)</em></p>
Landslides from Space - Glacier Bay Landslide, Alaska USA (28th June 2016)
<p>On 28th June 2016 seismometer recorded an event with a magnitude of 5.2. Later it was visually confirmed that this was caused by a landslide and not an earthquake.</p> <p>The pre-event acquisition is from 5th February 2016 (Sentinel-2) and the post-event acquisition is from 29th September 2016 (Sentinel-2).<br> <br> <em>Contains modified Copernicus Sentinel data (2016)</em></p>
Landslides from Space - Mocoa Debris Flow, Columbia (1st April 2017)
<p>More than 330 people died in a rainfall-triggered landslide which occured on 1st April 2017 in Mocoa, Columbia.<br> <br> The pre-event acquisition is from 13th February 2017 (Sentinel-2) and the post-event acquisition is from 4th April 2017 (Sentinel-2). A false colour composite with near-infrared, red and green band is visualised as RGB image.<br> <br> <em>Contains modified Copernicus Sentinel data (2017)</em></p>
Landslides from Space - Landslide between Aickens and Jacksons, New Zealand (18th January 2017)
<p>Heavy rainfall triggered on 18th January 2017 a landslide on the West Coast of New Zealand. The landslide blocked a street and disconnected the villages Aickens and Jacksons.<br> <br> The pre-event acquisition is from 4th January 2017 (Sentinel-2) and the post-event acquisition is from 24th April 2017 (Sentinel-2). A false colour composite with near-infrared, red and green band is visualised as RGB image.<br> <br> <em>Contains modified Copernicus Sentinel data (2017)</em></p>
Landslides from Space - Kakanj Mine Waste Landslide, Bosnia (24th February 2017)
<p>On February 24<sup>th</sup> 2017 a massive mine waste landslide from an open pit coal mine occurred. It had approximately the dimension of 600 metres in width and 800 metres in length. Through this slide the stream Ribnica was dammed up and created a small lake. Because of potential dam breach 150 people of two villages had to be evacuated downstream.<br> <br> The pre-event acquisition is from 14th February 2017 (Sentinel-2) and the post-event acquisition is from 16th March 2017 (Sentinel-2).<br> <br> <em>Contains modified Copernicus Sentinel data (2017)</em></p>
Landslides from Space - Koshe Garbage Landslide, Ethiopia (11th March 2017)
<p>On 11th March 2017 a garbage dump collapsed and caused a landslide in Koshe. Nearby buildings were buried and 115 people died.<br> <br> The pre-event acquisition is from 10th March 2017 (Sentinel-2) and the post-event acquisition is from 9th April 2017 (Sentinel-2).</p> <p><em>Contains modified Copernicus Sentinel data (2017)</em></p>
e-ITALICA (enhanced ITAlian rainfall-induced LandslIdes CAtalogue)
<p>The enhanced ITAlian rainfall-induced LandslIdes CAtalogue (e-ITALICA) currently lists 6312 records with information on rainfall-induced landslides that occurred over the Italian territory between January 1996 and December 2021. Information on rainfall-induced landslides has a high accuracy on their spatial and temporal location. e-ITALICA includes the triggering rainfall conditions associated with the landslides and the coordinates of the representative rain gauges. Moreover, details on elevation, slope, and land cover are also included.</p>
Mechanism of landslide induced by glacier-retreat on the Tungnakvíslarjökull area, Iceland
<p><strong>Introduction</strong></p> <p>This repository contains the data used for the study of the slope instability of Tungnakvíslarjökull, Iceland, described in Lacroix et al. (submitted). Specifically, the repository contains three time series in Tungnakvíslarjökull:</p> <ol> <li> <p>Time series of Digital Elevation Models (DEMs) from ASTER, 2000-2020.</p> </li> <li> <p>Time series of horizontal ground displacements, 1999-2019.</p> </li> <li> <p>Time series of earthquakes, 1995-2019.</p> </li> </ol> <p>Finally, we provide the map of the rate of elevation difference and the map of horizontal ground displacements for the whole period 2000-2019, as shown in Figure 1 of Lacroix et al. (submitted).</p> <p>The data and methods used for the elaboration of this data repository are described in detail in Lacroix et al. (submitted). In this repository we also provide a short summary and overview of the data and methods used.</p> <p><strong>Data</strong></p> <p>A total of 160 ASTER scenes were used to produce the time series of DEMs. A series of images from SPOT1, Landsat-7, ASTER and Landsat-8 was used in order to produce the horizontal ground displacements maps. The South-Iceland Lowlands (SIL) network (Jóndsdóttir et al., 2007) was obtained from Veðurstofan Íslands (www.vedur.is). Table 1 provides an overview of these data.</p> <table> <caption>Table1: Data used for the creation of this repository</caption> <tbody> <tr> <td>Application</td> <td>Platforms</td> <td>Acquisition dates</td> </tr> <tr> <td>DEM</td> <td>ASTER</td> <td>160 scenes from 2000-10-16 to 2020-08-27. <p>Format for the date is YYYYMMDD</p> </td> </tr> <tr> <td>Horizontal ground displacement</td> <td>SPOT1</td> <td>1987-08-05</td> </tr> <tr> <td> </td> <td>Landsat-7</td> <td>1999-07-26, 2000-08-20, 2001-09-24, 2002-07-09</td> </tr> <tr> <td> </td> <td>ASTER</td> <td>2003-08-04, 2004-09-18, 2007-08-15, 2011-08-10, 2013-07-24, 2014-08-18, 2016-08-07</td> </tr> <tr> <td> </td> <td>Landsat-8</td> <td>2014-08-12, 2015-09-16, 2016-08-24, 2017-08-20, 2018-09-14, 2019-08-10</td> </tr> <tr> <td>Seismicity</td> <td>SIL network</td> <td>370491 events recorded between 1995-2019 in the Mýrdalsjökull (S-Iceland) area and surroundings</td> </tr> </tbody> </table> <p><strong>Methods</strong></p> <p>The DEMs were created using the Ames StereoPipeline (ASP, Shean et al., 2016) with the same setup as used in Brun et al., (2017). Each DEM was then co-registered to a lidar DEM acquired in 2010 (Jóhannesson et al., 2013), using the co-registration methods from Berthier et al. (2007), and adding an across-track fifth-degree polynomial correction (Gardelle et al., 2013). The stack of elevations obtained from the DEM time series was linearly fitted in order to produce the map of elevation difference (file name 20000101_20210101_30x30m_UTM27N_DHDT_Lacroixetal2022.tif) of the period 2000-2020.</p> <p>The horizontal ground displacement maps were created using the offset tracking methodology described in Bontemps et al. (2018), consisting of: (1) pairwise image correlation using Mic-Mac (Rupnik et al., 2017), (2) masking of areas with low correlation coefficients (3) correction of co-registration bias by subtracting the mean values of the NS and EW displacement fields and (4) pixelwise fit of the horizontal ground displacements by least squares, using the time interval between measurements as weights and obtaining the full horizontal ground displacement for the analyzed period (file name 19990726_20200101_15x15m_UTM27N_HGD_Lacroixetal2022.tif)</p> <p>The time series of earthquakes obtained from the SIL network was filtered, and 2089 earthquakes with depth <5 km and magnitude <1.7 were used in this study and data repository (file name 19950814_20181118_SILvedur_time_lon_lat_dep_mag.txt).</p> <p><strong>Acknowledgements</strong></p> <p>We thank Bryndís Brandsdóttir for providing the seismic data used in this repository. E.B. and P.L. acknowledge the support from the French Space Agency (CNES) through the TOSCA, PNTS, SWH and ISIS programs.</p> <p><strong>Dataset attribution</strong></p> <p>This dataset is licensed under a Creative Commons CC BY 4.0 International License.</p> <p><strong>Dataset Citation</strong></p> <p>Lacroix, P., Belart, J.M.C., Berthier, E., Sæmundsson, Þ., Jónsdóttir, K.: Data Repository: Mechanism of landslide induced by glacier-retreat on the Tungnakvíslarjökull area, Iceland. Dataset distributed on Zenodo: 10.5281/zenodo.6388069</p>
Data for: A severe landslide event in the Alpine foreland under possible future climate and land-use changes
<p>Data underlying manuscript and supplementary figures of the corresponding publication, as well as the scripts to conduct the final analyses.</p>
Rapid Landslide Risk Zoning toward Multi-Slope Units of the Neikuihui Tribe for Preliminary Disaster Management repository
<p> Taiwan features steep terrain and a fragile geology environment accompanied by frequent earthquakes and typhoons annually. Meanwhile, with the booming economy and rapid population growth, activities pivot from metropolises to the Taiwan's suburban and mountain areas. However, for example, the Neikuihui tribe in northern Taiwan evolves landslide disasters during extreme rainfall events. To rapidly examine landslide risk in the tribe area for preliminary disaster management, the well-known principle of Risk, which comprises Hazard, Exposure, and Vulnerability, was carefully adapted to scrutinize 14 slope units around the Neikuihui tribe region. The framework of risk zoning is improved based on the previous quantified findings regarding the inventory of the deep-seated landslides in southern Taiwan. Moreover, the proposed procedures comprehensively assess susceptibility, activity, exposure, and vulnerability of each slope unit. The rapid risk zoning analysis of multi-slope units delivers a sloping unit with a high level of landslide risk, and this slope unit did suffer from landslide disasters in the 2016 typhoon event. This study preliminarily proves that the proposed framework and details of rapid risk zoning can help identify a relatively high-risk slope unit around a tribal region and address pre-countermeasures for disaster management.</p>
Landslide Susceptibility and 3-day Antecedent Rainfall generated by Artificial Neural Networks
<p>[ENGLISH]</p> <p>In this dataset, you can find:</p> <p>- Landslide susceptibility indexes for the Serra Geral geomorphic unit, from 0 (low susceptibility) to 1 (high susceptibility). Files starting in map_susc</p> <p>- 3-day Antecedent Rainfall Thresholds for rainfall-induced landslides in the Serra Geral geomorphic unit. Files starting in map_3day</p> <p>The figure tiles_location.png shows the locations of each tile within the states of Rio Grande do Sul and Santa Catarina, Brazil. Background map: OpenStreetMap contributors (2022)</p> <p>This dataset was produced within the research conducted for the PhD Thesis of Luísa Vieira Lucchese. The link to the Thesis will be added here when it is available. Reference:</p> <p>LUCCHESE, Luísa Vieira. Modelagem de Suscetibilidade e de Limiares de Precipitação para Deslizamentos de Terra utilizando métodos de Aprendizagem de Máquina. 2022. PhD Thesis (Water Resources and Environmental Sanitation) — Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, 2022.</p> <p> </p> <p>[PORTUGUÊS DO BRASIL]</p> <p>Neste conjunto de dados, você encontra:</p> <p>- Índices de suscetibilidade a deslizamentos de terra para a unidade geomorfológica da Serra Geral, de 0 (baixa suscetibilidade) até 1 (alta suscetibilidade). Os arquivos têm o prefixo map_susc</p> <p>- Precipitação antecedente de 3 dias para a ocorrência de deslizamentos de terra na unidade geomorfológica da Serra Geral. Os arquivos têm o prefixo map_3day</p> <p>A figura tiles_location.png mostra a localização de cada bloco dentro dos estados do Rio Grande do Sul e de Santa Catarina. Mapa de fundo: OpenStreetMap contributors (2022)</p> <p>Este conjunto de dados é produto da Tese de Doutorado de Luísa Vieira Lucchese. O link para a Tese será adicionado aqui, quando estiver disponível. Referência:</p> <p>LUCCHESE, Luísa Vieira. Modelagem de Suscetibilidade e de Limiares de Precipitação para Deslizamentos de Terra utilizando métodos de Aprendizagem de Máquina. 2022. Tese (Doutorado em Recursos Hídricos e Saneamento Ambiental) — Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande Sul, Porto Alegre, 2022.</p>
Landslides of the 2023 summer event of São Sebastião, southeastern Brazil
<p>In February 2023, anomalously heavy rainfall caused widespread landslides in the coastal city of São Sebastião (Southeastern Brazil). This report describes the first version of a landslide inventory dataset for this event. The inventory is based primarily on the analysis of aerial images with 10 cm spatial resolution acquired immediately after the event, as well as archive images from Google Earth and PlanetScope. Delimitation of the landslides relied on a comparison of the images along with the area's Digital Surface Model (DSM) and hydrography. The GIS vector dataset (shapefile and geopackage) contains 989 points representing the landslide's crowns, 1,116 polygons indicating the affected areas of landslides and 1 polygon of a debris flow.</p>
Storm Alex Landslide Inventory
<p>The storm Alex that in 2020 hit the Mediterranean Alps represented and extreme meteorological event triggering devastating floods and landslides in both Italy and France, with severe consequences for people and anthropic settlements. After the Storm Alex, a detailed inventory of rainfall-induced hillslope instability processes was prepared by means of the visual interpretation of VHR satellite imagery in two adjacent mountain catchments of the Liguria Region (northern Italy) impacted by intense rainfall. The inventory map included a total of 302 features classified in debris slide (214), debris slides/debris flow (79) and channelized flow erosion (9). </p>
Map of Co-Seismic Landslides for the M 7.8 Kaikoura, New Zealand Earthquake
<p>Prepared by the Research Group on Earthquake Geology in Greece (http://eqgeogr.weebly.com/)</p> <p>Version 2 (updated)</p> <p>With the release of new Sentinel-2 images, and other available resources for the M7.8 Kaikoura earthquake, we present an update of the Map of Co-Seismic Landslides and Surfaces Ruptures (As of 27/11/2016). Landslides were mapped using Sentinel-2 satellite images from Copernicus, European Space Agency, dated November and December 2016. Images were visually compared with previous last available S2A images without cloud cover (13 September and 26 October) and landslides and large slope failures were manually mapped. Areas covered by cloud are omitted and shown on map. 5875 landslide sites are shown in the map. A small number of landslides could have been mis-identified due to insufficient resolution of the images, small gaps of cloud cover or for other reasons. Also, re-activated landslides on the central mountainous area were unabled to identify due to imagery restrictions (medium resolution, relief shadows etc). Some local gaps in Sentinel imagery still exist due to cloud cover, but we believe the current map is very close to the major distribution of mass movement effects. Surface ruptures were mapped using Sentinel-2 imagery and approximate position from photos of the post-earthquake aerial surveys of Environment Canterbury Regional Council (http://ecan.govt.nz)</p> <p>KML file contains7355 landslide spots.</p>
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