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227 results for “landslides”
Patagonian Andes landslides inventory
<p>We present the dataset developed in the research "Patagonian Andes landslides inventory: The deep learning's way to their automatic detection", submitted to the journal Remote Sensing (<a href="https://doi.org/10.3390/rs14184622">https://doi.org/10.3390/rs14184622</a>).</p> <p>“Landslide_database” folder: contains two ESRI shapefile-type vector files, the first (Ground_Truth_database), corresponds to the manually outlined landslides for training the deep learning model. The file contains the Sentinel 2 tile number, area, and perimeter. The second (Ground_Truth_database_centroid) corresponds to the centroid of the outlined landslides, in addition to the previously mentioned fields, it contains the X and Y coordinates.</p> <p>“Model results” folder: contains an ESRI shapefile vector file (Pred_T18GYS), corresponding to the landslides detected and segmented by the deep learning algorithm in the Sentinel-2 test tile.</p> <p>“Model validation” folder: contains multiple ESRI shapefile vector files used during model validation. Study area (Study_Area), roads (Roads) and populated areas (Localities). Contains the predicted landslides in the study area (Predict_T18GYS_SA), the randomly selected predicted landslides (Predict_T18GYS_Random) and their geometries (Predict_T18GYS_Geometry). The folder also contains manually delineated landslides (Groud_Truth_T18GYS) constrained to the extent of the assessed mosaic and delineated landslides that spatially match the predicted landslides (Ground_Truth_Random). Finally, true positives (TP_T18GYS), false positives (FP_T18GYS), and false negatives (FN_T18GYS) are provided separately.</p>
Ensemble of global landslide hazard from PHELS
<p>Daily global landslide hazard from the Probabilistic Hydrological Estimation of LandSlides (PHELS) model on the 36-km EASE grid for different hydrological predictor variables alongside global landslide susceptibility estimates: A 7-day antecedent rainfall index (ARI7), daily rainfall, daily root-zone soil moisture (rzmc, 0-100cm depth) and the combination of rainfall&rzmc. PHELS is based on a quadratic exponential equation, fitted to 9367 landslide events. For all four hydrological predictor variable (combinations) deterministic hazard estimations are provided. Please note the different order of magnitude in the hazard values when using one or two hydrological predictor variables. For rainfall&rzmc results of ensemble simulations (100 members) are additionally provided, more specifically the ensemble average and standard deviation. The latter is a measure for the uncertainty of the estimated hazard. Details can be found in Felsberg, A., Heyvaert, Z., Poesen, J., Stanley, T., and De Lannoy, G. J. M. (2023): Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling (NHESS, https://doi.org/10.5194/egusphere-2023-869). The global landslide susceptibility estimation is described in Felsberg et al. (2022): Estimating global landslide susceptibility and its uncertainty through ensemble modelling (NHESS, https://doi.org/10.5194/nhess-22-3063-2022)</p>
Two multi-temporal datasets to track the enhanced landsliding after the 2008 Wenchuan earthquake
<p>We release two datasets that track the enhanced landsliding induced by the 2008 M<sub>w</sub> 7.9 Wenchuan earthquake over a portion of the Longmen mountains, at the eastern margin of the Tibetan plateau (Sichuan, China). The first dataset is a geo-referenced multi-temporal polygon-based inventory of pre- and coseismic landslides, post-seismic remobilisations of coseismic landslide debris, and post-seismic landslides (new failures). It covers 471 km<sup>2</sup> in the earthquake’s epicentral area, from 2005 to 2018. The second dataset records the debris flows that occurred from 2008 to 2017 in a larger area (~17,000 km<sup>2</sup>), together with information on their triggering rainfall as recorded by a network of rain gauges. For some well-monitored events, we provide more detailed data on rainfall, discharge, flow depth and density. The datasets can be used to analyse, at various scales, the patterns of landsliding caused by the earthquake. They can be compared to inventories relative to past or new earthquakes or other triggers to reveal common or distinctive controlling factors. To our knowledge, no other inventories that track the temporal evolution of earthquake-induced mass wasting have been made freely available thus far. Our datasets can be accessed from <a href="https://doi.org/10.5281/zenodo.1405490">https://doi.org/10.5281/zenodo.1405490</a>. We also encourage other researchers to share their datasets to facilitate research on post-seismic geological hazards.</p>
Tsunamis due to ice masses: Different calving mechanisms and linkage to landslide-tsunamis - Dataset
<p>Land ice melt and retreat is one of the most visible effects of climate change and contributes ≈1.5 mm/year to global sea-level rise (SLR) of a total of ≈2.7 mm/year. Global warming results in the shrinking of ice masses in most ice covered regions in the World, particularly in the Alps and in Greenland and the Greenlandic mass loss is estimated at –269 ±51 Gt/year. A significant part of this mass loss is through the detachment of icebergs at glacier fronts in a mechanism called iceberg calving. Such iceberg impacting into a water body generate tsunamis, such called "iceberg-tsunamis". Such an iceberg-tsunami reached a height of 50 m at the Eqip Sermia outlet glacier in 2014. These tsunamis pose a considerable hazard for the local community, the fishing industry and the increasing number of tourists in ice covered areas. Several iceberg calving mechanisms have been proposed including fall, over-turning and capsizing. Reliable guidance on the upper limit of iceberg-tsunami heights are currently unavailable. A main reason for this limited understanding is that reliable field data are rare, such that laboratory tests complemented with numerical simulations are important to advance this research field. This was the aim of this HYDRALAB+ funded study. The wave features (height, length, velocity) caused by icebergs in function of the iceberg calving mechanisms (fall, over-turning, capsizing), as well as the mass volume and kinematics, were modelled in unique large-scale experiments. This minimised both scale effects and wave reflection. The attached files and folders include information about and data from these experiments.</p>
Tsunamis due to ice masses: Different calving mechanisms and linkage to landslide-tsunamis - Data storage report
<p>Land ice melt and retreat is one of the most visible effects of climate change and contributes ≈1.5 mm/year to global sea-level rise (SLR) of a total of ≈2.7 mm/year. Global warming results in the shrinking of ice masses in most ice covered regions in the World, particularly in the Alps and in Greenland and the Greenlandic mass loss is estimated at –269 ±51 Gt/year. A significant part of this mass loss is through the detachment of icebergs at glacier fronts in a mechanism called iceberg calving. Such iceberg impacting into a water body generate tsunamis, such called "iceberg-tsunamis". Such an iceberg-tsunami reached a height of 50 m at the Eqip Sermia outlet glacier in 2014. These tsunamis pose a considerable hazard for the local community, the fishing industry and the increasing number of tourists in ice covered areas. Several iceberg calving mechanisms have been proposed including fall, over-turning and capsizing. Reliable guidance on the upper limit of iceberg-tsunami heights are currently unavailable. A main reason for this limited understanding is that reliable field data are rare, such that laboratory tests complemented with numerical simulations are important to advance this research field. This was the aim of this HYDRALAB+ funded study. The wave features (height, length, velocity) caused by icebergs in function of the iceberg calving mechanisms (fall, over-turning, capsizing), as well as the mass volume and kinematics, were modelled in unique large-scale experiments. This minimised both scale effects and wave reflection. The attached file is an HYDRALAB+ standard Data Storage Report about these experiments.</p>
LANDSLIDES RIO GRANDE DO SUL APRIL- MAY 2024
<p> </p> <p><strong>LANDSLIDES RIO GRANDE DO SUL APRIL- MAY 2024</strong></p> <p> </p> <p>Mapping of scars from the event triggered by rains in RS from April 23 to May 5, 2024. Images analyzed from Google (prior to the event), from PlanetScope on April 20 and 21, 2024 (prior to the event), and on May 6, 7, and 8, 2024 (after the event). The mapped features, therefore, correspond to those triggered by the first and most intense rain event that occurred in the state.</p> <p><strong>Data Description</strong>: GEO_RS_CEMADEN_WGS84_POLIGONOS_MM.shp - polygons of the landslides (may include small flows and the transport area) = 13,945 mapped scar polygons and 141 mapped flow polygons GEO_RS_CEMADEN_WGS84_PONTOS_MM.shp - points of landslide ruptures = 15,057 points</p> <p><strong>Executing Team</strong>: Harideva Marturano Egas, Rodrigo Augusto Stabile, Márcio Roberto Magalhães de Andrade, Tatiana Sussel Gonçalves Mendes, Danielle Silva de Paula, Tulius Dias Nery, João Paulo de Carvalho Araújo, Elisabete Weber Reckziegel, Demerval Aparecido Gonçalves.</p> <p><strong>Technical Note</strong>: https://www.gov.br/cemaden/pt-br/assuntos/monitoramento/notas-tecnicas/nota-tecnica-no-412-2024-sei-cemaden-mapeamento-dos-movimentos-de-massa-relacionados-ao-desastre-de-abril-maio-2024-no-estado-do-rio-grande-do-sul</p> <p>IMAGES PLANET Plataforma Brasil Mais (Rede MAIS/MJSP, includes material © 2023 Planet Labs Inc. All rights reserved) </p>
Thematic Layers of Causative Factors of Landslide in Palungtar Municipality, Gorkha, Nepal
<p><span>Eleven causative parameters responsible for instability in the area: Slope, Aspect, Land Use Land Cover, Plan Curvature, Profile Curvature, Distance from River, Distance from Road, Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Topographic Wetness Index (TWI), and Geology. </span></p>
Shallow landslide inventory for 2000-2019 (eastern DRC, Rwanda, Burundi)
<p>This shapefile contains 7944 shallow landslide instances for the North Tanganyika-Kivu Rift Region. These landslides were identified in © Google Earth. This inventory was also used in the paper "Interactions between deforestation, landscape rejuvenation, and shallow landslides in the North Tanganyika–Kivu rift region, Africa" published in Earth Surface Dynamics (https://doi.org/10.5194/esurf-9-445-2021). When using this dataset, you can refer to this work. This research was funded by the Belgian Science Policy Office (BELSPO) through the PAStECA project (BR/165/A3/PASTECA) entitled `Historical Aerial Photographs and Archives to Assess Environmental Changes in Central Africa' (http://pasteca.africamuseum.be/).</p>
Controls on post-seismic landslide behaviour in brittle rocks - Datasets
<p>Datasets summarising:</p> <p>1. DynBPS direct shear data;</p> <p>2. physical properties of the Tubul Sandstone analysed; and</p> <p>3. post-shear height profiles along samples, derived from Structure-from-Motion photogrammetry.</p>
Dataset for Nature Geoscience: Acceleration of a large deep-seated tropical landslide due to urbanisation feedbacks
<p>This archive file contains datafiles used in the manuscript: 'Acceleration of a large deep-seated tropical landslide due to urbanisation feedbacks' [https://doi.org/10.1038/s41561-022-01073-3]. It contains historical aerial orthomosaics, UAS DSM and orthomosaics, shapefiles, deformation maps, etc. for Funu landslide.</p>
Mud Creek landslide Sentinel-1 dataset processed with MiaplPy
<p>Displacement timeseries on Mud Creek landslide, USA</p> <p>Sensor: Sentinel-1 Descending track 42</p> <p>Time: 2015.03.01 - 2017.05.13, 63 acquisitions</p> <p>Processor: ISCE/topsStack</p> <p>This is the outputs from time series analysis with <a href="https://github.com/insarlab/MiaplPy">MiaplPy</a>.</p>
Non-susceptible landslide areas in Italy and in the Mediterranean region
<p>We used landslide information for 13 study areas in Italy and morphometric information obtained from the 3-arcseconds shuttle radar topography mission digital elevation model (SRTM DEM) to determine areas where landslide susceptibility is expected to be negligible in Italy and in the landmasses surrounding the Mediterranean Sea. The morphometric information consisted of the local terrain slope which was computed in a square 3 × 3-cell moving window, and in the regional relative relief computed in a circular 15 × 15-cell moving window. We tested three different models to classify the "non-susceptible" landslide areas, including a linear model (LNR), a quantile linear model (QLR), and a quantile, non-linear model (QNL). We tested the performance of the three models using independent landslide information presented by the Italian Landslide Inventory (<em>Inventario Fenomeni Franosi in Italia</em> – IFFI). Best results were obtained using the QNL model. The corresponding zonation of non-susceptible landslide areas was intersected in a geographic information system (GIS) with geographical census data for Italy. The result determined that 57.5% of the population of Italy (in 2001) was located in areas where landslide susceptibility is expected to be negligible. We applied the QNL model to the landmasses surrounding the Mediterranean Sea, and we tested the synoptic non-susceptibility zonation using independent landslide information for three study areas in Spain. Results showed that the QNL model was capable of determining where landslide susceptibility is expected to be negligible in the validation areas in Spain. We expect our results to be applicable in similar study areas, facilitating the identification of non-susceptible landslide areas, at the synoptic scale.</p>
Inventories of landslides triggered by the 2019 Cotabato - Davao del Sur (Philippines) seismic sequence
<p>These files are related to the paper “Environmental effects following a seismic sequence: the 2019 Cotabato - Davao del Sur (Philippines) earthquakes” by Ferrario M. F., Perez J., Livio F., Rimando J., Michetti A. M., currently under review.</p> <p>Files include a multi-temporal inventory of landslides triggered during a seismic sequence. In October – December 2019, the Cotabato and Davao del Sur Provinces (Philippine) were hit by four Mw > 6.0 earthquakes. The sequence started with a Mw 6.4 on 16 October (EQ1), then three earthquakes with Mw of 6.6, 6.5 and 6.8 occurred on 29 October (EQ2), 31 October (EQ3) and 15 December 2019 (EQ4).</p> <p>Landslides were manually mapped on 3-m resolution PlanetScope images. Shapefiles are in WGS84 UTM Zone 51n and include:</p> <ul> <li>Area study: shapefile of the study area (inventories 2 and 3)</li> <li>Area study_October 2019: shapefile of the study area for inventory 1</li> <li>Landslides_ott2019: polygonal inventory of landslides mapped on images acquired on 24 and 28/10/2019, that is after EQ1</li> <li>Landslides_nov2019: polygonal inventory of landslides mapped on images acquired on 15 and 16/11/2019, that is after EQ3</li> <li>Landslides_dic2019: polygonal inventory of landslides mapped on images acquired on 05/02/2020, that is after EQ4</li> </ul>
Red Relief Image Maps of the Ruinon Landslide, Northern Italy (2021-2022)
<p>Red Relief Image Maps of the Ruinon Landslide, Northern Italy.</p> <p>The maps are from UAV surveys carried out above the landslide on the following dates:</p> <ul> <li>06 July 2021</li> <li>29 October 2021</li> <li>02 May 2022</li> <li>05 July 2022</li> <li>05 October 2022</li> <li>29 October 2022</li> </ul> <p>The maps are in WGS 84/ UTM zone 32N (EPSG:32632). They <strong>are not finely</strong> co-registered.</p> <p>The map production process details are discussed in <em>Yordanov, V.; Truong, Q.X.; Brovelli, M.A. Estimating Landslide Surface Displacement by Combining Low-Cost UAV Setup, Topographic Visualization and Computer Vision Techniques. Drones <strong>2023</strong>, 7, 85. <a href="https://doi.org/10.3390/drones7020085">https://doi.org/10.3390/drones7020085</a> </em></p> <p>The maps are produced as part of the "Geoinformatics and Earth Observation for Landslide Monitoring" Italy-Vietnam.</p> <p>The work is partially funded by the Italian Ministry of Foreign Affairs and International Cooperation within the project “Geoinformatics and Earth Observation for Landslide Monitoring” CUP D19C21000480001 (Italian side) and partially funded by the Ministry of Science and Technology of Vietnam (MOST) (Vietnamese side) by the Bilateral Scientific Research project between Vietnam and Italy, code: NÐT/IT/21/14</p>
Multi-temporal landslide inventory for southern Sikkim State, India
<p>The <em>Multi-temporal landslide inventory for southern Sikkim State, India</em>, is based on two data-sources (mapped extent given in <strong>Shapefile A</strong>): Google Earth images (<strong>Shapefiles B–C</strong>) and stereoscopic Cartosat-1 satellite images (<strong>Shapefiles D–F</strong>). The landslide inventories were collected for the purpose of mapping landslide domains (regions with similar physical and environmental characteristics that specifically drive landslide style) and the data was used to give a general idea of landslides occurring in the region rather than a detailed overview. The landslide inventories are given as shapefiles with two sources of data described separately, after which a summary of all shapefiles is given.</p> <p>Google Earth landslides are mapped using images from 2002 to 2019 with a mapped extent of approximately 3000 km<sup>2</sup> and was ground-truthed during a 12-day field visit from 23 February to 6 March 2019. The resultant landslide inventory contains 440 landslides with three main landslide types identified: translational slides, debris flows, and rockfalls. Translational slides include debris slides, rock slides, and unclassified translational slides. In the landslide inventory, debris flows and rockfalls are mapped as points representing their source area and translational slides are mapped as polygons representing both the source and depositional area. A complete description of the landslide types and mapping is given in Heijenk (2022, Chapter 3, section 3.4.2) The final landslide inventory (refer to how they would access it here, so a reference, or shapefile) includes the following:</p> <ul> <li><strong>Year</strong>, the year of the first image that the landslide appears in is taken,</li> <li><strong>Geology</strong>, the geological unit that the landslide occurs in is taken from Mottram <em>et al.</em> (2004),</li> <li><strong>Area</strong>, for translational slides the area is given,</li> <li><strong>Topographic data</strong> (elevation, aspect, slope, and curvature), which is taken from ASTER GDEM (Version 3.0, 2018, 30 m horizontal resolution, 30 m vertical resolution).</li> </ul> <p>The Cartosat landslide inventory contains 44 features mapped from one pair of stereoscopic Cartosat-1 images (National Remote Sensing Centre, Cartosat-1 ID 197823411, https://www.nrsc.gov.in/, 2.5 m x 2.5 m) captured on 30 September 2011 with extents of 851 km<sup>2</sup> and 957 km<sup>2</sup>. Three main landslide types have been mapped: deep-seated landslides, multi-temporal landslide areas, and rockfall areas. For deep-seated landslides, the scarp is mapped separately from the depositional area. A complete description of the methodology is given in Heijenk (2022, Chapter 3, section 3.4.3).</p> <p>The following shapefiles are included in this dataset:</p> <ol> <li><strong>Google_Earth_mapped_extent_21Dec2021.shp</strong>: Shapefile with a polygon that denotes the mapped extent of southern Sikkim State.</li> <li><strong>Google_Earth_landslides_polygon_21Dec2021.shp</strong>: Shapefile with 255 polygon features, where the polygon includes the source and depositional area of translational landslides.</li> <li><strong>Google_Earth_landslides_point_21Dec2021.shp</strong>: Shapefile with 185 point features that denote the source area of both debris flows and rockfalls.</li> <li><strong>Cartosat_197823411_extents.shp</strong>: Shapefile with 2 polygon features that denote the extent of the Cartosat-1 image pair captured on 30 September 2011.</li> <li><strong>Cartosat_landslides_21Dec2021.shp</strong>: Shapefile with 67 polygon features that describe 44 landslide features. Some landslide features have been mapped with separate polygons for the scarp and the depositional area.</li> <li><strong>Cartosat_197823411clouds.shp</strong>: Shapefile with 5 polygon features that show an estimated area of the clouds that block landslide mapping in the 30 September 2011 Cartosat-1 image pair.</li> </ol> <p>All shapefiles are in an WGS 84 EPSG:3857 projection.</p> <p>This research was funded by the UK Natural Environment Research Council (NERC, Grant # NE/R012148/1) and the British Geological Survey (BGS, BUFI).</p> <p>References: </p> <p>Heijenk, R.A. (2022). Landslide Variables, Inventories, and Domains in Data-Poor Regions: A Case Study in East Sikkim, India. [PhD thesis]. King’s College London.</p>
Data from: Using network science to evaluate vulnerability of landslides on Big Sur Coast, California, USA
Open the record for dataset details and reuse information.
Potential rapidly moving landslide hazards in Western Oregon, clipped to Andrews Experimental Forest, 1999 to 2002
Landslides are a serious geologic hazard, threatening public safety, natural resources, and infrastructure, and costing millions of dollars for repairs each year in Oregon. This map of areas where rapidly moving landslides pose hazards in western Oregon is part of the State's attempt to protect lives and property. The overview map delineates zones that are prone to landslide hazards, especially rapidly moving landslides. These zones provide information to local governments about property that might require more site-specific evaluation. The map is in digital format and was produced with data at a scale of 1:24,000 (1 in. = 2,000 ft). Creation of the map involved the use of Geographic Information System (GIS) modeling, checking and calibrating with limited field evaluations, and comparing with historic landslide inventories. The extent and severity of the hazard posed by rapidly moving landslides varies considerably across western Oregon. In general, the most hazardous areas are mountainous terrains, which are usually sparsely populated, especially drainage channels and depositional fans associated with debris flows. Where hazard areas intersect with human development, use of the map can help to assess the risk and prioritize risk-reduction activities. Various options are available to reduce the risk of landslide losses. Risk-reduction activities can include engineering solutions, public education, warning systems, temporary road closures and evacuation, land use regulation, and many other options. Although this project addresses a range of rapidly moving landslides, this map is not a compilation of all possible landslide hazards.
Brazil's South Region landslide dataset for semantic segmentation
<p>This database contains images used for the semantic segmentation of landslide scars from a fully convolutional neural network U-Net of the three States of Brazil's South Region: Rio Grande do Sul, Santa Catarina and Paraná. Each .rar file contains 3 folders:</p> <p><strong>Image: </strong>GeoTIFF 8 bits images of locations with landslide scars.</p> <p><strong>Masks: </strong>PNG masks (scars indicated in white and background in black color).</p> <p><strong>Slope: </strong>GeoTIFF float rasters of slope (in degrees).</p>
Smartstone probe data of a landslide experiment
<p>The files contain data of four Smartstone probes recorded during a landslide experiment. The MATLAB data files are named after the particular pebbles (1 to 4), which were equipped with the probes. The files contain acceleration (acc) and gyroscope (gyr) data as well as a time code (time).</p>
Lago Lucchetti Watershed-Landslide & misc dataset
<p>Dataset supporting: </p> <p>"Sediment mobilization by hurricane-driven shallow landsliding in an actively cultivated wet subtropical landscape"</p> <p>C.E. Ramos-Scharrón<sup>1</sup>, E.Y. Arima<sup>1</sup>, A. Guidry<sup>1</sup>, D. Ruffe<sup>1</sup>, B. Vest<sup>1</sup></p> <p>Contains: roads & streams (lines), landslide scars, watershed, & lake (polys), 2016-17 1-m dem, 2018 1-m dem, 2010 land cover, geologic terrane, & soils (raster tif files)</p>
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