Data

 

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Citing the Data

The landslides you report with Landslide Reporter are shared with you and the world. The Cooperative Open Online Landslide Repository (COOLR) is openly accessible on Landslide Viewer and can be downloaded or referenced using its API.

COOLR data is used to validate the Landslide Hazard Assessment for Situational Awareness (LHASA) model. LHASA identifies locations with high potential for landslides by combining satellite-based precipitation estimates with landslide susceptibility variables. When using COOLR or LHASA for your research, please use the citations below.

COOLR

Citation for Report-Based Landslide Inventories:

Report-Based Landslide Inventories can be downloaded from a separate layer in the COOLR catalog named “nasa_coolr_reports”.

To use the citizen science data submitted through Landslide Reporter  in your research publication, you can filter the layer using a definition query (event_import_source = “LRC”) and we request that you reference the publication below:

  • Juang CS, Stanley TA, and Kirschbaum DB (2019) Using citizen science to expand the global map of landslides: Introducing the Cooperative Open Online Landslide Repository (COOLR). PLOS ONE 14(7): e0218657. doi:10.1371/journal.pone.0218657

To use the  NASA Global Landslide Catalog (GLC) in your research publication, you can filter the layer using a definition query (event_import_source = “GLC”) and we request that you reference the GLC using the publications below:

  • Kirschbaum, D.B., Stanley, T., & Zhou, Y. (2015). Spatial and temporal analysis of a global landslide catalog. Geomorphology, 249, 4-15. doi:10.1016/j.geomorph.2015.03.016
  • Kirschbaum, D.B., Adler, R., Hong, Y., Hill, S., & Lerner-Lam, A. (2010). A global landslide catalog for hazard applications: method, results, and limitations. Natural Hazards, 52, 561-575. doi:10.1007/s11069-009-9401-4

Citation for Event-Based Landslide Inventories:

Event-Based Landslide Inventories can be downloaded from separate layer in the COOLR catalog named “nasa_coolr_events”. You can further filter manual or automated landslides by using the following definition queries: method = “Manual” or method = “Automatic”.

If you use the Event-Based landslides in your research publication, we request you reference the inventories within the dataset. The citation for each event inventory is located within the attribute table field “citation”. Please reference each citation of the events used. For example, if you use the entire inventory layer, please reference all the event citations contained within the layer. If you use just the events past a certain date, or another subset of the layer, please query those citations out and reference those. In addition, a CSV containing all citations can be found on the download page: https://maps.nccs.nasa.gov/arcgis/apps/MapAndAppGallery/index.html?appid=574f26408683485799d02e857e5d9521

LHASA

Citation for LHASA 2.0

If you use the LHASA 2.0 in your research publication, we request that you reference the publication below:

  • Stanley, T. A., D. B. Kirschbaum, G. Benz, et al. 2021. "Data-Driven Landslide Nowcasting at the Global Scale." Frontiers in Earth Science, 9: 3389/feart.2021.640043

Citation for LHASA 1.1

If you use the LHASA 1.1 in your research publication, we request that you reference the publication below:

  • Stanley, T., and D. B. Kirschbaum. 2017. "A heuristic approach to global landslide susceptibility mapping." Natural Hazards, 1-20 1007/s11069-017-2757-y

 

Data Download and Reference Links

Data Source Notes Reference URL Citations Download URL
Global Gridded Landslide Inventory Collection of landslide databases were merged and converted to a grid with a daily 30 arc-second (1-km) resolution. To reduce file size, only grid cells with mapped landslides are listed. http://dx.doi.org/10.3389/feart.2021.640043 Stanley, T. A., D. B. Kirschbaum, G. Benz, et al. 2021. "Data-Driven Landslide Nowcasting at the Global Scale." Frontiers in Earth Science, 9: [10.3389/feart.2021.640043] https://maps.nccs.nasa.gov/arcgis/apps/MapAndAppGallery/index.html?appid=574f26408683485799d02e857e5d9521
NASA Global Landslide Catalog The GLC is a global inventory of rainfall-triggered landslides compiled by NASA since 2007. COOLR contains citizen science data (Landslide Reporter Catalog) and data from NASA's Global Landslide Catalog. This geodatabase contains both inventories, some displayed as points and some as polygons. https://doi.org/10.1007/s11069-009-9401-4 Kirschbaum, D.B., Adler, R., Hong, Y., Hill, S., & Lerner-Lam, A. (2010). A global landslide catalog for hazard applications: method, results, and limitations. Natural Hazards, 52, 561-575. doi:10.1007/s11069-009-9401-4 https://maps.nccs.nasa.gov/arcgis/apps/MapAndAppGallery/index.html?appid=574f26408683485799d02e857e5d9521
Landslide Reporter Catalog Landslides contributed by citizen scientists through Landslide Reporter and checked by NASA. Landslide Reporter was launched March 2018. https://doi.org/10.1371/journal.pone.0218657 Juang, C. S., T. A. Stanley, and D. B. Kirschbaum. (2019). Using citizen science to expand the global map of landslides: Introducing the Cooperative Open Online Landslide Repository (COOLR). PLOS ONE, 14 (7) doi:10.1371/journal.pone.0218657 https://maps.nccs.nasa.gov/arcgis/apps/MapAndAppGallery/index.html?appid=574f26408683485799d02e857e5d9521
Spatio-temporal distribution of slides (1999-2015) in Combeima's River hydrographic basin, Colombia Spatio-temporal distribution of slides (1999-2015) in Combeima's River hydrographic basin, Colombia. 366 slides collected from historical records, visual interpretation of images, and fieldwork for the identification of new slides and the verification of those that were visually interpreted. https://www.researchgate.net/publication/328229319_Spatio-temporal_distribution_of_slides_1999-2015_in_Combeima_s_River_hydrographic_basin_Colombia Leal Villamil, J., Perez Gomez, U., & Ortiz Lozano, N. E. (2018). Spatio-temporal distribution of slides (1999-2015) in Combeima's River hydrographic basin, Colombia. REVISTA GEOGRAFICA VENEZOLANA, 59(2), 346-365. https://maps.nccs.nasa.gov/arcgis/apps/MapAndAppGallery/index.html?appid=574f26408683485799d02e857e5d9521
Landslide Inventory for the central section of the Western branch of the East African Rift (LIWEAR) Landslide events along the western branch of the East African Rift collected from a variety of sources and validated, with known location and date over a span of 48 years from 1968 to 2016. Collected by Elise Monsieurs (Royal Museum for Central Africa) et al. https://doi.org/10.1007/s10346-018-1008-y Monsieurs, E., Jacobs, L., Michellier, C., Tchangaboba, J. B., Ganza, G. B., Kervyn, F., ... & Ndayisenga, A. (2018). Landslide inventory for hazard assessment in a data-poor context: a regional-scale approach in a tropical African environment. Landslides, 1-15. doi:10.1007/s10346-018-1008-y https://maps.nccs.nasa.gov/arcgis/apps/MapAndAppGallery/index.html?appid=574f26408683485799d02e857e5d9521
Republic of Macedonia Database Landslides in the Republic of Macedonia, collected by Igor Pesevski (Ss. Cyril and Methodius University of Skopje), Milorad Jovanovski (Ss. Cyril and Methodius University of Skopje), and Natasha Nedelkovska (Geohydroconsulting Ltd.). Imported to COOLR in August 2018. https://www.researchgate.net/publication/281274236_MODEL_FOR_GIS_LANDSLIDE_DATABASE_ESTABLISHMENT_AND_OPERATION_IN_REPUBLIC_OF_MACEDONIA Pesevski, I., Jovanovski, M., & Nedelkovska, N. (2018). Republic of Macedonia Database. Retrieved from https://landslides.nasa.gov/viewer. https://maps.nccs.nasa.gov/arcgis/apps/MapAndAppGallery/index.html?appid=574f26408683485799d02e857e5d9521
SERVIR-Mekong Myanmar Mapathon Landslides Landslides mapped in Myanmar from Google Earth imagery, collected by volunteers during two mapathon events in July and August 2018 hosted by Helen Eifert (NASA MSFC) and the NASA SERVIR team in Huntsville, AL. Imported to COOLR in August 2018. https://servir.adpc.net NASA SERVIR Science Coordination Office & SERVIR Mekong (2018). SERVIR-Mekong Myanmar Mapathon Landslides. Retrieved from https://landslides.nasa.gov/viewer. https://maps.nccs.nasa.gov/arcgis/apps/MapAndAppGallery/index.html?appid=574f26408683485799d02e857e5d9521
Czech Academy of Sciences Landslides Inventory Based on Media Reports Google Alerts and internet search are used to collect media report about new or reactivated landslides, which are manually processed and filled into database. https://www.irsm.cas.cz/landslides Klimeš J, Stemberk J, Blahut J, Krejcí V, Krejcí O, Hartvich F, Kycl P (2017) Challenges for landslide hazard and risk management in ‘low-risk’ regions, Czech Republic—landslide occurrences and related costs (IPL project no. 197). Landslides, 14, 771 – 780. https://maps.nccs.nasa.gov/arcgis/apps/MapAndAppGallery/index.html?appid=574f26408683485799d02e857e5d9521
Oregon Department of Transportation Landslide Inventory Oregon Department of Transportation mapped landslides from 2011 to 2016. https://gis.dogami.oregon.gov/maps/slido/, https://www.oregongeology.org/Landslide/landslidehome.htm Oregon Department of Transportation (2016). Landslide Inventory. Retrieved from https://landslides.nasa.gov/viewer. https://maps.nccs.nasa.gov/arcgis/apps/MapAndAppGallery/index.html?appid=574f26408683485799d02e857e5d9521
Reuleut Landslide Inventory Multitemporal landslide inventory created to understand the recovery rate after seismic events. https://doi.org/10.1007/s10064-021-02238-x Tanyas, H., Kirschbaum, D. & Lombardo, L. Capturing the footprints of ground motion in the spatial distribution of rainfall-induced landslides. Bull Eng Geol Environ (2021). https://doi.org/10.1007/s10064-021-02238-x https://maps.nccs.nasa.gov/arcgis/apps/MapAndAppGallery/index.html?appid=574f26408683485799d02e857e5d9521
Porgera Landslide Inventory Multitemporal landslide inventory created to understand the recovery rate after seismic events. https://doi.org/10.1007/s10064-021-02238-x Tanyas, H., Kirschbaum, D. & Lombardo, L. Capturing the footprints of ground motion in the spatial distribution of rainfall-induced landslides. Bull Eng Geol Environ (2021). https://doi.org/10.1007/s10064-021-02238-x https://maps.nccs.nasa.gov/arcgis/apps/MapAndAppGallery/index.html?appid=574f26408683485799d02e857e5d9521
Sulawesi-Kasiguncu-Palu Landslide Inventory Multitemporal landslide inventory created to understand the recovery rate after seismic events. https://doi.org/10.1007/s10064-021-02238-x Tanyas, H., Kirschbaum, D. & Lombardo, L. Capturing the footprints of ground motion in the spatial distribution of rainfall-induced landslides. Bull Eng Geol Environ (2021). https://doi.org/10.1007/s10064-021-02238-x https://maps.nccs.nasa.gov/arcgis/apps/MapAndAppGallery/index.html?appid=574f26408683485799d02e857e5d9521
Pokot Landslide Inventory Manual point inventory from Sentinel image. N/A Benz, G., and Stanley, T. (2020). Pokot Landslide Inventory. Greenbelt, Maryland, USA. NASA. https://maps.nccs.nasa.gov/arcgis/apps/MapAndAppGallery/index.html?appid=574f26408683485799d02e857e5d9521
Rainfall-induced landslide inventories for Lower Mekong This is an inventory of rainfall-induced landslides from 22 locations in Lower Mekong Region produced using Planet imagery and semi-automated mapping. https://doi.org/10.6084/m9.figshare.14199227.v1 Amatya, Pukar; Kirschbaum, Dalia; Stanley, Thomas (2021): Rainfall-induced landslide inventories for Lower Mekong. figshare. Dataset. https://doi.org/10.6084/m9.figshare.14199227.v1 https://maps.nccs.nasa.gov/arcgis/apps/MapAndAppGallery/index.html?appid=574f26408683485799d02e857e5d9521
Landslide Risk Assessment of Attica Region Database of slope failures in Attica from 1961 to 2020. https://doi.org/10.3390/land10020148 Tavoularis, N., Papathanassiou, G., Ganas, A., & Argyrakis, P. (2021). Development of the Landslide Susceptibility Map of Attica Region, Greece, Based on the Method of Rock Engineering System. Land, 10(2), 148. MDPI AG. http://dx.doi.org/10.3390/land10020148 https://maps.nccs.nasa.gov/arcgis/apps/MapAndAppGallery/index.html?appid=574f26408683485799d02e857e5d9521

Data Source Download locations Viewer locations
Landslide Exposure
Landslide Hazard

Forecast

The forecast model is currently running four times per day, but the results are not yet visible on Landslide Viewer. Limited amounts of forecast data are available upon request.

Post-Fire Debris Flow (PFDF)

The PFDF model is not currently running.

Seismic Overlay

Planned

Data Source Download Locations Viewer Locations
Landslide Hazard https://pmmpublisher.pps.eosdis.nasa.gov/ https://gpm.nasa.gov/data/visualizations/precip-apps

Data Source Download Locations Viewer Locations
Landslide Hazard Not Available https://lhasa-servir.adpc.net/

 

Download COOLR data into your system:

The Cooperative Open Online Landslide Inventory (COOLR) can be downloaded from Landslide Viewer by clicking on the link “Download Landslide Catalog” at the top of the application. A new window will open with the information for downloading the GLC and the option to save it to your computer as a file geodatabase (.gdb), shapefile (.shp), or comma-separated values (.csv) file. You can also create maps of the Landslide Viewer screen by screenshotting the current view. In the future, a “print” widget will be added to make creating maps of Landslide Viewer more seamless.

Reference COOLR using the ArcGIS REST API

  • Access the REST API through Landslide Viewer
    • Change screenshot of what the layer list looks like. (layers icon)
    • Edit text:
      • Click on the small arrow (arrow icon) next to the desired group of layers and click on the information button (info icon) next to the desired layer. This will bring you to the ArcGIS REST Services Directory with the URL that you will need to add to your map. More information can be found on the ArcGIS REST API Developers website.

External inventories referenced by COOLR

Here is a list of the inventories added to Landslide Viewer through their ArcGIS REST API. If you use any of these inventories, please check the respective websites for how to properly cite the data.

United States
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Contact Us

For any questions related to this system, please contact landslide_support@nccs.nasa.gov.