(EXERCISE - DaNCE March 2026) Landslides Mapped with Planet Imagery for the Brazil Floods May 2024
About this dataset
Columns (22)
Sample rows
| ev_id | ls_cat | method | ev_date | ev_desc | ev_time | ls_trig | OBJECTID | citation | comments | ev_title | img_type |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Landslide | Manual | 1714348800000 | Severe floods caused by heavy rains and storms have affected the Brazilian state of Rio Grande do Sul, resulting in widespread landslides and a dam collapse. | Heavy Rain | 1 | Soobitsky, Rachel, 2024, Manual inventory of landslides in Brazil, 2024-04-29. NASA GSFC. Juang CS, Stanley TA, and Kirschbaum DB. (2019). Using citizen science to expand the global map of landslides:… | Manual inventory of landslides in Brazil, 2024-04-29 | Planetscope | ||
| 0 | Landslide | Manual | 1714348800000 | Severe floods caused by heavy rains and storms have affected the Brazilian state of Rio Grande do Sul, resulting in widespread landslides and a dam collapse. | Heavy Rain | 2 | Soobitsky, Rachel, 2024, Manual inventory of landslides in Brazil, 2024-04-29. NASA GSFC. Juang CS, Stanley TA, and Kirschbaum DB. (2019). Using citizen science to expand the global map of landslides:… | Manual inventory of landslides in Brazil, 2024-04-29 | Planetscope | ||
| 0 | Landslide | Manual | 1714348800000 | Severe floods caused by heavy rains and storms have affected the Brazilian state of Rio Grande do Sul, resulting in widespread landslides and a dam collapse. | Heavy Rain | 3 | Soobitsky, Rachel, 2024, Manual inventory of landslides in Brazil, 2024-04-29. NASA GSFC. Juang CS, Stanley TA, and Kirschbaum DB. (2019). Using citizen science to expand the global map of landslides:… | Manual inventory of landslides in Brazil, 2024-04-29 | Planetscope | ||
| 0 | Landslide | Manual | 1714348800000 | Severe floods caused by heavy rains and storms have affected the Brazilian state of Rio Grande do Sul, resulting in widespread landslides and a dam collapse. | Heavy Rain | 4 | Soobitsky, Rachel, 2024, Manual inventory of landslides in Brazil, 2024-04-29. NASA GSFC. Juang CS, Stanley TA, and Kirschbaum DB. (2019). Using citizen science to expand the global map of landslides:… | Manual inventory of landslides in Brazil, 2024-04-29 | Planetscope | ||
| 0 | Landslide | Manual | 1714348800000 | Severe floods caused by heavy rains and storms have affected the Brazilian state of Rio Grande do Sul, resulting in widespread landslides and a dam collapse. | Heavy Rain | 5 | Soobitsky, Rachel, 2024, Manual inventory of landslides in Brazil, 2024-04-29. NASA GSFC. Juang CS, Stanley TA, and Kirschbaum DB. (2019). Using citizen science to expand the global map of landslides:… | Manual inventory of landslides in Brazil, 2024-04-29 | Planetscope | ||
| 0 | Landslide | Manual | 1714348800000 | Severe floods caused by heavy rains and storms have affected the Brazilian state of Rio Grande do Sul, resulting in widespread landslides and a dam collapse. | Heavy Rain | 6 | Soobitsky, Rachel, 2024, Manual inventory of landslides in Brazil, 2024-04-29. NASA GSFC. Juang CS, Stanley TA, and Kirschbaum DB. (2019). Using citizen science to expand the global map of landslides:… | Manual inventory of landslides in Brazil, 2024-04-29 | Planetscope | ||
| 0 | Landslide | Manual | 1714348800000 | Severe floods caused by heavy rains and storms have affected the Brazilian state of Rio Grande do Sul, resulting in widespread landslides and a dam collapse. | Heavy Rain | 7 | Soobitsky, Rachel, 2024, Manual inventory of landslides in Brazil, 2024-04-29. NASA GSFC. Juang CS, Stanley TA, and Kirschbaum DB. (2019). Using citizen science to expand the global map of landslides:… | Manual inventory of landslides in Brazil, 2024-04-29 | Planetscope | ||
| 0 | Landslide | Manual | 1714348800000 | Severe floods caused by heavy rains and storms have affected the Brazilian state of Rio Grande do Sul, resulting in widespread landslides and a dam collapse. | Heavy Rain | 8 | Soobitsky, Rachel, 2024, Manual inventory of landslides in Brazil, 2024-04-29. NASA GSFC. Juang CS, Stanley TA, and Kirschbaum DB. (2019). Using citizen science to expand the global map of landslides:… | Manual inventory of landslides in Brazil, 2024-04-29 | Planetscope |
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✦ Suggested by OrionAIDates of Images: 5/6/2024 Summary: The NASA GSFC landslides team produced this landslide polygon map. The landslide map was produced automatically from Planet imagery using the prototype Global Landslide Detection for Rapid response (GLiDEr) system, currently under development as a joint effort between the NASA GSFC landslides team and Data Science group. Pre-event imagery = mosaic of images from 04/20/2024 and 04/21/2024 Post-event imagery = mosaic of images from 05/05/2024 and 05/06/2024 This map has undergone manual false positive removal, but we have not corrected erroneous landslide areas, added missed landslides and resolved amalgamation. Suggested Use: This map can be used to identify location and size of landslides. For landslide mapping extent, refer to the mapping_AOI layer. Credits: NASA GSFC landslides team, NASA GSFC Data Science group, Planet Labs Esri REST Endpoint: See URL on the right side of the page. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/EX2602_planet_landslide_vector/MapServer/WMSServer
NOTE: Due to the higher resolution of this data, it may be slow to load or require the user to zoom to a smaller area of interest. Date of Images: 4/20/2024, 5/6/2024, 5/7/2024 Date of Next Image: Unknown Summary: The True Color RGB composite provides a product of how the surface would look to the naked eye from space. The RGB is created using the red, green, and blue channels of the respective instrument. Suggested Use: The True Color RGB provides a product of how the surface would look to the naked eye from space. The True Color RGB is produced using the 3 visible wavelength bands (red, green, and blue) from the respective sensor. Some minor atmospheric corrections have occurred. Satellite/Sensor: PlanetScope Resolution: 3 meters Esri REST Endpoint: See URL section on right side of page
Dates of Images: 5/6/2024 Summary: The NASA GSFC landslides team produced this landslide polygon map. The landslide map was produced automatically from Planet imagery using the prototype Global Landslide Detection for Rapid response (GLiDEr) system, currently under development as a joint effort between the NASA GSFC landslides team and Data Science group. Pre-event imagery = mosaic of images from 04/20/2024 and 04/21/2024 Post-event imagery = mosaic of images from 05/05/2024 and 05/06/2024 This map has undergone manual false positive removal, but we have not corrected erroneous landslide areas, added missed landslides and resolved amalgamation. Suggested Use: This map can be used to identify location and size of landslides. For landslide mapping extent, refer to the mapping_AOI layer. Credits: NASA GSFC landslides team, NASA GSFC Data Science group, Planet Labs Esri REST Endpoint: See URL on the right side of the page. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/EX2602_planet_landslide_vector/MapServer/WMSServer
Dates of Images: 4/28/2024, 4/29/2024, 4/30/2024, 5/1/2024, 5/2/2024, 5/5/2024, 5/6/2024 Summary: LHASA was created at Goddard Space Flight Center to identify the potential for rainfall-triggered landslides in real time. LHASA version 2 adopts machine learning to estimate the probability of landslide occurrence at a 30-arcsecond (~1 km) daily resolution. In addition, it estimates the potential exposure of human population and roads to landslide hazard and maps the basins likely to experience post-fire debris flows. Suggested Use: The global landslide nowcast shows where landslide hazard is highest on a given date. Resolution: ~1km Credits: Please cite: Stanley T. A., J. R. Sutton, R. S. Vershel, P. M. Amatya. 2025. "Better Satellite Precipitation Algorithms Slightly Improved Landslide Hazard Assessment." Journal of Applied Meteorology and Climatology 64 (10): 1379-1394 10.1175/jamc-d-25-0021.1 Esri REST Endpoint: See URL on the right side of the page. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/EX2602_lhasa/ImageServer/WMSServer
Dates of Images: 5/4/2024, 5/5/2024, 5/7/2024 Date of Next Image: Unknown Summary: The True Color RGB composite provides a product of how the surface would look to the naked eye from space. The RGB is created using the red, green, and blue channels of the respective instrument. The Short Wave Infrared (SWIR) RGB is a product that is created using the SWIR, NIR, and Red channels of the respective instrument. The Color Infrared composite is created using the near-infrared, red, and green channels, allowing for the ability to see areas impacted from the fires. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue. Suggested Use: The True Color RGB provides a product of how the surface would look to the naked eye from space. The True Color RGB is produced using the 3 visible wavelength bands (red, green, and blue) from the respective sensor. Some minor atmospheric corrections have occurred. The Short Wave Infrared (SWIR) RGB is a product that can provides value in flood detection. Areas of water will appear blue, healthy green vegetation will appear as a bright green, urban areas in various shades of magenta, snow will appear as a bright blue/cyan, and bare soils being multicolor dependent on their makeup. Compare pre-event imagery to post-event imagery to identify potential flooding. A Color Infrared composite depicts healthy vegetation as red, water as blue. Some minor atmospheric corrections have occurred. Satellite/Sensor: MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites Resolution: Sentinel-2: 20 meters Credits: NASA/MSFC, USGS, ESA Copernicus Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202405_FLOOD_BRAZIL/2407_sentinel2/ImageServer/WMSServer
Dates of Images: Post-Event: May 8, 2024; May 15, 2024 Pre-Event: April 6, 2024; April 21, 2024 Summary: True Color: The True Color RGB composite provides a product of how the surface would look to the naked eye from space. The RGB is created using the red, green, and blue channels of the respective instrument. Color Infrared: The Color Infrared composite is created using the near-infrared, red, and green channels, allowing for the ability to see areas impacted from the fires. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue. Suggested Use: True Color: provides a product of how the surface would look to the naked eye from space. The True Color RGB is produced using the 3 visible wavelength bands (red, green, and blue) from the respective sensor. Some minor atmospheric corrections have occurred. Color Infrared: depicts healthy vegetation as red, water as blue. Some minor atmospheric corrections have occurred. Satellite/Sensor: Landsat 9 Operational Land Imager (OLI) Resolution: 30 meters Credits: NASA/MSFC, USGS Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202405_FLOOD_BRAZIL/ex2602_landsat/ImageServer/WMSServer
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