(EXERCISE - DaNCE March 2026) Landslide Hazard Nowcast and Exposure (LHASA) for the Brazil Flood May 2024

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.

Dates Available: April 2021 - Present Update Frequency: Twice a day Summary: Landslides are a widespread and often underestimated natural hazard, causing thousands of deaths and significant economic damage each year. They routinely block roads, destroy infrastructure, and disrupt communities worldwide. While intense or prolonged rainfall is the most common landslide trigger, earthquakes and human activities can also cause them. The global Landslide Hazard Assessment for Situational Awareness (LHASA) model provides users with a comprehensive view of landslide hazard in near real-time. Using machine learning, LHASA combines rainfall data from the Global Precipitation Measurement (GPM) satellite with antecedent soil moisture from the Soil Moisture Active Passive (SMAP) satellite to identify areas currently at risk from landslides. The model shows where and when landslides are most probable, then calculates the exposed population and road length within each administrative district, helping users understand both the hazard and potential impacts on communities and infrastructure. To learn more please visit: https://landslides.nasa.gov/ Suggested Use: LHASA 2.1 was created to enable global monitoring of landslide hazard and is not a substitute for local site investigations. Red indicates high hazard and blue indicates low hazard. LHASA nowcast Hazard: "yesterday", "today", and "tomorrow": This map shows the relative probability of landslide occurrence yesterday, today, and tomorrow; respectively, based on rainfall estimates from GPM and forecasts from GEOS-FP. Satellite/Sensor: GPM, SMAP Resolution: 30 arcseconds (~1 kilometer) Credits: NASA GSFC Users are requested to cite the following paper: 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]

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

Update Frequency:Twice a day Summary:The global Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed to inform a wide range of users about landslide hazards. LHASA version 2.1 uses machine learning to show where and when landslides are most probable. Suggested Use:LHASA 2.1 was created to enable global monitoring of landslide hazard and is not a substitute for local site investigations. Red indicates high hazard and blue indicates low hazard.  LHASA nowcast "today": This map shows the relative probability of landslide occurrence today, based on rainfall estimates from GPM and forecasts from GEOS-FP. Satellite/Sensor:GPM, SMAP Resolution:30 arcseconds (~1 kilometer) Credits:NASA GSFC Users are requested to cite the following paper: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]

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: 5/11/2024 Summary: The NASA GSFC landslides team produced this landslide proxy heatmap from Sentinel-1 imagery. Potential landslides were detected by calculating the change in the backscatter coefficient before and after the triggering event using the log ratio approach (Iratio). The Iratio is generated by subtracting median pre- and post-event Radiometrically Terrain Corrected (RTC) gamma0 backscatter in decibel scale at 10 m resolution obtained from the Alaska Sar Facility’s Hybrid Pluggable Processing Pipeline (HyP3). The likely landslide pixels are identified using 99th percentile Iratio threshold considering areas greater than 10-degree in slope and not affected by radar shadow and layover effects. A heatmap is generated using identified landslide pixels for visualization ease. Pre-event imagery = median imagery generated using images from 09/14/2023, 09/26/2023 Post-event imagery = 05/11/2024 This map should be used as a guide to identify areas likely affected by landslides. This is a rapid response product. We have not done any form of manual verification or corrections to remove false positives. Please see the slope and shadow layover mask item for areas not considered in this analysis. Suggested Use: The red and yellow areas indicate potential zones of dense landsliding. False positives related to natural and human-made ground surface change, including but not limited to deformation from mining, construction, deforestation, agriculture, flooding, snow cover, and changes in reservoir water levels can also be within the red and yellow areas. Satellite/Sensor/Resolution: Synthetic Aperture Radar on European Space Agency's (ESA) Copernicus Sentinel-1 satellite, 10 meters Credits: NASA GSFC landslides team, Copernicus Sentinel-1 data Citation: Handwerger AL, Huang M-H, Jones SY, Amatya P, Kerner HR, Kirschbaum DB. 2022. Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine. Nat. Hazards Earth Syst. Sci. Copernicus Publications, 22(3): 753–773. https://doi.org/10.5194/nhess-22-753-2022. Esri REST Endpoint: See URL to the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/ex2602_sentinel1_landslide_heatmap/ImageServer/WMSServer