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 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]