Date of Image:

9/28/2024, 10/3/2024, 10/5/2024, 10/10/2024, 10/15/2024, 10/17/2024

Date of Next Image:

Unknown

Summary:

NASA used a Sentinel-1 SAR backscatter change approach developed in GEE (Handwerger et al., 2022) to detect areas with high landslide density. This approach detects potential landslides by calculating the change in the backscatter coefficient before and after the triggering event using the log ratio approach. False positives such as backscatter change due to flooding, agriculture, and more, are removed by using threshold-based masks made from the topographic slope from the 1 arcsec (∼30 m) resolution NASADEM (NASA JPL, 2020). Using stacks of SAR data reduces noise, and furthermore, the pre-event stack provides backscatter data that is more representative of the pre-event ground surface properties. Finally, to detect landslide areas, the backscatter change raster was thresholded using the 99th percentile to highlight strong signals, and the heatmap was calculated.

Pre-event time frame: 2024-01-10 to 2024-09-26

Post-event time frame: 2024-09-28 to 2024-10-17

This map should be used as a guidance to identify areas likely affected by landslides. This is a rapid response product. We have not done any form of manual corrections to remove false positives.

Suggested Use:

The red and yellow areas indicate potential zones of dense landsliding.

Disclaimer: not verified in field and optical imagery has clouds preventing verification.

Satellite/Sensor:

Copernicus Sentinel-1 Synthetic Aperture Radar (SAR)

Resolution:

10 meters

Credits:

NASA GSFC Landslides Team, Copernicus Sentinel-1 data

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 section on right side of page

Date of Image:

9/28/2024, 10/3/2024, 10/5/2024, 10/10/2024, 10/15/2024, 10/17/2024

Date of Next Image:

Unknown

Summary:

NASA used a Sentinel-1 SAR backscatter change approach developed in GEE (Handwerger et al., 2022) to detect areas with high landslide density. This approach detects potential landslides by calculating the change in the backscatter coefficient before and after the triggering event using the log ratio approach. False positives such as backscatter change due to flooding, agriculture, and more, are removed by using threshold-based masks made from the topographic slope from the 1 arcsec (∼30 m) resolution NASADEM (NASA JPL, 2020). Using stacks of SAR data reduces noise, and furthermore, the pre-event stack provides backscatter data that is more representative of the pre-event ground surface properties. Finally, to detect landslide areas, the backscatter change raster was thresholded using the 99th percentile to highlight strong signals, and the heatmap was calculated.

Pre-event time frame: 2024-01-10 to 2024-09-26

Post-event time frame: 2024-09-28 to 2024-10-17

This map should be used as a guidance to identify areas likely affected by landslides. This is a rapid response product. We have not done any form of manual corrections to remove false positives.

Suggested Use:

The red and yellow areas indicate potential zones of dense landsliding.

Disclaimer: not verified in field and optical imagery has clouds preventing verification.

Satellite/Sensor:

Copernicus Sentinel-1 Synthetic Aperture Radar (SAR)

Resolution:

10 meters

Credits:

NASA GSFC Landslides Team, Copernicus Sentinel-1 data

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 section on right side of page