Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine

Handwerger, Alexander L.; Huang, Mong-Han; Jones, Shannan Y.; Amatya, Pukar; Kerner, Hannah R.; Kirschbaum, Dalia B.

doi:https://doi.org/10.5194/nhess-22-753-2022

Articles | Volume 22, issue 3

https://doi.org/10.5194/nhess-22-753-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Remote sensing and Earth observation data in natural hazard...

https://doi.org/10.5194/nhess-22-753-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 3

Research article

|

09 Mar 2022

Research article |

| 09 Mar 2022

Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine

Alexander L. Handwerger, Mong-Han Huang, Shannan Y. Jones, Pukar Amatya, Hannah R. Kerner, and Dalia B. Kirschbaum

Data sets

NASADEM Merged DEM Global 1 arc second V001 NASA JPL https://doi.org/10.5067/MEaSUREs/NASADEM/NASADEM_HGT.001

Characteristics of landslides triggered by the 2018 Hokkaido Eastern Iburi Earthquake, North Japan S. Zhang, R. Li, F. Wang, and I. I. O. Akinori https://doi.org/10.5281/zenodo.2577300

Rapid Response Landslide Inventory for the 14 August 2021 M7.2 Nippes, Haiti, Earthquake S. N. Martinez, K. E. Allstadt, S. L. Slaughter, R. G. Schmitt, E. Collins, L. N. Schaefer, and S. Ellison https://doi.org/10.5066/P99MYPXK

Model code and software

alhandwerger/GEE_scripts_for_Handwerger_et_al_2022_NHESS A. L. Handwerger https://github.com/alhandwerger/GEE_scripts_for_Handwerger_et_al_2022_NHESS

Short summary

Rapid detection of landslides is critical for emergency response and disaster mitigation. Here we develop a global landslide detection tool in Google Earth Engine that uses satellite radar data to measure changes in the ground surface properties. We find that we can detect areas with high landslide density within days of a triggering event. Our approach allows the broader hazard community to utilize these state-of-the-art data for improved situational awareness of landslide hazards.