Articles | Volume 21, issue 10
Research article
07 Oct 2021
Research article |  | 07 Oct 2021

Integrating empirical models and satellite radar can improve landslide detection for emergency response

Katy Burrows, David Milledge, Richard J. Walters, and Dino Bellugi

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Cited articles

Aimaiti, Y., Liu, W., Yamazaki, F., and Maruyama, Y.: Earthquake-Induced Landslide Mapping for the 2018 Hokkaido Eastern Iburi Earthquake Using PALSAR-2 Data, Remote Sens., 11, 2351,, 2019. a, b, c, d, e, f
Allstadt, K. E., Jibson, R. W., Thompson, E. M., Massey, C. I., Wald, D. J., Godt, J. W., and Rengers, F. K.: Improving Near-Real-Time Coseismic Landslide Models: Lessons Learned from the 2016 Kaikōura, New Zealand, Earthquake Improving Near-Real-Time Coseismic Landslide Models, B. Seismol. Soc. Am., 108, 1649–1664, 2018. a
Au, T. C.: Random forests, decision trees, and categorical predictors: the “Absent levels” problem, J. Mach. Learn. Res., 19, 1737–1766, 2018. a, b
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. a, b, c
Burrows, K., Walters, R. J., Milledge, D., Spaans, K., and Densmore, A. L.: A New Method for Large-Scale Landslide Classification from Satellite Radar, Remote Sens., 11, 237,, 2019. a, b, c, d, e, f, g, h
Short summary
When cloud cover obscures optical satellite imagery, there are two options remaining for generating information on earthquake-triggered landslide locations: (1) models which predict landslide locations based on, e.g., slope and ground shaking data and (2) satellite radar data, which penetrates cloud cover and is sensitive to landslides. Here we show that the two approaches can be combined to give a more consistent and more accurate model of landslide locations after an earthquake.
Final-revised paper