Detecting Impacts of Historic and Undocumented Landslide Tsunamis at High Latitude Sites Using NDVI

In the last century most of the largest landslide tsunamis on record have occurred in remote regions of the world, which has led to a limited understanding of their impacts on landscapes. The goal of this work is to present a methodology to identify impacts of landslide tsunamis with a technique that does not require field work in extremely remote locations. Although landslide tsunamis have occurred that were not directly witnessed by anyone, evidence of landslide tsunamis can still be observed via satellite. Using multispectral satellite images, we calculated normalized difference vegetation index (NDVI) to identify tsunami-impacted sites, estimate tsunami wave runup, and quantify vegetation loss and vegetation recovery. This study focuses on four historic landslide tsunamis: 21 November 2000 in Paatuut, Greenland, 4 December 2007 in Chehalis Lake, British Columbia, Canada, 17 October 2015 in Taan Fiord, Alaska, and 17 June 2017 in Karrat Fjord, Greenland. We found that differenced NDVI allows for clear delineation of tsunami-impacted sites and estimates of tsunami runup heights to be reasonably close to measurements from previous studies. Time series analysis of NDVI at the high latitude study sites indicates areas stripped of vegetation by these landslide tsunamis subsequently require 10 to 45 years for vegetation to recover to pre-tsunami coverage. Tsunami trimlines formed in environments covered by trees and tall shrubs are sharper and better preserved over time compared to trimlines formed in low-lying shrub and grassland environments. We apply the NDVI methodology to Doroshin valley, Alaska to document an unobserved landslide tsunami from the Winter of 2002/2003. The tsunami affected approximately 0.12 km2 of land and had a maximum runup height of 38 m. The Doroshin event shows how even in the 21st century the catalog of landslide tsunamis undercounts actual events and demonstrates how difficult it is to assess landslide tsunami frequency relying only on human observation. The NDVI techniques tested in this study can detect past landslide tsunamis in unmonitored locations and are useful for emergency management planners to help remotely monitor hazardous locations. The technique can also aid in recovery and response of future tsunamis as well, by rapidly delineating tsunami-impacted sites