Skip to main content

Assessing uncertainty due to elevation error in a landslide susceptibility model

Buy Article:

$51.00 plus tax (Refund Policy)

Abstract:

A landslide susceptibility model, employing a digital elevation model (DEM) and geological data, was used in a GIS to predict slope stability in a region of the H J Andrews Long‐Term Research Forest, located in the Western Cascade Range in Oregon, USA. To evaluate the contribution of error in elevation to the uncertainty of the final output of the model, several different, but equally probable, versions of the input DEM were created through the addition of random, spatially autocorrelated noise (error) files. The realized DEMs were then processed to produce a family of slope stability maps from which the uncertainty effects of elevation error upon landslide susceptibility could be assessed. The ability to assess this uncertainty has the potential to help us better understand the inherent strengths and weaknesses of applying digital data and spatial information systems to this application, and to facilitate improved natural resource management decisions in relation to timber harvesting and slope stability problems.

Document Type: Original Article

DOI: https://doi.org/10.1111/j.1467-9671.1997.tb00058.x

Affiliations: 1: Los Alamos National Laboratory, PO Box 1663, XCM-MS F645, TA3 / SM43 / 13S, Los Alamos, New Mexico 87545, USA. 2: Centre for GIS and Modelling, Department of Geomatics, University of Melbourne, Parkville, Victoria 3052, Australia.

Publication date: 1997-12-01

  • Access Key
  • Free ContentFree content
  • Partial Free ContentPartial Free content
  • New ContentNew content
  • Open Access ContentOpen access content
  • Partial Open Access ContentPartial Open access content
  • Subscribed ContentSubscribed content
  • Partial Subscribed ContentPartial Subscribed content
  • Free Trial ContentFree trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more