ABSTRACT
An understanding of topographic surfaces and a system of geomorphometric variables underlies the effective use of Digital Elevation Models (DEMs) in remote sensing. This paper is focussed on the basic concepts and structure of geomorphometric variable types relevant to geophysical and biophysical remote sensing applications. In general, remote sensing analysts must be satisfied that there is a reasonable expectation that geomorphometric processing will benefit the remote sensing project at hand based on physical or mathematical relationships. Careful selection of local, textural, and contextual geomorphometric variables is required. The characteristics of these data can help optimize the analysis approach, which increasingly involves machine learning algorithms and Object-based DEM Analysis (OBDA) methods. Examples from the literature and a DEM of the Peterborough Drumlin Field are used to illustrate the discussion.
Acknowledgements
Thank you to the editor and to two anonymous reviewers for suggestions, which helped to improve the manuscript.
Disclosure statement
No potential conflict of interest was reported by the author.