Synopsis of Topic
There are many different techniques for estimating error, ranging from spatially uniform, simple classification (e.g. differentiating between wet, dry and vegetated), and various statistical methods. In this topic we will discuss several of these methods and techniques and then leverage ToPCAT (Topographic Point Cloud Analysis Toolkit) to derive roughness using a locally detrended standard deviation from a high resolution point cloud.
Why we're Covering it
Estimating DEM uncertainty (typically as a vertical DEM error) independently for each DEM survey is a critical step in change detection. In this topic and corresponding exercise (I) we will get experience doing just this.
Learning Outcomes Supported
This topic will help fulfill the following primary learning outcome(s)
for the workshop:
A comprehensive overview of the theory underpinning geomorphic change detection
The fundamental background
necessary to design effective repeat topographic monitoring campaigns
and distinguish geomorphic changes from noise (with particular focus on
- Hands-on instruction on use of the GCD software through group-led and self-paced exercises
Data and Materials for Exercises
In this exercise we will look at using the ToPCAT tool to derive roughness from high resolution point clouds and add then how to use the derived roughness service as a GCD error model. We will use some multi-beam echo sounding (i.e. SONAR) survey.
Relevant Online Help or Tutorials for this Topic
Slides and/or Handouts
Relevant or Cited Literature
- Brasington J, Vericat D and Rychkov I. 2012. Modeling river bed morphology, roughness, and surface sedimentology using high resolution terrestrial laser scanning. Water Resources Research. 48(11). DOI: 10.1029/2012wr012223.
- Milan DJ, Heritage GL, Large ARG and Fuller IC. 2011. Filtering spatial error from DEMs: Implications for morphological change estimation. Geomorphology. 125(1): 160-171. DOI: 10.1016/j.geomorph.2010.09.012.