K. Statistical Methods for Error Modelling from High Resolution Data


Synopsis of Topic

DEM error is a function of many factors, and space (x-y location) is certainly one of them. In other words, DEM error is not spatially uniform but varys in space (and time for each DEM). There are a variety of statistical techniques that can help you gain insights into such error. Some techniques can be used on ground-based rtkGPS or total stations (e.g. boot-strapping at right), whereas others require high resolution point coluds from TLS, SFM, MBES, etc.  

Why we're Covering it

To give you the background and some of the tools to help you model DEM Error appropriately. In some instances, it may be appropriate to use the outputs from such analyses directly as a proxy for DEM error. In other cases, they may become inputs to more complicated error models. 

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 restoration applications)
  • Methods for interpreting and segregating morphological sediment budgets quantitatively in terms of both geomorphic processes and changes in physical habitat
  • Hands-on instruction on use of the GCD software through group-led and self-paced exercises
  • An opportunity to interact with experts on geomorphic monitoring and the software developers of GCD to help you make better use of your own data


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.
  • Lane, S.N., Westaway, R.M. and Hicks, D.M., 2003. Estimation of erosion and deposition volumes in a large, gravel-bed, braided river using synoptic remote sensing. Earth Surface Processes and Landforms, 28(3): 249-271. DOI: 10.1002/esp.483.

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