Synopsis of Topic
In Wheaton et al. (2010), a new method for looking at the spatial coherence of erosion and deposition was presented, which proposed that the probability that DoD predicted change is real depends in part on what is going on around you. In other words, if you are in a cell experiencing minor erosion (perhaps below the minimum level of detection), but every cell around you is also erosional, there is a higher probability that you actually are erosional. By contrast, if you are in a cell experiencing minor erosion and everything around you is depositional, then there is a lower probability that you are actually erosional. This simple concept was used to develop a 'spatial coherence filter', which is then converted into a conditional probability. Bayes theorem can then be evoked to modify the a priori probability and calculate a new probability (posterior) that change is real.
Although this is a powerful concept, it can be misapplied. If your dataset exhibits systematic errors and bias, this filter can be problematic.
Why we're Covering it
This is one of the options in the GCD Change Detection panel and it is important to understand how it works and when it is appropriate to apply.
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
- 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