## Spatially Uniform DEM Error

### Further Reading on Spatially Uniform Error Specification

- See pages 78-90 of:
- Chapter 4 of Wheaton JM. 2008. Uncertainty in Morphological Sediment Budgeting of Rivers. Unpublished PhD Thesis, University of Southampton, Southampton, 412 pp.
- See page 140 of:
- Wheaton JM, Brasington J, Darby SE and Sear D. 2010. Accounting for Uncertainty in DEMs from Repeat Topographic Surveys: Improved Sediment Budgets. Earth Surface Processes and Landforms. 35 (2): 136-156. DOI: 10.1002/esp.1886.

## Spatially Variable DEM Error (Using Fuzzy Inference Systems)

### Background on Fuzzy Inference Systems

This video tutorial goes through a portion of the background covered in the Geomorphic Change Detection workshop on fuzzy inferences systems as they are applied to geomorphic change detection.### Specifying Fuzzy Inference Systems in GCD 4.0

This tutorial covers how you load a customized or previously saved (non-default) fuzzy inference system in the GCD software.One of the inputs that we commonly use in our fuzzy inference systems is a point density raster. Deriving this point density raster as a clipped concurrent grid is a little tricky in ArcGIS. It requires not only consistently setting the Point Density tool parameters correctly (we typically use a 3m x 3m square moving window and units of points per square meter), but also controlling the environment settings to enforce grid concurrency with your DEM as well as an additional clipping step.This model and toolbox simplifies that process into one step (made for ArcGIS 10; see here for how to add a toolbox):

### Customizing Fuzzy Inference Systems using Matlab Fuzzy Logic Toolbox

These video tutorial goes through how you customize a Fuzzy Inference System in Matlab for use in the GCD. Warning, these videos are only useful if you have Matlab on your computer installed with the Fuzzy Logic Toolbox. If you want to make your own fuzzy inference systems or edit systems you can still do so (scroll down to 'Customizing Fuzzy Inference Systems with a Text Editor').#### Part I - Getting Around in the Fuzzy Logic Toolobox

#### Part II -Looking at the Default Fuzzy Inference System for GCD 4.0

This goes through how the default two input fuzzy inference system for GCD 4.0 is set up, which is described in both Wheaton (2008) and Wheaton et al. (2010). The video shows you how to explore how the fuzzy inference system works and how it is organized.#### Part III - Modifying a Fuzzy Inference System

This video goes through modifying inputs and rules in Matlab's Fuzzy Logic Toolbox.### Customizing Fuzzy Inference Systems with a Text Editor

This video goes through how you customize a Fuzzy Inference System using either GCD or a text editor for use in GCD.

### Modifying or Adding to the Default Fuzzy Inference System(s) in GCD 4.0

### Further Reading on Fuzzy Inference Systems

- See pages 97-108 of:
- Chapter 4 of Wheaton JM. 2008. Uncertainty in Morphological Sediment Budgeting of Rivers. Unpublished PhD Thesis, University of Southampton, Southampton, 412 pp.
- See page 142-146 of:
- Wheaton JM, Brasington J, Darby SE and Sear D. 2010. Accounting for Uncertainty in DEMs from Repeat Topographic Surveys: Improved Sediment Budgets. Earth Surface Processes and Landforms. 35 (2): 136-156. DOI: 10.1002/esp.1886.
- Matlab Fuzzy Logic Toolbox Documentation

## Spatially Variable DEM Error (User Defined)

This option is not yet supported in GCD 4.0. This video explains what it entails.### Further Reading on Spatially Variable Error Specification

- See pages 162-165 of:
- Milan DJ, Heritage GL, Large ARG and Fuller IC. 2010. Filtering spatial error from DEMs: Implications for morphological change estimation. Geomorphology. 125(1): 160-171. DOI: 10.1016/j.geomorph.2010.09.012.

- See separate treatment of wet and dry areas in:
- Lane SN, Westaway RM and Hicks DM. 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.