Uncertainties in Digital Elevation Models
& Interpreting Morphological Sediment Budgets
2008 ~ read abstract),
which sought to address these two uncertainties (unreliability
and structural uncertainty) through development of new analytical and
interpretive methods. These tools are available as GCD (Geomorphic Change Detection Software).
Repeat topographic surveys are often used to monitor geomorphic
change in rivers. These yield digital elevation models (DEMs), which are differenced against each other to produce spatially distributed maps of elevation changes called DEMs of difference (DoD - See Figure below). Both
areal and volumetric budgets of erosion and deposition can be calculated
from DoDs. However, questions arise about the reliability of the analyses
and what they mean. This project was the focus of my PhD Thesis (|
BackgroundDigital elevation models (DEMs) are ubiquitous in geomorphic
studies, yet the uncertainty in their representations of the earth's surface
are rarely accounted for (Wechsler 2000 & 2003). Morphological sediment
budgets are derived by subtracting the DEM of an earlier survey from the
DEM of a later survey (e.g. Brasington et al. 2003; Church et al. 2001;
Milne and Sear 1997; Fuller et al. 2003). The so-called DEMs of Difference
(DoD) represent an estimate of the change in storage terms over the time
step between the surveys. Most studies ignore uncertainties in DoD calculations,
and those that have considered it explicitly (e.g. Lane et al. 2003) typically
assume that these uncertainties are spatially uniform.
A Schematic of the morphological method. On the left, an example of a DoD
(bottom) derived from the two DEMs above it is shown. In the upper right,
a plan form perspective of that DoD is shown, and and the inset maps show
how the DoD is calculated on a cell-by-cell basis by subtracting the elevation
values in the older DEM from the newer DEM. In the lower right is an example
of an areal (top) and volumetric (bottom) elevation change distribution
from the same DoD. (Figure 3.5 from Wheaton (2008) ©).
Methodological Development In this research, we have developed techniques to:
- Quantify spatially variable elevation uncertainties in individual
DEM surface representations with a fuzzy inference system
- Propagate spatially variable elevation uncertainties from two DEMs
through to assess their influence on a DoD estimates probabilistically
- Update these probabilities using a spatial neighborhood analysis and
Bayes Theorem, which assess the likelihood that changes are real based
on the changes in nearby cells
- Segregate the budget results and elevation change distributions with
geomorphologically meaningful masks
Minimum Level of Detection Threshold Sensitivity
An example of DoD sensitivity to confidence interval threshold minimum levels
of detection (minLoD). Confidence Intervals: A) Unthresholded, B) 50% CI,
C) 68% CI, D) 95% CI, E) 99% CI. Note the gross non-thresholded values are
plotted in red on each volumetric elevation change distribution (bottom)
to indicate information loss. Example from River Feshie, Scotland. (Figure
4.33 from Wheaton (2008) ©)
Map of the 2004 to 2005 DoD (A), its geomorphic interpretation (B), and
the relative proportion of each category of change (C). Relative proportions
are calculated volumetrically with reference to the total volume of material
net change recorded by the DoD (both erosion and deposition). The flow direction
is up the page. Example from River Feshie, Scotland. (Figure 8.7 from Wheaton
A Geomorphic Interpretation Mask
Typically, DoDs are only used to give gross reach scale estimates of volumetric
change and make qualitative interpretations of sub-reach and bar-scale changes.
By using simple spatial masks, the budget can be segregated by any classification
deemed useful to interpreting the DoD. Some examples of masks developed
and tested in Wheaton (2008) include:
An example of a geomorphic interpretation is shown at left.
- Standard classifications (e.g. geomorphic classification, habitat
classification, ecohydraulic habitat suitability model results, sub-reach
- Classification of Difference
- Geomorphic Interpretation (expert-based)
There are a variety of ways that the significance of geomorphic change to
physical habitat (e.g. for fish) can be assessed. In Wheaton (2008) various
types of masks were used to break up the budget. Examples include:
An example of a comparison of change in habitat quality according to DoD
recorded changes in morphology is shown below.
- Redd surveys - to look at what changes took place where salmon spawned
(i.e. changes due to spawning activity or floods and/or implications
for embryo survival)
- Habitat quality predictions (from ecohydraulic model simulations)-
to look at how certain types of geomorphic changes influence habitat
- Habitat quality differences - comparison of habitat quality before
The elevation change distributions (A-C) from masks based on a habitat quality
(defined by 2D ecohydraulic model simulations) classification of difference
(E - see also Figure 7.36) derived from the thresholded DoD for TS6 (D).
The ECDs correspond to the three different CoD categories: habitat quality
stable (beige in E), habitat quality improved (green in E), and habitat
quality degraded (orange in E). Example from Mokelumne River, California.
(Figure 7.37 from Wheaton (2008) ©)
A series of tools for performing the above analyses have been developed
in Matlab and are available
here. Additionally, a stand-alone Windows GUI and ArcGIS toolbar plugin
are under development (Library in C++; plugin and GUI in C#). These software
tools will be posted here
for free download upon completion.
| In Wheaton (2008),
these techniques were tested at three case studies sites:
In addition the techniques are now being employed on the following Rivers/Streams:
Yuba River, California, US
Rees River, South Island, New Zealand
- Bear Creek, Middle-Fork Salmon River Headwaters, Idaho, US
- The Provo River, UT
- The Logan River, UT
- Asotin Creek, WA
- Bridge Creek, OR
- Salmon River, ID
- Salmon Falls, ID
- Green River, UT
- Yampa River, CO
- Colorado River, Grand Canyon, AZ
| In these studies, the estimation of surface representation uncertainty
has been primarily derived from ground-based surveys (e.g. total station
and RTK-GPS). Through ongoing research in this area we hope to extend these
techniques to aerial surveys (e.g. LIDAR, Aerial photogrammetry) and we
have already started extending this to ground-based LiDAR (a.k.a. terrestrial
laser scanning). Regardless of the technique to quantify the spatially variable
surface representation uncertainties in an individual surface, the methodology
to propagate these uncertainties through to the DoD and explore their influence
on morphometric sediment budget estimates will still apply.
In addition, the following areas will be pursued:
- using the tools on bigger, higher resolution data sets of different
types over larger areas
- improving and extending the DoD Uncertainty Analysis (e.g. incorporation
of roughness into fuzzy inference system)
- closing the sediment budget, by incorporation of flux terms (sediment
- using the techniques to interrogate outputs from morphodynamic and
| This work started in 2004 as a collaboration between Dr. James Brasington and myself. It evolved to become the focus of my PhD
and has had significant contributions from my supervisors Dr. Steve Darby and Professor David Sear. In addition, Professor
Greg Pasternack has been a collaborator on the Mokelumne River and Sulphur
Creek case studies. Dr.
Damia Vericat, Dr. Brasington and I are now using and extending the tools for analyses
of terrestrial laser scan data on the Feshie and Rees Rivers (Vericat et
al. 2007; Brasington et al. 2007). Dr. Igor Rychkov and I are now working
on extending the Matlab software to an ArcGIS plugin and stand-alone application.
The field work has been possible thanks to the hard labor of numerous individuals
(see acknowledgements in Wheaton 2008), but among the most regular helpers
are Dr. Rebecca Hodge,
Clare Cox, and Richard Williams.
Publications from this Research:
- 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.
- Wheaton JM, Brasington J, Darby SE, Merz JE, Pasternack GB, Sear DA and
Vericat D. 2009. Linking Geomorphic Changes to Salmonid Habitat at a
Scale Relevant to Fish. River Research and Applications. DOI: 10.1002/rra.1305.
Presentations of this Research:
- Presentation: Wheaton JM, Brasington J, Darby SE, Sear D
and Vericat D. 2008. Beyond the gross reach-scale sediment budget –
using repeat topographic surveys for mechanistic geomorphic interpretation,
for Geomorphology Annual Meeting, Exeter, UK.
- Talk: Wheaton, JM, Vericat
D, Brasington J, Darby S, Sear D, Pasternack GB. 2008. Linking
Morphological Sediment Budgeting to Salmonid Ecohydraulics, BHS
Meeting: Ecohydraulics at Scales Relevant to Organisms, Loughborough.
- Poster: Wheaton JM. 2008. Do Geomorphic Dynamics Matter to
Fish?, MYRES 2008: Dynamic Interactions
of Life and its Landscape. New Orleans.
- Invited Presentation:
Brasington J, Vericat D, Wheaton JM and Hodge R. 2008. Reach-scale
retrieval of alluvial bed roughness, Geophysical Research Abstracts:
EGU General Assembly. European
Geophysical Union: Vienna, Austria, pp. EGU2008-A-01295. (See here
for details on Remote Sensing of Rivers Session).
- Wheaton JM et al. 2007. Improved
Fluvial Geomorphic Interpretation from DEM Differencing. Eos Trans.
AGU. 88(52): Fall Meet. Suppl., Abstract H43E-1672.
- Vericat D, Brasington J, Wheaton JM and Hodge R. 2007. Reach-Scale
Retrieval of Alluvial Bed Roughness. Eos Trans. AGU. 88(52): Fall Meet.
Suppl., Abstract H51E-0799.
- Brasington J, Wheaton JM, Vericat D and Hodge R. 2007. Modelling Braided
River Morphodynamics With Terrestrial Laser Scanning. Eos Trans. AGU.
88(52): Fall Meet. Suppl., Abstract H51L-02.
- Brasington J, Wheaton JM and
Williams RD. 2004. Sub-Reach
Scale Morphological Interpretations from DEM Differencing: Accounting
for DEM Uncertainty. Eos Trans. AGU. 85(47): Fall Meeting Supplement,
- Wheaton JM, Brasington J and
Williams RD. 2004. Modelling
Fluvial Sediment Budgets Under Uncertainty. Eos Trans. AGU. 85(47):
Fall Meeting Supplement, Abstract H53C-1264.
- Brasington, J., Langham, J. and Rumsby, B., 2003. Methodological sensitivity
of morphometric estimates of coarse fluvial sediment transport. Geomorphology,
- Church, M., Ham, D. and Weatherly,
H., 2001. Gravel
Management in the Lower Fraser River, Department of Geography, The
University of British Columbia, Vancourver, British Columbia.
- Fuller, I.C., Large, A.R.G., Charlton, M.E., Heritage, G.L. and Milan,
D.J., 2003. Reach-Scale Sediment Transfers: An Evaluation of Two Morphological
Budgeting Approaches. Earth Surface Processes and Landforms, 28: 889-903.
- Gaeuman, D.A., Schmidt, J.C. and Wilcock, P.R., 2003. Evaluation of
in-channel gravel storage with morphology-based gravel budgets developed
from planimetric data. Journal of Geophysical Research- Earth Surface,
- 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.
- Merz, J.E., Pasternack, G.B.
and Wheaton, J.M., 2006. Sediment
budget for salmonid spawning habitat rehabilitation in a regulated river.
Geomorphology. 76(1-2): 207-228
- Milne, J.A. and Sear, D., 1997. Modelling river channel topography
using GIS. International Journal of Geographical Information Science,
- Wechsler, S.P., 2003. Perceptions
of Digital Elevation Model Uncertainty by DEM Users. URISA Journal,
- Wechsler, S.P., 2000. Effect of DEM Uncertainty on Topographic Parameters,
DEM Scale and Terrain Evaluation, State University of New York, Syracuse,
New York, 380 pp.