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A. Shortridge (2000). Characterizing Uncertainty in Digital Elevation Models. Chapter 11 in C. Hunsaker, M. Goodchild, M. Friedl, & T. Case (Editors), Perspectives on Uncertainty in Spatial Data for Ecological Analyses. Springer Verlag, in press.
Abstract:
Topography plays an important role in many environmental processes.
Discrepancies exist between digital elevation models (DEMs) and the real-world
surfaces they represent. Using an uncertainty model, a researcher can propagate
DEM uncertainty through the analysis to identify its impact upon the results
of the application. This is accomplished by producing, via Monte Carlo
simulation, a set of equiprobable realizations of the DEM. This chapter
provides a through discussion of DEMs and uncertainty, as well as indicating
general approaches to modeling uncertainty in data for spatially continuous
phenomena. Some examples are presented to illustrate these modeling approaches.
P. C. Kyriakidis, A. M. Shortridge, & M. F. Goodchild (1999). Geostatistics for conflation and accuracy assessment of digital elevation models. International Journal of Geographical Information Science, 13(7): 677-707.
Abstract
A geostatistical methodology is proposed for integrating elevation
estimates derived from digital elevation models (DEMs) and elevation measurements of higher accuracy, e.g., elevation spot heights. The sparse elevation measurements (hard data) and the abundant DEM-reported elevations (soft data) are employed for modeling the unknown higher accuracy (reference) elevation surface in a way that properly reflects the relative reliability
of the two sources of information. Stochastic conditional simulation is
performed for generating alternative, equiprobable images (numerical models)
of the unknown reference elevation surface using both hard and soft data.
These numerical models reproduce the hard elevation data at their measurement
locations, and a set of auto and cross-covariance models quantifying spatial
correlation between data of the two sources of information at various spatial
scales. From this set of alternative representations of the reference elevation,
the probability that the unknown reference value is greater than the DEM-reported
one at each node is determined. Joint uncertainty associated with spatial
features observed in the DEM, e.g., the probability for an entire ridge
to exist, is also modeled from this set of alternative images.
A case study illustrating the proposed conflation procedure is presented
for a portion of a USGS one-degree DEM. It is suggested that maps of local
probabilities for over or underestimation of the unknown reference elevation
values from the DEM-reported ones, and joint probability values attached
to different spatial features, be provided to DEM users in addition to
traditionally reported summary statistics used to quantify DEM accuracy.
Such a metadata element would be a valuable tool
for subsequent decision-making processes that are based on the DEM-reported
elevation surface, or for targeting areas where more accurate elevation
measurements are required.
A. M. Shortridge and M. F. Goodchild (1999). Towards
a turn-key approach to modeling spatial data uncertainty. 95th
AAG Meeting, Honolulu. (HTML'd Powerpoint presentation)
A. M. Shortridge (1998). Using
derivative surface operators. UNIT 43 in V. Gray, Editor, GIS Core
Curriculum for Technical Programs. (on-line GIS tutorial)
M.F. Goodchild, A. M. Shortridge, & P. Fohl (1998) Encapsulating
simulation models with geospatial data sets. 3rd International
Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental
Sciences, Quebec City, Canada. (HTML'd Powerpoint presentation)
A. M. Shortridge and K. C. Clarke (1998) On
some limitations of square raster cell structures for digital elevation
data modeling. 3rd International Symposium on Spatial Accuracy
Assessment in Natural Resources and Environmental Sciences, Quebec City,
Canada. (HTML'd Powerpoint presentation)
Ehlschlaeger, C.R. & A. Shortridge (1996). Modeling
Elevation Uncertainty in Geographical Analyses. Proceedings
of the International Symposium on Spatial Data Handling, Delft, Netherlands.
9B.15-9B.25. (full text & graphics)