1 Spatial Modelling of the Terrestrial Environment - Georeferencial

6
Remotely Sensed Topographic Data
for River Channel Research: The
Identification, Explanation and
Management of Error
Stuart N. Lane, Simon C. Reid, Richard M. Westaway and D. Murray Hicks
6.1 Introduction
The quality of Digital Elevation Model (DEM) data is all too often overlooked (Cooper,
1998; Lane, 2000) which can have serious implications for geo-morphological and hydrological
study (Wise, 1998). There are a number of reasons why this can be the case. First,
there is a growth in the availability of digital data sources, some of which have unknown
or poorly specified data quality. A global measure of error (e.g. root mean square error,
RMSE) can be misleading (1): if it is based upon test sites that bear little resemblance to the
site of interest; and (2) if it fails to recognize the spatial structure of the error field. Second,
when digital data are supplied that have been derived from a number of processes (e.g. photogrammetric
generation of the initial topographic data, analysis to produce a DEM, digital
contouring, construction of a DEM from digital contours), there is the possibility of propagation
of error at each stage of the process. This can only be assessed through acquisition of
the raw source data and repetition of the process. This raw data may not be available. This is
especially problematic as research has shown that there can be substantial magnification of
propagated error into DEM-derived geo-morphological and hydrological parameters (e.g.
Wise, 1998, 2000; Lane et al., 2000). Third, new methods of data generation have involved
progressively greater levels of automation, with a corresponding increase in the amount
of data whose quality must be determined (Lane, 2000) and a reduction in the amount of
Spatial Modelling of the Terrestrial Environment. Edited by R. Kelly, N. Drake, S. Barr.
C○ 2004 John Wiley & Sons, Ltd. ISBN: 0-470-84348-9.