Modelling the spatial distribution of DEM Error

Carlisle, Bruce (2005) Modelling the spatial distribution of DEM Error. Transactions in GIS, 9 (4). pp. 521-540. ISSN 1361-1682

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Official URL: http://dx.doi.org/10.1111/j.1467-9671.2005.00233.x

Abstract

Assessment of a DEM’s quality is usually undertaken by deriving a measure of DEM accuracy – how close the DEM’s elevation values are to the true elevation. Measures such as Root Mean Squared Error and standard deviation of the error are frequently used. These measures summarise elevation errors in a DEM as a single value. A more detailed description of DEM accuracy would allow better understanding of DEM quality and the consequent uncertainty associated with using DEMs in analytical applications. The research presented addresses the limitations of using a single root mean squared error (RMSE) value to represent the uncertainty associated with a DEM by developing a new technique for creating a spatially distributed model of DEM quality – an accuracy surface. The technique is based on the hypothesis that the distribution and scale of elevation error within a DEM are at least partly related to morphometric characteristics of the terrain. The technique involves generating a set of terrain parameters to characterise terrain morphometry and developing regression models to define the relationship between DEM error and morphometric character. The regression models form the basis for creating standard deviation surfaces to represent DEM accuracy. The hypothesis is shown to be true and reliable accuracy surfaces are successfully created. These accuracy surfaces provide more detailed information about DEM accuracy than a single global estimate of RMSE.

Item Type: Article
Additional Information: © Bruce Carlisle {2005} The full text of this article is published in Transactions in GIS , 9, 4, 521-540. It is available online at http://dx.doi.org/10.1111/j.1467-9671.2005.00233.x
Subjects: F800 Physical and Terrestrial Geographical and Environmental Sciences
Department: Faculties > Engineering and Environment > Geography
Depositing User: Bruce Carlisle
Date Deposited: 24 Sep 2012 16:17
Last Modified: 14 May 2017 03:05
URI: http://nrl.northumbria.ac.uk/id/eprint/9166

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