Effect of error in the DEM on environmental variables for predictive vegetation modelling Van Niel, Kimberley en_US Laffan, Shawn en_US Lees, Brian en_US 2021-11-25T13:09:48Z 2021-11-25T13:09:48Z 2004 en_US
dc.description.abstract Question: Predictive vegetation modelling relies on the use of environmental variables, which are usually derived from a base data set with some level of error, and this error is propagated to any subsequently derived environmental variables. The question for this study is: What is the level of error and uncertainty in environmental variables based on the error propagated from a Digital Elevation Model (DEM) and how does it vary for both direct and indirect variables? Location: Kioloa region, New South Wales, Australia. Methods: The level of error in a DEM is assessed and used to develop an error model for analysing error propagation to derived environmental variables. We tested both indirect (elevation, slope, aspect, topographic position) and direct (average air temperature, net solar radiation, and topographic wetness index) variables for their robustness to propagated error from the DEM. Results: It is shown that the direct environmental variable net solar radiation is less affected by error in the DEM than the indirect variables aspect and slope, but that regional conditions such as slope steepness and cloudiness can influence this outcome. However, the indirect environmental variable topographic position was less affected by error in the DEM than topographic wetness index. Interestingly, the results disagreed with the current assumption that indirect variables are necessarily less sensitive to propagated error because they are less derived. Conclusions: The results indicate that variables exhibit both systematic bias and instability under uncertainty. There is a clear need to consider the sensitivity of variables to error in their base data sets in addition to the question of whether to use direct or indirect variables. en_US
dc.description.uri en_US
dc.identifier.issn 1100-9233 en_US
dc.language English
dc.language.iso EN en_US
dc.rights CC BY-NC-ND 3.0 en_US
dc.rights.uri en_US
dc.source Legacy MARC en_US
dc.title Effect of error in the DEM on environmental variables for predictive vegetation modelling en_US
dc.type Journal Article en
dcterms.accessRights metadata only access
dspace.entity.type Publication en_US
unsw.relation.faculty Science
unsw.relation.ispartofjournal Journal of Vegetation Science en_US
unsw.relation.ispartofpagefrompageto 747-756 en_US
unsw.relation.ispartofvolume 15 en_US
unsw.relation.originalPublicationAffiliation Van Niel, Kimberley en_US
unsw.relation.originalPublicationAffiliation Laffan, Shawn, Biological, Earth & Environmental Sciences, Faculty of Science, UNSW en_US
unsw.relation.originalPublicationAffiliation Lees, Brian en_US School of Biological, Earth & Environmental Sciences *
Resource type