Sensitivity analysis of a Decision Tree classification to input data errors using a general Monte-Carlo error sensitivity model Huang, Zhi en_US Laffan, Shawn en_US 2021-11-25T13:13:49Z 2021-11-25T13:13:49Z 2009 en_US
dc.description.abstract We analysed the sensitivity of a decision tree derived forest type mapping to simulated data errors in input DEM, geology and remotely sensed (Landsat Thematic Mapper) variables. We used a stochastic Monte Carlo simulation model coupled with a one-at-a-time approach. The DEM error was assumed to be spatially autocorrelated with its magnitude being a percentage of the elevation value. The error of categorical geology data was assumed to be positional and limited to boundary areas. The Landsat data error was assumed to be spatially random and follow a Gaussian distribution. Each layer was perturbed using its error model with increasing levels of error, and the effect on the forest type mapping was assessed. The results of the three sensitivity analyses were markedly different, with the classification being most sensitive to the DEM error, then to the Landsat data errors, but with only a limited sensitivity to the geology data error used. A linear increase in error resulted in non-linear increases in effect for the DEM and Landsat errors, while it was linear for geology. As an example, a DEM error of as small as ±2% reduced the overall test accuracy by more than 2%. More importantly, the same uncertainty level has caused nearly 10% of the study area to change its initial class assignment at each perturbation, on average. A spatial assessment of the sensitivities indicates that most of the pixel changes occurred within those forest classes expected to be more sensitive to data error. In addition to characterising the effect of errors on forest type mapping using decision trees, this study has demonstrated the generality of employing Monte Carlo analysis for the sensitivity and uncertainty analysis of categorical outputs which have distinctive characteristics from that of numerical outputs. en_US
dc.identifier.issn 1365-8816 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.subject.other GIS en_US
dc.subject.other Decision trees en_US
dc.subject.other Uncertainty en_US
dc.subject.other predicitve vegetation mapping en_US
dc.subject.other Monte Carlo en_US
dc.title Sensitivity analysis of a Decision Tree classification to input data errors using a general Monte-Carlo error sensitivity model en_US
dc.type Journal Article en
dcterms.accessRights open access
dspace.entity.type Publication en_US
unsw.description.publisherStatement This electronic version is embargoed for 12 months from publication. This is an electronic version of an article published in the International Journal of Geographical Information Science, 23(11), pp.1433-1452. The International Journal of Geographical Information Science is available online at: Embargo period expired November, 2010. en_US
unsw.relation.faculty Science
unsw.relation.ispartofissue 11 en_US
unsw.relation.ispartofjournal International Journal of Geographical Information Science en_US
unsw.relation.ispartofpagefrompageto 1433-1452 en_US
unsw.relation.ispartofvolume 23 en_US
unsw.relation.originalPublicationAffiliation Huang, Zhi en_US
unsw.relation.originalPublicationAffiliation Laffan, Shawn, Biological, Earth & Environmental Sciences, Faculty of Science, UNSW en_US School of Biological, Earth & Environmental Sciences *
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
1.03 MB
Resource type