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Sensitivity analysis of a decision tree classification to input data errors using a general Monte Carlo error sensitivity model

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dc.contributor.authorHuang, Zhi
dc.contributor.authorLaffan, Shawn
dc.date.accessioned2015-12-10T22:39:47Z
dc.date.issued2009
dc.date.updated2016-02-24T10:50:55Z
dc.description.abstractWe analysed the sensitivity of a decision tree derived forest type mapping to simulated data errors in input digital elevation model (DEM), geology and remotely sensed (Landsat Thematic Mapper) variables. We used a stochastic Monte Carlo simulation model
dc.identifier.issn1365-8816
dc.identifier.urihttp://hdl.handle.net/1885/57334
dc.publisherTaylor & Francis Group
dc.sourceInternational Journal of Geographical Information Science
dc.subjectKeywords: digital elevation model; error analysis; Gaussian method; land cover; land use; Landsat; Monte Carlo analysis; sensitivity analysis; terrain; uncertainty analysis; vegetation mapping Error modelling; Land use and land cover; Terrain analysis; Vegetation mapping and modelling
dc.titleSensitivity analysis of a decision tree classification to input data errors using a general Monte Carlo error sensitivity model
dc.typeJournal article
local.bibliographicCitation.issue11
local.bibliographicCitation.lastpage1452
local.bibliographicCitation.startpage1433
local.contributor.affiliationHuang, Zhi, College of Medicine, Biology and Environment, ANU
local.contributor.affiliationLaffan, Shawn, University of New South Wales
local.contributor.authoruidHuang, Zhi, u4026175
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor040699 - Physical Geography and Environmental Geoscience not elsewhere classified
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.ariespublicationU4279067xPUB396
local.identifier.citationvolume23
local.identifier.doi10.1080/13658810802634949
local.identifier.scopusID2-s2.0-70350779978
local.type.statusPublished Version

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