Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/28304
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dc.contributor.authorBrinkhoff, Jamesen
dc.contributor.authorVardanega, Justinen
dc.contributor.authorRobson, Andrew Jen
dc.date.accessioned2020-03-30T03:21:19Z-
dc.date.available2020-03-30T03:21:19Z-
dc.date.issued2020-
dc.identifier.citationRemote Sensing, 12(1), p. 1-26en
dc.identifier.issn2072-4292en
dc.identifier.urihttps://hdl.handle.net/1959.11/28304-
dc.description.abstractLand cover mapping of intensive cropping areas facilitates an enhanced regional response to biosecurity threats and to natural disasters such as drought and flooding. Such maps also provide information for natural resource planning and analysis of the temporal and spatial trends in crop distribution and gross production. In this work, 10 meter resolution land cover maps were generated over a 6200 km² area of the Riverina region in New South Wales (NSW), Australia, with a focus on locating the most important perennial crops in the region. The maps discriminated between 12 classes, including nine perennial crop classes. A satellite image time series (SITS) of freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery was used. A segmentation technique grouped spectrally similar adjacent pixels together, to enable object-based image analysis (OBIA). K-means unsupervised clustering was used to filter training points and classify some map areas, which improved supervised classification of the remaining areas. The support vector machine (SVM) supervised classifier with radial basis function (RBF) kernel gave the best results among several algorithms trialled. The accuracies of maps generated using several combinations of the multispectral and radar bands were compared to assess the relative value of each combination. An object-based post classification refinement step was developed, enabling optimization of the tradeoff between producers’ accuracy and users’ accuracy. Accuracy was assessed against randomly sampled segments, and the final map achieved an overall count-based accuracy of 84.8% and area-weighted accuracy of 90.9%. Producers’ accuracies for the perennial crop classes ranged from 78 to 100%, and users’ accuracies ranged from 63 to 100%. This work develops methods to generate detailed and large-scale maps that accurately discriminate between many perennial crops and can be updated frequently.en
dc.languageenen
dc.publisherMDPI AGen
dc.relation.ispartofRemote Sensingen
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleLand Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Dataen
dc.typeJournal Articleen
dc.identifier.doi10.3390/rs12010096en
dcterms.accessRightsUNE Greenen
local.contributor.firstnameJamesen
local.contributor.firstnameJustinen
local.contributor.firstnameAndrew Jen
local.subject.for2008070699 Horticultural Production not elsewhere classifieden
local.subject.seo2008820299 Horticultural Crops not elsewhere classifieden
local.profile.schoolSchool of Science and Technologyen
local.profile.schoolSchool of Science and Technologyen
local.profile.emailjbrinkho@une.edu.auen
local.profile.emailarobson7@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.publisher.placeSwitzerlanden
local.identifier.runningnumber96en
local.format.startpage1en
local.format.endpage26en
local.identifier.scopusid85080907751en
local.peerreviewedYesen
local.identifier.volume12en
local.identifier.issue1en
local.access.fulltextYesen
local.contributor.lastnameBrinkhoffen
local.contributor.lastnameVardanegaen
local.contributor.lastnameRobsonen
dc.identifier.staffune-id:jbrinkhoen
dc.identifier.staffune-id:arobson7en
local.profile.orcid0000-0002-0721-2458en
local.profile.orcid0000-0001-5762-8980en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/28304en
local.date.onlineversion2019-12-26-
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleLand Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Dataen
local.relation.fundingsourcenoteRiverina Local Land Servicesen
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorBrinkhoff, Jamesen
local.search.authorVardanega, Justinen
local.search.authorRobson, Andrew Jen
local.open.fileurlhttps://rune.une.edu.au/web/retrieve/87a5ed96-830e-4349-b3ee-52f0fb8d6a3ben
local.istranslatedNoen
local.uneassociationYesen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.identifier.wosid000515391700096en
local.year.available2019en
local.year.published2020en
local.fileurl.openhttps://rune.une.edu.au/web/retrieve/87a5ed96-830e-4349-b3ee-52f0fb8d6a3ben
local.fileurl.openpublishedhttps://rune.une.edu.au/web/retrieve/87a5ed96-830e-4349-b3ee-52f0fb8d6a3ben
local.subject.for2020300899 Horticultural production not elsewhere classifieden
local.subject.seo2020260599 Horticultural crops not elsewhere classifieden
dc.notification.tokend4719952-2952-4d23-a623-f6baa7c109d0en
local.codeupdate.date2021-12-07T08:18:16.588en
local.codeupdate.epersonjbrinkho@une.edu.auen
local.codeupdate.finalisedtrueen
local.original.for2020undefineden
local.original.seo2020undefineden
Appears in Collections:Journal Article
School of Environmental and Rural Science
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