Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/29464
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dc.contributor.authorBrinkhoff, Jamesen
dc.contributor.authorRobson, Andrew Jen
dc.date.accessioned2020-09-21T05:52:17Z-
dc.date.available2020-09-21T05:52:17Z-
dc.date.issued2020-07-13-
dc.identifier.citationRemote Sensing, 12(14), p. 1-13en
dc.identifier.issn2072-4292en
dc.identifier.urihttps://hdl.handle.net/1959.11/29464-
dc.description.abstractAccurate estimates of tree crop orchard age and historical crop area are important to develop yield prediction algorithms, and facilitate improving accuracy in ongoing crop forecasts. This is particularly relevant for the increasingly productive macadamia industry in Australia, where knowledge of tree age, as well as total planted area, are important predictors of productivity, and the area devoted to macadamia orchards is rapidly increasing. We developed a technique to aggregate more than 30 years of historical imagery, generate summary tables from the data, and search multiple combinations of parameters to find the most accurate planting year prediction algorithm. This made use of known planting dates of more than 90 macadamia blocks spread across multiple growing regions. The selected algorithm achieved a planting year mean absolute error of 1.7 years. The algorithm was then applied to all macadamia features in east Australia, as defined in an recent Australian tree crops map, to determine the area planted per year and the total cumulative area of macadamia orchards in Australia. The area estimates were refined by improving the resolution of the mapped macadamia features, by removing non-productive areas based on an optimal vegetation index threshold.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.titleMacadamia Orchard Planting Year and Area Estimation at a National Scaleen
dc.typeJournal Articleen
dc.identifier.doi10.3390/rs12142245en
dcterms.accessRightsUNE Greenen
local.contributor.firstnameJamesen
local.contributor.firstnameAndrew Jen
local.subject.for2008070104 Agricultural Spatial Analysis and Modellingen
local.subject.seo2008820206 Macadamiasen
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.runningnumber2245en
local.format.startpage1en
local.format.endpage13en
local.identifier.scopusid85088644817en
local.peerreviewedYesen
local.identifier.volume12en
local.identifier.issue14en
local.access.fulltextYesen
local.contributor.lastnameBrinkhoffen
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.identifier.unepublicationidune:1959.11/29464en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleMacadamia Orchard Planting Year and Area Estimation at a National Scaleen
local.relation.fundingsourcenoteAustralian Government Department of Agriculture and Water Resources; Horticulture Innovation Australia Ltd (ST19008)en
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorBrinkhoff, Jamesen
local.search.authorRobson, Andrew Jen
local.open.fileurlhttps://rune.une.edu.au/web/retrieve/230cfd75-9de9-4404-9c34-e8461829e246en
local.uneassociationYesen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.identifier.wosid000558204400001en
local.year.available2020en
local.year.published2020en
local.fileurl.openhttps://rune.une.edu.au/web/retrieve/230cfd75-9de9-4404-9c34-e8461829e246en
local.fileurl.openpublishedhttps://rune.une.edu.au/web/retrieve/230cfd75-9de9-4404-9c34-e8461829e246en
local.subject.for2020300206 Agricultural spatial analysis and modellingen
local.subject.seo2020260507 Macadamiasen
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School of Science and Technology
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