The classification of hill country vegetation from hyperspectral imagery

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dc.contributor.authorCushnahan, Ten_US
dc.contributor.authorYule, IJen_US
dc.contributor.authorGrafton, MCEen_US
dc.contributor.authorPullanagari, Ren_US
dc.contributor.authorWhite, Men_US
dc.contributor.editorCurrie, Len_US
dc.contributor.editorHedley, Men_US
dc.coverage.spatialPalmerston North, New Zealanden_US
dc.date.available10/04/2017en_US
dc.date.finish-date9/02/2017en_US
dc.date.issued10/04/2017en_US
dc.date.start-date7/02/2017en_US
dc.description.abstractRemotely sensed hyperspectral data provides the possibility to categorise and quantify the farm landscape in great detail, supplementing local expert knowledge and adding confidence to decisions. This paper examines the novel use of hyperspectral aerial imagery to classify various components of the hill country farming landscape. As part of the Ravensdown / MPI PGP project, “Pioneering to Precision”, eight diverse farms, five in the North and three in the South Island were sampled using the AisaFENIX hyperspectral imager. The resulting images had a 1m spatial resolution (approx.) with 448 spectral bands from 380 – 2500 nm. The primary aim of the PGP project is to develop soil fertility maps from spectral information. Images were collected in tandem with ground sampling and timed to coincide with spring and autumn seasons. Additional classification of the pasture components of two farms are demonstrated using various data pre-processing and classification techniques to ascertain which combination would provide the best accuracy. Classification of pasture with Support Vector Machines (SVM) achieved 99.59% accuracy. Classification of additional landscape components on the same two farms is demonstrated. Components classified as non-pasture ground cover included; water, tracks/soil, Manuka, scrub, gum, poplar and other tree species. The techniques were successfully used to classify the components with high levels of accuracy. The ability to classify and quantify landscape components has numerous applications including; fertiliser and farm operational management, rural valuation, strategic farm management and planning.en_US
dc.description.confidentialFALSEen_US
dc.format.extent1 - 9 (9)en_US
dc.identifier.citationOccasional Report Number 30, 2017, pp. 1 - 9 (9)en_US
dc.identifier.eissn2230-3944en_US
dc.identifier.elements-id339980
dc.identifier.harvestedMassey_Dark
dc.identifier.issn0112-9902en_US
dc.identifier.urihttps://hdl.handle.net/10179/10890
dc.publisherFertilizer & Lime Research Centreen_US
dc.relation.isPartOfOccasional Report Number 30en_US
dc.sourceFertilizer and Lime Research Centreen_US
dc.titleThe classification of hill country vegetation from hyperspectral imageryen_US
dc.typeConference Paper
pubs.notesNot knownen_US
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
pubs.organisational-group/Massey University/College of Sciences/School of Agriculture & Environment
pubs.organisational-group/Massey University/College of Sciences/School of Agriculture & Environment/Agritech
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