Hyperspectral Data Can Classify Plant Functional Groups Within New Zealand Hill Farm Pasture

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dc.citation.issue7
dc.citation.volume17
dc.contributor.authorCushnahan T
dc.contributor.authorGrafton M
dc.contributor.authorPearson D
dc.contributor.authorRamilan T
dc.contributor.editorHasenauer H
dc.date.accessioned2025-03-26T19:50:56Z
dc.date.available2025-03-26T19:50:56Z
dc.date.issued2025-03-21
dc.description.abstractReliable evidence of species composition or habitat distribution is essential to advance pasture management and decision making, including the definition of fertiliser rates for aerial top dressing. This is more difficult in a diverse environment such as New Zealand hill country farms. The simplification of the landscape character using plant functional types and species dominance has proven useful in ecological studies and in modelling grasslands. This study used hyperspectral imagery to map hill country pasture into plant functional groups (PFGs) as a proxy for pasture quality. We validated a farm scale map generated using support vector machines (SVMs), with ground reference data, to an overall accuracy of 88.75%. We discuss how that information can improve on-farm decision making and allow for better coordination with off-farm consultants. This form of farm-wide mapping is also critical for the successful application of variable-rate aerial topdressing technology as input for the allocation of fertiliser rates.
dc.description.confidentialfalse
dc.edition.editionApril-1 2025
dc.identifier.citationCushnahan TA, Grafton M, Pearson D, Ramilan T. (2025). Hyperspectral Data Can Classify Plant Functional Groups Within New Zealand Hill Farm Pasture. Remote Sensing. 17. 7.
dc.identifier.doi10.3390/rs17071120
dc.identifier.eissn2072-4292
dc.identifier.elements-typejournal-article
dc.identifier.issn2072-4292
dc.identifier.number1120
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/72694
dc.languageEnglish
dc.publisherMDPI AG
dc.publisher.urihttps://www.mdpi.com/2072-4292/17/7/1120
dc.relation.isPartOfRemote Sensing
dc.rights(c) 2025 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectplant functional groups
dc.subjectaerial hyperspectral imagery
dc.subjectsupport vector machines (SVMs)
dc.subjectpasture classification
dc.subjectAISA Fenix
dc.titleHyperspectral Data Can Classify Plant Functional Groups Within New Zealand Hill Farm Pasture
dc.typeJournal article
pubs.elements-id500169
pubs.organisational-groupOther
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