Browsing by Author "Cushnahan T"
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- ItemHyperspectral Data Can Classify Plant Functional Groups Within New Zealand Hill Farm Pasture(MDPI AG, 2025-03-21) Cushnahan T; Grafton M; Pearson D; Ramilan T; Hasenauer HReliable 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.
- ItemHyperspectral Data Can Classify Plant Functional Groups Within New Zealand Hill Farm Pasture(MDPI AG, 2025-03-21) Cushnahan T; Grafton M; Pearson D; Ramilan T; Hasenauer H
- ItemHyperspectral Data Can Differentiate Species and Cultivars of C3 and C4 Turf despite Measurable Diurnal Variation. Remote Sens. 2024, 16, 3142. https://doi.org/10.3390/rs16173142(MDPI AG, 2024-08-26) Cushnahan T; Grafton MCE; Pearson D; Ramilan T; Verreslt J
- ItemIdentifying grass species using hyperspectral sensing(Fertilizer and Lime Research Centre, ) Cushnahan T; Yule IJ; Pullanagari RR; Grafton MCE; Currie, L; Singh, R
