Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/13528
Title: Defining Sheep Grazing Environments Using Remotely Sensed Data at a Range of Scales
Contributor(s): Whelan, Michael Barry (author); Cottle, David (supervisor)orcid ; Lamb, David (supervisor); Geenty, Ken (supervisor)
Conferred Date: 2013
Copyright Date: 2012
Open Access: Yes
Handle Link: https://hdl.handle.net/1959.11/13528
Abstract: The grazing environment (i.e. the quantity and quality of pasture available) drives sheep production in Australia. The long-term viability of the sheep industry is dependent on the sustainable use of pasture, which requires monitoring. Remotely sensed data have the potential to monitor changes in pasture resources within and between seasons. Remotely sensed data have the potential to; map pasture resources within a paddock, differentiate paddocks within a farm, differentiate farms within a region and differentiate grazing environments across the country. This thesis examines the application of remotely sensed data in the sheep industry at three scales (within a paddock, at the paddock/farm scale and at a continental scale). Data from a hand-held active sensor (Crop Circle™) were used to estimate green dry matter (GDM) within a paddock and produce a map that highlighted the variability within the paddock. The normalised difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI) were used to estimate GDM in mixed annual and perennial swards over three years at two sites. Comparisons between NDVI, SAVI, pasture height and GDM indicated that producers should continue to use pasture height to estimate GDM but the Crop Circle™ could be used to map GDM variability within a paddock.
Publication Type: Thesis Doctoral
Field of Research Codes: 070301 Agro-ecosystem Function and Prediction
Rights Statement: Copyright 2012 - Michael Barry Whelan
HERDC Category Description: T2 Thesis - Doctorate by Research
Other Links: http://www.aaabg.org/proceedings18/files/whelan052.pdf
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Appears in Collections:School of Science and Technology
Thesis Doctoral

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