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https://hdl.handle.net/1959.11/57237
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DC Field | Value | Language |
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dc.contributor.author | Crabbe, Richard Azu | en |
dc.contributor.author | Lamb, David William | en |
dc.contributor.author | Trotter, Mark Graeme | en |
dc.date.accessioned | 2024-01-10T01:23:23Z | - |
dc.date.available | 2024-01-10T01:23:23Z | - |
dc.date.created | 2019-12 | - |
dc.date.issued | 2020-05-06 | - |
dc.identifier.uri | https://hdl.handle.net/1959.11/57237 | - |
dc.description | Please contact rune@une.edu.au if you require access to this thesis for the purpose of research or study. | en |
dc.description.abstract | <p>Pasture grasses are an important feed-base for the livestock industry. The ability to identify and characterise pasture type, species composition and quantify the available biomass in fields is invaluable to the sustainability and profitability of our livestock industries. Pasture species composition, biomass and canopy structural variations have been measured at different spatial scales using varied optical methods/tools such as active optical sensors, as well as aerial and spaceborne passive optical sensors. At large spatial scale, optical satellite sensors are often used. However, the utilisation of these sensors is affected by cloudy weather conditions and the fact that they are really only responsive to photosynthetically active biomass.</p> <p>Satellite-based Synthetic Aperture Radar (SAR) sensors, though not popular yet in pasture studies, have the potential to offset this limitation of optical sensing as the microwave energy emitted by these sensors penetrate clouds and that these wavelengths are also sensitive to volumetric scattering processes rendering them, potentially useful to situations involving significant, senesced, plant material (e.g. during drought) . This thesis predominantly focussed on Sentinel-1 C-band SAR with the whole research project comprising of three main components: (i) discrimination of pasture species based on C3 and C4 photosynthetic mechanisms and diversity of the botanical composition; (ii) estimating pasture biophysical variables with emphasis on aboveground biomass; and (iii) detection of surface heterogeneity due to selective grazing in pasture fields.</p> <p>In discriminating pasture species into C3, C4 and mixed C3/C4 classes, Random Forest classification was used and the highest overall classification accuracy (86%) was achieved with a combination of grey-level co-occurrence textural metrics and polarimetric SAR metrics. Moreover, the combined strengths of Sentinel-1 SAR and Sentinel-2 optical information parameterised into K-Nearest Neighbours, Random Forest and Support Vector Machine classifiers, produced the highest overall accuracy estimates of 89%, 96% and 95%, respectively. Regression models such as the generalised additive model estimated pasture biomass with a root mean square error of prediction of 392 kg/ha over AGB estimates between 443–2642 kg/ha. Here pasture LAI ranged from 0.27 to 1.87, and sward height from 3.25 cm to 13.75 cm. In the final study, canopy heterogeneity due to selective grazing was detectable with the Sentinel-1 SAR. Particularly, the range estimates (dispersion measure) of the polarimetric scattering entropy produced the strongest, statistically significant, linear correlation with a metric of patchiness (R<sup>2</sup> =0.74). Altogether, this thesis has demonstrated that Sentinel-1 SAR on its own as well as when integrated with optical data, could be a useful tool providing data to aid in pasture management.</p> | en |
dc.language | en | en |
dc.publisher | University of New England | - |
dc.relation.uri | https://hdl.handle.net/1959.11/62402 | en |
dc.relation.uri | https://hdl.handle.net/1959.11/62403 | en |
dc.title | Monitoring Pasture Species, Biomass and Canopy Heterogeneity Using Sentinel-1 Synthetic Aperture Radar Data | en |
dc.type | Thesis Doctoral | en |
local.contributor.firstname | Richard Azu | en |
local.contributor.firstname | David William | en |
local.contributor.firstname | Mark Graeme | en |
local.hos.email | st-sabl@une.edu.au | en |
local.thesis.passed | Passed | en |
local.thesis.degreelevel | Doctoral | en |
local.thesis.degreename | Doctor of Philosophy - PhD | en |
local.contributor.grantor | University of New England | - |
local.profile.school | School of Science and Technology | en |
local.profile.school | School of Science and Technology | en |
local.profile.school | School of Environmental and Rural Science | en |
local.profile.email | richcrabbe@gmail.com | en |
local.profile.email | dlamb@une.edu.au | en |
local.profile.email | mtrotte3@une.edu.au | en |
local.output.category | T2 | en |
local.record.place | au | en |
local.record.institution | University of New England | en |
local.publisher.place | Armidale, Australia | - |
local.contributor.lastname | Crabbe | en |
local.contributor.lastname | Lamb | en |
local.contributor.lastname | Trotter | en |
dc.identifier.staff | une-id:dlamb | en |
dc.identifier.staff | une-id:mtrotte3 | en |
local.profile.orcid | 0000-0002-2917-2231 | en |
local.profile.role | author | en |
local.profile.role | supervisor | en |
local.profile.role | supervisor | en |
local.identifier.unepublicationid | une:1959.11/57237 | en |
dc.identifier.academiclevel | Student | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
local.thesis.bypublication | Yes | en |
local.title.maintitle | Monitoring Pasture Species, Biomass and Canopy Heterogeneity Using Sentinel-1 Synthetic Aperture Radar Data | en |
local.relation.fundingsourcenote | UNERA International Fee and Stipend Scholarship. | en |
local.output.categorydescription | T2 Thesis - Doctorate by Research | en |
local.relation.doi | 10.3390/rs11030253 | en |
local.relation.doi | 10.1016/j.jag.2019.101978 | en |
local.relation.doi | 10.3390/rs11070872 | en |
local.relation.doi | 10.1080/01431161.2020.1812129 | en |
local.school.graduation | School of Science & Technology | en |
local.thesis.borndigital | Yes | - |
local.search.author | Crabbe, Richard Azu | en |
local.search.supervisor | Lamb, David William | en |
local.search.supervisor | Trotter, Mark Graeme | en |
local.uneassociation | Yes | en |
local.atsiresearch | No | en |
local.sensitive.cultural | No | en |
local.year.conferred | 2020 | en |
local.subject.for2020 | 300202 Agricultural land management | en |
local.subject.for2020 | 300206 Agricultural spatial analysis and modelling | en |
local.subject.for2020 | 300402 Agro-ecosystem function and prediction | en |
local.subject.seo2020 | 100401 Beef cattle | en |
local.subject.seo2020 | 100503 Native and residual pastures | en |
local.profile.affiliationtype | UNE Affiliation | en |
local.profile.affiliationtype | UNE Affiliation | en |
local.profile.affiliationtype | UNE Affiliation | en |
Appears in Collections: | School of Environmental and Rural Science School of Science and Technology Thesis Doctoral |
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