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https://hdl.handle.net/1959.11/19870
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DC Field | Value | Language |
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dc.contributor.author | Rahman, Muhammad Moshiur | en |
dc.contributor.author | Robson, Andrew | en |
dc.date.accessioned | 2017-01-23T09:35:00Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | Advances in Remote Sensing, 5(2), p. 93-102 | en |
dc.identifier.issn | 2169-2688 | en |
dc.identifier.issn | 2169-267X | en |
dc.identifier.uri | https://hdl.handle.net/1959.11/19870 | - |
dc.description.abstract | Quantifying sugarcane production is critical for a wide range of applications, including crop management and decision making processes such as harvesting, storage, and forward selling. This study explored a novel model for predicting sugarcane yield in Bundaberg region from time series Landsat data. From the freely available Landsat archive, 98 cloud free (<40%) Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) images, acquired between November 15th to July 31st (2001-2015) were sourced for this study. The images were masked using the field boundary layer vector files of each year and the GNDVI was calculated. An analysis of average green normalized difference vegetation index (GNDVI) values from all sugarcane crops grown within the Bundaberg region over the 15 year period identified the beginning of April as the peak growth stage and, therefore, the optimum time for satellite image based yield forecasting. As the GNDVI is an indicator of crop vigor, the model derived maximum GNDVI was regressed against historical sugarcane yield data, which showed a significant correlation with R² = 0.69 and RMSE = 4.2 t/ha. Results showed that the model derived maximum GNDVI from Landsat imagery would be a feasible and a modest technique to predict sugarcane yield in Bundaberg region. | en |
dc.language | en | en |
dc.publisher | Scientific Research Publishing, Inc | en |
dc.relation.ispartof | Advances in Remote Sensing | en |
dc.title | A Novel Approach for Sugarcane Yield Prediction Using Landsat Time Series Imagery: A Case Study on Bundaberg Region | en |
dc.type | Journal Article | en |
dc.identifier.doi | 10.4236/ars.2016.52008 | en |
dcterms.accessRights | Gold | en |
dc.subject.keywords | Agricultural Spatial Analysis and Modelling | en |
local.contributor.firstname | Muhammad Moshiur | en |
local.contributor.firstname | Andrew | en |
local.subject.for2008 | 070104 Agricultural Spatial Analysis and Modelling | en |
local.subject.seo2008 | 820304 Sugar | en |
local.profile.school | School of Science and Technology | en |
local.profile.school | School of Science and Technology | en |
local.profile.email | mrahma37@une.edu.au | en |
local.profile.email | arobson7@une.edu.au | en |
local.output.category | C1 | en |
local.record.place | au | en |
local.record.institution | University of New England | en |
local.identifier.epublicationsrecord | une-20170112-154729 | en |
local.publisher.place | United States of America | en |
local.format.startpage | 93 | en |
local.format.endpage | 102 | en |
local.peerreviewed | Yes | en |
local.identifier.volume | 5 | en |
local.identifier.issue | 2 | en |
local.title.subtitle | A Case Study on Bundaberg Region | en |
local.access.fulltext | Yes | en |
local.contributor.lastname | Rahman | en |
local.contributor.lastname | Robson | en |
dc.identifier.staff | une-id:mrahma37 | en |
dc.identifier.staff | une-id:arobson7 | en |
local.profile.orcid | 0000-0001-6430-0588 | en |
local.profile.orcid | 0000-0001-5762-8980 | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.identifier.unepublicationid | une:20062 | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
local.title.maintitle | A Novel Approach for Sugarcane Yield Prediction Using Landsat Time Series Imagery | en |
local.output.categorydescription | C1 Refereed Article in a Scholarly Journal | en |
local.search.author | Rahman, Muhammad Moshiur | en |
local.search.author | Robson, Andrew | en |
local.uneassociation | Unknown | en |
local.year.published | 2016 | en |
local.subject.for2020 | 300206 Agricultural spatial analysis and modelling | en |
local.subject.seo2020 | 260607 Sugar | en |
Appears in Collections: | Journal Article |
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