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Predicting inter-seasonal aboveground grass biomass using Sentinel-2 MSI and machine learning in the Umngeni catchment, KwaZulu-Natal, South Africa.

dc.contributor.advisorLottering, Romano Trent.
dc.contributor.advisorMutanga, Onisimo.
dc.contributor.authorVawda, Mohamed Ismail.
dc.date.accessioned2023-07-27T13:59:51Z
dc.date.available2023-07-27T13:59:51Z
dc.date.created2023
dc.date.issued2023
dc.descriptionMasters Degree. University of KwaZulu-Natal, Pietermaritzburg.en_US
dc.description.abstractAbstract available in PDF.en_US
dc.identifier.urihttps://researchspace.ukzn.ac.za/handle/10413/22027
dc.language.isoenen_US
dc.subject.otherGrasslands.en_US
dc.subject.otherConvolutional Neural Network.en_US
dc.subject.otherArtificial Neural Network.en_US
dc.subject.otherGrassland productivity monitoring.en_US
dc.titlePredicting inter-seasonal aboveground grass biomass using Sentinel-2 MSI and machine learning in the Umngeni catchment, KwaZulu-Natal, South Africa.en_US
dc.typeThesisen_US

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