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dc.contributor.authorAlabi, Tunrayo
dc.contributor.authorHaertel, M.
dc.contributor.authorChiejile, S.
dc.contributor.authorAlabi, Tunrayo
dc.date.accessioned2016-06-29T08:16:45Z
dc.date.available2016-06-29T08:16:45Z
dc.date.issued2016
dc.identifier.citationAlabi, T., Haertel, M. & Chiejile, S. (2016). Investigating the use of high resolution multi-spectral satellite imagery for crop mapping in Nigeria crop and landuse classification using WorldView-3 high resolution multispectral imagery and LANDSAT8 data. proceedings of the 2nd International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2016). (pp. 109-120), Setubal, Portugalen_US
dc.identifier.isbn978-989-758-188-5
dc.identifier.urihttp://hdl.handle.net/10568/75882
dc.description.abstractImagery from recently launched high spatial resolution WorldView-3 offers new opportunities for crop identification and landcover assessment. Multispectral WorldView-3 at 1.6m spatial resolution and LANDSAT8 images covering an extent of 100Km² in humid ecology of Nigeria were used for crop and landcover identification. Three supervised classification techniques (maximum likelihood(MLC), Neural Net clasifier(NNC) and support vector machine(SVM)) were used to classify WorldView-3 and LANDSAT8 into four crop classes and seven non-crop classes. For accuracy assessment, kappa coefficient, producer and user accuracies were used to evaluate the performance of all three supervised classifiers. NNC performed best with an overall accuracy(OA) of 92.20, kappa coefficient(KC) of 0.83 in landcover identification using WorldView-3. This was closely followed by SVM with an OA of 91.77%, KC of 0.83. MLC performed slightly lower at an OA of 91.25% and KC of 0.82. Classification of crops and landcover with LANDSAT8 was best with MLC classifier with an OA of 92.12% , KC of 0.89. Cassava at younger than 3 months old could not be identified correctly by all classifiers using WorldView-3 and LANDSAT8 products. In summary WorldView-3 and LANDSAT8 data had satisfactory performance in identifying different crop and landcover types though at varying degrees of accuracies.en_US
dc.format.extent109-120en_US
dc.language.isoenen_US
dc.subjectCASSAVAen_US
dc.subjectMAIZEen_US
dc.subjectNIGERIAen_US
dc.subjectNEURAL NETWORKen_US
dc.titleInvestigating the use of high resolution multi-spectral satellite imagery for crop mapping in Nigeria crop and landuse classification using WorldView-3 high resolution multispectral imagery and LANDSAT8 dataen_US
dc.typeConference Proceedingsen_US
cg.authorship.typesCGIAR single centreen_US
cg.subject.iitaCASSAVAen_US
cg.subject.iitaCROP SYSTEMSen_US
cg.subject.iitaFARMING SYSTEMSen_US
cg.subject.iitaFOOD SECURITYen_US
cg.subject.iitaLAND USEen_US
cg.subject.iitaMAIZEen_US
cg.identifier.statusRestricted Accessen_US
cg.contributor.affiliationInternational Institute of Tropical Agricultureen_US
cg.targetaudienceSCIENTISTSen_US
cg.placeSetubal, Portugalen_US
cg.coverage.regionWEST AFRICAen_US
cg.coverage.countryNIGERIAen_US
cg.contributor.crpClimate Change, Agriculture and Food Securityen_US


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