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dc.contributor.authorDuke, O.P.
dc.contributor.authorAlabi, T.R.
dc.contributor.authorNeeti, N.
dc.contributor.authorAdewopo, J.
dc.date.accessioned2022-09-05T08:50:18Z
dc.date.available2022-09-05T08:50:18Z
dc.date.issued2022
dc.identifier.citationDuke, O.P., Alabi, T.R., Neeti, N. & Adewopo, J. (2022). Comparison of UAV and SAR performance for crop type classification using machine learning algorithms: a case study of humid forest ecology experimental research site of west Africa. International Journal of Remote Sensing, 43(11), 4259-4286.
dc.identifier.issn0143-1161
dc.identifier.urihttps://hdl.handle.net/20.500.12478/7711
dc.description.abstractFood insecurity is one of the major challenges facing African countries; therefore, timely and accurate information on agricultural production is essential to feed the growing population on the continent. A synergistic approach comprising a high-resolution multispectral UAV optical dataset and synthetic aperture radar (SAR) can help understand spectral features of target objects, especially with crop type identification. We conducted this work on the experimental plots using high spatial resolution multispectral UAV data (12 cm, re-sampled to 50 cm) in combination with the Sentinel 1C Synthetic Aperture Radar (SAR) dataset. We generated 11 agronomically relevent vegetation indices from the UAV multispectral image. Multiple combinations of the UAV datasets were analysed to assess the impact of canopy height model (CHM) on classification accuracy and to determine the optimum dataset (including spatial resolution) for the land cover classification. We also appraise the impact of variable spatial resolution on classification accuracy. A combination of VH and VV polarizations of Sentinel-1 SAR data was also analysed to classify the crop types while comparing its accuracy with the UAV-derived models. Our results show that model accuracy is improved- for all the data combination pairs, when CHM is added to the modelling. We also observed a decreasing trend in classification accuracy with respect to increasing spatial resolution. Generally, the support vector machine (SVM) classifier produced an overall accuracy of 94.78% and 81.72% for UAV and SAR datasets, respectively. In comparison, the random forest (RF) achieved an accuracy of 93.84% and 92.58%, for UAV and SAR datasets, respectively. The outputs from ground-based validation corroborate the results from model-based classification coupled with acceptable simple models’ agreement ratio (SMAR), exceeding 90% in some cases. The combined techniques can be useful in precision agriculture over small and large agricultural fields to support food security assessment and planning.
dc.format.extent4259-4286
dc.language.isoen
dc.subjectFoods
dc.subjectFood Security
dc.subjectForests
dc.subjectAgriculture
dc.subjectNigeria
dc.subjectMachine Learning
dc.titleComparison of UAV and SAR performance for crop type classification using machine learning algorithms: a case study of humid forest ecology experimental research site of west Africa
dc.typeJournal Article
cg.contributor.crpRoots, Tubers and Bananas
cg.contributor.affiliationTERI School of Advanced Studies, India
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.coverage.regionAfrica
cg.coverage.regionWest Africa
cg.coverage.countryNigeria
cg.coverage.hubCentral Africa Hub
cg.coverage.hubHeadquarters and Western Africa Hub
cg.researchthemeNatural Resource Management
cg.identifier.bibtexciteidDUKE:2022
cg.isijournalISI Journal
cg.authorship.typesCGIAR and developing country institute
cg.iitasubjectClimate Change
cg.iitasubjectFood Security
cg.iitasubjectNatural Resource Management
cg.journalInternational Journal of Remote Sensing
cg.notesPublished online: 22 Aug 2022
cg.accessibilitystatusLimited Access
cg.reviewstatusPeer Review
cg.usagerightslicenseCopyrighted; all rights reserved
cg.targetaudienceScientists
cg.identifier.doihttps://dx.doi.org/10.1080/01431161.2022.2109444
cg.iitaauthor.identifierTunrayo Alabi: 0000-0001-5142-6990
cg.iitaauthor.identifierJulius Adewopo: 0000-0003-4831-2823
cg.futureupdate.requiredNo
cg.identifier.issue11
cg.identifier.volume43


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