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    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

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    Journal Article (10.86Mb)
    Date
    2022
    Author
    Duke, O.P.
    Alabi, T.R.
    Neeti, N.
    Adewopo, J.
    Type
    Journal Article
    Review Status
    Peer Review
    Target Audience
    Scientists
    Metadata
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    Abstract/Description
    Food 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.
    https://dx.doi.org/10.1080/01431161.2022.2109444
    Multi standard citation
    Permanent link to this item
    https://hdl.handle.net/20.500.12478/7711
    IITA Authors ORCID
    Tunrayo Alabihttps://orcid.org/0000-0001-5142-6990
    Julius Adewopohttps://orcid.org/0000-0003-4831-2823
    Digital Object Identifier (DOI)
    https://dx.doi.org/10.1080/01431161.2022.2109444
    Research Themes
    Natural Resource Management
    IITA Subjects
    Climate Change; Food Security; Natural Resource Management
    Agrovoc Terms
    Foods; Food Security; Forests; Agriculture; Nigeria; Machine Learning
    Regions
    Africa; West Africa
    Countries
    Nigeria
    Hubs
    Central Africa Hub; Headquarters and Western Africa Hub
    Journals
    International Journal of Remote Sensing
    Collections
    • Journal and Journal Articles4835
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