Integrating hierarchical statistical models and machine-learning algorithms for ground-truthing drone images of the vegetation: taxonomy, abundance and population ecological models

    Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

    Abstract

    In order to fit population ecological models, e.g., plant competition models, to new drone-aided image data, we need to develop statistical models that may take the new type of measurement uncertainty when applying machine-learning algorithms into account and quantify its importance for statistical inferences and ecological predictions. Here, it is proposed to quantify the uncertainty and bias of image predicted plant taxonomy and abundance in a hierarchical statistical model that is linked to ground-truth data obtained by the pin-point method. It is critical that the error rate in the species identification process is minimized when the image data are fitted to the population ecological models, and several avenues for reaching this objective are discussed. The outlined method to statistically model known sources of uncertainty when applying machine-learning algorithms may be relevant for other applied scientific disciplines.

    OriginalsprogEngelsk
    Artikelnummer1161
    TidsskriftRemote Sensing
    Vol/bind13
    Nummer6
    Antal sider7
    ISSN2072-4292
    DOI
    StatusUdgivet - mar. 2021

    Fingeraftryk

    Dyk ned i forskningsemnerne om 'Integrating hierarchical statistical models and machine-learning algorithms for ground-truthing drone images of the vegetation: taxonomy, abundance and population ecological models'. Sammen danner de et unikt fingeraftryk.

    Citationsformater