DOI: 10.1101/491381Dec 9, 2018Paper

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

BioRxiv : the Preprint Server for Biology
Christian Damgaard


In order to fit population ecological models, e.g. plant competition models, to the new drone-aided image data, we need to develop statistical models that may take the new type of measurement uncertainty 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 pinpoint 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.

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Population Group

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