RT Journal Article
SR Electronic
T1 Integrating hierarchical statistical models and machine-learning algorithms for ground-truthing drone images of the vegetation: taxonomy, abundance and population ecological models
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 491381
DO 10.1101/491381
A1 Christian Damgaard
YR 2020
UL http://biorxiv.org/content/early/2020/07/15/491381.abstract
AB 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.Competing Interest StatementThe authors have declared no competing interest.