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

TY - JOUR

T1 - Integrating hierarchical statistical models and machine-learning algorithms for ground-truthing drone images of the vegetation:

T2 - taxonomy, abundance and population ecological models

AU - Damgaard, Christian

PY - 2021/3

Y1 - 2021/3

N2 - 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.

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.

KW - Hierarchical statistical model

KW - Machine-learning algorithms

KW - Measurement uncertainty

KW - Plant competition models

U2 - 10.3390/rs13061161

DO - 10.3390/rs13061161

M3 - Journal article

SN - 2072-4292

VL - 13

JO - Remote Sensing

JF - Remote Sensing

IS - 6

M1 - 1161

ER -