  Remote sensing data are increasingly available and frequently used to produce
forest attributes maps. The sampling strategy of the calibration plots may
directly affect predictions and map qualities. The aim of this manuscript is to
evaluate models transferability at different spatial scales according to the
sampling efforts and the calibration domain of these models. Forest inventory
plots from locals and regionals networks were used to calibrate randomForest
(RF) models for stand basal area predictions. Auxiliary data from ALS flights
and a Sentinel-2 image were used. Model transferability was assessed by
comparing models developed over a given area and applied elsewhere.
Performances were measured in terms of precision (RMSE and bias), coefficient
of determination (R2) and the proportion of extrapolated predictions. Regional
networks were also thinned to evaluate the effect of sampling efforts on
models' performances. Local models showed large bias and extrapolation issues
when applied elsewhere. Local issues of regional models were also observed,
raising transferability and extrapolation concerns. An increase in sampling
efforts was shown to reduce extrapolation issues. The outcoming results of this
study underline the importance of considering models' validity domain while
producing forest attribute maps, since their transferability is of crucial
importance from a forest management perspective.
