Abstract
Many efforts are underway to produce broad-scale forest attribute
maps by modelling forest class and structure variables collected
in forest inventories as functions of satellite-based and biophysical
information. Typically, variants of classification and regression
trees implemented in Rulequest�s� See5 and Cubist (for binary and
continuous responses, respectively) are the tools of choice in many
of these applications. These tools are widely used in large remote
sensing applications, but are not easily interpretable, do not have
ties with survey estimation methods, and use proprietary unpublished
algorithms. Consequently, three alternative modelling techniques
were compared for mapping presence and basal area of 13 species located
in the mountain ranges of Utah, USA. The modelling techniques compared
included the widely used See5/Cubist, generalized additive models
(GAMs), and stochastic gradient boosting (SGB). Model performancewas
evaluated using independent test data sets. Evaluation criteria for
mapping species presence included specificity, sensitivity, Kappa,
and area under the curve (AUC). Evaluation criteria for the continuous
basal area variables included correlation and relative mean squared
error. For predicting species presence (setting thresholds to maximize
Kappa), SGB had higher values for the majority of the species for
specificity and Kappa, while GAMs had higher values for the majority
of the species for sensitivity. In evaluating resultant AUC values,
GAM and/or SGB models had significantly better results than the See5
models where significant differences could be detected between models.
For nine out of 13 species, basal area prediction results for all
modelling techniques were poor (correlations less than 0.5 and relative
mean squared errors greater than 0.8), but SGB provided the most
stable predictions in these instances. SGB and Cubist performed equally
well for modelling basal area for three species with moderate prediction
success, while all three modelling tools produced comparably good
predictions (correlation of 0.68 and relative mean squared error
of 0.56) for one species.
Users
Please
log in to take part in the discussion (add own reviews or comments).