Abstract
Broad-scale maps of forest characteristics are needed throughout the
United States for a wide variety of forest land management applications.
Inexpensive maps can be produced by modelling forest class and structure
variables collected in nationwide forest inventories as functions
of satellite-based information. But little work has been directed
at comparing modelling techniques to determine which tools are best
suited to mapping tasks given multiple objectives
and logistical constraints. Consequently, five modelling techniques
were compared for mapping forest characteristics in the Interior
Western United States. The modelling techniques included linear models
(LMs), generalized additive models (GAMs), classification and regression
trees (CARTs), multivariate adaptive regression splines (MARS), and
artificial neural networks (ANNs). Models were built for two discrete
and four continuous forest response variables using a variety of
satellite-based predictor variables within each of five ecologically
different regions. All techniques proved themselves workable in an
automated environment. When their potential mapping ability was explored
through simulations, tremendous advantages were seen in use of MARS
and ANN for prediction over LMs, GAMs, and CART. However, much smaller
differences were seen when using real data. In some instances, a
simple linear approach worked virtually as well as the more complex
models, while small gains were seen using more complex models in
other instances. In real data runs, MARS and GAMS performed (marginally)
best for prediction of forest characteristics.
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