Regional-Scale Forest Production Modeling using Process-Based 2025

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The Model Forest approach shifts forest value from solely being about timber to a vision where social, environmental, economic and cultural benefits, as well as trade-offs are equally considered.
Forest ecologists recognize four phases of forest succession, or what is sometimes called forest development. These phases are: (i) stand initiation, (ii) stem exclusion, (iii) understory reinitiation, and (iv) steady state.
The Forest Model tool creates a model that constructs a set of decision tree models to predict a target variable based on one or more predictor variables. The different models are constructed using random samples of the original data, a procedure known as bootstrapping.
Random forest is a machine learning model that works by utilizing many decision trees, collectively called a forest, to make a prediction. Decision trees can be visualized as a flow chart of conditions that can be used to most accurately classify or predict outcomes.
Models used to assist in forest management consist of several types. First and most prevalent are growth and yield models, which predict the development of stands of trees through time. Initially, these models focused on single species, single age stands, such as one would find in plantations.
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The 3-PG model (Physiological Processes Predicting Growth) was developed by Landsberg and Waring (1997). It was developed to bridge the gap between conventional, mensuration-based growth and yield, and process-based carbon balance models.

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