Category:Functions and models in forest inventory

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The direct observation of some tree variables, such as tree volume, but also tree height, is time consuming and therefore expensive. Thus, we may establish a statistical relationship between the target variable and variables that are easier to observe such as dbh. These relationships are formulated as mathematical functions which are used as prediction models: they allow us predicting the value of the target variable (dependent variable) once the value of the easy-to-measure variable (independent variable) is known. In forest inventory, two important models exist:

  • height curves which predict the tree height from \(dbh:height=f(dbh)\) and
  • volume functions which predict tree volume from \(dbh\) or from \(dbh\) and height or from other sets of independent variables:
    • \(volume=f(dbh)\), or
    • \(volume=f(\mbox{dbh, height})\), or
    • \(volume=f(\mbox{dbh, upper diameter, height})\).

In order to build these models, one needs to define a mathematical function that shall be used during regression analysis, and one needs to select a set of sample trees at which all variables (the dependent variable and the independent variables) are observed. Then, the model has to be fitted to the sample data in a way that prediction errors are minimized. The resulting function with the best fit is then used as prediction model.

Pages in category "Functions and models in forest inventory"

The following 4 pages are in this category, out of 4 total.

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