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| − | ==Introduction==
| + | This category contains all articles about mathematical functions and models in forest inventory. |
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| − | The direct observation of some [[:Category:Single tree variables|tree variables]], such as [[Stem volume|tree volume]], but also [[Tree height|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 [[Diameter at breast height|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:
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| − | *height curves which predict the tree height from <math>dbh:height=f(dbh)</math> and
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| − | *volume functions which predict tree volume from <math>dbh</math> or from <math>dbh</math> and height or from other sets of independent variables:
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| − | **<math>volume=f(dbh)</math>, or
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| − | **<math>volume=f(\mbox{dbh, height})</math>, or
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| − | **<math>volume=f(\mbox{dbh, upper diameter, height})</math>.
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| − | In order to build these models, one needs to define a mathematical function that shall be used, 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.
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| − | '''This category contains all articles about mathematical functions and models in forest inventory.'''
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| | [[Category:Forest mensuration]] | | [[Category:Forest mensuration]] |
Revision as of 19:20, 18 February 2011
This category contains all articles about mathematical functions and models in forest inventory.
Pages in category "Functions and models in forest inventory"
The following 4 pages are in this category, out of 4 total.
