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Publication Information

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Title: Accuracy and efficiency of area classifications based on tree tally

Author: Williams, Michael S.; Schreuder, Hans T.; Czaplewski, Raymond L. Ph.D.

Date: 2001

Source: Canadian Journal of Forest Research. 31: 556-560.

Publication Series: Scientific Journal (JRNL)

Description: Inventory data are often used to estimate the area of the land base that is classified as a specific condition class. Examples include areas classified as old-growth forest, private ownership, or suitable habitat for a given species. Many inventory programs rely on classification algorithms of varying complexity to determine condition class. These algorithms can be simple decision trees applied in the field or computer calculations applied on a field data recorder or after the data are collected. The advantages to using these algorithms are consistent classification of the condition class, reduced crew training, and the ability to define new condition classes after the data are collected, which will be referred to as post-classification. We discuss three types of the errors that can occur when these types of algorithms are employed and quantify the potential for error with examples. The examples are substantial oversimplifications of the true problem, but they show how difficult it is to determine anything but the most general condition classes using plot data alone. A discussion of how condition class is scale dependent and some general guidelines and recommendations are given.

Keywords: area classifications, inventory data, classification algorithms

Publication Notes:

  • We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
  • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

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Citation:


Williams, Michael S.; Schreuder, Hans T.; Czaplewski, Raymond L. 2001. Accuracy and efficiency of area classifications based on tree tally. Canadian Journal of Forest Research. 31: 556-560.

 


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