Accuracy and efficiency of area classifications based on tree tally

Abstract

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 postclassification. 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.