Frequently Asked Questions

What is the FIA?

The FIA, or Forest Inventory and Analysis, is the name given to a federally-led (by the USDA Forest Service) program of inventory and monitoring of all U.S. forest lands.  The program is cooperative with states, and is the only program that regularly measures and monitors forest resources nationwide.  The FIA reports on a state-by-state basis, but periodically uses these results to assemble statistics and trends nationally.   Click here to visit the FIA Web site.

How is forest land defined?

Forest land is generally broken into three types: timberland, reserved, and other (typically unproductive forest).  Specific definitions are given in the glossary.

How often is FIA conducted for a state?

FIA inventories have been conducted every 10-15 years for most states.  In Minnesota, reports are available from 1936, 1953, 1962, 1977, and 1990.  In recent years, the inventory has been converted to an annual basis, i.e., 10-20 percent of the inventory plots are observed each year.  In Minnesota, approximately 20% of the field plots are observed each year.

This new Minnesota inventory began in 1999.  The sample consists of a systematic distribution of field sample plots over the entire state.  This large sample was then divided into five inter-penetrating, non-overlapping subsamples or panels.  Then one panel is identified each year for measurement (of all the plots in that panel). That panel is then remeasured in 5 years.  For example, the panel of field plots measured in 1999 will be remeasured in 2004.  Thus, with a panel measured each year, the full set of field sample plots (five panels) is observed over the course of 5 years.

Since this site describes only 4 of the 5 planned inventory panels, results are obviously tentative and subject to sampling error and some differences due to changes in procedure since the 1990 inventory.  Also, at times there may be alternative approaches to computations or estimations that may lead to differences in results.

In this set of reports, sampling error, in particular, can be quite large where there are few sample plots to draw upon.  For example, the white spruce type is less common than aspen; there are few plots in the white spruce forest type and results for this type are much less reliable than those for aspen.  However, the addition of data from future panels will eventually improve the precision of results.