Field Methods And Data Processing Techniques Associated With Mapped Inventory Plots’

The U.S. Forest Inventory and Analysis (FIA) and Forest Health Monitoring (FHM) programs utilize a frxttd-area mapped-plot design as the national standard for extensive forest inventories. The mapped-plot design is explained, as well as the rationale for its selection as the national standard. Ratio-of-means estimators are presented as a method to process data from mapped inventory plots. It is particularly timely that we have convened to develop a sampling framework for forest ecosystems that is unified across the entire North American continent. In the United States we have been engagedin a similar effort to standardize our inventory system for the past decade. The purpose of this paper is to describe the sampling design implemented nationally in the U.S., provide background information about how this design evolved, explain how the resulting data can be processed, and describe our experience with this design since its implementation in 1991. U.S. National Plot Design The plot design currently utilized for forest inventory and monitoring in all regions of the U.S. is based on a cluster of four points spaced 36.6 m (120 ft) apart (Figure 1). Each plot consists of a central point surrounded by three satellite points separated by 120 degrees. Each of the four points is circled by a 7.3 m (24.0 ft) fured-radius subplot, where trees 12.7 cm (5.0 inches) in diameter and larger are measured. Together, all four subplots cover ?45 hectare (% acre). Each subplot includes a 2.1 m (6.8 ft) fixed-radius microplot used to measure seedlings and saplings smaller than 12.7 cm (5.0 inches) in diameter. Together, all four microplots encompass about %as hectare (‘?+5 acre). Microplots are offset from subplot centers to reduce the impact of trampling by field crews. Each subplot is surrounded by an annular plot. The annular plot is a ring extending from the edge of each subplot to a total distance of 18 m (58.9 ft) from each subplot center. ‘Paper presented at the North American Science Symposium: Toward a Unified Framework for Inventorying and Monitoring Forest Ecosystem Resources, Guadalajara, Mexico, November 1-6, 1998. 2William A. Bechtold is a Research Forester, and USDA Forest Service, Southern Research Station, 200 Weaver Blvd., Asheville, NC, 28802. e-mail: wbecbtold/srs,[email protected] 3Stanley J. Zamoch is a Mathematical Statistician, USDA Forest Service, Southern Research Station, 200 Weaver Blvd., Asheville, NC, 28802. e-mail: ssamoch/srs,[email protected] The annular plot is optional, designed to sample large trees with diameters of 100 cm (40 inches) or greater in regions characterized by large trees. Annular plots are currently considered for implementation only in the Pacific Northwest region of the U.S. In addition to the trees measured on these plots, data are also gathered about the area, or setting in which the trees are located. Area data are particularly useful for subdivision of the forest into meaningful categories, and to enable poststratification. Some of these area attributes are measured (e.g., percent slope), some are assigned by definition (e.g., disturbance history), and some are computed from the tree data (e.g., percent stocking). For proper analysis it is crucial that the tree data recorded on these plots are properly associated with the area data. To accomplish this, all plots are mapped by “condition class.” Survey crews assign an arbitrary number (usually 1) to the first condition class encountered on a plot. This number is then defined by a series of predetermined discrete variables