The Forest Inventory and Analysis (FIA) Program of the Forest Service, U.S. Department of Agriculture, conducts a national inventory of forests across the United States. A systematic subset of permanent inventory plots in 45 States is currently sampled every year for numerous forest health indicators. One of these indicators, crown-condition classification, is designed to estimate tree crown dimensions and assess the impact of crown stressors. The indicator features eight tree-level field measurements in addition to variables traditionally measured in conjunction with FIA inventories: vigor class, uncompacted live crown ratio, crown light exposure, crown position, crown density, crown dieback, foliage transparency, and crown diameter. Indicators of crown health derived from the crown data are intended for analyses at the State, regional, and national levels, and contribute to the core tabular output in standard FIA reports. Crown-condition measurements were originally implemented as part of the Forest Health Monitoring (FHM) Program in 1990. Except for crown diameter, these measurements were continued when the FIA Program assumed responsibility for FHM plot-based detection monitoring in 2000. This report describes in detail the data collection and analytical techniques recommended for crown-condition classification.

Indicators of forest health used in previous studies have focused on crown variables analyzed individually at the tree level by summarizing over all species. This approach has the virtue of simplicity but does not account for the three-dimensional attributes of a tree crown, the multivariate nature of the crown variables, or variability among species. To alleviate these difficulties, we define composite crown indicators based on geometric principles to better quantify the entire tree crown. These include crown volume, crown surface area, and crown production efficiency. These indicators were then standardized to a mean of 0 and variance of 1 to enable direct comparison among species. Residualized indicators, which can also be standardized, were defined as the deviation from a regression model that adjusted for tree and plot conditions. Distributional properties were examined for the three composite crown indicators and their standardized-residualized counterparts for 6167 trees from 250 permanent plots distributed across Virginia, Georgia, and Alabama. Comparisons between the composite crown indicators and their associated standardized residual indicators revealed that only two or three plots were jointly classified as poor by both when thresholds were set at the lower 5 percentiles of statistical distributions. In contrast, 19-21 other plots were classified differently, emphasizing that different aspects of crown condition are being summarized when the raw values are adjusted and standardized. Generally, crown volume and crown surface area behaved similarly, while crown production efficiency was substantially different.

Cover: The cover graphic illustrates FIAʼs sampling frame superimposed onto a landscape. The patchwork of green and brown under the hexagonal grid represents forest and nonforest areas. Each hexagon contains one ground plot symbolized by the cluster of four points located within each hexagon. The ground plots are not drawn to scale.

The mean crown diameters of stand-grown trees were modeled as a function of stem diameter, live-crown ratio, stand basal area, latitude, longitude, elevation, and Hopkins bioclimatic index for 87 tree species in the eastern United States. Stem diameter was statistically significant in all models, and a quadratic term for stem diameter was required for some species. Crown ratio and/or Hopkins index also improved the models for many species. Coefficients of variation from the regression solutions ranged from 18 to 35%, and model r-square values ranged from 0.15 to 0.88. Simpler models, based only on stem diameter and crown ratio, are also presented. South. J. Appl. For. 27(4):269–278.

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