William A. Bechtold

Status and future of the forest health indicators program of the USA

For two decades, the US Department of Agriculture, Forest Service, has been charged with implementing a nationwide field-based forest health monitoring effort. Given its extensive nature, the monitoring program has been gradually implemented across forest health indicators and inventoried states. Currently, the Forest Service’s Forest Inventory and Analysis program has initiated forest health inventories in all states, and most forest health indicators are being documented in terms of sampling protocols, data management structures, and estimation procedures. Field data from most sample years and indicators are available on-line with numerous analytical examples published both internally and externally. This investment in national forest health monitoring has begun to yield dividends by allowing evaluation of state/regional forest health issues (e.g., pollution and invasive pests) and contributing substantially to national/international reporting efforts (e.g., National Report on Sustainability and US EPA Annual Greenhouse Gas Estimates). With the emerging threat of climate change, full national implementation and remeasurement of a forest health inventory should allow for more robust assessment of forest communities that are undergoing unprecedented changes, aiding future land management and policy decisions.

Spatial cross-correlation of undisturbed, natural shortleaf pine stands in northern Georgia

In this study a cross-correlation statistic is used to analyse the spatial relationship among stand characteristics of natural, undisturbed shortleaf pine stands sampled during 1961–72 and 1972–82 in northern Georgia. Stand characteristics included stand age, site index, tree density, hardwood competition, and mortality. In each time period, the spatial cross-correlation statistic was used to construct cross-correlograms and cumulative cross-correlograms for all significant pairwise combination of stand characteristics. Both the cross-correlograms and cumulative cross-correlograms identified small-scale clustering and weak directional gradients for different stand characteristics in each time period. The cumulative cross-correlograms, which are based on inverse distance weighting were more sensitive in detecting small-scale clustering than the cross-correlograms based on a 0–1 weighting. Further analysis suggested that the significant cross-correlation observed among basal area growth and other stand characteristics were due, in a large part, on a subset of sample plots located in the northern part of the state, rather than regional or broad-scale variation as first thought. The ability to analyse the spatial relationship between two or more response surfaces should provide valuable insight in the development of ecosystem level models and assist decision makers in formulating pertinent policy on intelligent multiresource management.

Sampling and estimation procedures for the vegetation diversity and structure indicator

The Vegetation Diversity and Structure Indicator (VEG) is an extensive inventory of vascular plants in the forests of the United States. The VEG indicator provides baseline data to assess trends in forest vascular plant species richness and composition, and the relative abundance and spatial distribution of those species, including invasive and introduced species. The VEG indicator is one of several sets of measures collected by the Forest Inventory and Analysis (FIA) Program of the USDA Forest Service to assess forest health. This document describes the sampling design, field data collection methods, primary output objectives, and estimation procedures for summarizing FIA VEG data.

Evaluation of weighted regression and sample size in developing a taper model for loblolly pine

A stem profile model, fit using pseudo-likelihood weighted regression, was used to estimate merchantable volume of loblolly pine (Pinus taeda L.) in the southeast. The weighted regression increased model fit marginally, but did not substantially increase model performance. In all cases, the unweighted regression models performed as well as the weighted regression models, even for very small sample sizes.