Analyzing Forest Health Data

This report focuses on the Forest Health Monitoring Programs development and use of analytical procedures for monitoring changes in forest health and for expressing the corresponding statistical confidences. The programs assessments of long-term status, changes, and trends in forest ecosystem health use the Santiago Declaration: S2Criteria and Indicators for the Conservation and Sustainable Forest Management of Temporate and Boreal ForestsS3 (Montreal Process) as a reporting framework. Procedures used in five aspects of data analysis are presented. The analytical procedures used are based on mixed estimation procedures. Examples using the indicators are included, along with a clear link to the analytical procedures used (1) estimating change over time within groupsestimation of growth, harvest, mortality, and crown condition; (2) testing for differences in change over time among groupsfoliar transparency; (3) estimating change using covariatesimpact of drought on change in foliar transparency; (4) estimating plot values for unmeasured yearscomparison of observed and predicted (Best Linear Unbiased Predictions) values of foliar transparency, dieback, and total volume; and (5) estimating tree heightsexamples of using estimated tree heights to estimate tree volume.

The annual national report of the Forest Health Monitoring (FHM) Program of the Forest Service, U.S. Department of Agriculture, presents forest health status and trends from a national or multi-State regional perspective using a variety of sources, introduces new techniques for analyzing forest health data, and summarizes results of recently completed Evaluation Monitoring projects funded through the FHM national program.

"Forest Inventory and Analysis. The Forest Service Forest Inventory and Analysis (FIA) Program is the Nations continuous forest census. Since 1930, we have collected, analyzed, and reported information on the status and trends of Americas forests: how much forest exists, where it exists, who owns it, and how it is changing growing, dying, or being harvested. In response to widening customer interests, the FIA Program is developing a core program that will be implemented in the same manner on all U.S. forest lands. It includes sampling an extended suite of forest health indicators. The purpose of this brochure is to describe these health indicators: what we are measuring, why we believe these measurements are important, how we collect and interpret the data, and examples of what we have found to date. The FIA indicators discussed in this brochure are: crown condition, ozone injury, tree damage, tree mortality, lichen communities, down woody debris, vegetation diversity and structure, soil condition.

Lichens are one of several forest health indicators sampled every year for a subset of plots on the permanent grid established by the Forest Inventory and Analysis (FIA) Program of the U.S. Department of Agriculture Forest Service. This report reviews analysis procedures for standard FIA lichen indicator data. Analyses of lichen data contribute to state, regional, and national reports that evaluate spatial pattern and temporal trends in forest biodiversity, air quality, and climate. Data collection and management follow standard national protocols. A lichen species richness index (the number of species per FIA plot) is available for all areas soon after data collection. Air quality and climate indexes (for defined regional gradients and based on lichen species composition at plots) are developed from an FIA lichen gradient model. Critical steps in standard data analysis include screening plots to exclude biased data, selection of appropriate populations, then analysis, presentation, and interpretation of data.

The Forest Inventory and Analysis (FIA) program of the U.S. Forest Service collects vast amounts of field data to assess the condition and trends of the nations forest resources. A quality assurance (QA) program is implemented to assure that data are collected accurately with consistent protocols. A random subset of field plots is chosen to receive an additional, independent measurement by another field crew. This blind check approach allows for comparison of measurements between the two crews. The measurement differences are evaluated against measurement quality objectives (MQO), which specify a level of measurement precision for each attribute. In this report, differences in repeated measurements from blind check data are analyzed to assess the quality of forest health data nationwide. For the understory vegetation indicator, the ground cover variables attained the MQO or were slightly below the desired level. Variables related to canopy and species identifcation were substantially below the MQO. Tree crown attributes that effectively met the MQO include crown light exposure, crown dieback, and foliage transparency. The uncompacted crown ratio, vigor class, and crown density measurements did not attain the MQO standards. Quality assessment for the lichen indicator is evaluated using a second measurement by an expert and not an independent measurement by another field crew. Overall, the MQO was not attained, although compliance improved over time and western regions were close to achieving the standard. Most variables measured for the down woody material indicator were below MQO standards. Due to the sampling protocols for the ozone indicator, direct analyses of MQO attainment are not possible. Mean biosite index was not significantly different between crews, although there were some large discrepancies on individual plots. There was agreement on presence/absence of ozone damage for about 80 percent of the plots. For field measurements of forest soils, 10 of the 12 variables attained the MQO. Although MQOs are not specifically stated for laboratory analyses of soil properties, comparisons were performed to assess the variability of lab measurements.