Fia National Assessment Of Data Quality For Forest Health Indicators

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.

"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.

The Forest Inventory and Analysis program (FIA) is the key USDA Forest Service (USFS) program that provides the information needed to assess the status and trends in the environmental quality of the Nation's forests. The goal of the FIA Quality Assurance (QA) program is to provide a framework to assure the production of complete, accurate and unbiased forest information of known quality. Specific Measurement Quality Objectives (MQO) for precision are designed to provide a window of performance that we are striving to achieve for every field measurement. These data quality goals were developed from knowledge of measurement processes in forestry and forest ecology, as well as the program needs of FIA. This report is a national summary and compilation of MQO analyses by regional personnel and the National QA Advisor. The efficacy of the MQO, as well as the measurement uncertainty associated with a given field measurement, can be tested by comparing data from blind check plots where, in addition to the field measurements of the standard FIA crew, a second QA measurement of the plot was taken by a crew without knowledge of the first crew's results. These QA data were collected between 2000 and 2003 and analyzed for measurement precision between FIA crews. The charge of this task team was to use the blind-check data to assess the FIA program's ability to meet data quality goals as stated by the MQO. The results presented indicate that the repeatability was within project goals for a wide range of measurements across a variety of forest and nonforest environments. However, there were some variables that displayed noncompliance with MQO goals. In general, there were two types of noncompliance: the first is where all the regions were below the MQO standard, and the second is where a subset of the regions was below the MQO standards or was substantially different from the other remaining regions. Results for each regional analysis are presented in appendix tables. In the course of the study, the task team discovered that there were difficulties in analyzing seedling species and seedling count variables for MQO compliance, and recommends further study of the issue. Also the task team addresses the issue of trees missed or added and recommends additional study of this issue. Lastly, the team points out that traditional MQO analysis of the disturbance and treatment variables may not be adequate. Some attributes where regional compliance rates are dissimilar suggest that regional characteristics (environmental variables such as forest type, physiographic class, and forest fragmentation) may have an impact on the ability to obtain consistent measurements. Additionally, differences in data collection protocols may cause differences in compliance rates. For example, a particular variable may be measured with a calibrated instrument in one region, while ocularly estimated in another region.

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.

One of the critical issues of our time is the dwindling capacity of the planet to provide life support for a large and growing human population. Based on a symposium on ecosystem health, Managing for Healthy Ecosystems identifies key issues that must be resolved if there is to be progress in this complex area, such as: Evolving methods f