We present here the results of our initial effort to use FIA data to assess biodiversity in Maine's forests. Biodiversity is a complex issue and, from the start, it was apparent that the FIA data are inadequate for examining all facets of biodiversity. Nevertheless, the FIA provides the most comprehensive and detailed data on Maine's forests and can be used to measure some indicators of forest biodiversity, in particular those related to tree species and stand characteristics.

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.

The Swiss National Forest Inventory (NFI) is a forest survey on national level which started in 1982 and has already reached its 5th survey cycle (NFI5). It can be characterized as a multisource and multipurpose inventory where information is mainly collected from terrestrial field surveys using permanent sample plots. In addition, data from aerial photography, GIS and forest service questionnaires are also included. The NFI's main objective is to provide statistically reliable and sound figures to stakeholders such as politicians, researchers, ecologists, forest service, timber industry, national and international organizations as well as to international projects such as the Forest Resources Assessment of the United Nations. For Switzerland, NFI results are typically reported on national and regional level. State of the art methods are applied in all fields of data collection which have been proven to be of international interest and have even served as a basis for other European NFIs. The presented methods are applicable to any sample based forest inventory around the globe. In 2001 the Swiss NFI published its methods for the first time. Since then, many methodological changes and improvements have been introduced. This book describes the complete set of methods and revisions since NFI2. It covers various topics ranging from inventory design and statistics to remote sensing, field survey methods and modelling. It also describes data quality concepts and the software framework used for data storage, statistical analysis and result presentation.

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 inventories throughout the world have evolved gradually over time. The content as well as the concepts and de?nitions employed are constantly adapted to the users’ needs. Advanced inventory systems have been established in many countries within Europe, as well as outside Europe, as a result of development work spanning several decades, in some cases more than 100 years. With continuously increasing international agreements and commitments, the need for information has also grown drastically, and reporting requests have become more frequent and the content of the reports wider. Some of the agreements made at the international level have direct impacts on national economies and international decisions, e. g. , the Kyoto Protocol. Thus it is of utmost importance that the forest information supplied is collected and analysed using sound scienti?c principles and that the information from different countries is comparable. European National Forest Inventory (NFI) teams gathered in Vienna in 2003 to discuss the new challenges and the measures needed to get data users to take full advantage of existing NFIs. As a result, the European National Forest Inventory Network (ENFIN), a network of NFIs, was established. The ENFIN members decided to apply for funding for meetings and collaborative activities. COST– European Cooperation in Science and Technology - provided the necessary ?n- cial means for the realization of the program.