Author: James E. Pollard
Publisher:
Published: 2006
Total Pages: 52
ISBN-13:
DOWNLOAD EBOOKThe 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.
Book Synopsis Forest Inventory and Analysis National Data Quality Assessment Report for 2000 to 2003 by : James E. Pollard
Download or read book Forest Inventory and Analysis National Data Quality Assessment Report for 2000 to 2003 written by James E. Pollard and published by . This book was released on 2006 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
