Sampling and Estimation Documentation for the Enhanced Forest Inventory and Analysis Program: 2022

Sampling and Estimation Documentation for the Enhanced Forest Inventory and Analysis Program: 2022 PDF

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Published: 2022

Total Pages: 0

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The Forest Inventory and Analysis (FIA) program of the Forest Service, an Agency of the U.S. Department of Agriculture, provides what is arguably the most valuable forest resource dataset in the United States. These data are the basis for numerous inquiries across a wide range of forest-related attributes at various spatial and temporal scales. While user-friendly analytical tools are publicly available to facilitate the use of the data without expert knowledge, there is a need for detailed documentation of the underlying sampling and estimation procedures. The audience for this information entails the entire spectrum of both internal and external FIA data consumers. This document clarifies some aspects of existing documentation, provides the sampling and estimation methods used for key program areas including Urban FIA, National Woodland Owner Survey, Timber Products Output, and Carbon, and provides an examination of burgeoning estimation topics relevant to the FIA program and its users. A broad overview is provided on several advanced estimation approaches of particular interest to the FIA community. While the exposition for each topic is necessarily coarse, links to more detailed research and informational material are provided for readers desiring to further study a specific area of interest.

The Enhanced Forest Inventory and Analysis Program--national Sampling Design and Estimation Procedures

The Enhanced Forest Inventory and Analysis Program--national Sampling Design and Estimation Procedures PDF

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Published: 2005

Total Pages: 96

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The Forest Inventory and Analysis (FIA) Program of the U.S. Department of Agriculture, Forest Service is in the process of moving from a system of quasi-independent, regional, periodic inventories to an enhanced program featuring greater national consistency, a complete and annual sample of each State, new reporting requirements, and integration with the ground sampling component of the Forest Health Monitoring Program. This documentation presents an overview of the conceptual design, describes the sampling frame and plot configuration, presents the estimators that form the basis of FIA's National Information Management System (NIMS), and shows how annual data are combined for analysis. It also references a number of Web-based supplementary documents that provide greater detail about some of the more obscure aspects of the sampling and estimation system, as well as examples of calculations for most of the common estimators produced by FIA.

Sampling Protocol, Estimation, and Analysis Procedures for the Down Woody Materials Indicator of the FIA Program

Sampling Protocol, Estimation, and Analysis Procedures for the Down Woody Materials Indicator of the FIA Program PDF

Author: Christopher Woodall

Publisher:

Published: 2005

Total Pages: 62

ISBN-13:

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Provides the rationale and context for a national inventory of down woody material. Documents the various woody material components sampled by the DWM indicator, the sampling protocol used to measure the DWM components, and estimation procedures. Provides guidance on managing and processing DWM data and incorporating that data into pertinent inventory analyses and research projects.

Adjustments to Forest Inventory and Analysis Estimates of 2001 Saw-log Volumes for Kentucky

Adjustments to Forest Inventory and Analysis Estimates of 2001 Saw-log Volumes for Kentucky PDF

Author: Stanley J. Zarnoch

Publisher:

Published: 2005

Total Pages: 8

ISBN-13:

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The 2001 Kentucky Forest Inventory and Analysis survey overestimated hardwood saw-log volume in tree grade 1. This occurred because 2001 field crews classified too many trees as grade 1 trees. Data collected by quality assurance crews were used to generate two types of adjustments, one based on the proportion of trees misclassified and the other on the proportion of saw-log volume misclassified. Measures of variability for the estimated proportions were based on a cluster sampling design. Both methods significantly reduced estimated saw-log volume in tree grade 1. We believe that the saw-log volume approach is superior to the tree approach, but that both approaches generate improved estimates of tree grade saw-log volumes. The standard errors of the adjustment proportions are given and can be used to calculate standard errors of the adjusted values.