James A. Westfall

Bio: James A. Westfall is an academic researcher from United States Forest Service. The author has contributed to research in topics: Forest inventory & Population. The author has an hindex of 22, co-authored 102 publications receiving 1620 citations. Previous affiliations of James A. Westfall include United States Department of Agriculture & Virginia Tech.

Effects of uncertainty in model predictions of individual tree volume on large area volume estimates

Abstract: Forest inventory estimates of tree volume for large areas are typically calculated by adding model predictions of volumes for individual trees. However, the uncertainty in the model predictions is generally ignored with the result that the precision of the large area volume estimates is overestimated. The primary study objective was to estimate the effects of model residual variability and model parameter uncertainty on large area volume estimates and their uncertainties for a study area in northeastern Minnesota, USA. Monte Carlo simulation approaches were used because of the complexities associated with multiple sources of uncertainty and the nonlinear nature of the models. Two conclusions were important. First, for this study, the effects of uncertainty in model predictions on the large area volume estimates and their uncertainties were small when the models were calibrated using an average of 100 or more observations per species and when the average proportion of variance explained by the models was at least 0.95. Second, large area estimates and their uncertainties based on coniferous/deciduous and nonspecific models deviated very little from large area estimates based on species-specific models.

A Call to Improve Methods for Estimating Tree Biomass for Regional and National Assessments

Abstract: Tree biomass is typically estimated using statistical models. This review highlights five limitations of most tree biomass models, which include the following: (1) biomass data are costly to collect and alternative sampling methods are used; (2) belowground data and models are generally lacking; (3) models are often developed from small and geographically limited data sets; (4) simplistic model forms and predictor variables are used; and (5) variation is commonly averaged or grouped rather than accounted for. The consequences of these limitations are highlighted and discussed. Several recommendations for future efforts are presented including the following: (1) collection of field measurements of tree biomass using consistent protocols; (2) compilation of existing data; (3) continued evaluation and improvement of existing models; (4) exploration of new models; and (5) adoption of state-of-the-art analytical and statistical techniques. Given the increasing importance of accurately estimating forest biomass, there is a critical need to understand, evaluate, and improve current tree biomass prediction methods.

Sample-based estimators used by the forest inventory and analysis national information management system

Abstract: Addendum: The supplementary documents referenced in this manuscript are posted on the Web site https://www.fia.fs.fed.us/library/sampling/index.phpThis chapter outlines prescribed core procedures for deriving population estimates from attributes measured in conjunction with the Phase 1 and Phase 2 samples. These estimation procedures also apply to those Phase 3 attributes in common with Phase 2. Given the sampling frame and plot design described in the previous two chapters, many estimation approaches can be applied. In fact, one goal of the overall design is to maximize flexibility, so Forest Inventory and Analysis (FIA) data can be used to address a variety of analytical needs.

Climate Change 2007: The Physical Science Basis.

Abstract: Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

Multiple Imputation for Nonresponse in Surveys

Abstract: 25. Multiple Imputation for Nonresponse in Surveys. By D. B. Rubin. ISBN 0 471 08705 X. Wiley, Chichester, 1987. 258 pp. £30.25.

Inventory of U.S. Greenhouse Gas Emissions and Sinks

Abstract: This memorandum documents the updates implemented in EPA’s 2020 Inventory of U.S. Greenhouse Gas Emissions and Sinks (GHGI) for gathering and boosting (G&B) stations. Additional considerations for G&B were previously discussed in memoranda released November 2019 (Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2018: Updates Under Consideration for Natural Gas Gathering & Boosting Station Emissions),1 October 2018 (Inventory of U.S. GHG Emissions and Sinks 1990-2017: Updates Under Consideration for Natural Gas Gathering & Boosting Emissions),2 and April 2019 (Inventory of U.S. GHG Emissions and Sinks 1990-2017: Updates to Natural Gas Gathering & Boosting Pipeline Emissions).3