James A. Westfall

Guidelines for documenting and reporting tree allometric equations

1 IntroductionGiven the pressing need to quantify carbon fluxes associatedwith terrestrial vegetation dynamics, an increasing number ofresearchers have sought to improve estimates of tree volume,biomass, and carbon stocks. Tree allometric equations arecritical tools for such purpose and have the potential toimprove our understanding about carbon sequestration inwoody vegetation, to support the implementation of policiesand mechanisms designed to mitigate climate change (e.g.CDM and REDD+; Agrawal et al. 2011), to calculate costsand benefits associated with forest carbon projects, and toimprove bioenergy systems and sustainable forest manage-ment (Henry et al. 2013).

A primer for nonresponse in the US forest inventory and analysis program

Nonresponse caused by denied access and hazardous conditions are a concern for the USDA Forest Service, Forest Inventory and Analysis (FIA) program, whose mission is to quantify status and trends in forest resources across the USA. Any appreciable amount of nonresponse can cause bias in FIA’s estimates of population parameters. This paper will quantify the magnitude of nonresponse and describe the mechanisms that result in nonresponse, describe and qualitatively evaluate FIA’s assumptions regarding nonresponse, provide a recommendation concerning plot replacement strategies, and identify appropriate strategies to pursue that minimize bias. The nonresponse rates ranged from 0% to 21% and differed by land owner group; with denied access to private land the leading cause of nonresponse. Current FIA estimators assume that nonresponse occurs at random. Although in most cases this assumption appears tenable, a qualitative assessment indicates a few situations where the assumption is not tenable. In the short-term, we recommend that FIA use stratification schemes that make the missing at random assumption tenable. We recommend the examination of alternative estimation techniques that use appropriate weighting and auxiliary information to mitigate the effects of nonresponse. We recommend the replacement of nonresponse sample locations not be used.

Annual inventory report for Pennsylvania's forests: results from the first two years.

In 2000, the USDA Forest Services Forest Inventory and Analysis (FIA) program implemented a new system for inventory and monitoring Pennsylvania?s forest resources. The most salient benefit of the new inventory process will be a nearly threefold improvement in timeliness. This report summarizes the results of the first 2 years of annual inventory measurements. The area of forest land in Pennsylvania has remained stable since a previous inventory in 1989. The Keystone States forests continue to mature as larger trees and an increase in inventory volume were recorded. A separate study of tree seedlings revealed a general lack of regeneration in one-third to one-half of the stands in which regeneration should be adequate.

Propagating uncertainty through individual tree volume model predictions to large-area volume estimates

Key messageThe effects on large-area volume estimates of uncertainty in individual tree volume model predictions were negligible when using simple random sampling estimators for large-area estimation, but non-negligible when using stratified estimators which reduced the effects of sampling variability.ContextForest inventory estimates of tree volume for large areas are typically calculated by adding model predictions of volumes for individual trees at the plot level and calculating the per unit area mean over plots. The uncertainty in the model predictions is generally ignored with the result that the precision of the large-area volume estimate is optimistic.AimsThe primary objective was to estimate the effects on large-area volume estimates of volume model prediction uncertainty due to diameter and height measurement error, parameter uncertainty, and model residual variance.MethodsMonte Carlo simulation approaches were used because of the complexities associated with multiple sources of uncertainty, the non-linear nature of the models, and heteroskedasticity.ResultsThe effects of model prediction uncertainty on large-area volume estimates of growing stock volume were negligible when using simple random sampling estimators. However, with stratified estimators that reduce the effects of sampling variability, the effects of model prediction uncertainty were not necessarily negligible. The adverse effects of parameter uncertainty and residual variance were greater than the effects of diameter and height measurement errors.ConclusionThe uncertainty of large-area volume estimates that do not account for model prediction uncertainty should be regarded with caution.

Pennsylvania's forest 2004.

Pennsylvanias forest-land base is stable, covering 16.6 million acres or 58 percent of the land area. Sawtimber volume totals 88.9 billion board feet, an average of about 5,000 board feet per acre. Currently, only half of the forest land that should have advance tree seedling and sapling regeneration is adequately stocked with high-canopy species, and only one-third has adequate regeneration for commercially desirable timber species. Several exotic diseases and insects threaten the health of Pennsylvanias forests. Stressors such as drought, acidic deposition, and ground-level ozone pollution are adversely affecting the States forests.

An overview of existing and promising technologies for national forest monitoring

The main goal of national forest programs is to lead and steer forest policy development and implementation processes in an inter-sectoral way (FAO 2006). National forest monitoring systems contribute to forest programs through monitoring forest changes and forest services over time (FAO 2013). To do so, they generally collect and analyze forest-related data and provide knowledge and recommendations at regular intervals. The collection of forest-related data and their analyses have continually evolved with technological and computational advances (Kleinn 2002).

Recommendations for the use of tree models to estimate national forest biomass and assess their uncertainty

Key messageThree options are proposed to improve the accuracy of national forest biomass estimates and decrease the uncertainty related to tree model selection depending on available data and national contexts.IntroductionDifferent tree volume and biomass equations result in different estimates. At national scale, differences of estimates can be important while they constitute the basis to guide policies and measures, particularly in the context of climate change mitigation.MethodFew countries have developed national tree volume and biomass equation databases and have explored its potential to decrease uncertainty of volume and biomasttags estimates. With the launch of the GlobAllomeTree webplatform, most countries in the world could have access to country-specific databases. The aim of this article is to recommend approaches for assessing tree and forest volume and biomass at national level with the lowest uncertainty. The article highlights the crucial need to link allometric equation development with national forest inventory planning efforts.ResultsModels must represent the tree population considered. Data availability; technical, financial, and human capacities; and biophysical context, among other factors, will influence the calculation process.ConclusionThree options are proposed to improve accuracy of national forest assessment depending on identified contexts. Further improvements could be obtained through improved forest stratification and additional non-destructive field campaigns.

The U.S. forest carbon accounting framework: stocks and stock change, 1990-2016

As a signatory to the United Nations Framework Convention on Climate Change, the United States annually prepares an inventory of carbon that has been emitted and sequestered among sectors (e.g., energy, agriculture, and forests). For many years, the United States developed an inventory of forest carbon by comparing contemporary forest inventories to inventories that were collected using different techniques and definitions from more than 20 years ago. Recognizing the need to improve the U.S. forest carbon inventory budget, the United States is adopting the Forest Carbon Accounting Framework, a new approach that removes this older inventory information from the accounting procedures and enables the delineation of forest carbon accumulation by forest growth, land use change, and natural disturbances such as fire. By using the new accounting approach with consistent inventory information, it was found that net land use change is a substantial contributor to the United States forest carbon sink, with the entire forest sink offsetting approximately 15 percent of annual U.S. carbon dioxide emissions from the burning of fossil fuels. The new framework adheres to accounting guidelines set forth by the Intergovernmental Panel on Climate Change while charting a path forward for the incorporation of emerging research, data, and the needs of stakeholders (e.g., reporting at small scales and boreal forest carbon).

Conducting tests for statistically significant differences using forest inventory data

Many forest inventory and monitoring programs are based on a sample of ground plots from which estimates of forest resources are derived. In addition to evaluating metrics such as number of trees or amount of cubic wood volume, it is often desirable to make comparisons between resource attributes. To properly conduct statistical tests for differences, it is imperative that analysts fully understand the underlying sampling design and estimation methods, particularly identifying situations where the estimates being compared do not arise from independent samples. Information from the Forest Inventory and Analysis (FIA) program of the U.S. Forest Service was used to demonstrate circumstances where samples were not independent, and correct calculation of the standard error (and associated confidence intervals) required accounting for covariance. Failure to include the covariance when making comparisons between attributes resulted in standard errors that were too small. Conversely, comparisons of the same attribute at two points in time suffered from exaggerated standard errors when the covariance was excluded. The results indicated the effect of the covariance depends on the attribute of interest as well as the structure of the population being sampled.

Strategies for the use of mixed-effects models in continuous forest inventories.

Forest inventory data often consists of measurements taken on field plots as well as values predicted from statistical models, e.g., tree biomass. Many of these models only include fixed-effects parameters either because at the time the models were established, mixed-effects model theory had not yet been thoroughly developed or the use of mixed models was deemed unnecessary or too complex. Over the last two decades, considerable research has been conducted on the use of mixed models in forestry, such that mixed models and their applications are generally well understood. However, most of these assessments have focused on static validation data, and mixed model applications in the context of continuous forest inventories have not been evaluated. In comparison to fixed-effects models, the results of this study showed that mixed models can provide considerable reductions in prediction bias and variance for the population and also for subpopulations therein. However, the random effects resulting from the initial model fit deteriorated rapidly over time, such that some field data is needed to effectively recalibrate the random effects for each inventory cycle. Thus, implementation of mixed models requires ongoing maintenance to reap the benefits of improved predictive behavior. Forest inventory managers must determine if this gain in predictive power outweighs the additional effort needed to employ mixed models in a temporal framework.