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When conducting a forest inventory, sometimes portions of plots cannot be measured due to inaccessibility. Two primary methods have been presented to account for partial nonresponse in the estimation phase: (i) use a ratio-to-size estimator or (ii) apply an adjustment factor to all plot observations in proportion to the missing area. Both approaches provide identical estimates of the population mean, but the estimates of variance differ when partial nonresponse is present. The performance of variance estimators was examined for a range of population forest area and partial nonresponse proportions in the sample. The ratio-to-size variance estimator performed unbiasedly with respect to simulation results, but the adjustment factor variance estimates were biased, with magnitude and direction dependent upon the forest area proportion and amount of partial nonresponse. The bias is relatively small when the partial nonresponse is small, which is often the case; however, the ratio-to-size method is preferred to ensure accurate variance estimation for a wide range of circumstances. [ABSTRACT FROM AUTHOR]

Models of relationships among forest inventory sampling efficiency and cluster plot configuration variables inform decisions by inventory planners. However, relationships vary under different spatial heterogeneity scenarios. To improve understanding of how spatial patterns of forests affect these relationships, we implemented a factorial experiment by simulating forest pattern at both the landscape and stand scales. We sampled these simulated forests with a variety of cluster plot configurations, calculated coefficient of variation (CV) of trees per hectare for each replicate, and tested the relationships among CV and the heterogeneity and cluster plot configuration factors within a linear mixed model framework. Both landscape- and stand-scale pattern aggregation had a significant relationship with CV. Changing cluster plot configuration factors did little to change the overall CV when using larger subplots but had some important effects when using smaller subplots. These impacts were stronger in the more uniform landscapes. Results were opposite for stand-scale heterogeneity; changing plot configuration in areas with aggregated patterns had a stronger impact than it did in areas with more uniform patterns. Results of this study reveal the importance of accounting for spatial pattern at multiple scales when making cluster configuration choices if the goal is statistical efficiency. [ABSTRACT FROM AUTHOR]

Post-stratification is often used to increase the precision of estimates arising from large-area forest inventories with plots established at permanent locations. Remotely sensed data and associated spatial products are often used for developing the post-stratification, which offers a mechanism to increase precision for less cost than increasing the sample size. While important variance reductions have been shown from post-stratification, it remains unknown where observed gains lie along the continuum of possible gains. This information is needed to determine whether efforts to further improve post-stratification outcomes are warranted. In this study, two types of optimal post-stratification were compared with typical production-based post-stratifications to estimate the magnitude of remaining gains possible. Although the optimal post-stratifications were derived using methods inappropriate for operational usage, the results indicated that substantial further increases in precision for estimates of both forest area and total tree biomass could be obtained with better post-stratifications. The potential gains differed by the attribute being estimated, the population being studied, and the number of strata. Practitioners seeking to optimize post-stratification face challenges, such as evaluation of numerous auxiliary data sources, temporal misalignment between plot observations and remotely sensed data acquisition, and spatial misalignment between plot locations and remotely sensed data due to positional errors in both data types. [ABSTRACT FROM AUTHOR]

Many national forest inventories (NFI) use auxiliary data to increase the precision of estimates. Typically, this is accomplished via stratified estimation techniques that rely on assignment of similar sample plot observations to strata constructed with the goal of lowering the variance of estimates. While early applications of stratification used strata constructed from photo-interpretation of aerial photography, current technology makes using wall-to-wall digital map information more appealing due to automated processing capabilities; however, there is generally a reduction in classification accuracy in comparison with photo-interpretation and a concomitant decrease in the precision of estimates. While most established NFI have permanent plots and employ post-stratification (PS) with stratum weights known from a map, it is unclear what are the compromises compared to using a photo-interpretation (PI) approach. In this study, differences in cost and precision were evaluated for post-stratification using strata derived from a digital map and double sampling for post-stratification (DSPS) with strata created from PI of aerial imagery. It was found that DSPS consistently provided better precision than PS for estimates of total biomass and forestland area with approximately 13 PI points per sample plot, which incurred a cost increase equivalent to 0.5% per ground plot. Increasing the number of PI points per plot resulted in further gains in precision, with cost increases proportional to the PI intensity. To attain specific precision goals, DSPS was generally less costly than increasing the sample size under PS, although the PS design was more cost-effective if the PI intensity was too low. The results of this study provide a decision framework for inventory planners considering sampling designs that rely on post-stratified estimation. [ABSTRACT FROM AUTHOR]

Abstract: The paper describes an efficient approach for mapping multiple individual tree species over large spatial domains. The method integrates vegetation phenology derived from MODIS imagery and raster data describing relevant environmental parameters with extensive field plot data of tree species basal area to create maps of tree species abundance and distribution at a 250-m pixel size for the entire eastern contiguous United States. The approach uses the modeling techniques of k-nearest neighbors and canonical correspondence analysis, where model predictions are calculated using a weighting of nearest neighbors based on proximity in a feature space derived from the model. The approach also utilizes a stratification derived from the 2001 National Land-Cover Database tree canopy cover layer. Data pre-processing is also described, which includes the use of Fourier series transformation for data reduction and characterizing seasonal vegetation phenology patterns that are apparent in the MODIS imagery. A suite of assessment procedures is applied to each of the modeled dataset presented. These indicate high accuracies, at the scales of assessments used, for total live-tree basal area per hectare and for many of the most common tree species found in the study area. The end result is an approach that enables the mapping of individual tree species distributions, while retaining much of the species covariance found on the forest inventory plots, at a level of spatial detail approaching that required for many regional management and planning applications. The proposed approach has the potential for operational application for simultaneously mapping the distribution and abundance of numerous common tree species across large spatial domains. [Copyright &y& Elsevier]

This article focuses on the functions of the Forest Inventory and Analysis (FIA) program of the U.S. Department of Agriculture Forest Service. The FIA program conducts comprehensive forest inventories of the U.S. to estimate the area of forestland. Traditionally, it has reported estimates of forest attributes for states and counties. Increasingly, however, users request plot data for estimation for their own areas of interest. But, the FIA program is constrained by law from disclosing exact locations and, instead, releases only approximate locations. To comply with the Freedom of Information Act of 1966, FIA plot locations on private lands made available to the public are perturbed, the locations of some plots are swapped with those of similar plots, and private ownership information is masked.

Globally, forests are a crucial natural resource, and their sound management is critical for human and ecosystem health and well-being. Efforts to manage forests depend upon reliable data on the status of and trends in forest resources. When these data come from well-designed natural resource monitoring (NRM) systems, decision makers can make science-informed decisions. National forest inventories (NFIs) are a cornerstone of NRM systems, but requires capacity and skills to implement. Efficiencies can be gained by incorporating auxiliary information derived from remote sensing (RS) into ground-based forest inventories. However, it can be difficult for countries embarking on NFI development to choose among the various RS integration options, and to develop a harmonized vision of how NFI and RS data can work together to meet monitoring needs. The NFI of the United States, which has been conducted by the USDA Forest Service's (USFS) Forest Inventory and Analysis (FIA) program for nearly a century, uses RS technology extensively. Here we review the history of the use of RS in FIA, beginning with general background on NFI, FIA, and sampling statistics, followed by a description of the evolution of RS technology usage, beginning with paper aerial photography and ending with present day applications and future directions. The goal of this review is to offer FIA's experience with NFI-RS integration as a case study for other countries wishing to improve the efficiency of their NFI programs. [ABSTRACT FROM AUTHOR]