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Continuous forest inventory planners can allocate the budget to more plots per acre or a shorter remeasurement cycle. A higher plot intensity benefits small area estimation and allows for more precision in current status estimates. Shorter cycles may provide better estimates of growth, removals and mortality. On a fixed budget, the planner can't have both greater plot intensity and shorter cycles. Therefore, it is important to understand the trade-offs involved. Growth over removals ratios are important indicators of sustainability, and can be adversely affected by changes in cycle length. However, it might be possible to ameliorate negative impacts of longer cycle lengths with judicious use of aerial imagery. Increasing the cycle length reduces the value of an inventory for monitoring, but reducing the number of plots increases the variance of both current status estimates and trend estimates. There may be no optimal statistical solution to this quandary, but the best solution will depend on policy and management objectives. Continuous forest inventories use permanent plots that are remeasured to provide information on growth, removals and mortality. Typically, all plots are remeasured within a narrow time span, but the USDA Forest Service has popularized a variant referred to as an annual forest inventory where a percentage of the permanent plots are remeasured every year. We discuss trade-offs between number of field plots and cycle length and provide some insight with example applications showing how these decisions impact growth and removals estimates. We also discuss a variant of the traditional growth over removals ratio estimator that limits degradation in estimate quality as cycle lengths increase. [ABSTRACT FROM AUTHOR]

Simple methods for estimating the proportion of land changing from forest to nonforest are developed. Variance estimators are derived to facilitate significance tests. A power analysis indicates that 400 inventory plots are required to reliably detect small changes in net or gross forest loss. This is an important result because forest certification programs may require additional precautions when wood from areas where forestland area loss is occurring is harvested or purchased. Net and gross forest area loss must be clearly differentiated to avoid confusion. Estimates of gross forest cover loss from satellite data should not be equated with net forest area loss, which can be better determined from remeasured forest inventory plots. Simultaneous tests of net and gross forest area loss should use multiple comparison procedures to ensure that overall error rates are correct. Examples of applications demonstrate how to properly perform these tests. A simulated example is used to verify that the variance estimators are reliable. An application to USDA Forest Service inventory data indicates that neither net nor gross forest loss at the state level was statistically significant for states that had sufficien remeasured plot data publicly available when this analysis was done. [ABSTRACT FROM AUTHOR]

Annual forest inventories present special challenges and opportunities for those analyzing the data arising from them. Here, we address one question currently being asked by analysts of the US Forest Service's Forest Inventory and Analysis Program's quickly accumulating annual inventory data. The question is simple but profound: When combining the next year's data for a particular variable with data from previous years, how does one know whether the same model as used in the past for this purpose continues to be applicable? Of the myriad approaches that have been developed for changepoint detection and anomaly detection, this report focuses on a simple quality-control approach known as a control chart that will allow analysts of annual forest inventory data to determine when a departure from a past trend is likely to have occurred. [ABSTRACT FROM AUTHOR]

In the past decade, the United States Department of Agriculture Forest Service's Forest Inventory and Analysis Program (FIA) has replaced regionally autonomous, periodic, state-wide forest inventories using various probability proportional to tree size sampling designs with a nationally consistent annual forest inventory design utilizing systematically spaced clusters of fixed-area plots. This has resulted in significant changes in the spatial-temporal distribution of observations on the nation's forest resource. This paper discusses the resulting changes in the observation of the distribution of tree diameters measured at 1.37 m above ground level. We show that in three of the four FIA regions, a significant portion of the upper end of the diameter distribution has thus far gone unobserved by the new inventory design. We conclude that the reason is not that the larger diameter trees have become less common but rather that there is a low enough probability of observing these trees that they are not being observed. This postulate is supported by the observation that large-diameter trees are not missing from the data acquired by the fourth FIA region (the Pacific Northwest), which uses an additional sampling mechanism for large-diameter trees. We explore the implications of this effect in addition to potential solutions. [ABSTRACT FROM PUBLISHER]

Mapped plots are used for the national forest inventory conducted by the US Forest Service. Mapped plots are also useful for single ownership inventories. Mapped plots can handle boundary overlap and can provide less variable estimates for specified forest conditions. Mapping is a good fit for fixed plot inventories where the fixed area plot is used for both mapping and tree sampling. However, mapping can be done with a colocated fixed area plot even though the tree inventory uses variable plots. In fact, the tree measurement plots can be completely decoupled from the plots used for measuring condition proportions, although this is not recommended. [ABSTRACT FROM AUTHOR]

Weighted estimation formulas are developed for producing stratified estimates of means and variances where data come from plots that can contain multiple forest conditions. Each plot is mapped to allow the analyst to focus on specific forest types or conditions. The weights required to accommodate mapped plots are somewhat more complicated than the weights for unmapped plots. In particular, these weights depend on the mapped condition of interest. The implication is that a single plot weight or expansion factor will not suffice for all analyses as it does for unmapped plots. The methods are demonstrated using USDA Forest Service inventory data. [ABSTRACT FROM AUTHOR]