Abstract
The design of forest inventories and development of new sampling methods useful in such inventories normally have a two-fold target of design unbiasedness and minimum variance in mind. Many considerations such as costs go into the choices of sampling method for operational and other levels of inventory. However, the variance in terms of meeting a specified level of precision is always among the most important criteria. Similarly, in designing new sampling methods, one always seeks to decrease the variance of the new method compared to existing methods. This paper provides a review of some graphical methods that may prove useful in these endeavors. In addition, in the case of the comparison of variances between new and existing methods, it introduces the use of wavelet filtering to decompose the sampling variance associated with the estimators under consideration into scale-based components of variance. This yields an analysis of variance of sorts regarding how the methods compare over different distance/area classes. The graphical tools are also shown to be applicable to the wavelet decomposition. These graphical tools may prove useful in summarizing the results for inventory design, while the wavelet results may prove helpful as we begin to look at sampling designs more in light of spatial processes for a given population of trees or downed coarse woody debris.
Highlights
There are numerous extant ways to inventory standing and downed components of forest ecosystems
Any well-designed areal sampling simulation program can be used for simple comparison of variances and for some of the methods discussed
Taylor diagrams do not seem to have been used for either data-model comparison or for sampling design comparisons in forestry; this latter application, as well as the extension to wavelet decomposition levels appears to be new
Summary
There are numerous extant ways to inventory standing and downed components of forest ecosystems. A ‘pre-cruise’ (or perhaps a database of inventory information from similar stands) is often recommended to help judge the intensity from a variability perspective, and refinements such as stratification can be used in this context to help reduce the number of sample points or lines. The choice of method for selecting the individuals of interest on a sample unit may be made because of familiarity with a particular method, favoring it over other available alternatives. These are all interesting, practical and useful points that require being addressed when undertaking an inventory, and the aforementioned list is by no means complete. The choice of a sampling method can be enhanced by targeting the attribute of greatest interest in the inventory and using a method that is either directly ‘optimized’ for that attribute or one that is correlated with the target quantity
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