Some Refinements on the Comparison of Areal Sampling Methods via Simulation

Abstract

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.

Floral strips adjacent to Manitoba crop fields attract beneficial insects shortly after establishment regardless of management type or landscape context

“…Land use data were extracted from the 2019 annual crop inventory from Agriculture and Agri‐Food Canada (Agriculture and Agri‐Food Canada, 2019) using the landscapemetrics, raster and sampSurf, packages in R (Gove, 2021; Hesselbarth et al, 2019; Hijmans, 2021; R Core Team, 2020). We separated crops into rewarding and non‐rewarding categories since insects respond differently to flowering versus non‐flowering crops (Holzschuh et al, 2013; Le Féon et al, 2013; Montero‐Castaño et al, 2016).…”

A note on the estimation of variance for big BAF sampling

“…The mixed northern hardwood tree population is a somewhat larger version of the population used in Gove (2017); it is completely synthetic and is contained on a tract with a total area A = 3.17 ha (31,684 grid cells). An external buffer with width 18 m encloses the internal stand with an area of 2 ha.…”

“…Figure 1 presents the results of the Monte Carlo simulations for the northern hardwoods in terms of the average standard errors by sample size and (F c , F v ) pair. Each panel corresponds to a 'Smith' plot (Smith 1938), with the inverse of BAF v used as a surrogate for average inclusion zone size under HPS (e.g., Arvanitis and O'Regan 1967;Gove 2017;Yang et al 2017). The reference for comparison in terms of the smallest standard error attainable under the simulations is that for the BAF c HPS variance.…”

Cost implications of cluster plot design choices for precise estimation of forest attributes in landscapes and forests of varying heterogeneity

“…The effect of each of these design parameters on inventory efficiency is hard to predict because of the interaction of different design variables and environmental heterogeneity scale and type. Impacts of cluster plot design on precision have previously been studied using data from mapped stands (Schreuder et al 1987;Zenner and Peck 2009;Picard et al 2018), existing forest inventory plots (e.g., Lynch, 2003 ;Picard et al, 2004;Yim et al, 2015), and simulated forest tree patterns (Arvanitis and O'Regan 1967;Mackisack and Wood 1990;Brink and Schreuder 1992;Hou et al 2015;Gove 2017). Based on our review of the literature, however, there are no studies that use a factorial simulation experiment to model the relationship between plot design parameters, spatial heterogeneity type and scale, and inventory efficiency and costs.…”