Two-dimensional wavelet variance estimation with application to sea ice SAR images

Geilhufe, Marc; Percival, Donald B.; Stern, Harry L.

doi:10.1016/j.cageo.2012.11.020

Cited by 8 publications

(12 citation statements)

References 20 publications

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“…One place where this could be more illuminating is where mapped populations are used in place of the synthetic population used here. Trees within mapped natural populations may be influenced by gradients in edaphic and other factors such that the anisotropic images that have been ignored here (because no such factors exist in the synthetic population) may prove much more useful, uniting scale and directional trends in the comparison of sampling methods (see, e.g., [63]). Regardless, based on the small case study presented here, wavelets can add a new dimension to the comparison of sampling methods by decomposition of the sampling surface variance.…”

Section: Discussionmentioning

confidence: 99%

“…Note that these figures show clear maxima for HPS, CHS and CPS, and minima for CMC. Geilhufe et al [63] provides more interpretation for these plots, including the link between the maxima and a characteristic scale [74] to the images.…”

Section: Distance-based Smith Plotsmentioning

confidence: 99%

“…Two-dimensional wavelet variance decomposition theory and applications include Mondal and Percival [62], Geilhufe et al [63] and Lark and Webster [64], the latter of which also includes some information on wavelet covariances when comparing two images (or, in our case, two sampling methods). A recent study using these methods is found in Ma and Zhang [65].…”

Section: The Two-dimensional Wavelet Variance and Covariancementioning

confidence: 99%

“…In general, to apply the two-dimensional wavelet decomposition, let X denote an N row by M column raster image indexed by (u, v) as X u,v [62,63], corresponding to our individual raster simulation results from Table 1. The MODWT is applied to X at levels j = 1, .…”

Section: Modwt Basicsmentioning

confidence: 99%

“…The pyramid algorithm treats rows and columns as univariate time series and filters these individually in a specific manner using the wavelet filter coefficients h 1 and scaling filter coefficients g 1 (for details, see [63,64]). In practice, the pyramid algorithm is more complex to implement, but is computationally more efficient than the matrix approach.…”

Section: Modwt Basicsmentioning

confidence: 99%

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Some Refinements on the Comparison of Areal Sampling Methods via Simulation

Gove

2017

Forests

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Distance-based Smith Plotsmentioning

confidence: 99%