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Sampling Density (sampling + density)

Distribution by Scientific Domains

Earth and Environmental Science	57%

Selected Abstracts

Survey augmentation using commercial vessels in the Mid-Atlantic Bight: Sampling density and relative catchability

JOURNAL OF APPLIED ICHTHYOLOGY, Issue 6 2006
E. N. Powell
Summary A series of side-by-side tows was conducted between a survey vessel and a commercial vessel in two seasons, spring and fall (autumn), to examine the use of commercial vessels to increase sampling density in trawl-based stock surveys. Both vessels caught more fish offshore in the spring. The commercial vessel caught more fish than the survey vessel in both seasons. Catches of nearly all species were contagiously distributed in the spring. Most were contagiously distributed in the fall; however, somewhat more species were characterized by random or even distributions. The variance-to-mean ratio was consistently higher for most species for commercial vessel catches, regardless of season. As both vessels sampled in the same region at the same time, the increased predilection for the survey vessel to assess the distribution pattern as less patchy than the commercial vessel must accrue from some difference in sampling dynamics rather than variation in species distribution. A simulated decrease in sampling effort from 59 to 30 or 15 hauls increased the variance-to-mean ratio. Reduced sampling effort increased the tendency for occasional large catches to vary the estimate of domain biomass. The sampling program included an onshore,offshore gradient in station density. Domain biomass was considerably underestimated with reduced station density for six species characterized by large catches offshore in that portion of the survey domain characterized by low station density. In this study, a factor of two variation in domain biomass became more likely in 40% of species when sampling effort was reduced to 15 hauls from 59. A factor of two in biomass may distinguish a sustainable fishery from one in which a species is overfished. As survey sampling effort in this area was 18 hauls, increasing sample number by inclusion of commercial vessel tows would be advantageous. A regression between paired tows failed to adequately predict catches of one vessel from catches of the other. Standardization of vessel catches by the ratio-of-mean catches provided a more realistic comparison because large catches accounted for a significant fraction of domain biomass; however, a single conversion coefficient between boats could not be used for both sampling periods. The underlying impediment in developing a general conversion factor between the two vessels seems to be rooted in the differential in variance-to-mean ratios of the catches; this differential exists despite sampling of the same distribution of fish. [source]

Imaging geophysical data,taking the viewer into account

ARCHAEOLOGICAL PROSPECTION, Issue 1 2004
T. J. Dennis
Abstract A common way of presenting geophysical data from two-dimensional sources is as a grey-scale image. Some theoretical background to discrete image representation is described, and the deleterious effects of inappropriate (too sparse) sampling and display of such images discussed in an archaeological context. In high-quality images, such as magazine illustrations or digital television, the sampling densities can be sufficiently high to avoid the appearance of artefacts. Geophysical images in contrast are often sampled at very low densities; if the effective area of each sample is significantly less than the sample spacing, then the classic effect called ,aliasing' in communication engineering, caused by the violation of Nyquist's criterion, will be seen. Knowledge of the sensor's footprint can be used to select an appropriate sample density, and so minimize this source of distortion. To maximize the visibility of what may be low-contrast structures immersed in a high level of background noise, it is helpful also to consider the bandpass nature of the spatial frequency response of the human visual system. The non-linear phenomenon of visual masking is shown to influence the choice on presentation methods. Copyright © 2004 John Wiley & Sons, Ltd. [source]

Magnitude and sources of uncertainties in soil organic carbon (SOC) stock assessments at various scales

EUROPEAN JOURNAL OF SOIL SCIENCE, Issue 5 2009
E. Goidts
Summary Uncertainties in soil organic carbon (SOC) stock assessments are rarely quantified even though they are critical in determining the significance of the results. Previous studies on this topic generally focused on a single variable involved in the SOC stock calculation (SOC concentration, sampling depth, bulk density and rock fragment content) or on a single scale, rather than using an integrated approach (i.e. taking into account interactions between variables). This study aims to apply such an approach to identify and quantify the uncertainties in SOC stock assessments for different scales and spatial landscape units (LSU) under agriculture. The error propagation method (, method) was used to quantify the relative contribution of each variable and interaction involved to the final SOC stock variability. Monte Carlo simulations were used to cross-check the results. Both methods converged (r2=0.78). As expected, the coefficient of variation of the SOC stock increased across scales (from 5 to 35%), and was higher for grassland than for cropland. Although the main source of uncertainty in the SOC stock varied according to the scale and the LSU considered, the variability of SOC concentration (due to errors from the laboratory and to the high SOC spatial variability) and of the rock fragment content were predominant. When assessing SOC stock at the landscape scale, one should focus on the precision of SOC analyses from the laboratory, the reduction of SOC spatial variability (using bulk samples, accurate re-sampling, high sampling density or stratified sampling), and the use of equivalent masses for SOC stock comparison. The regional SOC stock monitoring of agricultural soils in southern Belgium allows the detection of an average SOC stock change of 20% within 11 years if very high rates of SOC stock changes occur (1 t C ha,1 year,1). Amplitude et sources des incertitudes liées aux estimations des stocks de carbone organique dans le sol (COS) à différentes échelles Résumé Les erreurs associées aux estimations du stock de carbone organique dans le sol (COS) sont rarement quantifiées bien qu'elles puissent empêcher l'obtention de résultats significatifs. Les quelques études qui le font focalisent en général sur une seule variable nécessaire au calcul du stock de COS (concentration en COS, profondeur échantillonnée, densité apparente et contenu en fragments rocheux) ou sur une échelle spatiale particulière, sans utiliser d'approche intégrée (prenant en compte les interactions entre les variables). Cette étude a pour objectif d'utiliser une telle approche pour identifier et quantifier les incertitudes liées aux estimations de stock de COS à différentes échelles spatiales et pour diverses unités spatiales de paysages (USP) agricoles. La loi de propagation des erreurs (méthode ,) permet de quantifier la contribution relative de chaque variable et interaction à la variabilité finale du stock de COS. Les simulations de Monte Carlo sont utilisées pour la vérification croisée des résultats. Les deux méthodes ont convergé (r2= 0.78). Comme prévu, le coefficient de variation du stock de COS a proportionnellement augmenté avec l'échelle spatiale considérée (de 5 à 35%), et était plus élevé pour les cultures que pour les prairies. Bien que la principale source d'erreur sur le stock de COS soit fonction de l'échelle spatiale et du type d'USP considérés, la variabilité du contenu en COS (du fait des erreurs de laboratoire et de sa grande variabilité spatiale) et du contenu en fragments rocheux étaient prédominants. Lors de l'estimation des stocks de COS à l'échelle du paysage, l'attention devrait prioritairement porter sur la précision des analyses en COS du laboratoire, la réduction de la variabilité spatiale du COS (en utilisant des échantillons composites, un ré-échantillonnage précis, une densité d'échantillonnage élevée ou un échantillonnage stratifié), et sur l'utilisation de masses équivalentes pour comparer les stocks de COS. Le réseau régional de suivi des stocks de COS des sols agricoles dans le sud de la Belgique permet la détection d'un changement de stock de COS moyen de 20% en 11 ans pour un taux très élevé de changement en stock de COS (1 t C ha,1 year,1). [source]

Statistical characterization of the spatial variability of soil moisture in a cutover peatland

HYDROLOGICAL PROCESSES, Issue 1 2004
Richard M. Petrone
Abstract Soil moisture is a significant variable in its importance to the validation of hydrological models, but it is also the one defining variable that ties in all components of the surface energy balance and as such is of major importance to climate models and their surface schemes. Changing the scale of representation (e.g. from the observation to modelling scale) can further complicate the description of the spatial variability in any hydrological system. We examine this issue using soil moisture and vegetation cover data collected at two contrasting spatial scales and at three different times in the snow-free season from a cutover peat bog in Cacouna, Québec. Soil moisture was measured using Time Domain Reflectometry (TDR) over 90 000 m2 and 1200 m2 grids, at intervals of 30 and 2 m respectively. Analyses of statistical structure, variance and spatial autocorrelation were conducted on the soil moisture data at different sampling resolutions and over different grid sizes to determine the optimal spatial scale and sampling density at which these data should be represented. Increasing the scale of interest without adequate resolution in the measurement can lead to significant inconsistency in the representation of these variables. Furthermore, a lack of understanding of the nature of the variability of soil moisture at different scales may produce spurious representation in a modelling context. The analysis suggests that in terms of the distribution of soil moisture, the extent of sampling within a grid is not as significant as the density, or spacing, of the measurements. Both the scale and resolution of the sampling scheme have an impact on the mean of the distribution. Only approximately 60% of the spatial pattern in soil moisture of both the large and small grid is persistent over time, suggesting that the pattern of moisture differs for wetting and drying cycles. Copyright © 2003 John Wiley & Sons, Ltd. [source]

Survey augmentation using commercial vessels in the Mid-Atlantic Bight: Sampling density and relative catchability

Sodium MRI using a density-adapted 3D radial acquisition technique

MAGNETIC RESONANCE IN MEDICINE, Issue 6 2009
Armin M. Nagel
Abstract A density-adapted three-dimensional radial projection reconstruction pulse sequence is presented which provides a more efficient k -space sampling than conventional three-dimensional projection reconstruction sequences. The gradients of the density-adapted three-dimensional radial projection reconstruction pulse sequence are designed such that the averaged sampling density in each spherical shell of k -space is constant. Due to hardware restrictions, an inner sphere of k -space is sampled without density adaption. This approach benefits from both the straightforward handling of conventional three-dimensional projection reconstruction sequence trajectories and an enhanced signal-to-noise ratio (SNR) efficiency akin to the commonly used three-dimensional twisted projection imaging trajectories. Benefits for low SNR applications, when compared to conventional three-dimensional projection reconstruction sequences, are demonstrated with the example of sodium imaging. In simulations of the point-spread function, the SNR of small objects is increased by a factor 1.66 for the density-adapted three-dimensional radial projection reconstruction pulse sequence sequence. Using analytical and experimental phantoms, it is shown that the density-adapted three-dimensional radial projection reconstruction pulse sequence allows higher resolutions and is more robust in the presence of field inhomogeneities. High-quality in vivo images of the healthy human leg muscle and the healthy human brain are acquired. For equivalent scan times, the SNR is up to a factor of 1.8 higher and anatomic details are better resolved using density-adapted three-dimensional radial projection reconstruction pulse sequence. Magn Reson Med, 2009. © 2009 Wiley-Liss, Inc. [source]

One-bit sigma-delta quantization with exponential accuracy

COMMUNICATIONS ON PURE & APPLIED MATHEMATICS, Issue 11 2003
C. Si, nan Güntürk
One-bit quantization is a method of representing bandlimited signals by ±1 sequences that are computed from regularly spaced samples of these signals; as the sampling density , , ,, convolving these one-bit sequences with appropriately chosen filters produces increasingly close approximations of the original signals. This method is widely used for analog-to-digital and digital-to-analog conversion, because it is less expensive and simpler to implement than the more familiar critical sampling followed by fine-resolution quantization. However, unlike fine-resolution quantization, the accuracy of one-bit quantization is not well-understood. A natural error lower bound that decreases like 2,, can easily be given using information theoretic arguments. Yet, no one-bit quantization algorithm was known with an error decay estimate even close to exponential decay. In this paper, we construct an infinite family of one-bit sigma-delta quantization schemes that achieves this goal. In particular, using this family, we prove that the error signal for ,-bandlimited signals is at most O(2,.07,). © 2003 Wiley Periodicals, Inc. [source]