Optimizing the spatial pattern of networks for monitoring radioactive releases

Melles, Stephanie J.; Heuvelink, G.B.M.; Twenhöfel, C.; Dijk, Arjan van; Hiemstra, Paul H.; Baume, Olivier; Stöhlker, U.

doi:10.1016/j.cageo.2010.04.007

Cited by 44 publications

(25 citation statements)

References 31 publications

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“…The resultant map (i.e., of artificial pollen abundance in this case) describes where artificial pollen density was highest, given the sampling design. With kriging, predictions are associated with prediction errors, and prediction errors can be used to hone in on areas where uncertainty is high—or low—as the case may be (e.g., Melles et al., ). For example, kriging prediction errors can be used to optimize spatial sampling designs by minimizing mean prediction errors across a study area (Brus and Heuvelink, ; Melles et al., ).…”

Section: Methodsmentioning

confidence: 99%

Pollen sleuthing for terrestrial plant surveys: Locating plant populations by exploiting pollen movement

et al. 2018

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Premise of the StudyWe present an innovative technique for sampling, identifying, and locating plant populations that release pollen, without extensive ground surveys. This method (1) samples pollen at random locations within the target species’ habitat, (2) detects species’ presence using morphological pollen analysis, and (3) uses kriging to predict likely locations of populations to focus future search efforts.MethodsTo demonstrate, we applied the pollen sleuthing system to search for artificially constructed populations of Brassica rapa in an old field. Population size varied from 0–100 flowers labeled with artificial pollen (paint pellets). After characterizing the landscape, we pan‐trapped 2762 potential insect vectors from random locations across the field and washed particulate matter from their bodies to assess artificial pollen abundance with a microscope.ResultsPopulation size greatly influenced artificial pollen detection success; following random pollen trap sampling and interpolation, ground surveys would be best focused on identified areas with high pollen density and low variation in pollen density. Sampling sites most successfully detected artificial pollen when they were located at higher elevations, near showy flowering plants that were not grasses.DiscussionDetection of nascent populations using the proposed system is possible but accuracy will depend on local environmental factors (e.g., wind, elevation). Conservation and invasive species control programs may be improved by using this approach.

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Section: Methodsmentioning

confidence: 99%

Pollen sleuthing for terrestrial plant surveys: Locating plant populations by exploiting pollen movement

et al. 2018

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“…[30], [31]). The algorithm requires that the objective function can be calculated prior to sampling i.e.…”

Section: Optimization Of Sampling Schemesmentioning

confidence: 99%

Optimized multi-phase sampling for soil remediation surveys

et al. 2013

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We develop an algorithm to optimize the design of multi-phase soil remediation surveys.The locations of observations in later phases are selected to minimize the expected loss incurred from misclassification of the local contamination status of the soil. In contrast to existing multi-phase design methods, the location of multiple observations can be optimized simultaneously and the reduction in the expected loss can be forecast. Hence rational decisions can be made regarding the resources which should be allocated to further sampling. The geostatistical analysis uses a copula-based spatial model which can represent general types of variation including distributions which include extreme values.The algorithm is used to design a hypothetical second phase of a survey of soil lead in Glebe, Sydney. Observations in this phase are generally dispersed on the boundaries between areas which according to the first phase either require, or do not require, remediation. The algorithm is initially used to make remediation decisions at the point scale but we demonstrate how it can be used to inform over blocks.

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“…By repeating this process multiple times, the sampling design with the most comprehensive spatial coverage can be chosen. Researchers have also used universal kriging variance to optimize surveys for soil, groundwater dynamics and radioactive releases (Heuvelink et al 2006; Brus and Heuvelink, 2007; Melles et al 2011). By finding the configuration of sites that minimises the universal kriging variance, a balance is struck between optimizing sampling across feature and geographic space.…”

Section: Quantifying Continuous Spatial Dependencementioning

confidence: 99%

Spatial parasite ecology and epidemiology: a review of methods and applications

et al. 2012

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The distributions of parasitic diseases are determined by complex factors, including many that are distributed in space. A variety of statistical methods are now readily accessible to researchers providing opportunities for describing and ultimately understanding and predicting spatial distributions. This review provides an overview of the spatial statistical methods available to parasitologists, ecologists and epidemiologists and discusses how such methods have yielded new insights into the ecology and epidemiology of infection and disease. The review is structured according to the three major branches of spatial statistics: continuous spatial variation; discrete spatial variation; and spatial point processes.

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Optimizing the spatial pattern of networks for monitoring radioactive releases

Cited by 44 publications

References 31 publications

Pollen sleuthing for terrestrial plant surveys: Locating plant populations by exploiting pollen movement

Pollen sleuthing for terrestrial plant surveys: Locating plant populations by exploiting pollen movement

Optimized multi-phase sampling for soil remediation surveys

Spatial parasite ecology and epidemiology: a review of methods and applications

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