Sparse sampling: Spatial design for monitoring stream networks

Dobbie, Melissa J.; Henderson, Brent; Stevens, Donald L.

doi:10.1214/07-ss032

Cited by 66 publications

(35 citation statements)

References 99 publications

(128 reference statements)

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“…For a good general review on this topic we refer to Dobbie et al (2008), where also other approaches like stratified sampling, latin square sampling and randomized sampling are reviewed. Basic to the model-based approach is the fact that the spatial observations are modeled as realizations of a random field {Y(x)| x [ X}, where X is the spatial domain under investigation and Y(x) are random variables modeling the observations.…”

Section: Survey Of Model-based Spatial Sampling Designmentioning

confidence: 99%

Spatial sampling design and covariance-robust minimax prediction based on convex design ideas

Spöck

Pilz

2009

Stoch Environ Res Risk Assess

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This paper presents new ideas on sampling design and minimax prediction in a geostatistical model setting. Both presented methodologies are based on regression design ideas. For this reason the appendix of this paper gives an introduction to optimum Bayesian experimental design theory for linear regression models with uncorrelated errors. The presented methodologies and algorithms are then applied to the spatial setting of correlated random fields. To be specific, in Sect. 1 we will approximate an isotropic random field by means of a regression model with a large number of regression functions with random amplitudes, similarly to Fedorov and Flanagan (J Combat Inf Syst Sci: 23, 1997). These authors make use of the Karhunen Loeve approximation of the isotropic random field. We use the so-called polar spectral approximation instead; i.e. we approximate the isotropic random field by means of a regression model with sinecosine-Bessel surface harmonics with random amplitudes and then, in accordance with Fedorov and Flanagan (J Combat Inf Syst Sci: 23, 1997), apply standard Bayesian experimental design algorithms to the resulting Bayesian regression model. Section 2 deals with minimax prediction when the covariance function is known to vary in some set of a priori plausible covariance functions. Using a minimax theorem due to Sion (Pac J Math 8:171-176, 1958) we are able to formulate the minimax problem as being equivalent to an optimum experimental design problem, too. This makes the whole experimental design apparatus available for finding minimax kriging predictors. Furthermore some hints are given, how the approach to spatial sampling design with one a priori fixed covariance function may be extended by means of minimax kriging to a whole set of a priori plausible covariance functions such that the resulting designs are robust. The theoretical developments are illustrated with two examples taken from radiological monitoring and soil science.

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Section: Survey Of Model-based Spatial Sampling Designmentioning

confidence: 99%

Spatial sampling design and covariance-robust minimax prediction based on convex design ideas

Spöck

Pilz

2009

Stoch Environ Res Risk Assess

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“…There are inherent trade-offs between the number of streams or watersheds that can be assessed and the sampling intensity within each stream (Dobbie et al 2008), especially because time and personnel resources were both identified by stewardship staff as major limitations to monitoring efforts. Ultimately, within-reach sampling intensities may be affected by the extent of each First Nation's territory and their monitoring capacity, which varies among stewardship offices.…”

Section: Broader Sampling Considerationsmentioning

confidence: 99%

Design considerations for community-based stream monitoring to detect changes in Pacific salmon habitats

Lagasse¹,

Ou²,

Honka³

et al. 2014

E&S

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ABSTRACT. Communities in the Great Bear Rainforest of British Columbia, Canada are highly dependent on Pacific salmon (Oncorhynchus spp.) and the watersheds that support them, yet current monitoring efforts are likely inadequate for detecting changes in stream habitats that may affect salmon populations. The Coastal First Nations Regional Monitoring System is attempting to address these information gaps through a new stream assessment program that collects baseline information and tracks changes in stream habitats. Using the program's monitoring protocol, we assessed the habitat characteristics of eight streams within the Koeye and Namu watersheds, then used a statistical power simulation to determine within-stream sampling requirements for detecting changes in substrate composition that may affect salmon habitat suitability. We also assessed resource constraints and perceived threats to stream habitats via questionnaires to coastal First Nations' stewardship staff. Results suggest that the current recommended sample size of 6 withinstream transects has low statistical power for detecting biologically significant changes in fine sediment. Given limited monitoring resources, we recommend higher transect sampling intensities within productive riffle-pool streams, but an emphasis on monitoring site level characteristics, such as large woody debris and pool volume, in less productive, high gradient cascade-pool streams. Questionnaire results highlight the need for flexibility and local adaptation in monitoring efforts because of differences in resource constraints among First Nations communities. If successfully implemented, the stream assessment program can integrate local knowledge with western science to inform ecosystem-based management of watersheds within the Great Bear Rainforest.

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“…A monitoring design specifies the resource to be monitored, what will be measured, how it will be measured (i.e., the response design), where it will be monitored (i.e., the survey design), how frequently it will be monitored, and how measurements will be summarized [2]. In this paper I focus on survey design-often called sample design or spatial design [3]-and how best to generate a rigorous, useful, and flexible survey design that specifies where environmental data will be collected. Stehman [2] suggests that a good survey design should be probability-based; have a low and known estimated variance; be spatially-balanced, simple, and cost-effective; and have flexibility as a key characteristic because of real-world, practical challenges that environmental monitoring programs inevitably face [4].…”

Section: Introductionmentioning

confidence: 99%

A General-Purpose Spatial Survey Design for Collaborative Science and Monitoring of Global Environmental Change: The Global Grid

Theobald

2016

Remote Sensing

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Recent guidance on environmental modeling and global land-cover validation stresses the need for a probability-based design. Additionally, spatial balance has also been recommended as it ensures more efficient sampling, which is particularly relevant for understanding land use change. In this paper I describe a global sample design and database called the Global Grid (GG) that has both of these statistical characteristics, as well as being flexible, multi-scale, and globally comprehensive. The GG is intended to facilitate collaborative science and monitoring of land changes among local, regional, and national groups of scientists and citizens, and it is provided in a variety of open source formats to promote collaborative and citizen science. Since the GG sample grid is provided at multiple scales and is globally comprehensive, it provides a universal, readily-available sample. It also supports uneven probability sample designs through filtering sample locations by user-defined strata. The GG is not appropriate for use at locations above ±85 • because the shape and topological distortion of quadrants becomes extreme near the poles. Additionally, the file sizes of the GG datasets are very large at fine scale (resolution~600 m × 600 m) and require a 64-bit integer representation.

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Sparse sampling: Spatial design for monitoring stream networks

Cited by 66 publications

References 99 publications

Spatial sampling design and covariance-robust minimax prediction based on convex design ideas

Spatial sampling design and covariance-robust minimax prediction based on convex design ideas

Design considerations for community-based stream monitoring to detect changes in Pacific salmon habitats

A General-Purpose Spatial Survey Design for Collaborative Science and Monitoring of Global Environmental Change: The Global Grid

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