Spatially Balanced Sampling: Application to Environmental Surveys

Brown, Jennifer; Robertson, B. L.; McDonald, Trent L.

doi:10.1016/j.proenv.2015.07.108

Cited by 39 publications

(28 citation statements)

References 13 publications

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“…The Halton sequence constitutes a good source for a low discrepancy in two dimensions since the selection of small coprime bases ensures a minimal correlation between dimensions (Worley, ) and is therefore regularly used in ecological sampling (Brown et al, ; Kermorvant et al, ).…”

Section: Methodsmentioning

confidence: 99%

On the Optimal Spatial Design for Groundwater Level Monitoring Networks

Ohmer

Liesch

Goldscheider

2019

Water Resources Research

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Effective groundwater monitoring networks are important, as systematic data collected at observation wells provide a crucial understanding of the dynamics of hydrogeological systems as well as the basis for many other applications. This study investigates the influence of six groundwater level monitoring network (GLMN) sampling designs (random, grid, spatial coverage, and geostatistical) with varying densities on the accuracy of spatially interpolated groundwater surfaces. To obtain spatially continuous prediction errors (in contrast to point cross-validation errors), we used nine potentiometric groundwater surfaces from three regional MODFLOW groundwater flow models with different resolutions as a priori references. To assess the suitability of frequently-used cross-validation error statistics (MAE, RMSE, RMSSE, ASE, and NSE), we compared them with the actual prediction errors (APE). Additionally, we defined upper and lower thresholds for an appropriate spatial density of monitoring wells. Below the lower threshold, the observation density appears insufficient, and additional wells lead to a significant improvement of the results. Above the upper threshold, additional wells lead to only minor and inefficient improvements. According to the APE, systematic sampling lead to the best results but is often not suited for GLMN due to its nonprogressive characteristic. Geostatistical and spatial coverage sampling are considerable alternatives, which are in contrast progressive and allow evenly spaced and, in the case of spatial coverage sampling, yet reproducible coverage with accurate results. We found that the global cross-validation error statistics are not suitable to compare the performance of different sampling designs, although they allow rough conclusions about the quality of the GLMN.

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Section: Methodsmentioning

confidence: 99%

On the Optimal Spatial Design for Groundwater Level Monitoring Networks

Ohmer

Liesch

Goldscheider

2019

Water Resources Research

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“…This ability is particularly appropriate to monitor natural resources. A three dimensions survey is relevant for monitoring in water bodies (integrating deepness), and a four or more dimensions study design will be able to integrate information such as ecological threats, time intervals, species population structure, environmental data... (Brown et al 2015). If we found that BAS perform well for manila clam's monitoring program in Arcachon bay with only two dimensions (x and y coordinates), we could expect that it will perform even better with more dimensions.…”

Section: Basmentioning

confidence: 99%

Optimizing cost-efficiency of long term monitoring programs by using spatially balanced sampling designs: The case of manila clams in Arcachon bay

Kermorvant

Caill-Milly

Bru

et al. 2019

Ecological Informatics

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Funding lake is one major issue in ecology, in particular at local scale. It is known that sustainable management of a natural population requires a good understanding of its functioning, itself dependent on a good long term monitoring program. Such programs are usually very difficult to implement, especially for resources characterized by high spatio-temporal variation in their distribution, resulting in a trade off between efficiency and costs. Today, thanks to rapidly evolving statistical theory, new survey designs are developed, some with the characteristic of well balancing samples in the study area. This paper aims at demonstrating that theses advanced sampling designs perform better than the usual ones for long term monitoring program of local resources, with the added benefices of saving money and also increasing results accuracy. To prove it, and for it high spatio-temporal variation in it distribution, we choose the example of Manila clam's stock monitoring in Arcachon bay. This stock is under high scrutiny and last campaigns could not be done because of lack of funding (at least 50,000€/survey). We use a simulation study based on real data to assess and compare performances of news and older sampling designs on this survey. Three sampling designs are tested in both of the 6 past monitoring campaigns data and we estimate the cost of their application in the field. Selected sampling designs are: 1-simple random sampling (SRS-the one used in the past years of this monitoring program), 2generalized tessellation sampling (GRTS-a recent spatially balanced sampling design known for its high performance) and, 3-balanced acceptance sampling design (BAS-a newly developed spatially balanced sampling design, never tested yet in a real population). We first confirm that the two spatially balanced sampling designs perform better than simple random sampling. Both of the advanced sampling designs perform equally and allow achieving same accuracy in the results with almost half sampling intensity than SRS. This makes them so cost-effective that 30% of each campaign price could be saved if they were used. Moreover, the three sampling designs need a constant sample size thought years to achieve a fixed accuracy in results. This will permit us to fix one sample size that could be done for all future campaigns; and this, despite the existence of spatial and temporal variations in clam's distribution. Highlights ► Long term monitoring program at local scale are mandatory for resource management but often too expensive to undertake. ► For the example of manila clams resource in Arcachon bay, each campaign price can be lowered by 30% only in using spatially balanced sampling design instead of simple random sampling. ► More than lowering the survey price, wanted accuracy in the results can be fixed a priori. ► BAS sampling design, never compared to other sampling designs in real data previously perform very well.

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“…Generalized random‐tessellation stratified (GRTS) designs are widely used in environmental monitoring surveys. They represent a flexible technique for selecting a spatially balanced probability sampling design (Stevens & Olsen, ; Grafström, Lundström, & Schelin, ; Brown, Robertson, & McDonald, ) in which each potential sampling location has a known, non‐zero probability of being included in the sample. The design ensures that no points in the target population are too far from a sampled point (i.e., points are spread evenly) (Brown et al, ) and that few sampled points are close together.…”

Section: Non‐adaptive Geostatistical Design Strategiesmentioning

confidence: 99%

“…They represent a flexible technique for selecting a spatially balanced probability sampling design (Stevens & Olsen, 2004;Grafström, Lundström, & Schelin, 2012;Brown, Robertson, & McDonald, 2015) in which each potential sampling location has a known, non-zero probability of being included in the sample. The design ensures that no points in the target population are too far from a sampled point (i.e., points are spread evenly) (Brown et al, 2015) and that few sampled points are close together. A GRTS design is formulated using a restricted randomization, referred to as hierarchical randomization, which randomly orders the spatial addresses (Stevens & Olsen, 2003).…”

Section: Other Constructions For Spatially Balanced Designsmentioning

confidence: 99%

Inhibitory geostatistical designs for spatial prediction taking account of uncertain covariance structure

et al. 2016

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The problem of choosing spatial sampling designs for investigating an unobserved spatial phenomenon scriptS arises in many contexts, for example, in identifying households to select for a prevalence survey to study disease burden and heterogeneity in a study region scriptD. We studied randomized inhibitory spatial sampling designs to address the problem of spatial prediction while taking account of the need to estimate covariance structure. Two specific classes of design are inhibitory designs and inhibitory designs plus close pairs. In an inhibitory design, any pair of sample locations must be separated by at least an inhibition distance δ. In an inhibitory plus close pairs design, n − k sample locations in an inhibitory design with inhibition distance δ are augmented by k locations each positioned close to one of the randomly selected n − k locations in the inhibitory design, uniformly distributed within a disk of radius ζ. We present simulation results for the Matérn class of covariance structures. When the nugget variance is non‐negligible, inhibitory plus close pairs designs demonstrate improved predictive efficiency over designs without close pairs. We illustrate how these findings can be applied to the design of a rolling Malaria Indicator Survey that forms part of an ongoing large‐scale, 5‐year malaria transmission reduction project in Malawi.

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Spatially Balanced Sampling: Application to Environmental Surveys

Cited by 39 publications

References 13 publications

On the Optimal Spatial Design for Groundwater Level Monitoring Networks

On the Optimal Spatial Design for Groundwater Level Monitoring Networks

Optimizing cost-efficiency of long term monitoring programs by using spatially balanced sampling designs: The case of manila clams in Arcachon bay

Inhibitory geostatistical designs for spatial prediction taking account of uncertain covariance structure

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