Spatially Balanced Sampling of Natural Resources

By D. Stevens, Anthony R. Olsen. 2004

1,314 citations recorded by the source corpus.

Subfields: Environmental Engineering, Water Science and Technology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/db0e34ae2eed7363267a24c034a01b4f1bcedf01

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Citations in the corpus

A Structured Approach for Developing Indices of Biotic Integrity: Three Examples from Streams and Rivers in the Western USA (2007, neutral): “probability design, Stoddard et al. (2005b) estimated”
Inhibitory geostatistical designs for spatial prediction taking account of uncertain covariance structure (2016, methodology): “They represent a flexible technique for selecting a spatially balanced, probability sampling design (Stevens and Olsen, 2004; Grafström et al., 2012; Brown et al., 2015) in which each potential sampling location has a known, non-zero probability of being 6”
Measuring the spatial balance of a sample: A new measure based on the Moran's I index (2017, methodology): “opulation of real business units located in the province of Siena (Italy). Keywords: Spatial sampling, Spatial balance, Moran’s I index, Statistical measure 1. Introduction Since the seminal paper by Stevens and Olsen (2004), spatial balance is considered as one of the most important and useful prop”
Doubly balanced spatial sampling with spreading and restitution of auxiliary totals (2013, methodology): “The most usual methods are systematic sampling, spatial stratification Generalized Random-Tessellation Stratifie (GRTS) sampling (see among others Ripley, 1981; Thompson, 1992; Stevens and Olsen, 2003, 2004; Mandallaz, 2008; Marker and Stevens, 2009).”
Gaussian predictive process models for large spatial data sets (2008, background): “Stevens and Olsen (2004) showed that spatial balance of design locations is more efficient than simple random sampling.”
How to Select Representative Samples (2014, methodology): “Stevens & Olsen (2003, 2004) introduced a design called generalized random-tessellation stratified for spatially balanced sampling in two dimensions.”
A plan for the North American Bat Monitoring Program (NABat) (2015, methodology): “For NABat, the approach begins by assigning a spatially balanced and randomized ordering of all 100-km2 units from the finite grid-based sampling frame using the generalized random-tessellation stratified (GRTS) survey design algorithm (Stevens and Olsen 2004).”
Which trees die during drought? The key role of insect host‐tree selection (2019, methodology): “Plot locations were selected a priori using Generalized Random Tessellation sampling (GRTS), which provides a spatially bal - anced sample that has a true probability design, allowing valid infer - ence for the entire study area (Stevens & Olsen, 2004).”
Why Well Spread Probability Samples Are Balanced (2013, methodology): “As suggested in [2], we use Voronoi polytopes to measure how well spread a sample is.”
Sampling designs for accuracy assessment of land cover (2009, background): “Other selection protocols that merit some consideration for accuracy assessment applications are adaptive cluster sampling (ACS) (Thompson and Seber 1996) and general random tessellation stratified (GRTS) sampling (Stevens and Olsen 2004).”