Sampling for Natural Resource Monitoring

By J. J. Gruijter, Dick Brus, Marc F. P. Bierkens, M. Knotters. 2006

555 citations recorded by the source corpus.

Subfields: Mechanics of Materials, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/35482f9c220fff5fe497bae0e686f071a78594ce

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In Mechanics of Materials

In Water Science and Technology

Citations in the corpus

Taxonomist survey biases and the unveiling of biodiversity patterns (2009, background): “More powerful survey designs (De Gruijter et al., 2006) should be used if the patchiness in the distribution of species richness is high or when we aim to completely reveal the species richness pattern.”
Comparative interpretation of count, presence–absence and point methods for species distribution models (2012, methodology): “Following sampling design theory (Gruijter et al. 2006), we may also expect improvement in the approximation of the integral by placing points regularly, instead of randomly, in space.”
Machine learning for digital soil mapping: Applications, challenges and suggested solutions (2020, background): “Despite abundant evidence that the sampling design and sample size play a key role in the resulting map accuracy (De Gruijter et al., 2006), sampling designs suitable for mapping with ML are yet to be discovered.”
Bayesian Maximum Entropy prediction of soil categories using a traditional soil map as soft information (2008, background): “The areal fraction correctly classified was estimated by (De Gruijter et al., 2006): f̂ ¼ + L u¼1 au f̂u; ð21Þ where au is the weight (relative area) of stratum u, and f̂u is the estimated areal fraction of stratum u correctly classified.”
A review of spatial sampling (2012, methodology): “Purposive sampling is generally more efficient than probability sampling (Brus and Gruijter, 1997; Gruijter et al., 2006).”
Soils and climate change: potential impacts on carbon stocks and greenhouse gas emissions, and future research for Australian agriculture (2012, unknown): “This approach can reduce sampling error by decreasing the variance of sample estimates within each stratum and therefore yield SOC data with smaller uncertainties (see for example de Gruijter et al. 2006).”
Optimization of sample patterns for universal kriging of environmental variables (2007, background): “All rights reserved. doi:10.1016/j.geoderma.2006.10.016 We now know that for model-based mapping there is no direct need for probability sampling, and that purposive sampling is generally more efficient (Brus and de Gruijter, 1997; de Gruijter et al., 2006).”
Spatial prediction of USDA‐ great soil groups in the arid Zarand region, Iran: comparing logistic regression approaches to predict diagnostic horizons and soil types (2012, methodology): “The variance of the mean value for d (V (d St ) was calculated according to De Gruijter et al. (2006): V (d St ) = H∑ h=1 a2hV (d h), (11) where αh is the relative area of stratum h, d h is the stratum mean of d and V (d h) is the estimated variance of d h that can be calculated as follows: V (d h)…”
Sampling for validation of digital soil maps (2011, methodology): “…by making use of the design-specific sampling variance estimators or by multiplying the required sample size as computed for SI by an empirical factor, the so-called ‘design-effect’, to account for the greater (STSI, SY) or lesser (TS, CL) accuracy (see de Gruijter et al., 2006 for more details).”
Sampling designs for accuracy assessment of land cover (2009, background): “By partitioning the region of interest into equal area tessellation cells (e.g. squares or hexagons) and sampling from each cell with equal probability, the sample is assured to be spatially well distributed, much like a systematic sample (de Gruijter et al. 2006, p. 77).”