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Dennis Walvoort

Researcher at Wageningen University and Research Centre

Publications -  39
Citations -  2697

Dennis Walvoort is an academic researcher from Wageningen University and Research Centre. The author has contributed to research in topics: Sampling (statistics) & Soil water. The author has an hindex of 12, co-authored 34 publications receiving 2275 citations.

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Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties

TL;DR: In this article, partial least squares regression (PLSR) was used to construct calibration models which were independently validated for the prediction of various soil properties from the soil spectra, including soil pHCa,p H w, lime requirement (LR), organic carbon (OC), clay, silt, sand, cation exchange capacity, exchangeable calcium (Ca), exchangeable aluminium (Al), nitrate-nitrogen (NO3-N), available phosphorus (PCol), exchangeability potassium (K) and electrical conductivity (EC).
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Statistical mapping of tree species over Europe

TL;DR: In this article, the spatial distribution of twenty tree species groups over Europe at 1 km × 1 km resolution was mapped using a multinomial multiple logistic regression model with the National Forest Inventory (NFI) plot data.
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An R package for spatial coverage sampling and random sampling from compact geographical strata by k-means

TL;DR: A new R package for designing spatial coverage samples for mapping, and for random sampling from compact geographical strata for estimating spatial means, with the mean squared shortest distance chosen as objective function.
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Compositional Kriging: A Spatial Interpolation Method for Compositional Data

TL;DR: In this paper, compositional kriging is introduced as a straightforward extension of ordinary Kriging that complies with the constant sum and non-negativity constraints of compositional data.
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The variance quadtree algorithm: Use for spatial sampling design

TL;DR: This work proposed the variance quadtree algorithm for sampling in an area with prior information represented as ancillary or secondary environmental data, and the covariance structure of the anCillary variable is non-stationary.