Basisregistratie Ondergrond (BRO) actualisatie bodemkaart : Herkartering van de bodem in Flevoland

Brouwer, Fokke; Vries, F. de; Walvoort, D.J.J.

doi:10.18174/468672

Cited by 11 publications

(18 citation statements)

References 1 publication

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“…The provincial government and the regional water board are interested in the soil composition until the Pleistocene sand layer (Brouwer, de Vries, & Walvoort, 2018). This is because unripened clay and peat might be present above the Pleistocene substrate, which has properties that change under the influence of air.…”

Section: Case Study: Depth Of Pleistocene Sand Layer In Flevolandmentioning

confidence: 99%

“…In 2018, the soil in the province of Flevoland was sampled at 1,507 points (Brouwer et al, 2018) selected by spatial coverage sampling (Walvoort, Brus, & de Gruijter, 2010) over the area of interest. At each location, the depth of the Pleistocene cover sand layer below the land surface was determined.…”

Section: Case Study: Depth Of Pleistocene Sand Layer In Flevolandmentioning

confidence: 99%

“…The calibration and statistical validation locations, and the covariates are shown in Figure 3. Note that urban areas, swamps, waterbodies and areas where the Pleistocene sand layer is known to be at the surface were not or less intensively sampled because they are outside the area of interest (Brouwer et al, 2018).…”

Section: Case Study: Depth Of Pleistocene Sand Layer In Flevolandmentioning

confidence: 99%

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Mapping depth to Pleistocene sand with Bayesian generalized linear geostatistical models

Steinbuch

Brus

Heuvelink

2021

European J Soil Science

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Spatial soil applications frequently involve binomial variables. If relevant environmental covariates are available, using a Bayesian generalized linear model (BGLM) might be a solution for mapping such discrete soil properties. The geostatistical extension, a Bayesian generalized linear geostatistical model (BGLGM), adds spatial dependence and is thus potentially better equipped. The objective of this work was to evaluate whether it pays off to extend from a BGLM to a BGLGM for mapping binary soil properties, evaluated in terms of prediction accuracy and modelling complexity. As motivating example, we mapped the presence/absence of the Pleistocene sand layer within 120 cm from the land surface in the Dutch province of Flevoland, using the BGLGM implementation in the R‐package geoRglm. We found that BGLGM yields considerably better statistical validation metrics compared to a BGLM, especially with – as in our case – a large (n = 1,000) observation sample and few relevant covariates available. Also, the inferred posterior BGLGM parameters enable the quantification of spatial relationships. However, calibrating and applying a BGLGM is quite demanding with respect to the minimal required sample size, tuning the algorithm, and computational costs. We replaced manual tuning by an automated tuning algorithm (which eases implementing applications) and found a sample composition that delivers meaningful results within 50 h calculation time. With the gained insights and shared scripts spatial soil practitioners and researchers can – for their specific cases – evaluate if using BGLGM is feasible and if the extra gain is worth the extra effort. Highlights Does adding spatial correlation to a Bayesian GLM for mapping a binary soil variable pay off? We aim to make spatial Bayesian hierarchical modelling accessible for pedometricians. Most hierarchical models work well when enough observations are provided, even without covariates. Including spatial correlation might sometimes be worth the extra effort and computational costs.

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Section: Case Study: Depth Of Pleistocene Sand Layer In Flevolandmentioning

confidence: 99%

Section: Case Study: Depth Of Pleistocene Sand Layer In Flevolandmentioning

confidence: 99%

Section: Case Study: Depth Of Pleistocene Sand Layer In Flevolandmentioning

confidence: 99%

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Mapping depth to Pleistocene sand with Bayesian generalized linear geostatistical models

Steinbuch

Brus

Heuvelink

2021

European J Soil Science

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“…71 (Walvoort et al, 2010). Vervolgens zijn hierbij de dichtstbijzijnde locaties gezocht uit een spatial coverage sample met een hogere dichtheid, dat is gemaakt voor de actualisatie van de bodemkaart van Nederland, schaal 1 : 50.000 (Brouwer et al, 2018) Bij de stambuisregressie is rekening gehouden met de aanwezigheid van 'dieper dan'-of gecensoreerde waarnemingen, door niet-parametrische regressiemodellen te gebruiken (Helsel, 2012;Hoogland et al, 2014). Met de nauwkeurigheid van de parameters van het regressiemodel wordt rekening gehouden door middel van bootstrapping (Efron en Tibshirani, 1993), zie Hoogland et al (2014)…”

Section: Oppervlaktewaterunclassified

Basisregistratie Ondergrond-Gd-kartering Laag-Nederland 2018 : Provincie Flevoland

Stuyt¹,

Knotters²,

Walvoort³

et al. 2018

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“…Voor de kwalitatieve beoordeling van de validatieresultaten gebruiken we de gecensoreerde gegevens wel. Zie ookBrouwer et al (2018) waar deze aanpak ook is gevolgd voor Flevoland.…”

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Basisregistratie Ondergrond (BRO) Actualisatie bodemkaart : Herkartering van de veengebieden aan de flanken van de Utrechtse Heuvelrug

Brouwer¹,

Walvoort²

2020

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Sinds 2010 wordt door Wageningen Environmental Research (WENR) structureel gewerkt aan de actualisatie van de Bodemkaart van Nederland, schaal 1 : 50 000. De bodemkaart onderscheidt allerlei bodemeenheden met veenlagen ondiep in het profiel. Door oxidatie en klink neemt de veendikte geleidelijk af. Voor het beheer van veengebieden is het van belang om inzicht te hebben in de veendiktes. Dit onderzoek heeft geresulteerd in een veendiktekaart voor de veengebieden aan de flanken van de Utrechtse Heuvelrug en een geactualiseerde bodemkaart, schaal 1 : 50 000, van deze veengebieden.

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Basisregistratie Ondergrond (BRO) actualisatie bodemkaart : Herkartering van de bodem in Flevoland

Cited by 11 publications

References 1 publication

Mapping depth to Pleistocene sand with Bayesian generalized linear geostatistical models

Mapping depth to Pleistocene sand with Bayesian generalized linear geostatistical models

Basisregistratie Ondergrond-Gd-kartering Laag-Nederland 2018 : Provincie Flevoland

Basisregistratie Ondergrond (BRO) Actualisatie bodemkaart : Herkartering van de veengebieden aan de flanken van de Utrechtse Heuvelrug

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