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Mogens Humlekrog Greve

Predictive mapping of the acidifying potential for acid sulfate soils

Publikation: KonferencebidragKonferenceabstrakt til konferenceForskning

Standard

Predictive mapping of the acidifying potential for acid sulfate soils. / Boman, A; Beucher, Amélie; Mattbäck, S; Nørgaard, Henrik; Rosendahl, R.; Greve, Mogens Humlekrog.

2017. Abstract fra Pedometrics Conference, Wageningen, Holland.

Publikation: KonferencebidragKonferenceabstrakt til konferenceForskning

Harvard

Boman, A, Beucher, A, Mattbäck, S, Nørgaard, H, Rosendahl, R & Greve, MH 2017, 'Predictive mapping of the acidifying potential for acid sulfate soils' Pedometrics Conference, Wageningen, Holland, 26/06/2017 - 01/07/2017, .

APA

Boman, A., Beucher, A., Mattbäck, S., Nørgaard, H., Rosendahl, R., & Greve, M. H. (2017). Predictive mapping of the acidifying potential for acid sulfate soils. Abstract fra Pedometrics Conference, Wageningen, Holland.

CBE

Boman A, Beucher A, Mattbäck S, Nørgaard H, Rosendahl R, Greve MH. 2017. Predictive mapping of the acidifying potential for acid sulfate soils. Abstract fra Pedometrics Conference, Wageningen, Holland.

MLA

Vancouver

Boman A, Beucher A, Mattbäck S, Nørgaard H, Rosendahl R, Greve MH. Predictive mapping of the acidifying potential for acid sulfate soils. 2017. Abstract fra Pedometrics Conference, Wageningen, Holland.

Author

Boman, A ; Beucher, Amélie ; Mattbäck, S ; Nørgaard, Henrik ; Rosendahl, R. ; Greve, Mogens Humlekrog. / Predictive mapping of the acidifying potential for acid sulfate soils. Abstract fra Pedometrics Conference, Wageningen, Holland.

Bibtex

@conference{acdbb7c378a14f58a1d81441f92fc18f,
title = "Predictive mapping of the acidifying potential for acid sulfate soils",
abstract = "Developing methods for the predictive mapping of the potential environmental impact from acid sulfate soils is important because recent studies (e.g. Mattb{\"a}ck et al., under revision) have shown that the environmental hazards (e.g. leaching of acidity) related to acid sulfate soils vary depending on their texture (clay, silt, sand etc.). Moreover, acidity correlates, not only with the sulfur content, but also with the electrical conductivity (EC) measured after incubation. Electromagnetic induction (EMI) data collected from an EM38 proximal sensor also enabled the detailed mapping of acid sulfate soils over a field (Huang et al., 2014).This study aims at assessing the use of EMI data for the predictive mapping of the acidifying potential in an acid sulfate soil area in western Finland. Different supervised classification modelling techniques, such as Artificial Neural Networks (Beucher et al., 2015), will also be evaluated to generate predictive maps.In the study area, an EMI-survey using a DUALEM proximal sensor was carried out during the autumn of 2016. The collected apparent soil electrical conductivity (ECa) measurements were interpolated using ordinary kriging. An inversion software called Aarhus Workbench (Auken et al., 2015) was used to create 2-D models of EC from the measured ECa. An unsupervised classification method was applied on the interpolated ECa map and LiDAR-based elevation data to partition the study area into homogeneous units. A soil sampling scheme was carried out based on this information, samples being taken from each unit.From every sampling site, five soil cores were taken down to two meters depth with a manually operated soil corer; one primary soil core in the middle and four soil cores distributed evenly around the primary soil core. The primary soil core was used for detailed characterization of soil properties (grain size, structure, texture, field-pH, oxidation depth, ground water level) and acidifying potential (incubation-pH and titratable incubation acidity) whereas the four other cores were used for checking the soil variability. Soil observations from the primary cores are used as calibration and validation data within the modelling.In addition to the EMI data, the present study relies on other environmental predictors: Quaternary geology maps and remote sensing data, such as airborne gamma-radiometric data, a LiDAR-based Digital Elevation Model (DEM), and different land surface parameters derived from this DEM (e.g. slope gradient, distance to channel network, flow accumulation and wetness index).Preliminary results show that the acidifying potential is generally high and that it varies within the soil cores. The interpolated ECa map and EC models both appear to indicate the field drainage system. EC models could also enable detecting the transition zone, which constitutes the most acidic layer overlying the anoxic horizon (i.e. the sulfidic parent material with a high acidifying potential). Additionally, soil texture variation could not be identified because fine-grained sediments homogeneously covered the study area.",
author = "A Boman and Am{\'e}lie Beucher and S Mattb{\"a}ck and Henrik N{\o}rgaard and R. Rosendahl and Greve, {Mogens Humlekrog}",
year = "2017",
language = "English",
note = "null ; Conference date: 26-06-2017 Through 01-07-2017",
url = "http://www.pedometrics2017.org/",

}

RIS

TY - ABST

T1 - Predictive mapping of the acidifying potential for acid sulfate soils

AU - Boman, A

AU - Beucher, Amélie

AU - Mattbäck, S

AU - Nørgaard, Henrik

AU - Rosendahl, R.

AU - Greve, Mogens Humlekrog

PY - 2017

Y1 - 2017

N2 - Developing methods for the predictive mapping of the potential environmental impact from acid sulfate soils is important because recent studies (e.g. Mattbäck et al., under revision) have shown that the environmental hazards (e.g. leaching of acidity) related to acid sulfate soils vary depending on their texture (clay, silt, sand etc.). Moreover, acidity correlates, not only with the sulfur content, but also with the electrical conductivity (EC) measured after incubation. Electromagnetic induction (EMI) data collected from an EM38 proximal sensor also enabled the detailed mapping of acid sulfate soils over a field (Huang et al., 2014).This study aims at assessing the use of EMI data for the predictive mapping of the acidifying potential in an acid sulfate soil area in western Finland. Different supervised classification modelling techniques, such as Artificial Neural Networks (Beucher et al., 2015), will also be evaluated to generate predictive maps.In the study area, an EMI-survey using a DUALEM proximal sensor was carried out during the autumn of 2016. The collected apparent soil electrical conductivity (ECa) measurements were interpolated using ordinary kriging. An inversion software called Aarhus Workbench (Auken et al., 2015) was used to create 2-D models of EC from the measured ECa. An unsupervised classification method was applied on the interpolated ECa map and LiDAR-based elevation data to partition the study area into homogeneous units. A soil sampling scheme was carried out based on this information, samples being taken from each unit.From every sampling site, five soil cores were taken down to two meters depth with a manually operated soil corer; one primary soil core in the middle and four soil cores distributed evenly around the primary soil core. The primary soil core was used for detailed characterization of soil properties (grain size, structure, texture, field-pH, oxidation depth, ground water level) and acidifying potential (incubation-pH and titratable incubation acidity) whereas the four other cores were used for checking the soil variability. Soil observations from the primary cores are used as calibration and validation data within the modelling.In addition to the EMI data, the present study relies on other environmental predictors: Quaternary geology maps and remote sensing data, such as airborne gamma-radiometric data, a LiDAR-based Digital Elevation Model (DEM), and different land surface parameters derived from this DEM (e.g. slope gradient, distance to channel network, flow accumulation and wetness index).Preliminary results show that the acidifying potential is generally high and that it varies within the soil cores. The interpolated ECa map and EC models both appear to indicate the field drainage system. EC models could also enable detecting the transition zone, which constitutes the most acidic layer overlying the anoxic horizon (i.e. the sulfidic parent material with a high acidifying potential). Additionally, soil texture variation could not be identified because fine-grained sediments homogeneously covered the study area.

AB - Developing methods for the predictive mapping of the potential environmental impact from acid sulfate soils is important because recent studies (e.g. Mattbäck et al., under revision) have shown that the environmental hazards (e.g. leaching of acidity) related to acid sulfate soils vary depending on their texture (clay, silt, sand etc.). Moreover, acidity correlates, not only with the sulfur content, but also with the electrical conductivity (EC) measured after incubation. Electromagnetic induction (EMI) data collected from an EM38 proximal sensor also enabled the detailed mapping of acid sulfate soils over a field (Huang et al., 2014).This study aims at assessing the use of EMI data for the predictive mapping of the acidifying potential in an acid sulfate soil area in western Finland. Different supervised classification modelling techniques, such as Artificial Neural Networks (Beucher et al., 2015), will also be evaluated to generate predictive maps.In the study area, an EMI-survey using a DUALEM proximal sensor was carried out during the autumn of 2016. The collected apparent soil electrical conductivity (ECa) measurements were interpolated using ordinary kriging. An inversion software called Aarhus Workbench (Auken et al., 2015) was used to create 2-D models of EC from the measured ECa. An unsupervised classification method was applied on the interpolated ECa map and LiDAR-based elevation data to partition the study area into homogeneous units. A soil sampling scheme was carried out based on this information, samples being taken from each unit.From every sampling site, five soil cores were taken down to two meters depth with a manually operated soil corer; one primary soil core in the middle and four soil cores distributed evenly around the primary soil core. The primary soil core was used for detailed characterization of soil properties (grain size, structure, texture, field-pH, oxidation depth, ground water level) and acidifying potential (incubation-pH and titratable incubation acidity) whereas the four other cores were used for checking the soil variability. Soil observations from the primary cores are used as calibration and validation data within the modelling.In addition to the EMI data, the present study relies on other environmental predictors: Quaternary geology maps and remote sensing data, such as airborne gamma-radiometric data, a LiDAR-based Digital Elevation Model (DEM), and different land surface parameters derived from this DEM (e.g. slope gradient, distance to channel network, flow accumulation and wetness index).Preliminary results show that the acidifying potential is generally high and that it varies within the soil cores. The interpolated ECa map and EC models both appear to indicate the field drainage system. EC models could also enable detecting the transition zone, which constitutes the most acidic layer overlying the anoxic horizon (i.e. the sulfidic parent material with a high acidifying potential). Additionally, soil texture variation could not be identified because fine-grained sediments homogeneously covered the study area.

M3 - Conference abstract for conference

ER -