Three-dimensional imaging of active acid sulfate soil using a DUALEM-21S and EM inversion software

One of the major environmental issues in Finland is the presence of large tracts of acid sulfate soil (ASS) landscapes along the coast. Accurately identifying the distribution of ASS sediments, and in particular soil pH, is essential for developing targeted management strategies. One approach is the...

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Veröffentlicht in:Journal of environmental management Jg. 212; S. 99 - 107
Hauptverfasser: Zare, E., Beucher, A., Huang, J., Boman, A., Mattbäck, S., Greve, M.H., Triantafilis, J.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Elsevier Ltd 15.04.2018
Schlagworte:
acid sulfate soils
Active acid sulfate soil
algorithms
coasts
computer software
digital images
electrical conductivity
Environmental management
Finland
landscapes
neural networks
Quasi 3-d inversion
sediments
Soil acidity mapping
soil pH
soil surveys
Three-dimensional imaging of soil pH
topsoil
Finland
Quasi 3-d inversion
Active acid sulfate soil
Soil acidity mapping
Three-dimensional imaging of soil pH
Environmental management
ISSN:0301-4797, 1095-8630, 1095-8630
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Zusammenfassung:One of the major environmental issues in Finland is the presence of large tracts of acid sulfate soil (ASS) landscapes along the coast. Accurately identifying the distribution of ASS sediments, and in particular soil pH, is essential for developing targeted management strategies. One approach is the use of digital soil mapping (DSM) with various ancillary information. Although electromagnetic (EM) induction data has shown potential in mapping ASS, few studies have been conducted to map the spatial distribution of pH at different depths. In this study, a DUALEM-21S was used to collect apparent soil electrical conductivity (ECa) data across a 23-ha field near Vaasa, which lies along the western coast of Finland. A quasi-3D inversion algorithm was used to calculate the estimated true electrical conductivity (σ – mS m−1). A calibration relationship was developed between σ and incubation-pH measured at various depths from topsoil (0–0.2 m), subsurface (0.2–0.4 m) and subsoil (e.g. 0.4–0.6 and 1.8–2 m) using an artificial neural network (ANN) model. The performance of the ANN model was good given the large R2 values for calibration (0.72) and validation (0.65). It was concluded that the combination of ECa data and quasi-3D inversion algorithm (in EM4Soil) was able to map the spatial distribution of incubation-pH associated within an ASS landscape. The approach has the potential to be applied across the coastal areas of Finland and elsewhere to map incubation-pH and identify active-ASS areas and thereby improve the management of these areas. •Proximal ancillary data was used to map soil acidity.•Inversion software was used to estimate true electrical conductivity (σ) from DUALEM-21S ECa.•A calibration relationship was developed between σ and incubation-pH using ANN modelling.•Topsoil and subsoil incubation-pH were predicted.•Spatial distribution of incubation-pH associated within an acid sulfate soil landscape was mapped.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0301-4797
1095-8630
1095-8630
DOI:10.1016/j.jenvman.2018.02.008