Mapping potential acid sulfate soils in Denmark using legacy data and LiDAR-based derivatives

A Beucher, K Adhikari, H Breuning-Madsen, M Greve, P Österholm, S Fröjdö, Niels H. Jensen, MH Greve

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Leaching large amounts of acidity and metals into recipient watercourses and estuaries, acid sulfate (a.s.) soils constitute a substantial environmental issue worldwide. Mapping of these soils enables measures to be taken to prevent pollution in high risk areas. In Denmark, legislation prohibits drainage of areas classified as potential a.s. soils without prior permission from environmental authorities. The mapping of these soils was first conducted in the 1980’s. Wetlands, in which Danish potential a.s. soils mostly occur, were targeted and the soils were surveyed through conventional mapping. In this study, a probability map for potential a.s. soil occurrence was constructed for the wetlands located in Jutland, Denmark (c. 6500 km2), using the digital soil mapping (DSM) approach. Among the variety of available DSM techniques, artificial neural networks (ANNs) were selected. More than 8000 existing soil observations and 16 environmental variables, including geology, landscape type, land use and terrain parameters, were available as input data within the modeling. Prediction models based on various network topologies were assessed for different selections of soil observations and combinations of environmental variables. The overall prediction accuracy based on a 30% hold-back validation data reached 70%. Furthermore, the conventional map indicated 32% of the study area (c. 2100 km2) as having a high frequency for potential a.s. soils while the digital map displayed about 46% (c. 3000 km2) as high probability areas for potential a.s. soil occurrence. ANNs, thus, demonstrated promising predictive classification abilities for the mapping of potential a.s. soils on a large extent.
Sider (fra-til)363-372
Antal sider10
StatusUdgivet - 15 dec. 2017


  • Acid sulfate soils
  • Digital soil mapping
  • Artificial neural networks
  • LiDAR-based derivatives

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