Abstract

Comparisons between field data and available maps show that 64% of wet areas in the boreal landscape are missing on current maps. Primarily forested wetlands and wet soils near streams and lakes are missing, making them difficult to manage. One solution is to model missing wet areas from high-resolution digital elevation models, using indices such as topographical wetness index and depth to water. However, when working across large areas with gradients in topography, soils and climate, it is not possible to find one method or one threshold that works everywhere. By using soil moisture data from the National Forest Inventory of Sweden as a training dataset, we show that it is possible to combine information from several indices and thresholds, using machine learners, thereby improving the mapping of wet soils (kappa = 0.65). The new maps can be used to better plan roads and generate riparian buffer zones near surface waters.

Keywords

Digital elevation model; LiDAR; Machine learning; Random Forest; Soil classification; Wet area mapping

Published in

AMBIO: A Journal of the Human Environment
2020, Volume: 49, number: 2, pages: 475-486

SLU Authors

• Lidberg, William

  • Department of Forest Ecology and Management, Swedish University of Agricultural Sciences
    

• Nilsson, Mats

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

• Ågren, Anneli

  • Department of Forest Ecology and Management, Swedish University of Agricultural Sciences
    

Sustainable Development Goals

Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation


UKÄ Subject classification

Remote Sensing
Forest Science


Publication identifier

DOI: https://doi.org/10.1007/s13280-019-01196-9

Permanent link to this page (URI)

https://res.slu.se/id/publ/103137