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Research article2020Peer reviewedOpen access

Using machine learning to generate high-resolution wet area maps for planning forest management: A study in a boreal forest landscape

Lidberg, William; Nilsson, Mats; Agren, Anneli


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.


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