This study presents a decision-tree (DT) approach to classifying heterogeneous land cover types within a northern watershed located in the zone of discontinuous permafrost using airborne LiDAR and high resolution spectral datasets. Results are compared with a more typically applied supervised classification. Increasing errors in discharge resulting from an inaccurate classification are quantified using a distributed hydrological model.
The hierarchical classification was accurate between 88% and 97% of the validation sub-area, whereas the parallelepiped classification was accurate between 38% and 74% of the same area (despite overall accuracy of ~ 91%, kappa = 0.91). Topographical derivatives were best able to explain variations in land cover types (82% to 96%), whilst spectral and vegetation structural derivatives were less accurate. When compared with field measurements, the hierarchical classification of plateau edges (adjacent to a fen) was within 2 m of measured, 60% of the time, whilst this occurred only 40% of the time when using a spectral classification. When examining the impacts of land cover classification accuracy on modelled discharge, we find that the length of the Hydrological Response Unit defined by the classification (and subject to varying levels of errors) was linearly related to discharge (m3) such that an increase in permafrost plateau area would increase discharge by 26% of the total. The methodology presented in this paper clarifies previous classification and modelling studies using Landsat and IKONOS data for the same basin. This study greatly improves upon past classifications in the same area, furthers our understanding of the distribution of connected bogs and fens (as conveyors of water to the basin outlet) within the watershed, and current spatial extents of rapidly thawing permafrost plateaus, which are critical for better understanding the impacts of climate change on these northern environments.