Land cover classification and mapping of a polar desert in the Canadian Arctic Archipelago

The use of remote sensing for developing land cover maps in the Arctic has
grown considerably in the last two decades, especially for monitoring the
effects of climate change. The main challenge is to link information
extracted from satellite imagery to ground covers due to the fine-scale
spatial heterogeneity of Arctic ecosystems. There is currently no commonly
accepted methodological scheme for high-latitude land cover mapping, but
the use of remote sensing in Arctic ecosystem mapping would benefit from a
coordinated sharing of lessons learned and best practices. Here, we aimed
to produce a highly accurate land cover map of the surroundings of the
Canadian Forces Station Alert, a polar desert on the northeastern tip of
Ellesmere Island (Nunavut, Canada) by testing different predictors and
classifiers. To account for the effect of the bare soil background and
water limitations that are omnipresent at these latitudes, we included as
predictors soil-adjusted vegetation indices and several hydrological
predictors related to waterbodies and snowbanks. We compared the results
obtained from an ensemble classifier based on a majority voting algorithm
to eight commonly used classifiers. The distance to the nearest snowbank
and soil-adjusted indices were the top predictors allowing the
discrimination of land cover classes in our study area. The overall
accuracy of the classifiers ranged between 75 and 88%, with the ensemble
classifier also yielding a high accuracy (85%) and producing less bias
than the individual classifiers. Some challenges remained, such as shadows
created by boulders and snow covered by soil material. We provide
recommendations for further improving classification methodology in the
High Arctic, which is important for the monitoring of Arctic ecosystems
exposed to ongoing polar amplification.