Émilie Desjardins

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.

The startling warming of the Arctic is driving important environmental changes, but vegetation responses have been spatially heterogeneous and difficult to predict. In this context, establishing new ecological baselines and initiating monitoring schemes are essential. We conducted systematic plot-based surveys in the polar desert surrounding Alert (Nunavut, Canada). We aimed at (1) identifying distinct plant communities, (2) characterizing community attributes, including diversity and abundance, as well as environmental variables associated with each community, and (3) establishing a georeferenced baseline with permanent field markers allowing robust re-surveying. We used hierarchical clustering to categorize cover values of vascular plant species, cryptogams, and ground substrates from 1,320 quadrats (1 m² each) surveyed in 264 vegetation plots. Five plant communities were identified, with one community associated with each of the barren and mesic habitats, and three communities associated with wetlands. The mean biotic covers were generally higher at Alert (13–98%) compared to other polar deserts in the Canadian Arctic Archipelago. A total of 250 quadrats from 50 vegetation plots were permanently marked, and a database describing all plots is available online. This study improves our understanding of High-Arctic plant communities and establishes an important vegetation monitoring reference at the northernmost permanently inhabited settlement on Earth.

Long-term monitoring is critical to guide conservation strategies and assess the impacts of climatic changes and anthropogenic activities. In High Arctic ecosystems, information on distribution and population trends of plants is dramatically lacking. During two field expeditions in 2018 and 2019, we conducted a systematic floristic survey together with opportunistic collecting in the polar desert surrounding Alert (Nunavut, Canada) to update past vascular plant inventories. We recorded 58 species, of which 54 species were recorded over the last seven decades, and four species that are additions to the local flora (Draba pauciflora R. Brown, Festuca edlundiae S.G. Aiken, Consaul, & Lefkovitch, Festuca hyperborea Holmen ex Frederiksen, and ×Pucciphippsia vacillans (T. Fries) Tzvelev). With the addition of 19 species that were previously reported but not found in our survey, we estimate the species richness in the study area at 77 species.