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 methodology and the land cover classes are described in the related works:

Émilie Desjardins, Sandra Lai, Laurent Houle, Alain Caron, Véronique Thériault, Andrew Tam, François Vézina & Dominique Berteaux (2023) Algorithms and Predictors for Land Cover Classification of Polar Deserts: A Case Study Highlighting Challenges and Recommendations for Future Applications. Remote Sensing, 15:12, 3090, DOI: 10.3390/rs15123090

Émilie Desjardins, Sandra Lai, Serge Payette, François Vézina, Andrew Tam & Dominique Berteaux (2021) Vascular plant communities in the polar desert of Alert (Ellesmere Island, Canada): Establishment of a baseline reference for the 21st century, Écoscience, 28:3-4, 243-267, DOI: 10.1080/11956860.2021.1907974

The dataset includes a shapefile named reference.shp, which consists of a collection of files with a common filename prefix, stored in the same directory. The shapefile stores the location, shape (point in this case), and attribute of the 467 reference points. The attribute of each point includes the land cover class among the following: bareground (refers to forb-dominated barren), mesic (refers to forb-dominated tundra), wetgrass (refers to grass-dominated wetland), wetsedge (refers to sedge-dominated wetland), wetmoss (refers to moss-dominated wetland), water, and snow. The shapefile can be opened in geographic information system (GIS) software such as QGIS (QGIS Development Team) and ArcGIS (ESRI). The geographic coordinate system is NAD 1983 (EPSG:4269) and the projected coordinate system is NAD 1983 UTM Zone 20N (EPSG:26920).

The dataset also includes a file named ensemble_classifier.tif. It is a raster GIS file in GeoTIFF format with 0.5 x 0.5 meter resolution. The uncompressed size is 382,48 MB. It can be opened in GIS software such as QGIS and ArcGIS. The geographic coordinate system is NAD 1983 (EPSG:4269) and the projected coordinate system is NAD 1983 UTM Zone 20N (EPSG:26920). Dimensions of the raster are 26870 rows x 29852 columns. The values in the raster represent the land cover classes: 1 = forb-dominated barren, 2= forb-dominated tundra, 3= snow, 4= water, 5= grass-dominated wetland, 6= moss-dominated wetland, 7= sedge-dominated wetland, 8= human infrastructure, and 9 = shadow (NoData value = 15).

The two other files in this dataset are R scripts that can be opened in RStudio software (RStudio Team). One script, named selection_classification_validation.R contains the different steps necessary to select the most relevant predictors prior to classification, to train the seven nonparametric classifiers (Artificial Neural Networks, Classification And Regression Trees, K-Nearest Neighbors, Linear Discriminant Analysis, Naive Bayes, Random Forests, and Support Vector Machines), and to evaluate the accuracy of the resulting classifications. The second script, named majority_voting.R, is the ensemble classifier with majority voting algorithm in which classified maps (rasters in geotiff format) of four of the classifiers (Random Forests, Linear Discriminant Analysis, Classification And Regression Trees, Maximum Likelihood) are combined. Note that the classified maps for the three non-parametric classifiers were created from the first script, while the classified map from the parametric classifier, Maximum Likelihood, was created using ArcGIS Pro.