DOI: 10.5061/dryad.3bk3j9kpk
title: Land cover classification and mapping of a polar desert in the Canadian Arctic Archipelago

description: 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.
description: 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
description: 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.