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 he...
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ftzenodo:oai:zenodo.org:7693292 2023-07-16T03:55:42+02:00 Land cover classification and mapping of a polar desert in the Canadian Arctic Archipelago Desjardins, Émilie Lai, Sandra Houle, Laurent Caron, Alain Thériault, Véronique Tam, Andrew Vézina, François Berteaux, Dominique 2023-06-26 https://zenodo.org/record/7693292 https://doi.org/10.5061/dryad.3bk3j9kpk unknown doi:10.1080/11956860.2021.1907974 doi:10.7910/DVN/OHHUKH doi:10.3390/rs15123090 doi:10.5281/zenodo.7689322 doi:10.5281/zenodo.7689324 https://zenodo.org/communities/dryad https://zenodo.org/record/7693292 https://doi.org/10.5061/dryad.3bk3j9kpk oai:zenodo.org:7693292 info:eu-repo/semantics/openAccess https://creativecommons.org/publicdomain/zero/1.0/legalcode Remote sensing Satellite image predictor selection majority voting ensemble model Snow vegetation water info:eu-repo/semantics/other dataset 2023 ftzenodo https://doi.org/10.5061/dryad.3bk3j9kpk10.1080/11956860.2021.190797410.7910/DVN/OHHUKH10.3390/rs1512309010.5281/zenodo.768932210.5281/zenodo.7689324 2023-06-27T23:00:10Z 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 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 ... Dataset Arctic Archipelago Arctic Canadian Arctic Archipelago Climate change Ellesmere Island Nunavut polar desert Zenodo Arctic Nunavut Ellesmere Island Canadian Arctic Archipelago Canada Canadian Forces Station Alert ENVELOPE(-62.350,-62.350,82.499,82.499) |
institution |
Open Polar |
collection |
Zenodo |
op_collection_id |
ftzenodo |
language |
unknown |
topic |
Remote sensing Satellite image predictor selection majority voting ensemble model Snow vegetation water |
spellingShingle |
Remote sensing Satellite image predictor selection majority voting ensemble model Snow vegetation water Desjardins, Émilie Lai, Sandra Houle, Laurent Caron, Alain Thériault, Véronique Tam, Andrew Vézina, François Berteaux, Dominique Land cover classification and mapping of a polar desert in the Canadian Arctic Archipelago |
topic_facet |
Remote sensing Satellite image predictor selection majority voting ensemble model Snow vegetation water |
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. 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 ... |
format |
Dataset |
author |
Desjardins, Émilie Lai, Sandra Houle, Laurent Caron, Alain Thériault, Véronique Tam, Andrew Vézina, François Berteaux, Dominique |
author_facet |
Desjardins, Émilie Lai, Sandra Houle, Laurent Caron, Alain Thériault, Véronique Tam, Andrew Vézina, François Berteaux, Dominique |
author_sort |
Desjardins, Émilie |
title |
Land cover classification and mapping of a polar desert in the Canadian Arctic Archipelago |
title_short |
Land cover classification and mapping of a polar desert in the Canadian Arctic Archipelago |
title_full |
Land cover classification and mapping of a polar desert in the Canadian Arctic Archipelago |
title_fullStr |
Land cover classification and mapping of a polar desert in the Canadian Arctic Archipelago |
title_full_unstemmed |
Land cover classification and mapping of a polar desert in the Canadian Arctic Archipelago |
title_sort |
land cover classification and mapping of a polar desert in the canadian arctic archipelago |
publishDate |
2023 |
url |
https://zenodo.org/record/7693292 https://doi.org/10.5061/dryad.3bk3j9kpk |
long_lat |
ENVELOPE(-62.350,-62.350,82.499,82.499) |
geographic |
Arctic Nunavut Ellesmere Island Canadian Arctic Archipelago Canada Canadian Forces Station Alert |
geographic_facet |
Arctic Nunavut Ellesmere Island Canadian Arctic Archipelago Canada Canadian Forces Station Alert |
genre |
Arctic Archipelago Arctic Canadian Arctic Archipelago Climate change Ellesmere Island Nunavut polar desert |
genre_facet |
Arctic Archipelago Arctic Canadian Arctic Archipelago Climate change Ellesmere Island Nunavut polar desert |
op_relation |
doi:10.1080/11956860.2021.1907974 doi:10.7910/DVN/OHHUKH doi:10.3390/rs15123090 doi:10.5281/zenodo.7689322 doi:10.5281/zenodo.7689324 https://zenodo.org/communities/dryad https://zenodo.org/record/7693292 https://doi.org/10.5061/dryad.3bk3j9kpk oai:zenodo.org:7693292 |
op_rights |
info:eu-repo/semantics/openAccess https://creativecommons.org/publicdomain/zero/1.0/legalcode |
op_doi |
https://doi.org/10.5061/dryad.3bk3j9kpk10.1080/11956860.2021.190797410.7910/DVN/OHHUKH10.3390/rs1512309010.5281/zenodo.768932210.5281/zenodo.7689324 |
_version_ |
1771541839464103936 |