Algorithms and Predictors for Land Cover Classification of Polar Deserts: A Case Study Highlighting Challenges and Recommendations for Future Applications
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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ftdoajarticles:oai:doaj.org/article:1a8337c343834cda839d3d4c33744cc6 2023-07-23T04:17:10+02:00 Algorithms and Predictors for Land Cover Classification of Polar Deserts: A Case Study Highlighting Challenges and Recommendations for Future Applications Émilie Desjardins Sandra Lai Laurent Houle Alain Caron Véronique Thériault Andrew Tam François Vézina Dominique Berteaux 2023-06-01T00:00:00Z https://doi.org/10.3390/rs15123090 https://doaj.org/article/1a8337c343834cda839d3d4c33744cc6 EN eng MDPI AG https://www.mdpi.com/2072-4292/15/12/3090 https://doaj.org/toc/2072-4292 doi:10.3390/rs15123090 2072-4292 https://doaj.org/article/1a8337c343834cda839d3d4c33744cc6 Remote Sensing, Vol 15, Iss 3090, p 3090 (2023) High Arctic remote sensing multispectral imagery WorldView-2/3 ensemble classifier majority voting Science Q article 2023 ftdoajarticles https://doi.org/10.3390/rs15123090 2023-07-02T00:37:11Z 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. Article in Journal/Newspaper Arctic Climate change Ellesmere Island Nunavut polar desert Directory of Open Access Journals: DOAJ Articles Arctic Canada Canadian Forces Station Alert ENVELOPE(-62.350,-62.350,82.499,82.499) Ellesmere Island Nunavut Remote Sensing 15 12 3090 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
High Arctic remote sensing multispectral imagery WorldView-2/3 ensemble classifier majority voting Science Q |
spellingShingle |
High Arctic remote sensing multispectral imagery WorldView-2/3 ensemble classifier majority voting Science Q Émilie Desjardins Sandra Lai Laurent Houle Alain Caron Véronique Thériault Andrew Tam François Vézina Dominique Berteaux Algorithms and Predictors for Land Cover Classification of Polar Deserts: A Case Study Highlighting Challenges and Recommendations for Future Applications |
topic_facet |
High Arctic remote sensing multispectral imagery WorldView-2/3 ensemble classifier majority voting Science Q |
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. |
format |
Article in Journal/Newspaper |
author |
Émilie Desjardins Sandra Lai Laurent Houle Alain Caron Véronique Thériault Andrew Tam François Vézina Dominique Berteaux |
author_facet |
Émilie Desjardins Sandra Lai Laurent Houle Alain Caron Véronique Thériault Andrew Tam François Vézina Dominique Berteaux |
author_sort |
Émilie Desjardins |
title |
Algorithms and Predictors for Land Cover Classification of Polar Deserts: A Case Study Highlighting Challenges and Recommendations for Future Applications |
title_short |
Algorithms and Predictors for Land Cover Classification of Polar Deserts: A Case Study Highlighting Challenges and Recommendations for Future Applications |
title_full |
Algorithms and Predictors for Land Cover Classification of Polar Deserts: A Case Study Highlighting Challenges and Recommendations for Future Applications |
title_fullStr |
Algorithms and Predictors for Land Cover Classification of Polar Deserts: A Case Study Highlighting Challenges and Recommendations for Future Applications |
title_full_unstemmed |
Algorithms and Predictors for Land Cover Classification of Polar Deserts: A Case Study Highlighting Challenges and Recommendations for Future Applications |
title_sort |
algorithms and predictors for land cover classification of polar deserts: a case study highlighting challenges and recommendations for future applications |
publisher |
MDPI AG |
publishDate |
2023 |
url |
https://doi.org/10.3390/rs15123090 https://doaj.org/article/1a8337c343834cda839d3d4c33744cc6 |
long_lat |
ENVELOPE(-62.350,-62.350,82.499,82.499) |
geographic |
Arctic Canada Canadian Forces Station Alert Ellesmere Island Nunavut |
geographic_facet |
Arctic Canada Canadian Forces Station Alert Ellesmere Island Nunavut |
genre |
Arctic Climate change Ellesmere Island Nunavut polar desert |
genre_facet |
Arctic Climate change Ellesmere Island Nunavut polar desert |
op_source |
Remote Sensing, Vol 15, Iss 3090, p 3090 (2023) |
op_relation |
https://www.mdpi.com/2072-4292/15/12/3090 https://doaj.org/toc/2072-4292 doi:10.3390/rs15123090 2072-4292 https://doaj.org/article/1a8337c343834cda839d3d4c33744cc6 |
op_doi |
https://doi.org/10.3390/rs15123090 |
container_title |
Remote Sensing |
container_volume |
15 |
container_issue |
12 |
container_start_page |
3090 |
_version_ |
1772178498435153920 |