Possible use of remote sensing for reforestation processes in Arctic zone of European Russia
This article considers the possibility of using remote sensing to monitor reforestation as exemplified in the Severodvinsk and Onezhsk forestry districts of the Arkhangelsk region of Russia's Arctic zone. Remote sensing makes use of medium spatial resolution satellite images and high resolution...
Published in: | Arctic Environmental Research |
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Northern (Arctic) Federal University named after M.V. Lomonosov
2018
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Online Access: | https://doi.org/10.3897/issn2541-8416.2018.18.3.106 |
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ftzenodo:oai:zenodo.org:2534977 2024-09-15T17:50:08+00:00 Possible use of remote sensing for reforestation processes in Arctic zone of European Russia Demina, NA Karpov, AA Voronin, VV Lopatin, EV Bognanov, AP 2018-11-02 https://doi.org/10.3897/issn2541-8416.2018.18.3.106 unknown Northern (Arctic) Federal University named after M.V. Lomonosov https://zenodo.org/communities/biosyslit https://doi.org/10.3897/issn2541-8416.2018.18.3.106 oai:zenodo.org:2534977 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Arctic Environmental Research, 18((3)), 106-113, (2018-11-02) reforestation forest monitoring forest cutting forest dynamic boreal forest Landsat Sentinel remote sensing ERS unmanned aerial vehicle UAV info:eu-repo/semantics/article 2018 ftzenodo https://doi.org/10.3897/issn2541-8416.2018.18.3.106 2024-07-26T05:34:47Z This article considers the possibility of using remote sensing to monitor reforestation as exemplified in the Severodvinsk and Onezhsk forestry districts of the Arkhangelsk region of Russia's Arctic zone. Remote sensing makes use of medium spatial resolution satellite images and high resolution unmanned aerial vehicle (UAV) images. In the course of work on the project, a preliminary method was developed for reforesting land previously subjected to cutting, fire, or windfall. Steps include detecting a reduction in forest cover and collecting field data through the use of UAVs to create a training set, which is used to classify satellite images according to the two classes of 'restored' or 'not restored'. Various data processing tools are used to perform these steps. The Tasseled Cap multi-channel satellite image transformation method is employed as a tool for detecting a reduction in forest cover and analysing reforestation. The k-nearest neighbour algorithm is employed to classify satellite images. This article provides a step-by-step algorithm for monitoring and an assessment is provided of the situation in relation to forest regeneration in the Severodvinsk and Onezhsk forestry districts. The work carried out has shown that it is possible to use UAV images to monitor forest recovery, which is of significant importance for the conditions of the Arctic zone of European Russia. Article in Journal/Newspaper Arctic Arkhangelsk Zenodo Arctic Environmental Research 18 3 106 113 |
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collection |
Zenodo |
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ftzenodo |
language |
unknown |
topic |
reforestation forest monitoring forest cutting forest dynamic boreal forest Landsat Sentinel remote sensing ERS unmanned aerial vehicle UAV |
spellingShingle |
reforestation forest monitoring forest cutting forest dynamic boreal forest Landsat Sentinel remote sensing ERS unmanned aerial vehicle UAV Demina, NA Karpov, AA Voronin, VV Lopatin, EV Bognanov, AP Possible use of remote sensing for reforestation processes in Arctic zone of European Russia |
topic_facet |
reforestation forest monitoring forest cutting forest dynamic boreal forest Landsat Sentinel remote sensing ERS unmanned aerial vehicle UAV |
description |
This article considers the possibility of using remote sensing to monitor reforestation as exemplified in the Severodvinsk and Onezhsk forestry districts of the Arkhangelsk region of Russia's Arctic zone. Remote sensing makes use of medium spatial resolution satellite images and high resolution unmanned aerial vehicle (UAV) images. In the course of work on the project, a preliminary method was developed for reforesting land previously subjected to cutting, fire, or windfall. Steps include detecting a reduction in forest cover and collecting field data through the use of UAVs to create a training set, which is used to classify satellite images according to the two classes of 'restored' or 'not restored'. Various data processing tools are used to perform these steps. The Tasseled Cap multi-channel satellite image transformation method is employed as a tool for detecting a reduction in forest cover and analysing reforestation. The k-nearest neighbour algorithm is employed to classify satellite images. This article provides a step-by-step algorithm for monitoring and an assessment is provided of the situation in relation to forest regeneration in the Severodvinsk and Onezhsk forestry districts. The work carried out has shown that it is possible to use UAV images to monitor forest recovery, which is of significant importance for the conditions of the Arctic zone of European Russia. |
format |
Article in Journal/Newspaper |
author |
Demina, NA Karpov, AA Voronin, VV Lopatin, EV Bognanov, AP |
author_facet |
Demina, NA Karpov, AA Voronin, VV Lopatin, EV Bognanov, AP |
author_sort |
Demina, NA |
title |
Possible use of remote sensing for reforestation processes in Arctic zone of European Russia |
title_short |
Possible use of remote sensing for reforestation processes in Arctic zone of European Russia |
title_full |
Possible use of remote sensing for reforestation processes in Arctic zone of European Russia |
title_fullStr |
Possible use of remote sensing for reforestation processes in Arctic zone of European Russia |
title_full_unstemmed |
Possible use of remote sensing for reforestation processes in Arctic zone of European Russia |
title_sort |
possible use of remote sensing for reforestation processes in arctic zone of european russia |
publisher |
Northern (Arctic) Federal University named after M.V. Lomonosov |
publishDate |
2018 |
url |
https://doi.org/10.3897/issn2541-8416.2018.18.3.106 |
genre |
Arctic Arkhangelsk |
genre_facet |
Arctic Arkhangelsk |
op_source |
Arctic Environmental Research, 18((3)), 106-113, (2018-11-02) |
op_relation |
https://zenodo.org/communities/biosyslit https://doi.org/10.3897/issn2541-8416.2018.18.3.106 oai:zenodo.org:2534977 |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode |
op_doi |
https://doi.org/10.3897/issn2541-8416.2018.18.3.106 |
container_title |
Arctic Environmental Research |
container_volume |
18 |
container_issue |
3 |
container_start_page |
106 |
op_container_end_page |
113 |
_version_ |
1810291984215048192 |