Modelling Landscape Changes and Detecting Land Cover Types by Means of Remote Sensing Data and ILWIS GIS
The emphasis of this article is placed on the technical application of the remote sensing tools and methods for studies of vegetation coverage in northern ecosystems. The study area is located in Yamal peninsula, the Russian Federation. Landsat imagery covering study area in 1988, 2001 and 2011 has...
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Format: | Conference Object |
Language: | unknown |
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Zenodo
2015
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Online Access: | https://doi.org/10.6084/m9.figshare.7211639 |
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author | Lemenkova Polina |
author_facet | Lemenkova Polina |
author_sort | Lemenkova Polina |
collection | Zenodo |
description | The emphasis of this article is placed on the technical application of the remote sensing tools and methods for studies of vegetation coverage in northern ecosystems. The study area is located in Yamal peninsula, the Russian Federation. Landsat imagery covering study area in 1988, 2001 and 2011 has been analyzed using ILWIS GIS. The image processing was performed using semi-automated method of image interpretation. The remote sensing data classification from ILWIS menu enabled to map vegetation coverage over research area, which helped to identify land cover types and distribution in Yamal. Results show that Landsat TM imagery with 30 m mesh spacing is useful for landscape mapping and the interpretation of the vegetation cover types. P. Lemenkova. "Modelling Landscape Changes and Detecting Land Cover Types by Means of Remote Sensing Data and ILWIS GIS". In: Information Technologies. Problems and Solutions. Proceedings of the 3rd International Research Conference (Ufa State Petroleum Technological University, May 20–22, 2015). Ed. by F. U. Enikeev. Vol. 1. 2. Ufa, Russia: Vostochnaya Pechat, 2015, pp. 265–271. isbn: 978-5-905220-50-4. doi:10.6084/m9.figshare.7211639. |
format | Conference Object |
genre | Arctic Yamal Peninsula |
genre_facet | Arctic Yamal Peninsula |
geographic | Arctic Yamal Peninsula |
geographic_facet | Arctic Yamal Peninsula |
id | ftzenodo:oai:zenodo.org:2317833 |
institution | Open Polar |
language | unknown |
long_lat | ENVELOPE(69.873,69.873,70.816,70.816) |
op_collection_id | ftzenodo |
op_doi | https://doi.org/10.6084/m9.figshare.7211639 |
op_relation | https://d-nb.info/gnd/978-5-905220-50-4 https://doi.org/10.6084/m9.figshare.7211639 oai:zenodo.org:2317833 |
op_rights | info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode |
op_source | Information Technologies. Problems and Solutions, Ufa (Bashkortostan), Russia, May 20–22, 2015 |
publishDate | 2015 |
publisher | Zenodo |
record_format | openpolar |
spelling | ftzenodo:oai:zenodo.org:2317833 2025-01-16T20:39:54+00:00 Modelling Landscape Changes and Detecting Land Cover Types by Means of Remote Sensing Data and ILWIS GIS Lemenkova Polina 2015-05-20 https://doi.org/10.6084/m9.figshare.7211639 unknown Zenodo https://d-nb.info/gnd/978-5-905220-50-4 https://doi.org/10.6084/m9.figshare.7211639 oai:zenodo.org:2317833 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Information Technologies. Problems and Solutions, Ufa (Bashkortostan), Russia, May 20–22, 2015 Modelling Mapping GIS land cover types land cover changes landscapes Arctic image processing satellite images Landsat TM info:eu-repo/semantics/conferencePaper 2015 ftzenodo https://doi.org/10.6084/m9.figshare.7211639 2024-12-05T16:05:12Z The emphasis of this article is placed on the technical application of the remote sensing tools and methods for studies of vegetation coverage in northern ecosystems. The study area is located in Yamal peninsula, the Russian Federation. Landsat imagery covering study area in 1988, 2001 and 2011 has been analyzed using ILWIS GIS. The image processing was performed using semi-automated method of image interpretation. The remote sensing data classification from ILWIS menu enabled to map vegetation coverage over research area, which helped to identify land cover types and distribution in Yamal. Results show that Landsat TM imagery with 30 m mesh spacing is useful for landscape mapping and the interpretation of the vegetation cover types. P. Lemenkova. "Modelling Landscape Changes and Detecting Land Cover Types by Means of Remote Sensing Data and ILWIS GIS". In: Information Technologies. Problems and Solutions. Proceedings of the 3rd International Research Conference (Ufa State Petroleum Technological University, May 20–22, 2015). Ed. by F. U. Enikeev. Vol. 1. 2. Ufa, Russia: Vostochnaya Pechat, 2015, pp. 265–271. isbn: 978-5-905220-50-4. doi:10.6084/m9.figshare.7211639. Conference Object Arctic Yamal Peninsula Zenodo Arctic Yamal Peninsula ENVELOPE(69.873,69.873,70.816,70.816) |
spellingShingle | Modelling Mapping GIS land cover types land cover changes landscapes Arctic image processing satellite images Landsat TM Lemenkova Polina Modelling Landscape Changes and Detecting Land Cover Types by Means of Remote Sensing Data and ILWIS GIS |
title | Modelling Landscape Changes and Detecting Land Cover Types by Means of Remote Sensing Data and ILWIS GIS |
title_full | Modelling Landscape Changes and Detecting Land Cover Types by Means of Remote Sensing Data and ILWIS GIS |
title_fullStr | Modelling Landscape Changes and Detecting Land Cover Types by Means of Remote Sensing Data and ILWIS GIS |
title_full_unstemmed | Modelling Landscape Changes and Detecting Land Cover Types by Means of Remote Sensing Data and ILWIS GIS |
title_short | Modelling Landscape Changes and Detecting Land Cover Types by Means of Remote Sensing Data and ILWIS GIS |
title_sort | modelling landscape changes and detecting land cover types by means of remote sensing data and ilwis gis |
topic | Modelling Mapping GIS land cover types land cover changes landscapes Arctic image processing satellite images Landsat TM |
topic_facet | Modelling Mapping GIS land cover types land cover changes landscapes Arctic image processing satellite images Landsat TM |
url | https://doi.org/10.6084/m9.figshare.7211639 |