Linking GIS and Remote Sensing Data to Study Vegetation Patterns
International audience The paper studies changes in land cover types in tundra landscapes during the past two decades. The study area is located in the Yamal Peninsula, north-central Russia. The main objective of this research is to analyse changes in vegetation distribution and land cover types ove...
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Online Access: | https://doi.org/10.6084/m9.figshare.7210388 https://hal.archives-ouvertes.fr/hal-01986732/file/Lemenkova_Naberezhniye_Chelny.pdf https://hal.archives-ouvertes.fr/hal-01986732 |
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fttriple:oai:gotriple.eu:10670/1.oyakhq 2023-05-15T15:10:48+02:00 Linking GIS and Remote Sensing Data to Study Vegetation Patterns Lemenkova, Polina Ocean University of China (OUC) Kazan National Research Technical University n.a. A.N. Tupolev KNITU-KAI O. Y. Gerasimova E. L. Gunicheva G. S. Mullagayanova Naberezhnye Chelny (Tatarstan), Russia 2015-11-27 https://doi.org/10.6084/m9.figshare.7210388 https://hal.archives-ouvertes.fr/hal-01986732/file/Lemenkova_Naberezhniye_Chelny.pdf https://hal.archives-ouvertes.fr/hal-01986732 en eng HAL CCSD hal-01986732 doi:10.6084/m9.figshare.7210388 10670/1.oyakhq https://hal.archives-ouvertes.fr/hal-01986732/file/Lemenkova_Naberezhniye_Chelny.pdf https://hal.archives-ouvertes.fr/hal-01986732 lic_creative-commons other Hyper Article en Ligne - Sciences de l'Homme et de la Société New Technologies of Knowledge-Intensive Engineering: Priorities of Development and Training New Technologies of Knowledge-Intensive Engineering: Priorities of Development and Training, Kazan National Research Technical University n.a. A.N. Tupolev KNITU-KAI, Nov 2015, Naberezhnye Chelny (Tatarstan), Russia. pp.174-178, ⟨10.6084/m9.figshare.7210388⟩ Landsat TM and ETM data Image classification Satellite image interpretation Spatial analysis Spatial analysis statistics GIS Analysis SIG Systèmes d'information géographique SIG et aménagement SIG et modélisation spatiale geo manag Conference Output https://vocabularies.coar-repositories.org/resource_types/c_c94f/ 2015 fttriple https://doi.org/10.6084/m9.figshare.7210388 2023-01-22T17:38:48Z International audience The paper studies changes in land cover types in tundra landscapes during the past two decades. The study area is located in the Yamal Peninsula, north-central Russia. The main objective of this research is to analyse changes in vegetation distribution and land cover types over the area of Yamal Peninsula. Methodology of the work aims at technical application of the remote sensing and GIS tools for studies and includes georeferencing, creation of color composites, supervised classification. The research data includes Landsat scenes. The research method consists in Landsat image processing (Fig.1), georeferencing via the Google Earth and spatial analysis performed in ILIWIS GIS. The choice of Landsat scenes for land cover mapping is explained by their well-known advantages of application in geosciences and cartography. The final outcomes show changes in the vegetation coverage and land cover classes in Bovanenkovo region of the Yamal Peninsula, which happened during the past two decades. The results are received by comparing and analyzing of two classified maps covering the same geographic region with time span of 23 years: in 1988 and 2011. The changes mostly concern types of land covers and overall increase of shrubland and willows. It can be explained by the complex environmental changes in Arctic regions, which leads to ―greenness‖ processes, or unnatural increase of willows. Other/Unknown Material Arctic Tundra Yamal Peninsula Unknown Arctic Bovanenkovo ENVELOPE(68.437,68.437,70.354,70.354) Yamal Peninsula ENVELOPE(69.873,69.873,70.816,70.816) |
institution |
Open Polar |
collection |
Unknown |
op_collection_id |
fttriple |
language |
English |
topic |
Landsat TM and ETM data Image classification Satellite image interpretation Spatial analysis Spatial analysis statistics GIS Analysis SIG Systèmes d'information géographique SIG et aménagement SIG et modélisation spatiale geo manag |
spellingShingle |
Landsat TM and ETM data Image classification Satellite image interpretation Spatial analysis Spatial analysis statistics GIS Analysis SIG Systèmes d'information géographique SIG et aménagement SIG et modélisation spatiale geo manag Lemenkova, Polina Linking GIS and Remote Sensing Data to Study Vegetation Patterns |
topic_facet |
Landsat TM and ETM data Image classification Satellite image interpretation Spatial analysis Spatial analysis statistics GIS Analysis SIG Systèmes d'information géographique SIG et aménagement SIG et modélisation spatiale geo manag |
description |
International audience The paper studies changes in land cover types in tundra landscapes during the past two decades. The study area is located in the Yamal Peninsula, north-central Russia. The main objective of this research is to analyse changes in vegetation distribution and land cover types over the area of Yamal Peninsula. Methodology of the work aims at technical application of the remote sensing and GIS tools for studies and includes georeferencing, creation of color composites, supervised classification. The research data includes Landsat scenes. The research method consists in Landsat image processing (Fig.1), georeferencing via the Google Earth and spatial analysis performed in ILIWIS GIS. The choice of Landsat scenes for land cover mapping is explained by their well-known advantages of application in geosciences and cartography. The final outcomes show changes in the vegetation coverage and land cover classes in Bovanenkovo region of the Yamal Peninsula, which happened during the past two decades. The results are received by comparing and analyzing of two classified maps covering the same geographic region with time span of 23 years: in 1988 and 2011. The changes mostly concern types of land covers and overall increase of shrubland and willows. It can be explained by the complex environmental changes in Arctic regions, which leads to ―greenness‖ processes, or unnatural increase of willows. |
author2 |
Ocean University of China (OUC) Kazan National Research Technical University n.a. A.N. Tupolev KNITU-KAI O. Y. Gerasimova E. L. Gunicheva G. S. Mullagayanova |
format |
Other/Unknown Material |
author |
Lemenkova, Polina |
author_facet |
Lemenkova, Polina |
author_sort |
Lemenkova, Polina |
title |
Linking GIS and Remote Sensing Data to Study Vegetation Patterns |
title_short |
Linking GIS and Remote Sensing Data to Study Vegetation Patterns |
title_full |
Linking GIS and Remote Sensing Data to Study Vegetation Patterns |
title_fullStr |
Linking GIS and Remote Sensing Data to Study Vegetation Patterns |
title_full_unstemmed |
Linking GIS and Remote Sensing Data to Study Vegetation Patterns |
title_sort |
linking gis and remote sensing data to study vegetation patterns |
publisher |
HAL CCSD |
publishDate |
2015 |
url |
https://doi.org/10.6084/m9.figshare.7210388 https://hal.archives-ouvertes.fr/hal-01986732/file/Lemenkova_Naberezhniye_Chelny.pdf https://hal.archives-ouvertes.fr/hal-01986732 |
op_coverage |
Naberezhnye Chelny (Tatarstan), Russia |
long_lat |
ENVELOPE(68.437,68.437,70.354,70.354) ENVELOPE(69.873,69.873,70.816,70.816) |
geographic |
Arctic Bovanenkovo Yamal Peninsula |
geographic_facet |
Arctic Bovanenkovo Yamal Peninsula |
genre |
Arctic Tundra Yamal Peninsula |
genre_facet |
Arctic Tundra Yamal Peninsula |
op_source |
Hyper Article en Ligne - Sciences de l'Homme et de la Société New Technologies of Knowledge-Intensive Engineering: Priorities of Development and Training New Technologies of Knowledge-Intensive Engineering: Priorities of Development and Training, Kazan National Research Technical University n.a. A.N. Tupolev KNITU-KAI, Nov 2015, Naberezhnye Chelny (Tatarstan), Russia. pp.174-178, ⟨10.6084/m9.figshare.7210388⟩ |
op_relation |
hal-01986732 doi:10.6084/m9.figshare.7210388 10670/1.oyakhq https://hal.archives-ouvertes.fr/hal-01986732/file/Lemenkova_Naberezhniye_Chelny.pdf https://hal.archives-ouvertes.fr/hal-01986732 |
op_rights |
lic_creative-commons other |
op_doi |
https://doi.org/10.6084/m9.figshare.7210388 |
_version_ |
1766341752037310464 |