Mapping land cover changes using Landsat TM: a case study of Yamal ecosystems, Arctic Russia
International audience 1. Introduction. This paper details changes in land cover types in tundra landscapes (Yamal) during since 1988. The research method is supervised classification (Minimal Distance) of the Landsat TM scenes. The new approach of the current work is application of ILWIS GIS and RS...
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HAL CCSD
2012
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Online Access: | https://hal.archives-ouvertes.fr/hal-01972875 https://hal.archives-ouvertes.fr/hal-01972875/document https://hal.archives-ouvertes.fr/hal-01972875/file/Lemenkova_etal_Poster_Yamal.pdf https://doi.org/10.13140/RG.2.2.32044.72329 |
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Open Polar |
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Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
op_collection_id |
ftccsdartic |
language |
English |
topic |
Image processing Image recognition Image processing Pattern recognition Image processing Digital image processing Segmentation Classification Landsat 7 ETM+ Landsat Imagery Satellite image analysis Satellite image interpretation GIS & Spatial Analyses GIS Analysis SIG et modélisation spatiale SIG Systèmes d'information géographique SIG Raster SIG topographie photogrammétrie méthodologie ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.10: Image Representation/I.4.10.3: Statistical ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.1: Models ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.3: Clustering ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.3: Clustering/I.5.3.0: Algorithms ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.3: Clustering/I.5.3.1: Similarity measures ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.4: Applications/I.5.4.0: Computer vision ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.0: General/I.4.0.0: Image displays ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.0: General/I.4.0.1: Image processing software ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.1: Digitization and Image Capture/I.4.1.1: Imaging geometry ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.6: Segmentation ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.6: Segmentation/I.4.6.1: Pixel classification ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.10: Image Representation [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] [SDV.EE.ECO]Life Sciences [q-bio]/Ecology environment/Ecosystems [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing [SDE.MCG]Environmental Sciences/Global Changes [SDE.BE]Environmental Sciences/Biodiversity and Ecology [SDE.ES]Environmental Sciences/Environmental and Society [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC] [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] |
spellingShingle |
Image processing Image recognition Image processing Pattern recognition Image processing Digital image processing Segmentation Classification Landsat 7 ETM+ Landsat Imagery Satellite image analysis Satellite image interpretation GIS & Spatial Analyses GIS Analysis SIG et modélisation spatiale SIG Systèmes d'information géographique SIG Raster SIG topographie photogrammétrie méthodologie ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.10: Image Representation/I.4.10.3: Statistical ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.1: Models ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.3: Clustering ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.3: Clustering/I.5.3.0: Algorithms ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.3: Clustering/I.5.3.1: Similarity measures ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.4: Applications/I.5.4.0: Computer vision ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.0: General/I.4.0.0: Image displays ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.0: General/I.4.0.1: Image processing software ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.1: Digitization and Image Capture/I.4.1.1: Imaging geometry ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.6: Segmentation ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.6: Segmentation/I.4.6.1: Pixel classification ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.10: Image Representation [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] [SDV.EE.ECO]Life Sciences [q-bio]/Ecology environment/Ecosystems [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing [SDE.MCG]Environmental Sciences/Global Changes [SDE.BE]Environmental Sciences/Biodiversity and Ecology [SDE.ES]Environmental Sciences/Environmental and Society [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC] [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] Lemenkova, Polina Forbes, Bruce, Kumpula, Timo Mapping land cover changes using Landsat TM: a case study of Yamal ecosystems, Arctic Russia |
topic_facet |
Image processing Image recognition Image processing Pattern recognition Image processing Digital image processing Segmentation Classification Landsat 7 ETM+ Landsat Imagery Satellite image analysis Satellite image interpretation GIS & Spatial Analyses GIS Analysis SIG et modélisation spatiale SIG Systèmes d'information géographique SIG Raster SIG topographie photogrammétrie méthodologie ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.10: Image Representation/I.4.10.3: Statistical ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.1: Models ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.3: Clustering ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.3: Clustering/I.5.3.0: Algorithms ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.3: Clustering/I.5.3.1: Similarity measures ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.4: Applications/I.5.4.0: Computer vision ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.0: General/I.4.0.0: Image displays ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.0: General/I.4.0.1: Image processing software ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.1: Digitization and Image Capture/I.4.1.1: Imaging geometry ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.6: Segmentation ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.6: Segmentation/I.4.6.1: Pixel classification ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.10: Image Representation [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] [SDV.EE.ECO]Life Sciences [q-bio]/Ecology environment/Ecosystems [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing [SDE.MCG]Environmental Sciences/Global Changes [SDE.BE]Environmental Sciences/Biodiversity and Ecology [SDE.ES]Environmental Sciences/Environmental and Society [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC] [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] |
description |
International audience 1. Introduction. This paper details changes in land cover types in tundra landscapes (Yamal) during since 1988. The research method is supervised classification (Minimal Distance) of the Landsat TM scenes. The new approach of the current work is application of ILWIS GIS and RS tools for Bovanenkovo region. The research area is geographically located on the Bovanenkovo region, the north-western part of Yamal Peninsula, West Siberia, Russia. The Yamal Peninsula is a flat homogeneous lowland region with low-lying plains of heights <90m. Such geographic settings create specific local environmental conditions in the region. Thus, Yamal is the worlds largest high-latitude wetland system covering in total 900,000 km2 of peatlands, complex system of wetlands, dense lake and river network. Typical for this region are seasonal flooding, active erosion processing, permafrost distribution and intensive local landslides. 2. Data.The research data are orthorectified Landsat TM scenes covering north-west of Yamal. The images have a time span of 23 years: 1988-08-07 and 2011-07-14, taken in growing season when vegetation coverage is clearly visible.3. Methods.The research methods consist of image classification, spatial analysis and thematic mapping, technically performed in ILIWIS GIS. Research steps: 1. Data pre-processing: a) import .img into ASCII raster format (GDAL). After converting, each image contained collection of 7 Landsat raster bands b) visual color and contrast enhancement c) geographic referencing of Landsat scenes: UTM (Universal Transverse Mercator), Eastern Zone 42, Northern Zone W, WGS 1984 datum (Georeference Corner Editor, ILWIS). 2. Research area selection. The area of interest (AOI) was identified and cropped on the raw images (Fig.3). This area shows Bovanenkovo region in a large scale. The AOI area best represents typical tundra landscapes. 3. Image classification method is supervised classification (Minimal Distance), which is based on the spatial analysis of spectral ... |
author2 |
Ocean University of China (OUC) Fellowship of the Center for International Mobility (CIMO) of Finland. Contract No. TM-10-7124 (Decision 9.11.2010). All Ukrainian Association of Geoinformatics (AUAG) |
format |
Conference Object |
author |
Lemenkova, Polina Forbes, Bruce, Kumpula, Timo |
author_facet |
Lemenkova, Polina Forbes, Bruce, Kumpula, Timo |
author_sort |
Lemenkova, Polina |
title |
Mapping land cover changes using Landsat TM: a case study of Yamal ecosystems, Arctic Russia |
title_short |
Mapping land cover changes using Landsat TM: a case study of Yamal ecosystems, Arctic Russia |
title_full |
Mapping land cover changes using Landsat TM: a case study of Yamal ecosystems, Arctic Russia |
title_fullStr |
Mapping land cover changes using Landsat TM: a case study of Yamal ecosystems, Arctic Russia |
title_full_unstemmed |
Mapping land cover changes using Landsat TM: a case study of Yamal ecosystems, Arctic Russia |
title_sort |
mapping land cover changes using landsat tm: a case study of yamal ecosystems, arctic russia |
publisher |
HAL CCSD |
publishDate |
2012 |
url |
https://hal.archives-ouvertes.fr/hal-01972875 https://hal.archives-ouvertes.fr/hal-01972875/document https://hal.archives-ouvertes.fr/hal-01972875/file/Lemenkova_etal_Poster_Yamal.pdf https://doi.org/10.13140/RG.2.2.32044.72329 |
op_coverage |
Kiev, Ukraine |
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 permafrost Tundra Yamal Peninsula Siberia |
genre_facet |
Arctic permafrost Tundra Yamal Peninsula Siberia |
op_source |
11th International Conference on Geoinformatics: Theoretical and Applied Aspects https://hal.archives-ouvertes.fr/hal-01972875 11th International Conference on Geoinformatics: Theoretical and Applied Aspects, May 2012, Kiev, Ukraine. 2012, ⟨10.13140/RG.2.2.32044.72329⟩ http://geoinformatics.org.ua/eng/conferences/pages-and-navigation/gis2012/ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.13140/RG.2.2.32044.72329 hal-01972875 https://hal.archives-ouvertes.fr/hal-01972875 https://hal.archives-ouvertes.fr/hal-01972875/document https://hal.archives-ouvertes.fr/hal-01972875/file/Lemenkova_etal_Poster_Yamal.pdf doi:10.13140/RG.2.2.32044.72329 |
op_rights |
http://creativecommons.org/publicdomain/zero/1.0/ info:eu-repo/semantics/OpenAccess |
op_rightsnorm |
CC0 PDM |
op_doi |
https://doi.org/10.13140/RG.2.2.32044.72329 |
_version_ |
1766350394645020672 |
spelling |
ftccsdartic:oai:HAL:hal-01972875v1 2023-05-15T15:20:10+02:00 Mapping land cover changes using Landsat TM: a case study of Yamal ecosystems, Arctic Russia Lemenkova, Polina Forbes, Bruce, Kumpula, Timo Ocean University of China (OUC) Fellowship of the Center for International Mobility (CIMO) of Finland. Contract No. TM-10-7124 (Decision 9.11.2010). All Ukrainian Association of Geoinformatics (AUAG) Kiev, Ukraine 2012-05-14 https://hal.archives-ouvertes.fr/hal-01972875 https://hal.archives-ouvertes.fr/hal-01972875/document https://hal.archives-ouvertes.fr/hal-01972875/file/Lemenkova_etal_Poster_Yamal.pdf https://doi.org/10.13140/RG.2.2.32044.72329 en eng HAL CCSD info:eu-repo/semantics/altIdentifier/doi/10.13140/RG.2.2.32044.72329 hal-01972875 https://hal.archives-ouvertes.fr/hal-01972875 https://hal.archives-ouvertes.fr/hal-01972875/document https://hal.archives-ouvertes.fr/hal-01972875/file/Lemenkova_etal_Poster_Yamal.pdf doi:10.13140/RG.2.2.32044.72329 http://creativecommons.org/publicdomain/zero/1.0/ info:eu-repo/semantics/OpenAccess CC0 PDM 11th International Conference on Geoinformatics: Theoretical and Applied Aspects https://hal.archives-ouvertes.fr/hal-01972875 11th International Conference on Geoinformatics: Theoretical and Applied Aspects, May 2012, Kiev, Ukraine. 2012, ⟨10.13140/RG.2.2.32044.72329⟩ http://geoinformatics.org.ua/eng/conferences/pages-and-navigation/gis2012/ Image processing Image recognition Image processing Pattern recognition Image processing Digital image processing Segmentation Classification Landsat 7 ETM+ Landsat Imagery Satellite image analysis Satellite image interpretation GIS & Spatial Analyses GIS Analysis SIG et modélisation spatiale SIG Systèmes d'information géographique SIG Raster SIG topographie photogrammétrie méthodologie ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.10: Image Representation/I.4.10.3: Statistical ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.1: Models ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.3: Clustering ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.3: Clustering/I.5.3.0: Algorithms ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.3: Clustering/I.5.3.1: Similarity measures ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.4: Applications/I.5.4.0: Computer vision ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.0: General/I.4.0.0: Image displays ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.0: General/I.4.0.1: Image processing software ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.1: Digitization and Image Capture/I.4.1.1: Imaging geometry ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.6: Segmentation ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.6: Segmentation/I.4.6.1: Pixel classification ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION/I.4.10: Image Representation [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] [SDV.EE.ECO]Life Sciences [q-bio]/Ecology environment/Ecosystems [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing [SDE.MCG]Environmental Sciences/Global Changes [SDE.BE]Environmental Sciences/Biodiversity and Ecology [SDE.ES]Environmental Sciences/Environmental and Society [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC] [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] info:eu-repo/semantics/conferenceObject Poster communications 2012 ftccsdartic https://doi.org/10.13140/RG.2.2.32044.72329 2020-12-24T23:17:48Z International audience 1. Introduction. This paper details changes in land cover types in tundra landscapes (Yamal) during since 1988. The research method is supervised classification (Minimal Distance) of the Landsat TM scenes. The new approach of the current work is application of ILWIS GIS and RS tools for Bovanenkovo region. The research area is geographically located on the Bovanenkovo region, the north-western part of Yamal Peninsula, West Siberia, Russia. The Yamal Peninsula is a flat homogeneous lowland region with low-lying plains of heights <90m. Such geographic settings create specific local environmental conditions in the region. Thus, Yamal is the worlds largest high-latitude wetland system covering in total 900,000 km2 of peatlands, complex system of wetlands, dense lake and river network. Typical for this region are seasonal flooding, active erosion processing, permafrost distribution and intensive local landslides. 2. Data.The research data are orthorectified Landsat TM scenes covering north-west of Yamal. The images have a time span of 23 years: 1988-08-07 and 2011-07-14, taken in growing season when vegetation coverage is clearly visible.3. Methods.The research methods consist of image classification, spatial analysis and thematic mapping, technically performed in ILIWIS GIS. Research steps: 1. Data pre-processing: a) import .img into ASCII raster format (GDAL). After converting, each image contained collection of 7 Landsat raster bands b) visual color and contrast enhancement c) geographic referencing of Landsat scenes: UTM (Universal Transverse Mercator), Eastern Zone 42, Northern Zone W, WGS 1984 datum (Georeference Corner Editor, ILWIS). 2. Research area selection. The area of interest (AOI) was identified and cropped on the raw images (Fig.3). This area shows Bovanenkovo region in a large scale. The AOI area best represents typical tundra landscapes. 3. Image classification method is supervised classification (Minimal Distance), which is based on the spatial analysis of spectral ... Conference Object Arctic permafrost Tundra Yamal Peninsula Siberia Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) Arctic Bovanenkovo ENVELOPE(68.437,68.437,70.354,70.354) Yamal Peninsula ENVELOPE(69.873,69.873,70.816,70.816) |