Mapping land cover changes using Landsat TM: a case study of Yamal ecosystems, Arctic Russia

• Main Authors: Lemenkova, Polina, Forbes, Bruce, Kumpula, Timo
• Other Authors: 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
• Language: English
• Published: HAL CCSD 2012
• Subjects: 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]; Arctic; Bovanenkovo; Yamal Peninsula; permafrost; Tundra; Siberia
• 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

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 ...