Mapping Land Cover Changes Using Landsat TM: A Case Study of Yamal Ecosystems, Arctic Russia - Lemenkova Polina (Zenodo, 2012)
This poster shows application of the GIS/RS techniques for satellite image processing aimed at land cover chang detection. The changes in land cover types in tundra landscapes (Yamal) are detected 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 (Fig.1). 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. Methods. The research methods consist of image classification, spatial analysis and thematic mapping, technically performed in ILIWIS GIS. Research steps: 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). 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. Image classification method is supervised classification (Minimal Distance), which is based on the spatial analysis of spectral signatures of object variables, i.e. vegetation types. The classes sampling was performed using Sample Set tool in ILWIS GIS. The training pixels for each land cover type were selected as representative samples and stored as classification key. They have contrasting colors, visually visible and distinguishable on the image. ...