Mapping land cover changes using Landsat TM: a case study of Yamal ecosystems, Arctic Russia
Résumé
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 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. The defined classes include shrub tundra, willows, tall willows, short shrub tundra, sparse short shrub tundra, dry grass heath, sedge grass tundra, dry short shrub tundra, dry short shrub sedge tundra, wet peatland, peatland (sphagnum). The pixels were associated with land cover classes, using their DNs, similar to the key samples. 4. Thematic mapping: layout of main research results, represented as maps of the land cover classes. The created domain Land classes includes legend with representation colors visualizing each category.
4. Results.
The research output includes following results: 1) two thematic maps of land cover types in Bovanenkovo area, Yamal (Fig.4) 2) calculation of the areas in ha of land cover types. The assessment of the areas of all land cover classes shows following results. Willows covers 2750,57 ha in 2011, which is more than in 1988, when it covered 1547,52 ha (both ’tall willows’ & ’willows’ classes). Noticeable is increase in tundra vegetation: ’short shrub tundra’, ’sparse short shrub tundra’ and ’dry short shrub tundra’ have more areas covered in 2011 comparing to 1988: almost 5442,00 ha vs 1823,00 ha.
Domaines
Traitement des images [eess.IV] Ecosystèmes Traitement du signal et de l'image [eess.SP] Milieux et Changements globaux Biodiversité et Ecologie Environnement et Société Interfaces continentales, environnement Interface homme-machine [cs.HC] Vision par ordinateur et reconnaissance de formes [cs.CV]
Origine : Fichiers produits par l'(les) auteur(s)
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