To the question of the environmental education: how Landsat TM, ETM+ and MSS images can be processed by GIS-techniques for geospatial research

The emphasis of this research is to demonstrate application of Landsat satellite imagery as a major resource for student and educational research. Landsat images are highly useful and strongly recommended for educational purposes as they are provided free of charge and timely regular geospatial data with 30-m resolution covering any places of the Earth. The case study describes mapping land cover types in ecosystems. It details how exactly satellite images can be used for geospatial research step by step. In the current research I used orthorectified Landsat Thematic Mapper (TM), MSS and Enhanced Thematic Mapper (ETM+) data in Geographic Tagged Image-File Format (GeoTIFF) acquired over the area of Bovanenkovo region, Yamal. The images cover study area for different time periods. The choice of Landsat data application for land cover mapping is explained by its 30-m high spatial resolution, well-known advantages of application of the Landsat scenes in research and cartography, almost 40 year old history of the image record, successful distribution and open availability. Landsat scenes were selected for the pair analysis: Landsat TM scenes for 1988-08-07 and 2011-07-14 and Landsat ETM+, 2001. The research methodology is based spatial analysis tools of the open source GIS software: Quantum GIS and ILWIS GIS. The images were georeferenced, preprocessed and imported to ILWIS from .img into ILWIS .mpr raster map format (ASCII) using GDAL (Geospatial Data Abstraction Library) in main ILWIS. Minimal Distance method was sued to classify images. After converting, each image contained collection of 7 Landsat raster bands, as well as theirs metadata stored in Map List (.mpl) file, information about georeference (.grf) and coordinate system in .csy file. To visualize spectral information from the Landsat image, a color composite map has been created using combination of three raster images of the individual bands. Supervised classification of the raster imagery includes image analysis aimed at recognizing class membership for each pixel. The respective pixels are selected in Sample Set Editor, ILWIS GIS. The research method used in current research is supervised classification, which enabled to assign land cover classes by adjusting classification parameters and thresholds in DN values of spectral signature of pixels. The principle of Minimum Distance method, used for pixels classification is based on the calculating of shortest straight-line distance in Euclidian coordinate system from each pixel’s DN to the pattern pixels of land cover classes.