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    Wyświetlanie 1-2 z 2
    Tytuł:
    Multi-temporal analysis of vegetation reflectance using MERIS data in the Czech Republic
    Autorzy:
    Štych, Přemysl
    Malíková, Lucie
    Kříž, Jan
    Holman, Lukáš
    Powiązania:
    https://bibliotekanauki.pl/articles/2037394.pdf
    Data publikacji:
    2014-06-25
    Wydawca:
    Uniwersytet Warszawski. Wydział Geografii i Studiów Regionalnych
    Tematy:
    High temporal data
    MERIS
    Czech Republic
    land cover
    classification
    Opis:
    Accurate high temporal resolution data is a very important source of information for understanding processes in the landscape. High temporal and spectral resolution data enable the monitoring of dynamic landscape processes. For this reason, since 2008 a receiving station for Metosat, NOAA and Envisat data has been installed at the Department of Applied Geoinformatics and Cartography, Faculty of Science, Charles University in Prague. The aim of this study is to analyse the spectral characteristics of vegetation using MERIS data in the Czech Republic. Spectral characteristics of vegetation were examined both by analysing changes in reflectivity as well as by utilising vegetation indices. Vegetation in forests and agricultural land was evaluated. The results present the spectral characteristics of selected associations of vegetation based on MERIS data and a discussion of the methods of multitemporal classification of land cover.
    Źródło:
    Miscellanea Geographica. Regional Studies on Development; 2014, 18, 2; 30-34
    0867-6046
    2084-6118
    Pojawia się w:
    Miscellanea Geographica. Regional Studies on Development
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Mapping vegetation communities of the Karkonosze National Park using APEX hyperspectral data and Support Vector Machines
    Autorzy:
    Marcinkowska, Adriana
    Zagajewski, Bogdan
    Ochtyra, Adrian
    Jarocińska, Anna
    Raczko, Edwin
    Kupková, Lucie
    Stych, Premysl
    Meuleman, Koen
    Powiązania:
    https://bibliotekanauki.pl/articles/2037399.pdf
    Data publikacji:
    2014-06-25
    Wydawca:
    Uniwersytet Warszawski. Wydział Geografii i Studiów Regionalnych
    Tematy:
    Hyperspectral data
    APEX
    Karkonosze National Park
    mapping/ classification
    vegetation communities
    Opis:
    This research aims to discover the potential of hyperspectral remote sensing data for mapping mountain vegetation ecosystems. First, the importance of mountain ecosystems to the global system should be stressed due to mountainous ecosystems forming a very sensitive indicator of global climate change. Furthermore, a variety of biotic and abiotic factors influence the spatial distribution of vegetation in the mountains, producing a diverse mosaic leading to high biodiversity. The research area covers the Szrenica Mount region on the border between Poland and the Czech Republic - the most important part of the Western Karkonosze and one of the main areas in the Karkonosze National Park (M&B Reserve of the UNESCO). The APEX hyperspectral data that was classified in this study was acquired on 10th September 2012 by the German Aerospace Center (DLR) in the framework of the EUFAR HyMountEcos project. This airborne scanner is a 288-channel imaging spectrometer operating in the wavelength range 0.4-2.5 μm. For reference patterns of forest and non-forest vegetation, maps (provided by the Polish Karkonosze National Park) were chosen. Terrain recognition was based on field walks with a Trimble GeoXT GPS receiver. It allowed test and validation dominant polygons of 15 classes of vegetation communities to be selected, which were used in the Support Vector Machines (SVM) classification. The SVM classifier is a type of machine used for pattern recognition. The result is a post classification map with statistics (total, user, producer accuracies, kappa coefficient and error matrix). Assessment of the statistics shows that almost all the classes were properly recognised, excluding the fern community. The overall classification accuracy is 79.13% and the kappa coefficient is 0.77. This shows that hyperspectral images and remote sensing methods can be support tools for the identification of the dominant plant communities of mountain areas.
    Źródło:
    Miscellanea Geographica. Regional Studies on Development; 2014, 18, 2; 23-29
    0867-6046
    2084-6118
    Pojawia się w:
    Miscellanea Geographica. Regional Studies on Development
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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