Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

Wyszukujesz frazę "Zagajewski, Bogdan" wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
Assessment of Imaging Spectroscopy for rock identification in the Karkonosze Mountains, Poland
Autorzy:
Mierczyk, Monika
Zagajewski, Bogdan
Jarocińska, Anna
Knapik, Roksana
Powiązania:
https://bibliotekanauki.pl/articles/1035939.pdf
Data publikacji:
2016
Wydawca:
Uniwersytet Warszawski. Wydział Geografii i Studiów Regionalnych
Tematy:
Rock identification
Imaging Spectroscopy
APEX hyperspectral airborne imagery data
Spectral Angle Mapper
Linear Spectral Unmixing
Matched Filtering
Opis:
Based on laboratory, field and airborne-acquired hyperspectral data, this paper aims to analyse the dominant minerals and rocks found in the Polish Karkonosze Mountains. Laboratory spectral characteristics were measured with the ASD FieldSpec 3 spectrometer and images were obtained from VITO’s Airborne Prism EXperiment (APEX) scanner. The terrain is covered mainly by lichens or vascular plants creating significant difficulties for rock identification. However, hyperspectral airborne imagery allowed for subpixel classifications of different types of granites, hornfels and mica schist within the research area. The hyperspectral data enabled geological mapping of bare ground that had been masked out using three advanced algorithms: Spectral Angle Mapper, Linear Spectral Unmixing and Matched Filtering. Though all three methods produced positive results, the Matched Filtering method proved to be the most effective. The result of this study was a set of maps and post classification statistical data of rock distribution in the area, one for each method of classification.
Źródło:
Miscellanea Geographica. Regional Studies on Development; 2016, 20, 1; 34-40
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

    Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies