- 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