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Laboratory and image spectroscopy for evaluating the biophysical state of meadow vegetation in the Krkonoše National Park

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Jelének J., Kupková L., Zagajewski B., Březina S., Ochytra A., Marcinkowska A.

Miscellanea Geographica. Regional Studies on Development

2014

vol. 18

issue 2

15-22

The paper deals with the evaluation of mountain meadow vegetation condition using in-situ measurements of the fraction of Accumulated Photosynthetically Active Radiation (fAPAR) and Leaf Area Index (LAI). The study analyses the relationship between these parameters and spectral properties of meadow vegetation and selected invasive species with the goal of finding out vegetation indices for the detection of fAPAR and LAI. The developed vegetation indices were applied on hyperspectral data from an APEX (Airborne Prism Experiment) sensor in the area of interest in the Krkonoše National Park. The results of index development on the level of the field data were quite good. The maximal sensitivity expressed by the coefficient of determination for LAI was R2 = 0.56 and R2 = 0.79 for fAPAR. However, the sensitivity of all the indices developed at the image level was quite low. The output values of in-situ measurements confirmed the condition of invasive species as better than that of the valuable original meadow vegetation, which is a serious problem for national park management.

Mapping vegetation communities of the Karkonosze National Park using APEX hyperspectral data and Support Vector Machines

84%

Marcinkowska A., Zagajewski B., Ochtyra A., Jarocińska A., Raczko E., Kupková L., Stych P., Meuleman K.

Miscellanea Geographica. Regional Studies on Development

2014

vol. 18

issue 2

23-29

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.

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2 Miscellanea Geographica. Regional Studies on Development

2 Kupková L.

2 Marcinkowska A.

2 Zagajewski B.

1 Březina S.

1 Jarocińska A.

1 Jelének J.

1 Meuleman K.

1 Ochtyra A.

1 Ochytra A.

1 Raczko E.

1 Stych P.

2 2014

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Laboratory and image spectroscopy for evaluating the biophysical state of meadow vegetation in the Krkonoše National Park

Mapping vegetation communities of the Karkonosze National Park using APEX hyperspectral data and Support Vector Machines