The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 371–376, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-371-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 371–376, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-371-2020

  21 Aug 2020

21 Aug 2020

SEASONAL SPECTRAL SEPARABILITY OF SELECTED GRASSES: CASE STUDY FROM THE KRKONOŠE MTS. TUNDRA ECOSYSTEM

L. Červená, L. Kupková, M. Potůčková, and J. Lysák L. Červená et al.
  • Department of Applied Geoinformatics and Cartography, Faculty of Science, Charles University, Albertov 6, Prague 2, Czech Republic

Keywords: laboratory spectroscopy, image spectroscopy, Krkonoše Mts., grasses, tundra, spectral separability, seasonal changes

Abstract. This paper focuses on spectral separability of closed alpine grasslands dominated with Nardus stricta and competitive grasses Calamagrostis villosa and Molinia caerulea in the relict arctic-alpine tundra located in the Krkonoše Mountains National Park, Czech Republic. The spectral data were acquired and compared at three levels: spectra of a single layer of leaves measured with the ASD FieldSpec4 Wide-Res spectroradiometer coupled with a contact probe in a laboratory (leaf level), canopy spectra measured in a field with the same spectroradiometer using the fiber optic cable with a pistol grip (canopy level), and hyperspectral image data acquired by Nano-Hyperspec® fastened to the DJI Matrice 600 Pro drone (image level). All the measurements were repeated three times during the 2019 vegetation season – in June, July and August. Using the methods of analysis of variance and Welch's (unpaired) t-test, it was proven that there were differences in the results for all three spectra sources. But in general, for each combination of species and each data source a suitable date and intervals of the spectral bands for species separation exist. The most suitable term for data acquisition in order to differentiate all the species is July. At the leaf level, the best species separability was observed in the near-infrared and shortwave infrared spectral ranges. At the canopy and image levels, the visible bands are of higher importance for discriminating the species.