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Mapping Plant Functional Groups in Subalpine Grassland of the Greater Caucasus

Magiera, Anja ; Feilhauer, Hannes ; Waldhardt, Rainer ; Wiesmair, Martin ; Otte, Annette

Originalveröffentlichung: (2018) Mountain Research and Development 38(1):10 doi: 10.1659/MRD-JOURNAL-D-17-00082.1
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URN: urn:nbn:de:hebis:26-opus-146264

Freie Schlagwörter (Englisch): remote sensing , subalpine grassland composition , random forest , spatial distribution of grass , grass cover
Sammlung: Open Access - Publikationsfonds
Universität Justus-Liebig-Universit√§t Gie√üen
Institut: Division of Landscape Ecology and Landscape Planning, Institute of Landscape Ecology and Resources Management
Fachgebiet: Agrarwissenschaften und Umweltmanagement
DDC-Sachgruppe: Landwirtschaft
Dokumentart: Aufsatz
Sprache: Englisch
Erstellungsjahr: 2018
Publikationsdatum: 20.05.2019
Kurzfassung auf Englisch: Plant functional groups - in our case grass, herbs, and legumes - and their spatial distribution can provide information on key ecosystem functions such as species richness, nitrogen fixation, and erosion control. Knowledge about the spatial distribution of plant functional groups provides valuable information for grassland management. This study described and mapped the distribution of grass, herb, and legume coverage of the subalpine grassland in the high-mountain Kazbegi region, Greater Caucasus, Georgia. To test the applicability of new sensors, we compared the predictive power of simulated hyperspectral canopy reflectance, simulated multispectral reflectance, simulated vegetation indices, and topographic variables for modeling plant functional groups. The tested grassland showed characteristic differences in species richness; in grass, herb, and legume coverage; and in connected structural properties such as yield. Grass (Hordeum brevisubulatum) was dominant in biomass-rich hay meadows. Herb-rich grassland featured the highest species richness and evenness, whereas legume-rich grassland was accompanied by a high coverage of open soil and showed dominance of a single species, Astragalus captiosus. The best model fits were achieved with a combination of reflectance, vegetation indices, and topographic variables as predictors. Random forest models for grass, herb, and legume coverage explained 36%, 25%, and 37% of the respective variance, and their root mean square errors varied between 12-15%. Hyperspectral and multispectral reflectance as predictors resulted in similar models. Because multispectral data are more easily available and often have a higher spatial resolution, we suggest using multispectral parameters enhanced by vegetation indices and topographic parameters for modeling grass, herb, and legume coverage. However, overall model fits were merely moderate, and further testing, including stronger gradients and the addition of shortwave infrared wavelengths, is needed.
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