Mapping Plant Functional Groups in Subalpine Grassland of the Greater Caucasus

Mapping Plant Functional Groups in Subalpine Grassland of the Greater Caucasus

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 info...

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Veröffentlicht in:Mountain Research and Development. - International Mountain Society. - 38(2018), 1, Seite 63-72
1. Verfasser: Magiera, Anja (VerfasserIn)
Weitere Verfasser: Feilhauer, Hannes, Waldhardt, Rainer, Wiesmair, Martin, Otte, Annette
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2018
Zugriff auf das übergeordnete Werk:Mountain Research and Development
Schlagworte:Remote sensing subalpine grassland composition random forest spatial distribution of grass grass cover herb cover legume cover Georgia Applied sciences Biological sciences
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520 |a 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 biomassrich 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. 
540 |a © 2018 Mingyu Yang et al. 
650 4 |a Remote sensing 
650 4 |a subalpine grassland composition 
650 4 |a random forest 
650 4 |a spatial distribution of grass 
650 4 |a grass cover 
650 4 |a herb cover 
650 4 |a legume cover 
650 4 |a Georgia 
650 4 |a Applied sciences  |x Food science  |x Foodstuffs  |x Food  |x Edible seeds  |x Legumes 
650 4 |a Biological sciences  |x Agriculture  |x Agricultural products  |x Plant products  |x Herbs 
650 4 |a Biological sciences  |x Biology  |x Botany  |x Plant ecology  |x Vegetation 
650 4 |a Biological sciences  |x Ecology  |x Ecosystems  |x Biomes  |x Grasslands  |x Mountain grasslands 
650 4 |a Biological sciences  |x Ecology  |x Applied ecology  |x Environmental management  |x Natural resource management  |x Ecosystem management  |x Grassland management 
650 4 |a Applied sciences  |x Research methods  |x Vegetation index 
650 4 |a Biological sciences  |x Biology  |x Botany  |x Plants  |x Grasses 
650 4 |a Biological sciences  |x Biology  |x Botany  |x Plants 
650 4 |a Applied sciences  |x Research methods  |x Modeling 
650 4 |a Applied sciences  |x Research methods  |x Modeling  |x Simulations  |x MountainResearch  |x Systems knowledge 
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700 1 |a Waldhardt, Rainer  |e verfasserin  |4 aut 
700 1 |a Wiesmair, Martin  |e verfasserin  |4 aut 
700 1 |a Otte, Annette  |e verfasserin  |4 aut 
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