Mapping Plant Functional Groups in Subalpine Grassland of the Greater Caucasus

dc.contributor.authorMagiera, Anja
dc.contributor.authorFeilhauer, Hannes
dc.contributor.authorWaldhardt, Rainer
dc.contributor.authorWiesmair, Martin
dc.contributor.authorOtte, Annette
dc.date.accessioned2022-11-18T09:53:46Z
dc.date.available2019-05-20T13:43:03Z
dc.date.available2022-11-18T09:53:46Z
dc.date.issued2018
dc.description.abstractPlant 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.en
dc.identifier.urihttp://nbn-resolving.de/urn:nbn:de:hebis:26-opus-146264
dc.identifier.urihttps://jlupub.ub.uni-giessen.de//handle/jlupub/9461
dc.identifier.urihttp://dx.doi.org/10.22029/jlupub-8849
dc.language.isoende_DE
dc.rightsNamensnennung 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectremote sensingen
dc.subjectsubalpine grassland compositionen
dc.subjectrandom foresten
dc.subjectspatial distribution of grassen
dc.subjectgrass coveren
dc.subject.ddcddc:630de_DE
dc.titleMapping Plant Functional Groups in Subalpine Grassland of the Greater Caucasusen
dc.typearticlede_DE
local.affiliationFB 09 - Agrarwissenschaften, Ökotrophologie und Umweltmanagementde_DE
local.opus.fachgebietAgrarwissenschaften und Umweltmanagementde_DE
local.opus.id14626
local.opus.instituteDivision of Landscape Ecology and Landscape Planning, Institute of Landscape Ecology and Resources Managementde_DE
local.source.freetextMountain Research and Development 38(1):10de_DE
local.source.urihttps://doi.org/10.1659/MRD-JOURNAL-D-17-00082.1

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