Separating reflectance signatures of shrub species – a case study in the Central Greater Caucasus

Abstract

Questions Shrub encroachment has been observed in many alpine and arctic environments and is expected to significantly alter these ecosystems. Mapping these processes with remote sensing is a powerful tool for monitoring purposes. Thus, we test the distinctiveness of the reflectance signature of target species relative to their co‐occurring shrub species using uni‐ and multivariate analyses for an alpine ecosystem. We ask: (i) is it possible to differentiate shrub species with a unique growth form by their reflectance signature; (ii) which of the tested multispectral sensors produces the best separation; and (iii) how are the results affected by the timing of data acquisition in the vegetation period? Location Kazbegi district, Central Greater Caucasus, Georgia. Method We analysed three shrub (Betula litwinowii, Rhododendron caucasicum, Hippophae rhamnoides) and one tall forb (Veratrum lobelianum) species occurring in the sub‐alpine to alpine belt The vegetation of 52 relevés was analysed using non‐metric multidimensional scaling and indicator species analysis. From field spectrometric data we simulated multispectral sensor bands (IKONOS, Quickbird 2, RapidEye, WorldView‐2) directly taken from the target species. We analysed the reflectance signature in RapidEye data from June and September. For all data sets we calculated the Jeffries‐Matusita distance (JMD) as a separation measure and tested the reflectance signature of the single bands for differences. Results Betula litwinowii and V. lobelianum always co‐occurred in our data. A high abundance of B. litwinowii could also be found in the Rhododendron cluster and vice versa, whereas the Hippophae cluster was more homogeneous. Simulated bands showed good overall separation (JMD 1.58–2.00) of the target species. The separation increased with the increase of number of bands and inclusion of the red edge band. There was a general trend in which the reflectance from satellite images produced a lower separation (JMD 1.20–1.55) than the simulations, with the best separation in the late vegetation period. Conclusion Our results showed the possibility to spectrally separate encroaching shrub species with a unique growth form in a high‐mountain environment using simulated multispectral data and satellite imagery.

Analysing Betula litwinowii encroachment and reforestation in the Kazbegi region, Greater Caucasus, Georgia

“…Although the resolution of the DEM is rather coarse, especially in the high‐mountain environment with its small‐scale changes, we consider it sufficient since we did not evaluate differences between single relevés. The DEM has already been successfully employed in similar vegetation studies (Magiera, Feilhauer, Tephnadze, Waldhardt, & Otte, ; Magiera, Feilhauer, Waldhardt, Wiesmair, & Otte, ). The variables of most importance during the modelling process reflect factors expected to influence the topographic distribution and structural differences between the vegetation types.…”

Land-Use Change Related to Topography and Societal Drivers in High-Mountains – A Case Study in the Upper Watershed of the Tergi (Kazbegi Region), Greater Caucasus

“…Reduced grazing in the valley bottom and in settlement-near locations results in the succession of woody vegetation as well and leads to a loss of montane grassland habitats (Barcella et al, 2016). In favorable locations for agricultural use, shrub (Elaeagnus rhamnoides) is expanding and will further expand when pastures stay abandoned (Magiera et al, 2016;Waldhardt et al, 2011). North of Stepantsminda, in the floodplain of the Tergi River, a huge area of fallow land was totally covered by shrub in 2015 (see Fig.…”

Mapping Plant Functional Groups in Subalpine Grassland of the Greater Caucasus

“…However, the spatial scale as a crucial sensor characteristic was not taken into account: the field of view of the spectrometer covers areas below 1 m 2 , depending on the average height above ground, whereas the rescalable, airborne AISA Eagle imagery has varying pixel sizes (,5 3 5 m) and the spaceborne RapidEye sensor delivers imagery with a pixel size of 5 3 5 m. The number of species in a pixel size increases with pixel size; we counteracted this problem by averaging spectra on 5 3 5m plots. Using actual imagery might result in different model qualities (Magiera et al 2016;Meyer et al 2017).…”