Relating Canopy Reflectance to the Vegetation Composition of Mountainous Grasslands in the Greater Caucasus

Abstract

Mountainous grassland landscapes increasingly experience modified management and land use, which are often triggered by socio-economic and climate change. Remote-sensing based mapping approaches are needed to monitor gradual changes in grassland composition and biodiversity of remote and inaccessible areas. Hyperspectral remote sensing in combination with regression and ordination techniques is a promising approach to map continuous representations of prominent floristic gradients at the landscape scale. This approach has, however, to date not been tested in alpine environments with difficult terrain. In the present study we tested whether hyperspectral data allows for the differentiation of species-rich grassland types of a subalpine pasture landscape in Georgia, Greater Caucasus. Due to the unavailability of space- or airborne hyperspectral data, we used field-spectrometric in situ data to investigate this potential. We sampled the vegetation of four grassland types (as classified by cluster analysis) using the Braun-Blanquet approach. For these 60 plots we made four measurements of their hyperspectral canopy reflectance (325-1075 nm) on two dates within a seven-day period at biomass peak. Floristic gradients were derived from Non-metric Multi Dimensional Scaling (NMDS) ordination. These gradients were subsequently subjected to Partial Least Square Regression (PLSR) to predict NMDS scores from the corresponding canopy reflectance. Cross validated Pearson R-2 values for the PLSR models derived from the four measurements ranged from 0.60 to 0.83. Reflectance data sampled on the same day yielded similar results in the respective models, while temporal transferability within the seven day period was limited. The reflectance values of the significant wavelengths in the best models were subjected to Principal Component Analysis (PCA) to display the spectral similarity of plots. The congruency between the similarity of species composition (NMDS) and spectral similarity (PCA) was satisfactorily assessed by Procrustes rotation. The floristic gradients in our study were directly related to pronounced biophysical and environmental gradients, which are responsible for differences in the spectral signatures. Our results indicate that hyperspectral data has the potential to differentiate the composition of Caucasian subalpine grassland types and offers multiple opportunities for very detailed vegetation mapping in difficult terrain. (c) 2013 Elsevier B.V. All rights reserved.

Publication
AGRICULTURE ECOSYSTEMS & ENVIRONMENT

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