Discrimination and characterization of management systems in semi-arid rangelands of South Africa using RapidEye time series

Abstract

In South African grasslands, rangeland management is strongly related to land tenure. Communal farms are reported to exhibit less desirable vegetation conditions for livestock than commercial farms. Time series of high spatial and temporal resolution imagery may be useful for improved evaluation of these rangelands as they provide information at a spatial scale similar to the typical scale of field assessments and may thus overcome the limited spatio-temporal representativeness of field measurements. A time series of 13 RapidEye images over one growing season (2010–2011) was used to explore spectral differences between and within two management systems (commercial vs. communal). Isomap ordination was applied to map continuous spectral dissimilarities of sample plots. Using regression with simultaneous autoregressive models (SAR), dissimilarities were subsequently related to ecological variables of plant and soil, including indicators for grazing effects. The largest differences were found between sample plots of communal and commercial farms. Vegetation attributes were significantly related to dissimilarities in reflectance, both from the growing season and the dormant period. However, these relationships did not suggest vegetation degradation on communal farms. They further suggest that a management-related pattern of grazing disturbance in the summer months led to spectral differences between farms but could have impaired the detailed characterization of spectral dissimilarities related to differences in vegetation composition.

Acknowledgements

We thank farmers and community headmen for allowing us to do research on their land and for their kind cooperation. Thanks to Mias van der Westhuizen and Herman Fouché for their valuable assistance in fieldwork set-up. We also thank Chris du Preez and Elmarie Kotzé for their continuous support during field campaigns. Thanks to Cristian Moreno, Johannes Schmidt, Hannah Steinschulte, and Petra Weber for their fieldwork assistance, and Andreas Tewes for his assistance in image data preparation. We thank DLR-RESA for the provision of RapidEye images (2010–2011, Project #448), and the Agricultural Research Council (ARC) for providing rainfall data. Thanks to the German Aerospace Center (DLR) for providing access to the CATENA software for image preprocessing.

Funding

This research was funded through the collaborative research project FOR 1501 by the German Science Foundation (DFG) and the Theodor-Brinkmann Graduate School, University of Bonn, Germany.