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Combined Use of Multi-Temporal Optical and Radar Satellite Images for Grassland Monitoring
Remote Sens. 2014, 6(8), 7732-7761; doi:10.3390/rs6087732

Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery

1,2,* , 1
1 Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, SE-223 62 Lund, Sweden 2 Department of Biology, Lund University, Sölvegatan 37, SE-223 62 Lund, Sweden 3 German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany 4 Institute of Biology/Geobotany and Botanical Garden, Martin Luther University of Halle Wittenberg, Am Kirchtor 1, 06108 Halle (Saale), Germany 5 Karlsruhe Institute of Technology (KIT), Institute of Geography and Geoecology, 76131 Karlsruhe, Germany
* Author to whom correspondence should be addressed.
Received: 8 April 2014 / Revised: 30 July 2014 / Accepted: 30 July 2014 / Published: 20 August 2014
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
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Plant communities differ in their species composition, and, thus, also in their functional trait composition, at different stages in the succession from arable fields to grazed grassland. We examine whether aerial hyperspectral (414–2501 nm) remote sensing can be used to discriminate between grazed vegetation belonging to different grassland successional stages. Vascular plant species were recorded in 104.1 m2 plots on the island of Öland (Sweden) and the functional properties of the plant species recorded in the plots were characterized in terms of the ground-cover of grasses, specific leaf area and Ellenberg indicator values. Plots were assigned to three different grassland age-classes, representing 5–15, 16–50 and >50 years of grazing management. Partial least squares discriminant analysis models were used to compare classifications based on aerial hyperspectral data with the age-class classification. The remote sensing data successfully classified the plots into age-classes: the overall classification accuracy was higher for a model based on a pre-selected set of wavebands (85%, Kappa statistic value = 0.77) than one using the full set of wavebands (77%, Kappa statistic value = 0.65). Our results show that nutrient availability and grass cover differences between grassland age-classes are detectable by spectral imaging. These techniques may potentially be used for mapping the spatial distribution of grassland habitats at different successional stages.
Keywords: arable-to-grassland succession; Ellenberg indicator values; HySpex spectrometer; imaging spectroscopy; partial least square discriminant analysis arable-to-grassland succession; Ellenberg indicator values; HySpex spectrometer; imaging spectroscopy; partial least square discriminant analysis
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Möckel, T.; Dalmayne, J.; Prentice, H.C.; Eklundh, L.; Purschke, O.; Schmidtlein, S.; Hall, K. Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery. Remote Sens. 2014, 6, 7732-7761.

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