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Remote Sens. 2016, 8(2), 112; doi:10.3390/rs8020112

Characterization of a Highly Biodiverse Floodplain Meadow Using Hyperspectral Remote Sensing within a Plant Functional Trait Framework

1
Department of Geography and Environmental Science; School of Archaeology, Geography and Environmental Science, The University of Reading, Whiteknights, PO Box 227, Reading RG6 6AB, UK
2
Department of Environment, Earth and Ecosystems, Open University, Milton Keynes MK7 6AA, UK
3
Department of Water Resources, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede 7500 AE, The Netherlands
4
British Geological Survey, Wallingford, Oxfordshire OX10 8BB, UK
5
Centre for Ecology and Hydrology, Wallingford, Oxfordshire OX10 8BB, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Clement Atzberger and Prasad S. Thenkabail
Received: 5 December 2015 / Revised: 26 January 2016 / Accepted: 28 January 2016 / Published: 3 February 2016
View Full-Text   |   Download PDF [4569 KB, uploaded 3 February 2016]   |  

Abstract

We assessed the potential for using optical functional types as effective markers to monitor changes in vegetation in floodplain meadows associated with changes in their local environment. Floodplain meadows are challenging ecosystems for monitoring and conservation because of their highly biodiverse nature. Our aim was to understand and explain spectral differences among key members of floodplain meadows and also characterize differences with respect to functional traits. The study was conducted on a typical floodplain meadow in UK (MG4-type, mesotrophic grassland type 4, according to British National Vegetation Classification). We compared two approaches to characterize floodplain communities using field spectroscopy. The first approach was sub-community based, in which we collected spectral signatures for species groupings indicating two distinct eco-hydrological conditions (dry and wet soil indicator species). The other approach was “species-specific”, in which we focused on the spectral reflectance of three key species found on the meadow. One herb species is a typical member of the MG4 floodplain meadow community, while the other two species, sedge and rush, represent wetland vegetation. We also monitored vegetation biophysical and functional properties as well as soil nutrients and ground water levels. We found that the vegetation classes representing meadow sub-communities could not be spectrally distinguished from each other, whereas the individual herb species was found to have a distinctly different spectral signature from the sedge and rush species. The spectral differences between these three species could be explained by their observed differences in plant biophysical parameters, as corroborated through radiative transfer model simulations. These parameters, such as leaf area index, leaf dry matter content, leaf water content, and specific leaf area, along with other functional parameters, such as maximum carboxylation capacity and leaf nitrogen content, also helped explain the species’ differences in functional dynamics. Groundwater level and soil nitrogen availability, which are important factors governing plant nutrient status, were also found to be significantly different for the herb/wetland species’ locations. The study concludes that spectrally distinguishable species, typical for a highly biodiverse site such as a floodplain meadow, could potentially be used as target species to monitor vegetation dynamics under changing environmental conditions. View Full-Text
Keywords: MG4 community; field spectroscopy; optical functional types; radiative transfer model; biophysical parameters; sedge and rush MG4 community; field spectroscopy; optical functional types; radiative transfer model; biophysical parameters; sedge and rush
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Punalekar, S.; Verhoef, A.; Tatarenko, I.V.; van der Tol, C.; Macdonald, D.M.J.; Marchant, B.; Gerard, F.; White, K.; Gowing, D. Characterization of a Highly Biodiverse Floodplain Meadow Using Hyperspectral Remote Sensing within a Plant Functional Trait Framework. Remote Sens. 2016, 8, 112.

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