Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery
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...
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doaj-15fa761d9a1340ef855417310ada97362020-11-25T01:01:07ZengMDPI AGRemote Sensing2072-42922014-08-01687732776110.3390/rs6087732rs6087732Classification of Grassland Successional Stages Using Airborne Hyperspectral ImageryThomas Möckel0Jonas Dalmayne1Honor C. Prentice2Lars Eklundh3Oliver Purschke4Sebastian Schmidtlein5Karin Hall6Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, SE-223 62 Lund, SwedenDepartment of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, SE-223 62 Lund, SwedenDepartment of Biology, Lund University, Sölvegatan 37, SE-223 62 Lund, SwedenDepartment of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, SE-223 62 Lund, SwedenGerman Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, GermanyKarlsruhe Institute of Technology (KIT), Institute of Geography and Geoecology, 76131 Karlsruhe, GermanyDepartment of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, SE-223 62 Lund, SwedenPlant 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.http://www.mdpi.com/2072-4292/6/8/7732arable-to-grassland successionEllenberg indicator valuesHySpex spectrometerimaging spectroscopypartial least square discriminant analysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Thomas Möckel Jonas Dalmayne Honor C. Prentice Lars Eklundh Oliver Purschke Sebastian Schmidtlein Karin Hall |
spellingShingle |
Thomas Möckel Jonas Dalmayne Honor C. Prentice Lars Eklundh Oliver Purschke Sebastian Schmidtlein Karin Hall Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery Remote Sensing arable-to-grassland succession Ellenberg indicator values HySpex spectrometer imaging spectroscopy partial least square discriminant analysis |
author_facet |
Thomas Möckel Jonas Dalmayne Honor C. Prentice Lars Eklundh Oliver Purschke Sebastian Schmidtlein Karin Hall |
author_sort |
Thomas Möckel |
title |
Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery |
title_short |
Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery |
title_full |
Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery |
title_fullStr |
Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery |
title_full_unstemmed |
Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery |
title_sort |
classification of grassland successional stages using airborne hyperspectral imagery |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2014-08-01 |
description |
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. |
topic |
arable-to-grassland succession Ellenberg indicator values HySpex spectrometer imaging spectroscopy partial least square discriminant analysis |
url |
http://www.mdpi.com/2072-4292/6/8/7732 |
work_keys_str_mv |
AT thomasmockel classificationofgrasslandsuccessionalstagesusingairbornehyperspectralimagery AT jonasdalmayne classificationofgrasslandsuccessionalstagesusingairbornehyperspectralimagery AT honorcprentice classificationofgrasslandsuccessionalstagesusingairbornehyperspectralimagery AT larseklundh classificationofgrasslandsuccessionalstagesusingairbornehyperspectralimagery AT oliverpurschke classificationofgrasslandsuccessionalstagesusingairbornehyperspectralimagery AT sebastianschmidtlein classificationofgrasslandsuccessionalstagesusingairbornehyperspectralimagery AT karinhall classificationofgrasslandsuccessionalstagesusingairbornehyperspectralimagery |
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