<div class="csl-bib-body">
<div class="csl-entry">Zlinszky, A., Mücke, W., Lehner, H., Briese, C., & Pfeifer, N. (2012). Categorizing wetland vegetation by airborne laser scanning on Lake Balaton and Kis-Balaton, Hungary. <i>Remote Sensing</i>. https://doi.org/10.3390/rs4061617</div>
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Outlining patches dominated by different plants in wetland vegetation provides information on species succession, microhabitat patterns, wetland health and ecosystem services. Aerial photogrammetry and hyperspectral imaging are the usual data acquisition methods but the application of airborne laser scanning (ALS) as a standalone tool also holds promises for this field since it can be used to quantify 3-dimensional vegetation structure. Lake Balaton is a large shallow lake in western Hungary with shore wetlands that have been in decline since the 1970s. In August 2010, an ALS survey of the shores of Lake Balaton was completed with 1 pt/m2 discrete echo recording. The resulting ALS dataset was processed to several output rasters describing vegetation and terrain properties, creating a sufficient number of independent variables for each raster cell to allow for basic multivariate classification. An expert-generated decision tree algorithm was applied to outline wetland areas, and within these, patches dominated by Typha sp. Carex sp., and Phragmites australis. Reed health was mapped into four categories: healthy, stressed, ruderal and die-back. The output map was tested against a set of 775 geo-tagged ground photographs and had a user’s accuracy of > 97% for detecting non-wetland features (trees, artificial surfaces and low density Scirpus stands), > 72% for dominant genus detection and > 80% for most reed health categories (with 62% for one category). Overall classification accuracy was 82.5%, Cohen’s Kappa 0.80, which is similar to some hyperspectral or multispectral-ALS fusion studies. Compared to hyperspectral imaging, the processing chain of ALS can be automated in a similar way but relies directly on differences in vegetation structure and actively sensed reflectance and is thus probably more robust. The data acquisition parameters are similar to the national surveys of several European countries, suggesting that these existing datasets could be used for vegetation mapping and monitoring.
en
dc.description.sponsorship
European Community’s 7th Framework Programme
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dc.description.sponsorship
EUFAR
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dc.language
English
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dc.language.iso
en
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dc.publisher
MDPI
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dc.relation.ispartof
Remote Sensing
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dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
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dc.subject
LIDAR
en
dc.subject
wetlands
en
dc.subject
Phragmites australis
en
dc.subject
Carex
en
dc.subject
Typha
en
dc.subject
ecosystem health
en
dc.subject
vegetation classification
en
dc.title
Categorizing wetland vegetation by airborne laser scanning on Lake Balaton and Kis-Balaton, Hungary
en
dc.type
Article
en
dc.type
Artikel
de
dc.rights.license
Creative Commons Namensnennung 4.0 International
de
dc.rights.license
Creative Commons Attribution 4.0 International
en
dc.relation.grantno
FP7/2008-20012
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dc.relation.grantno
2271
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dc.rights.holder
The Author(s) 2012
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dc.type.category
Original Research Article
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tuw.journal.peerreviewed
true
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tuw.peerreviewed
true
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tuw.version
vor
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dcterms.isPartOf.title
Remote Sensing
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tuw.publication.orgunit
E120 - Department für Geodäsie und Geoinformation
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tuw.publisher.doi
10.3390/rs4061617
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dc.identifier.eissn
2072-4292
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dc.identifier.libraryid
AC11360734
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dc.identifier.urn
urn:nbn:at:at-ubtuw:3-2536
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tuw.author.orcid
0000-0002-2348-7929
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dc.rights.identifier
CC BY 4.0
de
dc.rights.identifier
CC BY 4.0
en
wb.sci
true
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item.openaccessfulltext
Open Access
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item.languageiso639-1
en
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item.fulltext
with Fulltext
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research article
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Publications
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http://purl.org/coar/resource_type/c_2df8fbb1
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item.grantfulltext
open
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crisitem.author.dept
E120 - Department für Geodäsie und Geoinformation
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crisitem.author.dept
E120 - Department für Geodäsie und Geoinformation
-
crisitem.author.dept
E122 - Institut für Photogrammetrie und Fernerkundung