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Titel
Categorizing wetland vegetation by airborne laser scanning on Lake Balaton and Kis-Balaton, Hungary
VerfasserZlinszky, András ; Mücke, Werner ; Lehner, Hubert ; Briese, Christian ; Pfeifer, Norbert
Erschienen in
Remote sensing, 2012, Jg. 4, H. 6, S. 1617-1650
Erschienen2012
Ausgabe
Published version
SpracheEnglisch
DokumenttypAufsatz in einer Zeitschrift
Schlagwörter (EN)LIDAR / wetlands / Phragmites australis / Carex / Typha / ecosystem health / vegetation classification
URNurn:nbn:at:at-ubtuw:3-2536 Persistent Identifier (URN)
DOI10.3390/rs4061617 
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 Das Werk ist frei verfügbar
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Categorizing wetland vegetation by airborne laser scanning on Lake Balaton and Kis-Balaton, Hungary [2.09 mb]
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Zusammenfassung (Englisch)

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 users 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%, Cohens 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.