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Remote Sens. 2012, 4(6), 1617-1650; doi:10.3390/rs4061617

Categorizing Wetland Vegetation by Airborne Laser Scanning on Lake Balaton and Kis-Balaton, Hungary

1
Balaton Limnological Institute, Centre for Ecological Research of the Hungarian Academy of Sciences, Klebelsberg Kuno út 3, H-8237 Tihany, Hungary
2
Institute of Photogrammetry and Remote Sensing, TU Vienna, E122, Gußhausstraße 27-29, A-1040 Vienna, Austria
3
Ludwig Boltzmann Institute for Archaeological Prospection and Virtual Archaeology, Hohe Warte 38, A-1190 Vienna, Austria
*
Author to whom correspondence should be addressed.
Received: 18 April 2012 / Revised: 29 May 2012 / Accepted: 30 May 2012 / Published: 1 June 2012
(This article belongs to the Special Issue Remote Sensing of Biological Diversity)

Abstract

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. View Full-Text
Keywords: LIDAR; wetlands; Phragmites australis; Carex; Typha; ecosystem health; vegetation classification LIDAR; wetlands; Phragmites australis; Carex; Typha; ecosystem health; vegetation classification
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Zlinszky, A.; Mücke, W.; Lehner, H.; Briese, C.; Pfeifer, N. Categorizing Wetland Vegetation by Airborne Laser Scanning on Lake Balaton and Kis-Balaton, Hungary. Remote Sens. 2012, 4, 1617-1650.

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