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Remote Sens. 2014, 6(9), 8056-8087; doi:10.3390/rs6098056

Categorizing Grassland Vegetation with Full-Waveform Airborne Laser Scanning: A Feasibility Study for Detecting Natura 2000 Habitat Types

1
Research Groups Photogrammetry and Remote Sensing, Department of Geodesy and Geoinformation, Vienna University of Technology, Gußhausstraße 27–29, 1040 Vienna, Austria
2
Balaton Limnological Institute, Centre for Ecological Research, Hungarian Academy of Sciences, Klebelsberg Kuno út 3, 8237 Tihany, Hungary
3
YggdrasilDiemer, Dudenstr. 38, 10965 Berlin, Germany
4
ATMOTERM S.A., ul. Łangowskiego 4, 45-031 Opole, Poland
5
MTA-DE Biodiversity and Ecosystem Services Research Group, Egyetem tér 1, 4032 Debrecen, Hungary
6
Department of Geophysics and Space Science, Eötvös University, Pázmány Péter sétány 1/C, 1117 Budapest, Hungary
7
Interdisziplinäres Ökologisches Zentrum, TU Bergakademie Freiberg, Akademiestraße 6, 09596 Freiberg, Germany
*
Author to whom correspondence should be addressed.
Received: 20 June 2014 / Revised: 19 August 2014 / Accepted: 19 August 2014 / Published: 27 August 2014
(This article belongs to the Special Issue Remote Sensing and GIS for Habitat Quality Monitoring)
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Abstract

There is increasing demand for reliable, high-resolution vegetation maps covering large areas. Airborne laser scanning data is available for large areas with high resolution and supports automatic processing, therefore, it is well suited for habitat mapping. Lowland hay meadows are widespread habitat types in European grasslands, and also have one of the highest species richness. The objective of this study was to test the applicability of airborne laser scanning for vegetation mapping of different grasslands, including the Natura 2000 habitat type lowland hay meadows. Full waveform leaf-on and leaf-off point clouds were collected from a Natura 2000 site in Sopron, Hungary, covering several grasslands. The LIDAR data were processed to a set of rasters representing point attributes including reflectance, echo width, vegetation height, canopy openness, and surface roughness measures, and these were fused to a multi-band pseudo-image. Random forest machine learning was used for classifying this dataset. Habitat type, dominant plant species and other features of interest were noted in a set of 140 field plots. Two sets of categories were used: five classes focusing on meadow identification and the location of lowland hay meadows, and 10 classes, including eight different grassland vegetation categories. For five classes, an overall accuracy of 75% was reached, for 10 classes, this was 68%. The method delivers unprecedented fine resolution vegetation maps for management and ecological research. We conclude that high-resolution full-waveform LIDAR data can be used to detect grassland vegetation classes relevant for Natura 2000. View Full-Text
Keywords: remote sensing; LIDAR; Natura 2000; machine learning; grasslands; lowland hay meadows; habitat mapping remote sensing; LIDAR; Natura 2000; machine learning; grasslands; lowland hay meadows; habitat mapping
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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.; Schroiff, A.; Kania, A.; Deák, B.; Mücke, W.; Vári, Á.; Székely, B.; Pfeifer, N. Categorizing Grassland Vegetation with Full-Waveform Airborne Laser Scanning: A Feasibility Study for Detecting Natura 2000 Habitat Types. Remote Sens. 2014, 6, 8056-8087.

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