Categorizing Grassland Vegetation with Full-Waveform Airborne Laser Scanning: A Feasibility Study for Detecting Natura 2000 Habitat Types
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 habita...
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doaj-6405cd9f13434c428777772c851ef1072020-11-25T00:30:58ZengMDPI AGRemote Sensing2072-42922014-08-01698056808710.3390/rs6098056rs6098056Categorizing Grassland Vegetation with Full-Waveform Airborne Laser Scanning: A Feasibility Study for Detecting Natura 2000 Habitat TypesAndrás Zlinszky0Anke Schroiff1Adam Kania2Balázs Deák3Werner Mücke4Ágnes Vári5Balázs Székely6Norbert Pfeifer7Research Groups Photogrammetry and Remote Sensing, Department of Geodesy and Geoinformation, Vienna University of Technology, Gußhausstraße 27–29, 1040 Vienna, AustriaYggdrasilDiemer, Dudenstr. 38, 10965 Berlin, GermanyATMOTERM S.A., ul. Łangowskiego 4, 45-031 Opole, PolandMTA-DE Biodiversity and Ecosystem Services Research Group, Egyetem tér 1, 4032 Debrecen, HungaryResearch Groups Photogrammetry and Remote Sensing, Department of Geodesy and Geoinformation, Vienna University of Technology, Gußhausstraße 27–29, 1040 Vienna, AustriaYggdrasilDiemer, Dudenstr. 38, 10965 Berlin, GermanyDepartment of Geophysics and Space Science, Eötvös University, Pázmány Péter sétány 1/C, 1117 Budapest, HungaryResearch Groups Photogrammetry and Remote Sensing, Department of Geodesy and Geoinformation, Vienna University of Technology, Gußhausstraße 27–29, 1040 Vienna, AustriaThere 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.http://www.mdpi.com/2072-4292/6/9/8056remote sensingLIDARNatura 2000machine learninggrasslandslowland hay meadowshabitat mapping |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
András Zlinszky Anke Schroiff Adam Kania Balázs Deák Werner Mücke Ágnes Vári Balázs Székely Norbert Pfeifer |
spellingShingle |
András Zlinszky Anke Schroiff Adam Kania Balázs Deák Werner Mücke Ágnes Vári Balázs Székely Norbert Pfeifer Categorizing Grassland Vegetation with Full-Waveform Airborne Laser Scanning: A Feasibility Study for Detecting Natura 2000 Habitat Types Remote Sensing remote sensing LIDAR Natura 2000 machine learning grasslands lowland hay meadows habitat mapping |
author_facet |
András Zlinszky Anke Schroiff Adam Kania Balázs Deák Werner Mücke Ágnes Vári Balázs Székely Norbert Pfeifer |
author_sort |
András Zlinszky |
title |
Categorizing Grassland Vegetation with Full-Waveform Airborne Laser Scanning: A Feasibility Study for Detecting Natura 2000 Habitat Types |
title_short |
Categorizing Grassland Vegetation with Full-Waveform Airborne Laser Scanning: A Feasibility Study for Detecting Natura 2000 Habitat Types |
title_full |
Categorizing Grassland Vegetation with Full-Waveform Airborne Laser Scanning: A Feasibility Study for Detecting Natura 2000 Habitat Types |
title_fullStr |
Categorizing Grassland Vegetation with Full-Waveform Airborne Laser Scanning: A Feasibility Study for Detecting Natura 2000 Habitat Types |
title_full_unstemmed |
Categorizing Grassland Vegetation with Full-Waveform Airborne Laser Scanning: A Feasibility Study for Detecting Natura 2000 Habitat Types |
title_sort |
categorizing grassland vegetation with full-waveform airborne laser scanning: a feasibility study for detecting natura 2000 habitat types |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2014-08-01 |
description |
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. |
topic |
remote sensing LIDAR Natura 2000 machine learning grasslands lowland hay meadows habitat mapping |
url |
http://www.mdpi.com/2072-4292/6/9/8056 |
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