Title: Synergy of Airborne Digital Camera and LiDAR Data to Map Coastal Dune Vegetation
Citation: JOURNAL OF COASTAL RESEARCH vol. Fall 2009 no. Special Issue 53 p. 73-82
Publication Year: 2009
JRC N°: JRC50094
ISSN: 0749-0208
URI: http://www.jcronline.org/toc/coas//10053
DOI: 10.2112/SI53-009.1
Type: Articles in periodicals and books
Abstract: Driven by the successful applications of LiDAR in forestry, and the availability of LiDAR technology, new research on other ecosystems is being carried out. While LiDAR data have often been used to study tall forest ecosystems, the current study explored their possibilities for the lower-canopy ecosystems in the Belgian coastal dune belt. This area is largely covered by marram dune, moss dune, grassland, scrubs and some woodland. Small diameter (0.4m) footprint LiDAR was applied to derive the canopy height by analyzing the first and last pulse returns simultaneously. It was investigated whether the height of low-canopy ecosystems could be mapped with adequate accuracy. An error analysis was performed first on flat terrain (tennis court and parking lot) and then on vegetation canopy. The mapping of coastal dune vegetation is a necessary element in the definition of the strength of the dune belt. Dune vegetation fixes the sand dunes, protecting them from erosion and from possible breakthroughs threatening the historically reclaimed land (polders) situated inland from the dunes. Next to the LiDAR data, multispectral data was acquired from a digital camera with visual and near infrared channels. The digital camera overflight was not conducted on the same platform as the LiDAR. After ortho-rectification of the multispectral image, the data of both sources were fused. The limited spectral information delivered by the digital camera was not able to provide a sufficiently detailed and accurate vegetation map. The fusion with LiDAR data provides the extra information needed to obtain the desired vegetation and dune strength maps. A total of 14 classes were defined, from which 12 cover vegetation. It was shown that classification accuracy can be improved with 16% from 55% to 71% after data fusion.
JRC Directorate:Sustainable Resources

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