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<div class="csl-entry">Chen, Y.-C., Hollaus, M., Kukko, A., & Hyyppä, J. (2023, September 6). <i>Tree Species Classification using Multi-spectral LiDAR - First Result from an Austria Study Site</i> [Poster Presentation]. SilviLaser 2023, London, United Kingdom of Great Britain and Northern Ireland (the). http://hdl.handle.net/20.500.12708/188712</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/188712
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dc.description.abstract
In the framework of the project 4Map4Health, multi-spectral LiDAR (MS-LiDAR) is adopted to enhance the ability of information extraction in forest regions. One of the core ideas of using MS-LiDAR is tree species classification at the individual tree level. Hence, multiple MS-LiDAR campaigns have been carried out in several European countries, including Austria. The scanning equipment is mounted under a helicopter, consisting of three laser scanners in different wavelengths. This work is to present the initial view of this MS-LiDAR data set, as well as the first result of tree species classification using this data set. Firstly, we combine three wavelengths of information by aggregating point clouds near the canopy surface. Secondly, basic multi-spectral products, e.g., NDVI, are derived and help interpret the difference between each tree species. Furthermore, point clouds within the area of the single tree crown are also involved to classify tree species by analyzing spectral information in the vertical and horizontal distribution. The current results show the potential of MS-LiDAR in this task. Compared to traditional laser scanning with single wavelength information, various behaviors of tree species are already observable in raster products of MS-LiDAR. In future investigations, more features will be discovered to facilitate and improve the accuracy of tree species classification.
en
dc.language.iso
en
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dc.subject
Laserscanning
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dc.subject
tree species
en
dc.title
Tree Species Classification using Multi-spectral LiDAR - First Result from an Austria Study Site
