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A spectral clustering approach of vegetation components for describing plant topology and geometry from terrestrial waveform LiDAR data

Dobrina Boltcheva 1 Eric Casella 2 Rémy Cumont 3 Franck Hétroy 3 
1 ALICE - Geometry and Lighting
Inria Nancy - Grand Est, LORIA - ALGO - Department of Algorithms, Computation, Image and Geometry
3 MORPHEO - Capture and Analysis of Shapes in Motion
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : Computer models that treat plant architectures as a collection of interconnected elementary units (internode, petiole, leaf lamina), which are spatially distributed within the above- and/or the below-ground space, have become increasingly popular in the FSPM scientific community (DeJong et al. 2011). The core of such 3-D plant architecture models deal with contrasting reconstruction methods generally based on stochastic, fractal or L-system approaches, or by describing accurately the geometry of each plant component in situ using 3-D digitizing technology. These methods can approximate the geometry of many species for understanding and integrating plant development and ecophysiology, but have generally been applied at a small scale. High-resolution terrestrial Light Detection And Ranging (tLiDAR), a 3-D remote sensing technique, has recently been applied for measuring the 3-D characteristics of vegetation from grass to forest plant species (Dassot et al. 2011). The resulting data are known as a point cloud which shows the 3-D position of all the hits by the laser beam giving a raw sketch of the spatial distribution of plant elements in 3-D, but without explicit information on their geometry and connectivity. In this study we propose a new approach based on a delineation algorithm that clusters a point cloud into elementary plant units. The algorithm creates a graph (points + edges) to recover plausible neighbouring relationships between the points and embed this graph in a spectral space in order to segment the point-cloud into meaningful elementary plant units. Our approach is robust to inherent geometric outliers and/or noisy points and only considers the x, y, z coordinate tLiDAR data as an input.
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Submitted on : Thursday, June 6, 2013 - 3:18:50 PM
Last modification on : Wednesday, February 2, 2022 - 3:54:58 PM
Long-term archiving on: : Monday, April 3, 2017 - 9:30:17 AM


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  • HAL Id : hal-00817508, version 1


Dobrina Boltcheva, Eric Casella, Rémy Cumont, Franck Hétroy. A spectral clustering approach of vegetation components for describing plant topology and geometry from terrestrial waveform LiDAR data. FSPM2013 - 7th International Conference on Functional-Structural Plant Models, Jun 2013, Saariselkä, Finland. ⟨hal-00817508⟩



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