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A tree-based approach to estimate wood volume from Lidar data: a case study in a pine plantation

Abstract : We developed an-object based framework to assess individual tree volume from airborne LiDAR data in a pine-dominated forest. Individual tree crowns were extracted using a point-based segmentation algorithm and total tree volume was estimated using height and either tree or crown bounding volume information using nonlinear models. Tree-level models provided root mean squared errors (RMSE) around 30%. Scaling volume at the plot level allows to reduce RMSE by a factor 2, i.e. around 15%. This scale change may benefits from error compensation associated to segmentation involving false tree detections or tree omissions leading to crown fusions. Along with height, crown volume was found to be a good predictor of tree volume, but suffers from computational issues that may further induce variability in the models. Future work should integrate an analysis of tree neighborhood in order to improve tree- models by the use of indices reflecting competition and growth conditions.
Keywords : FORET SEGMENTATION
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Journal articles
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https://hal.inrae.fr/hal-02602432
Contributor : Migration Irstea Publications <>
Submitted on : Saturday, May 16, 2020 - 7:50:53 AM
Last modification on : Tuesday, September 7, 2021 - 3:55:02 PM

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  • HAL Id : hal-02602432, version 1
  • IRSTEA : PUB00046532

Citation

A. Hamrouni, Cédric Vega, J.P. Renaud, S. Durrieu, Marine Bouvier. A tree-based approach to estimate wood volume from Lidar data: a case study in a pine plantation. Revue Française de Photogrammétrie et de Télédétection, Société Française de Photogrammétrie et de Télédétection, 2015, pp.63-70. ⟨hal-02602432⟩

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