Canadian Forest Service Publications

Estimating leaf area distribution in savanna trees from terrestrial LiDAR measurements. 2011. Béland, M.; Widlowski, J.-L.; Fournier, R.A.; Côté, J.-F; Verstraete, M.M. Agricultural and Forest Meteorology 151: 1252-1266.

Year: 2011

Issued by: Atlantic Forestry Centre

Catalog ID: 34431

Language: English

Availability: PDF (request by e-mail)

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Vegetation structure parameters are key elements in the study of ecosystem functioning and global scale ecosystemic interactions. The detailed retrieval of many of these parameters by direct measurements is impractical due to the quantity of plant material in trees. Terrestrial LiDAR Scanners (TLSs) have been shown to hold great potential as an indirect means of estimating plant structure parameters with a high level of detail, while some studies identified a number of challenges inherent to this approach. In this study we investigate the use of a voxel-based approach to retrieve leaf area distribution of individual trees. The approach is based on the contact frequency method applied to co-registered TLS returns from two or more scanning positions. The contact frequency was computed for voxels being 10, 30, and 50 cm in size and subsequently corrected for the influence of occlusion effects, leaf inclination, the presence of non-photosynthetic material, and the laser beam size. The leaf area of voxels for which occlusion effects were too pronounced was estimated using modeled values based on the availability of light. We compared the TLS derived leaf area estimates against direct measurements, obtained by the harvesting of leaves, in a broad-leaved savanna of central Mali. The measured leaf area values of the sampled trees ranged from 30 to 530 m2, and crown LAI values between 0.8 and 7.2. The leaf area estimates lay on average 14% from the reference measurements (general bias). Our method provides vertical as well as radial distributions of leaf area in individual trees, and lends itself to the estimation of savanna vegetation structural parameters with a high level of detail.