Spatial quantification of vegetation density from terrestrial laser scanner data for characterization of 3D forest structure at plot level
2008
Durrieu, Sylvie | Allouis, T. | Fournier, Richard, A | Véga, C. | Albrech, L. | Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF) | Université de Sherbrooke = University of Sherbrooke (UdeS)
[Departement_IRSTEA]DS [TR1_IRSTEA]METHODO / SYNERGIE
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显示更多 [+] 显示较少 [-]英语. Precise description of forest 3D structure at plot level is required for sustainable ecosystem management. However, a detailed structure description from traditional field measurements is tedious. We propose an innovative method to quantify in 3D the spatial distribution of forest structure from terrestrial lidar data. The method rests on the hypothesis that the normalized number of laser returns within a given volume element is proportional to the density of vegetation material inside this volume. The developed model is based on analysis made inside Svoxels (spherical voxels) to compute a spatialized vegetation density index. The model was tested on two different scans of the same plot. The resulting vegetation density index well represents the vegetation structure as observed within the lidar point cloud. Quantitative analyses confirmed a global consistency of the results within and between scans. However, we observed a slight bias in the computed density indexes. It might be mainly explained by occlusions, which cause 1) a slight decrease of the density index with distance and 2) local differences in density index between scans.. Future work will focus on improving our algorithm and correcting biases. These results are promising for the development of quantitative measures of the 3D forest structure.
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