Automatic and Self-Adaptive Stem Reconstruction in Landslide-Affected Forests
AbstractTerrestrial laser scanning (TLS) is a promising technique for plot-wise acquisition of geometric attributes of forests. However, there still exists a need for TLS applications in mountain forests where tree stems’ growing directions are not vertical. This paper presents a novel method to model tree stems precisely in an alpine landslide-affected forest using TLS. Tree stems are automatically detected by a two-layer projection method. Stems are modeled by fitting a series of cylinders based on a 2D-3D random sample consensus (RANSAC)-based approach. Diameter at breast height (DBH) was manually measured in the field, and stem curves were measured from the point cloud as reference data. The results showed that all trees in the test area can be detected. The root mean square error (RMSE) of estimated DBH was 1.80 cm (5.5%). Stem curves were automatically generated and compared with reference data, as well as stem volumes. The results imply that the proposed method is able to map and model the stem curve precisely in complex forest conditions. The resulting stem parameters can be employed in single tree biomass estimation, tree growth quantification and other forest-related studies. View Full-Text
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Wang, D.; Hollaus, M.; Puttonen, E.; Pfeifer, N. Automatic and Self-Adaptive Stem Reconstruction in Landslide-Affected Forests. Remote Sens. 2016, 8, 974.
Wang D, Hollaus M, Puttonen E, Pfeifer N. Automatic and Self-Adaptive Stem Reconstruction in Landslide-Affected Forests. Remote Sensing. 2016; 8(12):974.Chicago/Turabian Style
Wang, Di; Hollaus, Markus; Puttonen, Eetu; Pfeifer, Norbert. 2016. "Automatic and Self-Adaptive Stem Reconstruction in Landslide-Affected Forests." Remote Sens. 8, no. 12: 974.
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