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Remote Sens. 2016, 8(12), 974; doi:10.3390/rs8120974

Automatic and Self-Adaptive Stem Reconstruction in Landslide-Affected Forests

Group of Photogrammetry, Department of Geodesy and Geoinformation, TU Wien, Vienna 1040, Austria
Centre of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute (FGI), National Land Survey of Finland, Masala 02430, Finland
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute (FGI), National Land Survey of Finland, Masala 02430, Finland
Author to whom correspondence should be addressed.
Academic Editors: Guangxing Wang, Erkki Tomppo, Dengsheng Lu, Huaiqing Zhang, Qi Chen, Lars T. Waser, Randolph H. Wynne and Prasad S. Thenkabail
Received: 28 August 2016 / Revised: 16 October 2016 / Accepted: 18 November 2016 / Published: 28 November 2016
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
View Full-Text   |   Download PDF [6158 KB, uploaded 28 November 2016]   |  


Terrestrial 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
Keywords: terrestrial laser scanning; point cloud; mountain forests; stem curve; diameter; volume terrestrial laser scanning; point cloud; mountain forests; stem curve; diameter; volume

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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.

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