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Forests 2014, 5(7), 1565-1583; doi:10.3390/f5071565

Correction of Erroneous LiDAR Measurements in Artificial Forest Canopy Experimental Setups

1,* , 2
1 Department of Biosystems, Katholieke Universiteit Leuven, M3-BIORES, Willem de Croylaan 34, BE-3001 Leuven, Belgium 2 Management Unit of the North Sea Mathematical Models, Gulledelle 100, BE-1200 Brussels, Belgium 3 Departamento de Ciencias Forestales, Universidad de La Frontera, Temuco PO-Box 54-D, Chile
* Author to whom correspondence should be addressed.
Received: 30 December 2013 / Revised: 28 May 2014 / Accepted: 10 June 2014 / Published: 3 July 2014
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Terrestrial laser scanning (TLS) data makes possible to directly characterize the three-dimensional (3D) distribution of canopy foliage elements. The scanned edges of these elements may result in incorrectly point measurements (i.e., “ghost points”) impacting the quality of point cloud data. Therefore, estimation of the ghost points’ spatial visibilities, measurement of their characteristics and their removal are essential. In order to quantify the improvements on data quality, a method is developed in this study to efficiently correct for ghost points. Since the occurrence of ghost points is governed by a number of factors, (e.g., scanning resolution and distance, object properties, incident angle); the developed method is based on the analysis of the effects of these factors under controlled conditions where canopy-like objects (i.e., leaves, branches and layers of leaves) were scanned using a continuous-wave TLS system that employs phase-shift technology. Manual extraction of ghost points was done in order to calculate the relative amount of ghost points per scan, or ghost points ratio (gpr). The gpr values were computed in order to: (i) analyze their relationships with variables representing the above factors; and (ii) be used as a reference to evaluate the performance of filters used for extraction of ghost points. The ghost points’ occurrence was modeled by fitting regression models using different predictor variables that represent the variables under study. The obtained results indicated that reduced models with three predictors were suitable for gpr estimation in artificial leaves and in artificial branches, with a relative root mean squared error (RMSE) of 4.7% and 3.7%, respectively; while the full model with four predictors was appropriate for artificial layers of leaves, with relative RMSE of 4.5%. According to the statistical analysis, scanning distance was identified as the most important variable for modeling ghost points occurrence. Results indicated that optimized distance-based filters relative to the scanning distance have improved the outcomes in ghost points detection, in comparison to standard filtering criteria. These results suggest that more accurate characterization of forest canopy 3D structures can be achieved by removing ghost points using the new developed method.
Keywords: continuous-wave LiDAR; mixed pixel effect; ghost points filter; experimental setup continuous-wave LiDAR; mixed pixel effect; ghost points filter; experimental setup
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Cifuentes, R.; Van der Zande, D.; Salas, C.; Farifteh, J.; Coppin, P. Correction of Erroneous LiDAR Measurements in Artificial Forest Canopy Experimental Setups. Forests 2014, 5, 1565-1583.

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