Erratum: A Machine Learning Method for Co-Registration and Individual Tree Matching of Forest Inventory and Airborne Laser Scanning Data. Remote Sens. 2017, 9, 505

Sebastian Lamprecht; Andreas Hill; Johannes Stoffels; Thomas Udelhoven

doi:10.3390/rs9070692

,

and

¹

Remote Sensing & Geoinformatics Department, Trier University, 54286 Trier, Germany

²

Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland

^*

Author to whom correspondence should be addressed.

Remote Sens.2017, 9(7), 692;https://doi.org/10.3390/rs9070692

Version Notes

Order Reprints

Since Equation (2) has been rearranged incorrectly during preparation for this article [1], the authors would like to correct the relevant text of Section 3.4.3 as follows:

3.4.3. Synthetic Tree Detections

A tree crown is defined by the tree species-specific parameters b and c (see Table 2), the tree height h, crown base height

c b h

, and its crown radius r (all provided by the Waldplaner 2.0 software, [2]). The height above ground

e_{i}

of a point with distance

d_{i}

to the tree location less than or equal to r is calculated by Equation (2). Please note that since the parameters of the Douglas fir were not given by Pretzsch [3], these were assumed to be similar to the parameters of the Norway spruce.

e_{i} = h - c \cdot (h - c b h) \cdot {(\frac{d_{i}}{r})}^{\frac{1}{b}}

(2)

For each plot, we generated uniquely-distributed xy-coordinates with the same pulse density as the original point cloud. For each point, the z-coordinate was calculated by applying Equation (2) for all trees and adding the value of the DTM (of the original ALS point cloud) at the tree location. Finally, the point is linked to the tree which resulted in the maximum z-coordinate and—for a subsequent evaluation—it is labeled with the ID of that tree.

We apologize for any inconvenience caused to the readers by these changes. The changes do not affect the scientific results. The manuscript will be updated and the original will remain online on the article webpage.

References

Lamprecht, S.; Hill, A.; Stoffels, J.; Udelhoven, H. A Machine Learning Method for Co-Registration and Individual Tree Matching of Forest Inventory and Airborne Laser Scanning Data. Remote Sens. 2017, 9, 505. [Google Scholar] [CrossRef]
Hansen, J.; Nagel, J. Waldwachstumskundliche Softwaresysteme auf Basis von TreeGrOSS-Anwendung und Theoretische Grundlagen; Niedersächsische Staats-und Universitätsbibliothek: Göttingen, Germany, 2014. [Google Scholar]
Pretzsch, H. Modellierung des Waldwachstums; Parey: Berlin, Germany, 2001. [Google Scholar]

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Erratum: A Machine Learning Method for Co-Registration and Individual Tree Matching of Forest Inventory and Airborne Laser Scanning Data. Remote Sens. 2017, 9, 505

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