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Erratum published on 5 July 2017, see Remote Sens. 2017, 9(7), 692.

Open AccessArticle
Remote Sens. 2017, 9(5), 505; doi:10.3390/rs9050505

A Machine Learning Method for Co-Registration and Individual Tree Matching of Forest Inventory and Airborne Laser Scanning Data

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.
Academic Editors: Lars T. Waser and Prasad S. Thenkabail
Received: 3 March 2017 / Revised: 10 May 2017 / Accepted: 16 May 2017 / Published: 19 May 2017
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Abstract

Determining the exact position of a forest inventory plot—and hence the position of the sampled trees—is often hampered by a poor Global Navigation Satellite System (GNSS) signal quality beneath the forest canopy. Inaccurate geo-references hamper the performance of models that aim to retrieve useful information from spatially high remote sensing data (e.g., species classification or timber volume estimation). This restriction is even more severe on the level of individual trees. The objective of this study was to develop a post-processing strategy to improve the positional accuracy of GNSS-measured sample-plot centers and to develop a method to automatically match trees within a terrestrial sample plot to aerial detected trees. We propose a new method which uses a random forest classifier to estimate the matching probability of each terrestrial-reference and aerial detected tree pair, which gives the opportunity to assess the reliability of the results. We investigated 133 sample plots of the Third German National Forest Inventory (BWI, 2011–2012) within the German federal state of Rhineland-Palatinate. For training and objective validation, synthetic forest stands have been modeled using the Waldplaner 2.0 software. Our method has achieved an overall accuracy of 82.7% for co-registration and 89.1% for tree matching. With our method, 60% of the investigated plots could be successfully relocated. The probabilities provided by the algorithm are an objective indicator of the reliability of a specific result which could be incorporated into quantitative models to increase the performance of forest attribute estimations. View Full-Text
Keywords: co-registration; individual tree detection; tree matching; point set registration; machine-learning; forest inventory co-registration; individual tree detection; tree matching; point set registration; machine-learning; forest inventory
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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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MDPI and ACS Style

Lamprecht, S.; Hill, A.; Stoffels, J.; Udelhoven, T. 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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