A Benchmark of Lidar-Based Single Tree Detection Methods Using Heterogeneous Forest Data from the Alpine Space
by
Lothar Eysn 1,*,
Markus Hollaus 1,
Eva Lindberg 1,2
,
Frédéric Berger 3,
Jean-Matthieu Monnet 3,
Michele Dalponte 4,
Milan Kobal 5,
Marco Pellegrini 6,
Emanuele Lingua 6,
Domen Mongus 7 and
Norbert Pfeifer 1
Lothar Eysn 1,*,
Markus Hollaus 1,
Eva Lindberg 1,2,
Frédéric Berger 3,
Jean-Matthieu Monnet 3,
Michele Dalponte 4,
Milan Kobal 5,
Marco Pellegrini 6,
Emanuele Lingua 6,
Domen Mongus 7 and
Norbert Pfeifer 1
1
TU Wien: Department of Geodesy and Geoinformation, Vienna University of Technology, Gußhausstraße 27–29, 1040 Vienna, Austria
2
SLU: Department of Forest Resource Management, Swedish University of Agricultural Sciences, 90183 Umeå, Sweden
3
Irstea, UR EMGR Écosystèmes Montagnards, centre de Grenoble, F-38402 Saint-Martin-d'Hères, France
4
FEM: Department of Sustainable Agro-Ecosystems and Bioresources, Research and Innovation Centre, Fondazione Edmund Mach, Via E. Mach 1, 38010 San Michele all'Adige (TN), Italy
5
SFI: Department of Forestry and Renewable Forest Resources, Biotechnical Faculty, University of Ljubljana, Večna pot 83, 1000 Ljubljana, Slovenia
6
TESAF: Department of Land, Environment, Agriculture and Forestry, University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
7
UM-FERI: Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova ulica 17, SI-2000 Maribor, Slovenia
*
Author to whom correspondence should be addressed.
Academic Editors: Joanne C. White and Eric J. Jokela
Forests 2015, 6(5), 1721-1747; https://doi.org/10.3390/f6051721
Received: 12 March 2015 / Revised: 24 April 2015 / Accepted: 8 May 2015 / Published: 15 May 2015
In this study, eight airborne laser scanning (ALS)-based single tree detection methods are benchmarked and investigated. The methods were applied to a unique dataset originating from different regions of the Alpine Space covering different study areas, forest types, and structures. This is the first benchmark ever performed for different forests within the Alps. The evaluation of the detection results was carried out in a reproducible way by automatically matching them to precise in situ forest inventory data using a restricted nearest neighbor detection approach. Quantitative statistical parameters such as percentages of correctly matched trees and omission and commission errors are presented. The proposed automated matching procedure presented herein shows an overall accuracy of 97%. Method based analysis, investigations per forest type, and an overall benchmark performance are presented. The best matching rate was obtained for single-layered coniferous forests. Dominated trees were challenging for all methods. The overall performance shows a matching rate of 47%, which is comparable to results of other benchmarks performed in the past. The study provides new insight regarding the potential and limits of tree detection with ALS and underlines some key aspects regarding the choice of method when performing single tree detection for the various forest types encountered in alpine regions.
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Keywords:
single tree extraction; airborne laser scanning; forest inventory; comparative testing; co-registration; mountain forests; Alpine Space; matching
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MDPI and ACS Style
Eysn, L.; Hollaus, M.; Lindberg, E.; Berger, F.; Monnet, J.-M.; Dalponte, M.; Kobal, M.; Pellegrini, M.; Lingua, E.; Mongus, D.; Pfeifer, N. A Benchmark of Lidar-Based Single Tree Detection Methods Using Heterogeneous Forest Data from the Alpine Space. Forests 2015, 6, 1721-1747.
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