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Remote Sens. 2012, 4(4), 950-974; doi:10.3390/rs4040950

An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning

1,* , 1
1 Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute, P.O.Box 15, FI-02431 Masala, Finland 2 Department of Forest Sciences, University of Helsinki, P.O. Box 27 (Latokartanonkaari 7), FI-00014 Helsinki, Finland 3 School of Science and Technology, Aalto University, FI-00076 Aalto, Finland 4 Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Nienburger Str. 1, D-30167 Hannover, Germany 5 Institute for Information and Communication Technologies, Joanneum Research Forschungsgesellschaft mbH, Steyrergasse 17, A-8010 Graz, Austria 6 Department of Geography, University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland 7 Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway 8 The Finnish Forest Research Institute, P.O. Box 68 (Yliopistokatu 6), FI-80101 Joensuu, Finland 9 Department of Ecosystem Science and Management, Texas A&M University, 2120 TAMU, College Station, TX 77843, USA 10 Department of Forest Resources, Norwegian forest and landscape institute, P.O. Box 115, NO-1431 Ås, Norway 11 Solving3D GmbH, Osteriede 8-10, D-30027 Garbsen, Germany 12 Department of Civil Engineering, National I-Lan University, No. 1, Sec. 1, Sheng-Lung Road, I-Lan City 260, Taiwan
* Author to whom correspondence should be addressed.
Received: 10 February 2012 / Revised: 15 March 2012 / Accepted: 15 March 2012 / Published: 30 March 2012
(This article belongs to the Special Issue Laser Scanning in Forests)
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The objective of the “Tree Extraction” project organized by EuroSDR (European Spatial data Research) and ISPRS (International Society of Photogrammetry and Remote Sensing) was to evaluate the quality, accuracy, and feasibility of automatic tree extraction methods, mainly based on laser scanner data. In the final report of the project, Kaartinen and Hyyppä (2008) reported a high variation in the quality of the published methods under boreal forest conditions and with varying laser point densities. This paper summarizes the findings beyond the final report after analyzing the results obtained in different tree height classes. Omission/Commission statistics as well as neighborhood relations are taken into account. Additionally, four automatic tree detection and extraction techniques were added to the test. Several methods in this experiment were superior to manual processing in the dominant, co-dominant and suppressed tree storeys. In general, as expected, the taller the tree, the better the location accuracy. The accuracy of tree height, after removing gross errors, was better than 0.5 m in all tree height classes with the best methods investigated in this experiment. For forest inventory, minimum curvature-based tree detection accompanied by point cloud-based cluster detection for suppressed trees is a solution that deserves attention in the future.
Keywords: tree detection; tree extraction; airborne laser scanning; EuroSDR; ISPRS; individual tree inventory; 3D; crown delineation tree detection; tree extraction; airborne laser scanning; EuroSDR; ISPRS; individual tree inventory; 3D; crown delineation
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Kaartinen, H.; Hyyppä, J.; Yu, X.; Vastaranta, M.; Hyyppä, H.; Kukko, A.; Holopainen, M.; Heipke, C.; Hirschmugl, M.; Morsdorf, F.; Næsset, E.; Pitkänen, J.; Popescu, S.; Solberg, S.; Wolf, B.M.; Wu, J.-C. An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning. Remote Sens. 2012, 4, 950-974.

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