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A Comparative Assessment of the Performance of Individual Tree Crowns Delineation Algorithms from ALS Data in Tropical Forests

1
AMAP, IRD, CNRS, INRA, Univ Montpellier, CIRAD, 34000 Montpellier, France
2
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91001, USA
3
Institute of the Environment and Sustainability, University of California, Los Angeles, CA 90095, USA
4
Department of Computer Science, University of Kentucky, Lexington, KY 40506, USA
5
Forest Ecology and Conservation Group, Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK
6
Pôle Recherche, Développement et Innovation de Nancy, Office National des Forêts, Site de Nancy-Brabois, 54600 Villers-lès-Nancy, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(9), 1086; https://doi.org/10.3390/rs11091086
Received: 22 March 2019 / Revised: 23 April 2019 / Accepted: 24 April 2019 / Published: 7 May 2019
(This article belongs to the Special Issue Remote Sensing of Tropical Phenology)
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Abstract

Tropical forest canopies are comprised of tree crowns of multiple species varying in shape and height, and ground inventories do not usually reliably describe their structure. Airborne laser scanning data can be used to characterize these individual crowns, but analytical tools developed for boreal or temperate forests may require to be adjusted before they can be applied to tropical environments. Therefore, we compared results from six different segmentation methods applied to six plots (39 ha) from a study site in French Guiana. We measured the overlap of automatically segmented crowns projection with selected crowns manually delineated on high-resolution photography. We also evaluated the goodness of fit following automatic matching with field inventory data using a model linking tree diameter to tree crown width. The different methods tested in this benchmark segmented highly different numbers of crowns having different characteristics. Segmentation methods based on the point cloud (AMS3D and Graph-Cut) globally outperformed methods based on the Canopy Height Models, especially for small crowns; the AMS3D method outperformed the other methods tested for the overlap analysis, and AMS3D and Graph-Cut performed the best for the automatic matching validation. Nevertheless, other methods based on the Canopy Height Model performed better for very large emergent crowns. The dense foliage of tropical moist forests prevents sufficient point densities in the understory to segment subcanopy trees accurately, regardless of the segmentation method. View Full-Text
Keywords: individual tree crown segmentation; airborne laser scanning; tropical forest; benchmark individual tree crown segmentation; airborne laser scanning; tropical forest; benchmark
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Aubry-Kientz, M.; Dutrieux, R.; Ferraz, A.; Saatchi, S.; Hamraz, H.; Williams, J.; Coomes, D.; Piboule, A.; Vincent, G. A Comparative Assessment of the Performance of Individual Tree Crowns Delineation Algorithms from ALS Data in Tropical Forests. Remote Sens. 2019, 11, 1086.

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