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Open AccessArticle

Understanding Tree-to-Tree Variations in Stone Pine (Pinus pinea L.) Cone Production Using Terrestrial Laser Scanner

1
Département de biologie, Chimie et Géographie, Université du Québec à Rimouski, 300 allée des Ursulines, Rimouski, QC G5N 1E8, Canada
2
Department of Forest Management and Dynamics, INIA-CIFOR., Ctra. A Coruña km 7.5., 28040 Madrid, Spain
3
AMAP, IRD, CNRS, CIRAD, INRA, Univ Montpellier, botAnique et Modélisation de l’Architecture, des Plantes et des Végétations, TA A51/PS2, CEDEX 05, 34398 Montpellier, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(1), 173; https://doi.org/10.3390/rs12010173
Received: 12 November 2019 / Revised: 13 December 2019 / Accepted: 21 December 2019 / Published: 3 January 2020
(This article belongs to the Special Issue 3D Forest Structure Observation)
Kernels found in stone pinecones are of great economic value, often surpassing timber income for most forest owners. Visually evaluating cone production on standing trees is challenging since the cones are located in the sun-exposed part of the crown, and covered by two vegetative shoots. Very few studies were carried out in evaluating how new remote sensing technologies such as terrestrial laser scanners (TLS) can be used in assessing cone production, or in trying to explain the tree-to-tree variability within a given stand. Using data from 129 trees in 26 plots located in the Spanish Northern Plateau, the gain observed by using TLS data when compared to traditional inventory data in predicting the presence, the number, and the average weight of the cones in an individual tree was evaluated. The models using TLS-derived metrics consistently showed better fit statistics, when compared to models using traditional inventory data pertaining to site and tree levels. Crown dimensions such as projected crown area and crown volume, crown density, and crown asymmetry were the key TLS-derived drivers in understanding the variability in inter-tree cone production. These results underline the importance of crown characteristics in assessing cone production in stone pine. Moreover, as cone production (number of cones and average weight) is higher in crowns with lower density, the use of crown pruning, abandoned over 30 years ago, might be the key to increasing production in combination with stand density management. View Full-Text
Keywords: stone pinecone production; terrestrial laser scanner; crown characteristics; modeling; inter-tree variability stone pinecone production; terrestrial laser scanner; crown characteristics; modeling; inter-tree variability
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MDPI and ACS Style

Schneider, R.; Calama, R.; Martin-Ducup, O. Understanding Tree-to-Tree Variations in Stone Pine (Pinus pinea L.) Cone Production Using Terrestrial Laser Scanner. Remote Sens. 2020, 12, 173. https://doi.org/10.3390/rs12010173

AMA Style

Schneider R, Calama R, Martin-Ducup O. Understanding Tree-to-Tree Variations in Stone Pine (Pinus pinea L.) Cone Production Using Terrestrial Laser Scanner. Remote Sensing. 2020; 12(1):173. https://doi.org/10.3390/rs12010173

Chicago/Turabian Style

Schneider, Robert; Calama, Rafael; Martin-Ducup, Olivier. 2020. "Understanding Tree-to-Tree Variations in Stone Pine (Pinus pinea L.) Cone Production Using Terrestrial Laser Scanner" Remote Sens. 12, no. 1: 173. https://doi.org/10.3390/rs12010173

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