Field characterization of olive ( Olea europaea L.) tree crown architecture using terrestrial laser scanning data

• Published in: Agricultural and forest meteorology 2011-02, Vol.151 (2), p.204-214
• Main Authors: Moorthy, Inian, Miller, John R., Berni, Jose Antonio Jimenez, Zarco-Tejada, Pablo, Hu, Baoxin, Chen, Jing
• Format: Article
• Language: English
• Subjects: Agricultural and forest climatology and meteorology. Irrigation. Drainage; Agronomy. Soil science and plant productions; Architecture; Biological and medical sciences; Biophysical parameters; canopy; data analysis; digital images; field experimentation; Fundamental and applied biological sciences. Psychology; General agronomy. Plant production; height; ILRIS-3D; Laser scanning; Lasers; leaves; lidar; measuring devices; methodology; new methods; Olea; Olea europaea; Olea europaea L; Olives; plant architecture; plant area index; Plants (organisms); remote sensing; Scanning; spatial distribution; terrestrial laser scanning; Three dimensional; tree crown; Trees; Vegetation; volume; width
• ISSN: 0168-1923; 1873-2240
• DOI: 10.1016/j.agrformet.2010.10.005

▶ ILRIS-3D data was used to characterize individual tree crown structural parameters. ▶ Crown width, crown height, crown volume, and plant area index were retrieved. ▶ Novel method to characterized crown-level clumping is described. Since the introduction of Terrestrial Laser Scanning (TLS) instruments, there now exists a means of rapidly digitizing intricate structural details of vegetation canopies using Light Detection and Ranging (LiDAR) technology. In this investigation, Intelligent Laser Ranging and Imaging System (ILRIS-3D) data was acquired of individual tree crowns at olive ( Olea europaea L.) plantations in Córdoba, Spain. In addition to conventional tripod-mounted ILRIS-3D scans, the unit was mounted on a platform (12 m above ground) to provide nadir (top–down) observations of the olive crowns. 24 structurally variable olive trees were selected for in-depth analysis. From the observed 3D laser pulse returns, quantitative retrievals of tree crown structure and foliage assemblage were obtained. Robust methodologies were developed to characterize diagnostic architectural parameters, such as tree height ( r 2 = 0.97, rmse = 0.21 m), crown width ( r 2 = 0.97, rmse = 0.13 m), crown height ( r 2 = 0.86, rmse = 0.14 m), crown volume ( r 2 = 0.99, rmse = 2.6 m 3), and Plant Area Index (PAI) ( r 2 = 0.76, rmse = 0.26 m 2 /m 2). With the development of such LiDAR-based methodologies to describe vegetation architecture, the forestry, agriculture, and remote sensing communities are now faced with the possibility of replacing current labour-intensive inventory practices with, modern TLS systems. This research demonstrates that TLS systems can potentially be the new observational tool and benchmark for precise characterization of vegetation architecture for improved agricultural monitoring and management.