Preprint Article Version 1 This version is not peer-reviewed

Using Multi-Spectral UAV Imagery to Extract Tree Crop Structural Properties and Assess Pruning Effects

Version 1 : Received: 15 April 2018 / Approved: 16 April 2018 / Online: 16 April 2018 (08:56:39 CEST)

A peer-reviewed article of this Preprint also exists.

Johansen, K.; Raharjo, T.; McCabe, M.F. Using Multi-Spectral UAV Imagery to Extract Tree Crop Structural Properties and Assess Pruning Effects. Remote Sens. 2018, 10, 854. Johansen, K.; Raharjo, T.; McCabe, M.F. Using Multi-Spectral UAV Imagery to Extract Tree Crop Structural Properties and Assess Pruning Effects. Remote Sens. 2018, 10, 854.

Journal reference: Remote Sens. 2018, 10, 854
DOI: 10.3390/rs10060854

Abstract

Unmanned aerial vehicles (UAV) provide an unprecedented capacity to monitor the development and dynamics of tree growth and structure through time. It is generally thought that the pruning of tree crops encourages new growth, has a positive effect on fruiting, makes fruit-picking easier, and may increase yield, as it increases light interception and tree crown surface area. To establish the response of pruning in an orchard of lychee trees, an assessment of changes in tree structure, i.e. tree crown perimeter, width, height, area and Plant Projective Cover (PPC), was undertaken using multi-spectral UAV imagery collected before and after a pruning event. While tree crown perimeter, width and area could be derived directly from the delineated tree crowns, height was estimated from a produced canopy height model and PPC was most accurately predicted based on the NIR band. Pre- and post-pruning results showed significant differences in all measured tree structural parameters, including an average decrease in tree crown perimeter of 1.94 m, tree crown width of 0.57 m, tree crown height of 0.62 m, tree crown area of 3.5 m2, and PPC of 14.8%. In order to provide guidance on data collection protocols for orchard management, the impact of flying height variations was also examined, offering some insight into the influence of scale and the scalability of this UAV based approach for larger orchards. The different flying heights (i.e. 30, 50 and 70 m) produced similar measurements of tree crown width and PPC, while tree crown perimeter, area and height measurements decreased with increasing flying height. Overall, these results illustrate that routine collection of multi-spectral UAV imagery can provide a means of assessing pruning effects on changes in tree structure in commercial orchards, and highlight the importance of collecting imagery with consistent flight configurations, as varying flying heights may cause changes to tree structural measurements.

Subject Areas

UAV; multi-spectral; lychee; pruning; tree crop structure; change detection

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