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Open AccessTechnical Note

Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment

1
Remote Sensing Research Centre, School of Earth and Environmental Sciences, The University of Queensland, St Lucia 4072, QLD, Australia
2
Joint Remote Sensing Research Program, The University of Queensland, St Lucia 4072, QLD, Australia
3
Hydrology, Agriculture and Land Observation Group, Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
4
Precision Agriculture Research Group, School of Science and Technology, University of New England, Armidale 2351, NSW, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(3), 269; https://doi.org/10.3390/rs11030269
Received: 20 December 2018 / Revised: 19 January 2019 / Accepted: 28 January 2019 / Published: 30 January 2019
Tree condition, pruning and orchard management practices within intensive horticultural tree crop systems can be determined via measurements of tree structure. Multi-spectral imagery acquired from an unmanned aerial system (UAS) has been demonstrated as an accurate and efficient platform for measuring various tree structural attributes, but research in complex horticultural environments has been limited. This research established a methodology for accurately estimating tree crown height, extent, plant projective cover (PPC) and condition of avocado tree crops, from a UAS platform. Individual tree crowns were delineated using object-based image analysis. In comparison to field measured canopy heights, an image-derived canopy height model provided a coefficient of determination (R2) of 0.65 and relative root mean squared error of 6%. Tree crown length perpendicular to the hedgerow was accurately mapped. PPC was measured using spectral and textural image information and produced an R2 value of 0.62 against field data. A random forest classifier was applied to assign tree condition into four categories in accordance with industry standards, producing out-of-bag accuracies >96%. Our results demonstrate the potential of UAS-based mapping for the provision of information to support the horticulture industry and facilitate orchard-based assessment and management. View Full-Text
Keywords: unmanned aerial system; horticulture; avocado; canopy structure; tree condition unmanned aerial system; horticulture; avocado; canopy structure; tree condition
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    Link: https://github.com/dobedobedo
    Description: The author's GitHub that contains Python scripts for this study.
MDPI and ACS Style

Tu, Y.-H.; Johansen, K.; Phinn, S.; Robson, A. Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment. Remote Sens. 2019, 11, 269.

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