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

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%.
Authors: Yu-Hsuan Tu, Kasper Johansen, Stuart Phinn and Andrew Robson
 
Journal: Remote Sens. 2019, 11(3), 269
 
Publisher: MDPI 
 
Abstract
 
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.

Photo: Graphical abstract (credits: Yu-Hsuan Tu, Kasper Johansen, Stuart Phinn and Andrew Robson)

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
 
 
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