PhD Scholarship: University of Canterbury, Christchurch, New Zealand

Phenotyping Trees with remote sensing for forest genetics


Phenotyping is the measurement of tree characteristics, such as height, or needle colour, which represent the combined effects of the tree’s genetics and growing environment. Emerging technologies such as laser scanning (LiDAR), dense image matching, and multispectral sensors, operated from UAV or ground-based platforms are providing opportunities to achieve high volume, high quality, cost effective, phenotypic data for tree breeders. Recently developed techniques such as deep learning, and machine learning offer opportunities to identify and segment objects such as trees from two- and three-dimensional data sets and identify relationships hidden in large data sets. There are significant challenges in processing high density data sets to extract tree phenotypes. There are further challenges in utilising such unprecedented levels of information in statistical analyses of trials. Current methods are designed to process phenotypic observations for a handful of traits. High throughput phenotyping could produce large numbers (tens to thousands) of phenotypic observations per tree. It can also provide detailed spatial information about the trees growing environment. Such as tree locations, terrain characteristics, and competition levels. Such large amounts and types of phenotypic data potentially demand novel analytical and modeling methods.

Scholarship Description:

The Radiata Pine Breeding Company (RPBC) is sponsoring a PhD scholarship, offered by the University of Canterbury, for the development of novel tree phenotyping methods to accelerate rates of genetic improvement. RBPC breeds elite genetic material, and provides knowledge, support and tools to continuously improve profitability for Australasian radiata pine (Pinus radiata) forest owners. RPBC and research partner Scion are committed to ongoing development and application of state-of-the-art methods to support these goals. The scholarship will cover university tuition fees, a stipend of $25,000 p.a. and some research-related costs. It is expected the PhD will be completed in a 3 year period. A preferred starting date is March 2020.

Research Topics:

Potential areas for research focus could include one or more of the following, or related, topics:

  • Robotic (UAV, ground), or personal platform-sensor systems for tree phenotyping.
  • Methods, possibly computer vision based, to obtain real-time sub-metre location under forest canopy.
  • Automated methods to spatially co-register remotely sensed data such as 3D point clouds and 2D spectral images.
  • Automated methods to identify individual trees and extract phenotypic data from remotely sensed data such as 3D point clouds and 2D spectral images.
  • Methods to immersively and interactively measure trees in 3D spatial and spectral datasets, and to visualise data and derivitaves including phenotypic and genotypic values.
  • Spatial analytical methods to generate measures of environmental effects on trees, including competition, microsite effects, and topgraphic analyses.
  • Analytical methods able to efficiently handle large volumes of phenotypic data.

Applicant Qualifications:

The ideal applicant will have a GPA of 8.0 (A-) or higher, a four-year bachelor degree with first class honours, or a Masters degree in a relevant field; this may include forestry, ecology, geomatics, computer science, mathematics, statistics or other related field. Preference maybe given to applicants that have demonstrated research skills in remote sensing. Candidates who have published research in peer-reviewed journals are strongly encouraged to apply. Candidates should have a valid drivers licence and be willing and capable to undertake field work, dependant on the nature of the agreed topic.

Contact Us:

Expressions of interest should include a CV, copy of transcript(s), and a 1-page research proposal.
These should be sent to:
      Dr Justin Morgenroth, School of Forestry, University of Canterbury
      Dr Luis Apiolaza, School of Forestry, University of Canterbury
      and Dr Mark Paget, RPBC
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