For more information about listed positions or to discuss potential opportunities, please email Professor Justin Morgenroth or Dr Vega Xu. When contacting us, please include a brief description of your background and research interests, why you are interested in working with FOReST, your academic transcripts, and a writing sample (e.g., published paper, thesis chapter, or research proposal).
University of Canterbury - Geospatial Research Institute Toi Hangarau PhD Scholarship 2026 - School of Earth and Environment &FOReST
Project aim:
Examining the 3-30-300 rule’s robustness as a neighbourhood quality indicator by developing scalable measurements and assessing its socio-spatial impacts through factors like housing prices.
Summary:
This project examines how access to trees, green space, and urban infrastructure influences environmental quality within and between neighbourhoods using the 3-30-300 rule. This rule proposes every resident should have visibility of at least 3 trees, 30% neighbourhood canopy cover, and green space access within 300 metres. Despite its adoption as a guideline in urban forestry and planning, the 3-30-300 rule’s evidence base remains limited in terms of consensus on measuring its components, scalability, and thresholds for socio-spatial impacts.
The research uses an explicitly spatial approach that combines multi-scale LiDAR-derived tree canopy cover, network-based accessibility, and GeoAI methods to capture experiential aspects of tree exposure, and the extent to which that exposure is equitably distributed. Exploring 3-30-300 rule component measurements, and how they interact with socio-spatial aspects of neighbourhoods, will produce a replicable, scalable approach and help determine its robustness as a neighbourhood quality indicator.
Skills required: Strong GIS or spatial data science background, including programming skills (R/Python); skill and experience working with remote sensing data including imagery and LiDAR data; experience with computer vision or GeoAI is a plus.
Funds awarded:
NZ$35,650/year, over 3 years full-time. This scholarship consists of:
One UC PhD scholarship ($32,650 per annum), plus A GRI top-up scholarship ($3,000 per annum).
Up to an additional $4,000 per year is available from the GRI for travel or other research related expenses (over three years full-time, or pro-rata for part-time study, subject to support from the senior PhD supervisor and approval of the GRI Director).
Funding period: The scholarship is tenable for the period necessary to complete up to 360 points of enrolment
Contact Information:
Dr. Lindsey Conrow, lindsey.conrow@canterbury.ac.nz
Prospective PhD student applications must include the following five items:
Cover letter explaining motivation for doing a PhD outlining interest and experience in geospatial methods and analysis.
Curriculum Vitae including a list of any prior publications.
Contact details of at least two academic or professional referees
A GPA report obtained from https://support.scholaro.com/portal/en/kb/articles/canterbury (those with New Zealand or United States qualifications are not required to use Scholaro).
Please send your completed application materials to:
Geospatial Research Institute: gri-enquiries@canterbury.ac.nz
The deadline for submission of applications is 29 May 2026, at: 17:00 NZ Time.
*Those that submit late, will not be considered.
In recognizing our Tiriti o Waitangi responsibilities and in valuing diversity in our institutions, we look forward to a diverse applicant pool. Māori scholars are especially encouraged to apply and would be connected with Māori academics and support staff at Te Whare Wānaga o Waitaha | University of Canterbury.
For information related to selection of PhD candidates please download this link: https://geospatial.ac.nz/wp-content/uploads/2026/04/05_selection_of_phd_candidates2026.pdf
The official advertisement and supplementary documents can be found at: https://geospatial.ac.nz/jobs-and-scholarships/
University of Canterbury - FOReST Lab
Background:
The FOReST (Forest Observation with Remote Sensing Technologies) Lab at the University of Canterbury's School of Forestry invites applications from outstanding students for PhD supervision opportunities commencing in 2026.
About the FOReST Lab:
Established in 2018 by Professor Justin Morgenroth, the FOReST Lab is co-directed by Professor Justin Morgenroth and Senior Lecturer Dr. Cong (Vega) Xu. Our research focuses on advancing the applications of remote sensing and geospatial information science to forested environments. Our current research portfolio includes:
Evaluation of ecosystem services provided by urban forests
Native and plantation forest mapping and monitoring
Advanced forest analysis using cutting-edge remote sensing technologies including drones, LiDAR, and satellite imagery
Funding Opportunities:
With an increase in doctoral scholarships available for 2026, successful candidates may be eligible for University of Canterbury Doctoral Scholarships (UCDS) or co-funded UC Connect Scholarships (stipends co-funded between UC and external organizations), providing $32,650 per annum.
Please note: This represents an opportunity to apply for UC doctoral scholarships under our supervision and does not guarantee funding.
Scholarship Eligibility:
High GPA from the recent past two years of study (either at Masters level or Honours undergraduate level)
Strong academic background in relevant fields (forestry, environmental science, geography, remote sensing, geospatial analysis, or related disciplines).
Chances Will Improve With: successful peer-reviewed publications; relevant work experience in remote sensing, forestry, or geospatial analysis.
How to Apply:
Interested candidates should contact the FOReST Lab to discuss potential research projects and supervision opportunities. Early engagement is encouraged to develop competitive scholarship applications.
Contact Information:
Professor Justin Morgenroth, justin.morgenroth@canterbury.ac.nz
Dr. Cong (Vega) Xu, cong.xu@canterbury.ac.nz
Background:
Genetic trials are central to breeding programmes: we use them for estimating genetic parameters, predicting breeding values, training genomic models, etc. Forestry trials often show strong within-trial trends, which means that we must remove substantial environmental noise to observe a clear genetic signal.
Historically, we could only remove some noise through experimental design and later improve this approach by using spatial analysis. We now routinely have additional data available from Unpiloted Aerial Vehicle (UAV) imagery and LiDAR, which we are not fully exploiting.
The project:
The NZ School of Forestry at the University of Canterbury (UC) is offering a 3-year PhD project to investigate how tree and terrain related data can be used to improve within-trial analysis, by adding spatial analysis, topographic indices and competition indices derived from UAV LiDAR data.
The project is funded by the NZ Radiata Pine Breeding Company (RPBC). The student will work closely with RPBC, which specialises in breeding elite radiata pine germplasm for New Zealand and Australian forest owners. The student will work with a research team led by Professor Luis Apiolaza and Dr Vega Xu from UC, and Dr Mark Paget and Dr Sai Arojju from RPBC.
The research project will include:
Investigate the derivation of multiple competition indices based on UAV LiDAR.
Evaluate the utility of multiple topographic indices and tree metrics derived from UAV LiDAR in the context of RPBC genetic trials.
Compare the base genetic analysis (experimental design + genomic pedigree) to base + spatial analysis, base + spatial + topographic + competition.
Candidate notes:
The ideal applicant will have a GPA of 7.0 (A-) or higher, and hold a four-year bachelor’s degree with first-class honours or a Master’s degree in forestry, remote sensing or data
science.
Applicants must demonstrate:
Strong data analysis skills, with proficiency in R or Python
Familiarity with remote sensing and geospatial data processing (e.g. LiDAR and UAV imagery)
Ability to learn and apply quantitative genetics and advanced statistical methods in the context of tree breeding.
A valid driver’s licence and willing to undertake fieldwork.
The scholarship is available from 1 October 2025 (start date negotiable). Review of applications will begin immediately and continue until the position is filled.
Applications must include:
A full Curriculum Vitae, including your university transcript (i.e. list of grades for all courses).
The names of at least two referees.
A statement of your research interests and your intended start date.
You must meet the eligibility criteria to enrol in UC’s Doctor of Philosophy programme (https://checkwhatyouneed.canterbury.ac.nz/checkrequirements).
The University of Canterbury is located in Christchurch, the largest city in the beautiful South Island of Aotearoa | New Zealand. The city’s central location in the South Island gives easy access to both coasts as well as the Southern Alps and a range of other unique environments.
Funding notes:
The available scholarship covers full university fees and a stipend of NZ$34,000 p.a. for three years
Contact Information:
Dr. Cong (Vega) Xu, cong.xu@canterbury.ac.nz
Background:
Radiata pine breeding company (RPBC) plays a central role in breeding elite genetic material to forest owners in Australasia, with emphasis on production traits. However, with changing climatic conditions a new set of challenges arise, for instance, increased drought and disease susceptibility. Among diseases, dothistroma needle blight (DNB) and red needle cast (RNC) are significant as they negatively affect productivity. High-throughput phenotyping for drought and disease resistance is of key interest to RPBC for breeding next generation of trees that are resilient to biotic and abiotic stresses.
The project:
The University of Canterbury (UC) is working alongside RPBC to develop remote sensing methods for high-throughput phenotyping, reducing the costs of field-based assessments to accurately monitor and quantify these drought and disease resistance traits. The methods developed will ultimately result in developing tolerant genotypes through selective breeding.
In this project, RPBC and UC want to explore the feasibility of using multispectral and thermal imaging technologies for high-throughput phenotyping of drought and disease progression in radiata pine. A combination of controlled experiments and field-based assessments will be carried out, coupled with advanced quantitative genetic analysis to quantify the learnings from both systems. These findings will help to develop a robust pipeline for phenotyping adaptive traits in the RPBC breeding programme.
Who we’re looking for:
The University of Canterbury wishes to hire a postdoctoral researcher (3-year contract, 1.0 FTE at between $61,007 – 91,511 per annum, depending on experience) who has demonstrated research capabilities in remote sensing image acquisition, processing, and analysis. Experience with deep learning and time-series/phenological datasets is desirable. An understanding of vegetation biology and function is preferred, but not necessary.
The successful candidate should be willing and able to begin work prior to 1 June 2024. The role requires the candidate to be based in Christchurch, New Zealand.
Who we are:
You will work alongside research and technical staff at the Radiata Pine Breeding Company (rpbc.co.nz) and researchers and graduate students at the University of Canterbury’s Remote Sensing and Geospatial Analysis research group (rsga.co.nz). Questions and expressions of interest should be sent to Assoc. Prof. Justin Morgenroth (justin.morgenroth@canterbury.ac.nz).
We are excited to offer a PhD position in the field of remote sensing at the New Zealand School of Forestry, University of Canterbury. This research project aims to advance our understanding of minor forest species in New Zealand by using image analysis and machine learning/deep learning. We are seeking a motivated and enthusiastic PhD candidate to join our team and contribute to cutting-edge research in forestry.
Description:
New Zealand’s plantation forests are predominantly composed of a single species - Pinus radiata. However, concerns have arisen about the risks associated with relying heavily on a single species, such as market demand fluctuations and the threat of devastating pest or disease outbreaks. Hence, there is growing interest in diversifying forest resources across the country. To effectively model the potential sustainable log supply from these minor species, it becomes crucial to accurately assess the extent and distribution of the existing resource. However, such information is currently lacking, as most of these forests are privately owned, small and fragmented. Therefore, this research will focus on developing innovative remote sensing techniques to identify, classify, and monitor these minor tree species throughout the country. By combining advanced image analysis and machine learning/deep learning methods, we aim to provide accurate and efficient tools to monitor and manage these valuable resources.
Requirements:
We are looking for a highly motivated individual with a strong background in remote sensing, forestry, or a related field. The ideal candidate should have the following qualifications:
A Master's degree (or equivalent) in forestry, environmental science, geospatial science, remote sensing or a related discipline.
Meet the English language requirements for admission for international students (UC requirement).
Proficiency in remote sensing techniques, including image processing and analysis, machine learning and deep learning.
Experience with programming languages such as R and Python.
Strong analytical and statistical skills.
Excellent communication and writing abilities.
Funding and support:
The successful candidate will have the tuition fee waived and will be supported by a competitive stipend (NZD$28,000) for the duration of the PhD program.
How to apply:
Please submit the following documents when applying:
A CV including your research experience
Academic transcripts
A cover letter outlining your research interests and motivation to join the project
Contact information for two academic referees
Submit all required documents to Dr Vega Xu (cong.xu@canterbury.ac.nz)
Application Deadline:
The application deadline is 30th Sep 2023.
Shortlisted candidates will be contacted for interviews within one month after the application deadline.
For further inquiries about the project or application process, please contact Dr Vega Xu (cong.xu@canterbury.ac.nz).
We look forward to welcoming an enthusiastic and dedicated PhD student to join our team in Christchurch, New Zealand.
