Satellite-based monitoring of softwood productivity through optimal site-specific silviculture.

About this project 

The objective is to develop remote sensing methodologies and cloud-based tools utilizing freely available satellite imagery data that allow Australian softwood growers to better target silvicultural interventions to improve overall productivity and economic returns. Growers often apply a limited number of silvicultural regimes to their plantation estate depending on broad site differences including soil group or productivity. Oversimplification of silvicultural systems or inadequate site characterisation means that some sites receive insufficient silvicultural inputs while others receive significantly more resources than necessary. This can lead to poor productivity outcomes or inefficient resource use. The project will explore the ability of different remote sensing technologies to estimate tree productivity metrics in Australian softwood plantations. Models will be developed across different plantation species and environmental conditions, while reducing noise from non-target understory species. Remote sensed metrics will be used to predict the response of softwood plantations to mid-rotation fertiliser and thinning, using data from grower research trials and routine plantations. 

What you’ll do 

The project will deliver remote sensing solutions integrated into cloud-based tools that enable growers to target silvicultural interventions more precisely. Remote sensing metrics and models will be produced to monitor plantation health and identify responsive sites for mid-rotation fertiliser applications and thinning. The ability to forecast plantation responses to silvicultural interventions will lead to improved plantation productivity and economic returns based on measured plantation requirements rather than standard prescriptions. Findings will be disseminated through academic publications. Engagement with industry partners will be facilitated through workshops and training sessions to demonstrate the use of new tools and models. Regular interaction with growers will ensure practical applicability, and field visits will allow the candidate to inspect research trials and participate in data collection. The future impact of this project includes long-term improvements in the productivity of Australian softwood plantations, widespread industry adoption of remote sensing tools, and contributions to the body of knowledge in forestry and remote sensing, paving the way for future innovations and improvements.
The proposed project will be linked to the industry funded project ‘Transforming future softwood productivity through optimal site-specific silviculture’, which was initiated to address low and static productivity in Australia’s softwood plantations. The productivity of Australia’s softwood plantation estate has been static for at least two decades and must be improved to meet future demand for timber. Determining the potential yield of plantations, estimating the difference between potential and actual yield, and attributing the difference to limiting factors, provides a framework for making decisions on silvicultural management to optimize yields from the existing estate and on the location of future plantations. While climate, soil and topographic conditions provide an overall limit to plantation productivity, a key component of the broader project will also be to identify silvicultural factors limiting productivity. This will include identifying established plantation areas with sub-optimal growth due to limiting nutrition or excessive competition. Innovative approaches such as remote sensing technologies will be explored to target later aged fertilising or thinning operations and improve productivity and value in these plantations. Remote sensing technologies have the potential to revolutionise Australian softwood forestry by providing the means to cost-effectively capture estate-wide data at regular intervals and target silvicultural interventions. A particularly promising metric is leaf area index (LAI), as tree canopy size is a strong indicator of resource availability. The ‘simple ratio’ of near-infrared light to red light, measured by the Sentinel-2 satellite, is a strong predictor of LAI in softwood plantations. This ratio is used to target stands for mid-rotation fertilising. However, similar remote sensing tools have not been developed for the unique species and environmental conditions of Australian softwood plantations

Where you’ll be based 

The PhD student will carry out their research within UniSA STEM's Environmental Science Group at the Sustainable Infrastructure and Resource Management Research Centre, benefiting from a collaborative and supportive environment. The group has secured substantial funding through Australian Research Council Discovery Projects and various industry sponsors. With expertise in Forestry, Ecology, Environmental Science, Remote Sensing, and Geospatial Science, team members have successfully supervised several PhD candidates to completion in recent years. The group includes experienced research fellows and PhD students working on related topics, with weekly seminars showcasing advancements across multiple areas of data mining and machine learning, including satellite remote sensing case studies. This collaborative environment fosters the success and professional development of both new research fellows and PhD students. In addition, the PhD student will become a member of UniSA’s Forestry Centre of Excellence.

Stefan Peters brings extensive expertise in Geospatial Intelligence and satellite remote sensing for forestry applications. With over 60 published research papers and as a chief investigator of 15 research grants, Stefan leads innovative research, including SmartSat-CRC-funded projects and NIFPI-based forestry research on imputation modeling. 

Jim O’Hehir, General Manager of the UniSA Forestry Centre of Excellence, has over 30 years in plantation forestry, including 10 in senior executive roles. His focus is on plantation growth, yield, and sustainability. Jim has led national research, developed growth models, and holds a Master of Forest Science and a PhD. 

John McGrath has 40 years of ecophysiological research experience in timber plantations and farm forestry across Australia, specializing in climatic, soil, water, and nutrient limitations to tree growth. He has held senior research management roles and now operates McGrath Forestry Services, delivering technical solutions for the forestry sector. 

John Senior, HQPlantations’ Silvicultural Scientist, has extensive expertise in silvicultural research and experimentation. He integrates geospatial and remote sensing insights with on-ground experiments for model training. 

Andrew Trlica, Research Scholar at NC State, specializes in remote sensing, GIS, and ecology. His research spans environmental economics, landscape ecology, and climate change impacts, supporting sustainable land management. 

Sami Rifai, University of Adelaide lecturer, specializes in Remote Sensing and Ecology. His research examines global change impacts on ecosystems, focusing on how meteorological extremes affect vegetation using remote sensing tools. The collective expertise of the supervisory team will ensure successful integration of forestry and silviculture science, and cloud-based satellite remote sensing for impactful research outcomes. Financial Support  

This project is funded for reasonable research expenses. Additionally, a living allowance scholarship of $35.200 per annum is available to eligible applicants. Australian Aboriginal and/or Torres Strait Islander applicants will be eligible to receive an increased stipend rate of $52,352 per annum (2025 rate). A fee-offset or waiver for the standard term of the program is also included. For full terms and benefits of the scholarship please refer to our scholarship information for domestic students or international students.

Eligibility and Selection 

This project is open to applications from both Domestic and International applicants.
Applicants must meet the eligibility criteria for entrance into a PhD. 
Additionally, applicants must meet the project selection criteria: 

• Completed course work in forestry or shows an interest silviculture. 
• An understanding of remote sensing, statistics/machine learning, and programming in Python.
• Strong interpersonal skills.
• High level organisational skills, including the ability to prioritise and manage workload.
• Preferably some understanding of plantation silviculture, or a willingness to learn in this area.

All applications that meet the eligibility and selection criteria will be considered for this project. A merit selection process will be used to determine the successful candidate.
The successful applicant is expected to study full-time and to be based at our Mawson Lakes Campus in the north of Adelaide. Note that international students on a student visa will need to study full-time.

Essential Dates 

Applicants are expected to start in a timely fashion upon receipt of an offer. Extended deferral periods are not available. Applications close on Thursday 9 January 2024