Development of novel, operational, forest monitoring tools from satellite imagery and airborne laser scanning technologies

Background:

Across the managed forest estate in Canada there is a need to improved monitoring of key forest stand metrics that drive the silvicultural decision-making process. Key to silvicultural activities such as thinning, fertilising and final harvesting, is accurate tree and stand level inventory information such as tree location, height, stand density, species composition, and health. Despite advances in the estimation of many of these attributes using LIDAR data within the Enhanced Forest Inventory (EFI) framework, to date these estimates have been inherently static. We are looking for a two-year postdoctoral research fellow (PDF) to determine which key stand attributes are well suited to being integrated into a continuous monitoring framework, at what level of spatial detail and at what level of accuracy. Once the framework is developed the PDF will apply it at a number of key sites across Canada.

This project is a part of Silva21, a large multi-year industry and federally funded project to provide tools and practical solutions for decision-makers, managers and planners to adapt their forest management practices to improve both resistance to stressors and resilience to disturbance, and thereby ensure the longevity of forest-based communities across Canada.

Position Criteria:

Knowledge of advanced remote sensing approaches and modelling focused on forest inventory needs and sustainable forest management is critical. We require a two year post doctoral fellow to develop the framework and then lead its implementation across a number of key sites. It is anticipated the framework could then be applied by other researchers as part of the ongoing Silva21 project.  Outcomes of this research will be shared via peer-reviewed publications and open-access software package.

Successful applicants should:

  • Hold a PhD in remote sensing, computer science, geography, forestry or related field;
  • Have a strong programming background and excellent programming and scripting skills in R, python, or similar;
  • Have experience in developing predictive models of forest inventory attributes, and a demonstrated ability to use and apply 3D remote sensing datasets, specifically airborne laser scanning data.
  • Have excellent oral and written communication skills with a strong publication record.
  • Experience in working with forest inventory, and growth and yield data is desirable, but not a requirement.

Details

The candidate will be based at the University of British Columbia (UBC) in Vancouver, Canada under the supervision of Professor Nicholas Coops. Applicants should send a letter explaining their motivation and relevant skill set, a CV and the names of three references to nicholas.coops@ubc.ca

The deadline for sending in applications is May 30th 2021, but we will consider applications until the position is filled. The expected start date would be July 1st  2021. The position is for a fixed length of 24 months.

 
Reference Number

Please mention reference number GPS-55067 in all your correspondence about this Postdoctoral Fellow position.

This position will be supervised by
 
 
 
 
 

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