Post Doctoral Fellow: Remote Sensing and Forest Resource Inventory

Laboratory for Remote Sensing of Earth and Environmental Systems (LaRSEES)
Department of Geography
Queen’s University
Kingston, Ontario Canada

“We are seeking an individual with a Ph.D. in one of the following disciplines: geography, forestry, environmental science, environmental engineering; with an emphasis on remote sensing, spatial data analysis and/or modeling for forestry. Experience with light detection and ranging (LiDAR) data analysis is a definite asset. Other skills and background of the applicant should include some of the following:

  • Knowledge of forest mensuration and field techniques (i.e., with an ability to lead the design, implementation and collection of forest inventory data);
  • Computer programming skills/experience;
  • Familiarity with the application of statistics/biostatistics;
  • Knowledge of image processing and GIS software; and
  • Ability to communicate effectively both verbally and in writing.

“The focus of the project is to derive accurate estimates of forest inventory variables (e.g., tree height, stem density, diameter and breast height, volume, biomass, etc.) using lidar and high resolution digital photos. The individual will be involved in the development of algorithms and procedures for extracting forest structural and terrain variables from the LiDAR data collected for over one million hectares of boreal forest near Hearst Ontario. The PDF will coordinate activities of the project, specifically the field-based activities during two summer field campaigns and the development and application of LiDAR height and density metrics to the LiDAR data collected for Hearst. The PDF will be responsible for supervising the application of the individual tree crown (ITC) method to the ADS40 imagery collected for the same forest. The successful candidate will have the opportunity to define their own research goals within the scope of the overall project (i.e., enhanced forest inventory using LiDAR and/or high resolution digital photography). The successful candidate will also assist the principal investigator with the administration and management of the project. The fellowship holder will be expected to collaborate and work closely with the research team at Queen’s and Nipissing Universities as well as government (e.g., OMNR, CFS) and industrial partners (Hearst Forest Management Inc., Tembec Inc., etc.).”