We are seeking a M.Sc. student to investigate forest-fire risk adaptation planning in British Columbia, Canada. The project will involve two components: using remote sensing data (LiDAR) to evaluate forest fire risk, and then evaluating how this information can be used to assist community driven fire adaptation planning. Work will be done at the University of Northern British Columbia in partnership with the Xáxli’p First Nation. Start date September 2019 (or possibly summer 2019). Graduate funding for two years secured. Application deadline February 11, 2019. Contact Scott Green (scott.gr...@unbc.ca) or Che Elkin (che.el...@unbc.ca).
*Project Title:* *High-resolution wildfire-fuel mapping using Aerial Laser Scanning (LiDAR) to support climate-change adaptation planning for the Xaxli’p First Nation, Lillooet, BC* *Project Summary* Working in partnership with the Xáxli’p First Nation in southern British Columbia, our multi-year project will evaluate forest-fire risk in ecologically and socially complex environments, and assess mitigation options. The aim of this work is to help inform a community-directed adaptation strategy to restore landscape resilience according to ecological and cultural coherence principles. Specifically, the representation of Xáxli’p cultural values on the landscape*, and *the continuance of traditional practices and relationships in their *‘Survival Territory’*, contained within a community forest license in the Fountain Valley near Lillooet, BC. The Xáxli’p First Nation has identified the risk of catastrophic forest fire as the priority climate-change vulnerability within their territory, which has been exacerbated by institutional management practices – in particular, suppression of natural wildfire and industrial forestry have dramatically altered the structure and density of these dry-land forests. This project will undertake the spatial description of wildfire fuel distributions in the Fountain Valley to inform Xáxli’p restoration activities. Recent advances in remote sensing technology, such as Aerial Laser Scanning *(ALS or LiDAR)*, have substantially improved both the grain and extent at which we can evaluate forest systems. While this new technology has principally been used to develop high-quality forest inventory data, it also provides the opportunity to evaluate forest structure and derive high-resolution measures of wildfire fuel loading, fuel moisture content and landscape level fire risk susceptibility. In this project we will use ALS data in combination with empirical field data and community expert knowledge to advance the projects short-term goals of supporting and informing Xáxli’p Community Forest strategic planning in their landscape restoration *(fuel reduction)* activities. *Desired Qualifications**:* The MSc student in this project will be expected to develop appropriate competencies in both community engagement and technical skills needed to develop the fuel maps. Ÿ*Community Engagement* – As a core objective in this project all quantitative information will be developed with an informed understanding of community objectives and the need to provide technical information *“operationable”* within the Xáxli’p Community Forest (XCF) planning / implementation structures. This objective requires a level of understanding about the project objectives from the community perspective, an understanding of the XCF planning process and a commitment to maintain ongoing discussions with community research partners to ensure that the project remains aligned with the community objectives and needs. Experience working in community-directed projects is desirable *(particularly in First Nations communities)*, but as a minimum requirement the candidate should have a strong interest in community-directed research working with First Nations and a willingness to engage independently with community partners ŸTechnical Skills – Ideal candidates will have a strong forest ecology background and previous experience working with remote sensing data and LiDAR *(ALS)* in particular. Strong quantitative skills and experience conducting data analysis and modelling in R or Python would be beneficial. *Start Date**:* The likely start date is September 2019, but an earlier start *(Summer 2019)* would be possible. *Funding**: *Funding for the first year of the project is confirmed *(with a student stipend of $19,000)*, with funding options for year two identified but not yet confirmed. *Applications**: **Applications should include a cover letter describing qualifications and interest along with a current resume / CV. *Applicants wanting to ensure consideration should have their applications received by *February 11, 2019*. But we will continue to accept applications until a suitable candidate is found. *Contact information**:* For more information or to submit your application, you can contact either of the Principal Investigators *Dr. Scott Green* University of Northern BC 3333 University Way Prince George, BC V2N4Z9 scott.gr...@unbc.ca 250-960-5817 *Dr. Che Elkin* University of Northern BC 3333 University Way Prince George, BC V2N4Z9 che.el...@unbc.ca 250-960-5004
