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

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



*Dr. Che Elkin*

University of Northern BC

3333 University Way

Prince George, BC V2N4Z9



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