GIS and remote sensing of forested environments
Faculty mentor/Supervisor: 
Michael Wing
Department Affiliation: 
Forest Engineering Resources & Management
Job Location: 
Corvallis with remote locations being possible during Covid
Description of project or research opportunity: 
The primary emphasis project will be on a NSF-funded project that is examining climate change impacts on southwestern white pine seedlings in Arizona. The AIS Lab has collected high resolution imagery using unmanned aircraft systems (UAS) from several Arizona locations over four years. The student would be involved in the on-going processing and analysis of these databases using GIS and remote sensing software, including generating Lidar-like point clouds of landscape features. This experience would include experience with a number of geospatial software packages and geoprocessing techniques. These skills should be very attractive to potential future employers.
Tasks student will perform: 
The student’s primary duty will be maintaining and analyzing large robust geospatial datasets. These datasets are primarily Lidar-like point clouds or high-resolution UAS-collected imagery but also include vector and raster layers. The student will process point clouds of the study area to extract seedling metrics including height and stand density. Most of the student work will involve using geospatial software to perform batch operations like clipping, creating terrain models, and extracting pixel values from Lidar and multispectral datasets. PhD student Matt Barker will be the primary mentor in using programming and geospatial analysis techniques to perform operations on the databases. The student will ideally work more independently once familiar with the processing techniques, and the work can be accomplished remotely by logging into one of the AIS Lab machines. Training and mentoring can also be accomplished remotely through Zoom.
Special skills required: 
The student will have foundational GIS, remote sensing, and/or programming experience. Students with advanced experience working with Lidar or large imagery datasets are preferred. Students with knowledge or interest in fire ecology and/or silviculture.
Hourly rate of pay: 
Proposed dates of employment: 
Monday, November 16, 2020 to Saturday, June 19, 2021
Anticipated hours worked per week: 
Proposal Type: 
Mentored Employment Program
COVID-19 Pandemic Response: 
The majority of work can be accomplished remotely using Zoom-based meetings and screen sharing. The student will have the ability to remotely log into AIS Lab computer workstations for spatial processing activities.