- Name: Barry Myers, Ph.D.
- Institution: Northwest Nazarene University
- Department: Mathematics and Computer Science
- Phone: 208-467-8670
- Email: firstname.lastname@example.org
Summary: NNU Computer Science is developing FireMAP, a remote sensing platform for monitoring wildland fire effects, and post fire ecosystem response. High intensity wildfires contribute to post fire erosion, soil loss, flooding events, loss of timber resources, and incur costs for rangeland managers; they often negatively impact wildlife habitat, ecosystem services, and recreational opportunities. FireMAP will provide a responsive, affordable and safe means by which land managers can evaluate the effect that a wildland fire is having on the ecosystem. This will be accomplished by combining:
• Unmanned aerial systems (UAS) with attached camera and sensors
• Software to process, geo-analyze and manage images and data.
FireMAP will provide information to assist with fuel inventories, suppression management, development of post fire recovery plans, and updating spatial fuel layers to reflect the effects of wildland fire on vegetation. This information will allow for realization of increased fire prevention, optimized management, and decreased associated costs by enabling data driven decision-making and will ultimately leave room for fire management decisions resulting in fire resilient ecosystems on Earth.
Minimum Classes: Students must have had a programming course and be strong in science and problem solving.
Projects: Students on the FireMAP team will be working on a variety of tasks including the development and refinement of software to georeference and mosaic imagery, manage large datasets (both spatial and non-spatial), and perform geoanalytics extracting actionable knowledge from data using image processing, pattern recognition and classification techniques. Students may also participate in modifying UAS in order to enhance image acquisition capabilities.
NNU Computer Science is also working to show that analysis and machine learning algorithms created to analyze data and imagery from drones over wildland fires is applicable and adaptable to bioinformatics applications, such as detecting prostate cancer.