Early Detection of Forest Road Fill Slope Failures Using Drone-Based Ground Penetrating Radar (GPR)
Faculty mentor/Supervisor
Heesung Woo
Email Address
Department Affiliation
Forest Engineering Resources & Management
Project Location
Research forest or industry partner properties
Project Description
This research aims to develop a drone-based ground penetrating radar (GPR) system for the early detection of fill slope failures along forest roads, a persistent challenge that threatens safety, supply chain reliability, and ecosystem integrity. Forest roads, essential for timber transport, wildfire suppression, and rural access, often fail due to hidden subsurface instabilities that traditional visual inspections cannot detect until catastrophic collapse occurs. By integrating lightweight UAV-mounted GPR with advanced machine learning algorithms, this project seeks to identify early signs of subsurface crack formation, monitor soil stability in real time, and provide forest managers with a scalable, preventive monitoring tool. The outcomes will reduce costly road closures, enhance operator safety, and establish a data-driven foundation for resilient forest infrastructure management.
Describe the type of work and tasks you anticipate the student will perform
Student Roles and Tasks
Students will be directly involved in field data collection, analysis, and reporting:
1. Operate drone-mounted GPR to survey selected forest roads.
2. Collect and organize field data (radargrams, soil data, slope conditions).
3. Process and interpret GPR outputs using specialized software.
4. Integrate results with GIS for visualization and spatial analysis.
5. Assist in drafting research progress reports and presenting findings at the Poster Symposium.
Skills Students Will Gain
1. Hands-on training with GPR sensors, drones, and geospatial analysis tools.
2. Data processing and analysis for subsurface imaging.
3. Application of research to real-world industrial forestry problems.
4. Professional experience in scientific communication and poster presentations.
Students will be directly involved in field data collection, analysis, and reporting:
1. Operate drone-mounted GPR to survey selected forest roads.
2. Collect and organize field data (radargrams, soil data, slope conditions).
3. Process and interpret GPR outputs using specialized software.
4. Integrate results with GIS for visualization and spatial analysis.
5. Assist in drafting research progress reports and presenting findings at the Poster Symposium.
Skills Students Will Gain
1. Hands-on training with GPR sensors, drones, and geospatial analysis tools.
2. Data processing and analysis for subsurface imaging.
3. Application of research to real-world industrial forestry problems.
4. Professional experience in scientific communication and poster presentations.
Hourly rate of pay
$15.05/hr
Certification
Yes
What is the expected timeline of this project?
Project Launch
Mid-November 2025 onward – Students may begin work after Student Employment clearance.
Nov–Dec 2025: Onboarding and training (safety, field techniques, GPR methods, data management).
Dec 2025–Feb 2026: Initial field surveys and equipment calibration on forest roads.
Research & Data Collection
Winter 2026 (Jan–Mar) – Pilot GPR surveys on selected forest road segments; begin preliminary analysis of subsurface anomalies.
Spring 2026 (Apr–May) – Expanded data collection with student teams; refinement of early detection models for slope instability.
May 27, 2026 – Spring Poster Symposium: Students present preliminary findings, challenges, and applied impacts.
Project Wrap-up
May–June 2026 – Finalize datasets, generate reports, and prepare materials for industry partners.
June 12, 2026 – MEP program funds expire. All research activities, reporting, and student employment must be concluded.
Mid-November 2025 onward – Students may begin work after Student Employment clearance.
Nov–Dec 2025: Onboarding and training (safety, field techniques, GPR methods, data management).
Dec 2025–Feb 2026: Initial field surveys and equipment calibration on forest roads.
Research & Data Collection
Winter 2026 (Jan–Mar) – Pilot GPR surveys on selected forest road segments; begin preliminary analysis of subsurface anomalies.
Spring 2026 (Apr–May) – Expanded data collection with student teams; refinement of early detection models for slope instability.
May 27, 2026 – Spring Poster Symposium: Students present preliminary findings, challenges, and applied impacts.
Project Wrap-up
May–June 2026 – Finalize datasets, generate reports, and prepare materials for industry partners.
June 12, 2026 – MEP program funds expire. All research activities, reporting, and student employment must be concluded.
Are special skills or knowledge required to work on this project?
No
Will training be provided?
Yes
How many hours per week do you anticipate a student to work?
3 to 4hrs
How many hours per week do you anticipate engaging in direct mentorship?
the expected direct mentorship time typically falls in the range of 2–4 hours per week.
The student will receive hands-on training in GPR-based forest road monitoring, including equipment operation, data collection, and analysis. I will provide weekly one-on-one meetings to review progress, clarify technical questions, and guide research tasks. The student will also participate in bi-weekly lab group meetings, gaining exposure to collaborative research environments and peer feedback.
In addition to technical training, I will mentor the student on professional development by providing opportunities to present at the Spring Poster Symposium and practice research communication skills. The student will be encouraged to maintain a research journal to reflect on progress, and I will provide ongoing written and verbal feedback on reports and presentations. My mentorship approach emphasizes experiential learning, structured guidance, and professional readiness, helping the student build both technical expertise and transferable career skills.
In addition to technical training, I will mentor the student on professional development by providing opportunities to present at the Spring Poster Symposium and practice research communication skills. The student will be encouraged to maintain a research journal to reflect on progress, and I will provide ongoing written and verbal feedback on reports and presentations. My mentorship approach emphasizes experiential learning, structured guidance, and professional readiness, helping the student build both technical expertise and transferable career skills.