Is this project an undergraduate, graduate, or faculty project?
Graduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Christopher Chandler, Graduate student Christopher M Saylor, Graduate student Marcus Thompson, Graduate student Tejash Zala, Graduate student
Lead Presenter's Name
Christopher Chandler
Lead Presenter's College
DB College of Aviation
Faculty Mentor Name
Dr. Chuyang Yang
Abstract
Airports are required to maintain obstruction-free airspace surfaces under 14 CFR Part 77; however, vegetation and perimeter obstruction management is often conducted through periodic ground inspections and contractor-led surveys that provide only snapshot conditions. These approaches may delay detection of encroachments into approach, transitional, horizontal, or primary surfaces, increasing the risk of operational impacts and reactive mitigation. This study proposes an FAA-aligned framework integrating small unmanned aircraft systems (sUAS) into obstruction monitoring workflows through recurring, repeatable perimeter inspections. High-resolution optical and LiDAR sensors, combined with RTK/PPK-enabled GNSS correction, generate centimeter-level geospatial datasets that are processed into canopy height models. These models are digitally compared against Part 77 surfaces to identify potential penetrations and monitor vegetation growth trends over time. The results demonstrate that sUAS-enabled monitoring enhances early detection, supports proactive compliance, and improves the consistency and operational value of obstruction management practices.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Included in
Small Unmanned Aerial Systems-Assisted Airport Obstruction and Perimeter Inspections
Airports are required to maintain obstruction-free airspace surfaces under 14 CFR Part 77; however, vegetation and perimeter obstruction management is often conducted through periodic ground inspections and contractor-led surveys that provide only snapshot conditions. These approaches may delay detection of encroachments into approach, transitional, horizontal, or primary surfaces, increasing the risk of operational impacts and reactive mitigation. This study proposes an FAA-aligned framework integrating small unmanned aircraft systems (sUAS) into obstruction monitoring workflows through recurring, repeatable perimeter inspections. High-resolution optical and LiDAR sensors, combined with RTK/PPK-enabled GNSS correction, generate centimeter-level geospatial datasets that are processed into canopy height models. These models are digitally compared against Part 77 surfaces to identify potential penetrations and monitor vegetation growth trends over time. The results demonstrate that sUAS-enabled monitoring enhances early detection, supports proactive compliance, and improves the consistency and operational value of obstruction management practices.