Drone imagery student assistant - Sociology

Throughout Northeast Florida, a unified methodology for oyster condition assessment has been recently developed and implemented by UCF, GTMNEER and UF. This effort represents the first attempt at creating a standard monitoring protocol for oyster reef health within Florida. While this information is extremely useful for natural resource managers, this assessment methodology is labor-intensive and requires a large amount of time per reef. Drone imagery could potentially enhance the oyster condition assessment process by expanding the footprint of reefs surveyed with minimal time commitment. Drone imagery combined with field collected data would be combined to train a supervised classification to predict reef health over the entire study area. This would cut down on the time required for field data collection while increasing the understanding of oyster health across large areas.

Project Dates

Start Date: 1/11/2022 - End Date: 4/27/2022

Students Needed

Type of Project


Student Responsibilities

Position Responsibilities: Student will focus on collecting drone imagery within Mosquito Lagoon, Florida. Then, the student will use software to process and georeference imagery to prepare for the supervised classification. The supervised classification and subsequent statistical analysis will be conducted by a graduate student.

Time Commitment

10 hours a week hour(s)

Student Requirements

Drone flight experience is required. Familiarity with computers, and ArcGIS and Drone2Map software is desirable. Student interest in manuscript publication based on our results would be a plus.

Interested in Working With the Following Programs

For EXCEL URE Students Only

Additional Notes

Position Overview

  • Date Posted: 10-05-2018
  • Location:
    • Orlando (Main Campus)
  • Paid: Yes