Theses and Dissertations

ORCID

0009-0003-6275-8925

Advisor

Boyd, Dylan

Committee Member

Ball, John

Committee Member

Diao, Junming

Date of Degree

8-12-2026

Original embargo terms

Immediate Worldwide Access

Document Type

Graduate Thesis - Open Access

Major

Electrical & Computer Engineering

Degree Name

Master of Science (M.S.)

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

Global Navigation Satellite System Transmissometry is a technique for forest remote sensing. Interpreting these measurements requires physically based models to relate observed signal behavior to underlying biophysical parameters. A limitation of current electromagnetic modeling approaches is the assumption that vegetation optical depth is static. In practice, forest canopies, and therefore vegetation optical depth, are dynamic systems continuously influenced by wind. These geometric changes alter the scattering environment and introduce temporal variability in the received signal, particularly through phase-sensitive multipath interactions. This thesis presents a methodology for simulating wind dynamics for first-order scattering. The approach integrates a procedural, stochastic aeroelastic wind model with the Signals of Opportunity Coherent Bistatic simulator to generate time-series electric field simulations under the distorted Born approximation. The simulation environments evaluate forest representations, including fractal and space colonization models, under different incidence angles, polarizations, and root position configurations. The integration of these two models exhibits expected trends for wind dynamics using a noise-based wind model. By capturing the time-evolving physical geometry of the canopy, this open-source approach provides a functional tool to compare simulated wind impacts with physical measurements. This model establishes a foundation for future efforts in comparing field data to wind-simulated tree motion for signals of opportunity using a computationally efficient, noise-based approach.

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