Date of Award:

5-2026

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Plants, Soils, and Climate

Committee Chair(s)

Grant Cardon

Committee

Grant Cardon

Committee

Colleen Jones

Committee

Larissa Yocom

Committee

Doug Ramsey

Committee

Chelsea Duball

Abstract

After a wildfire, land managers are required to monitor large tracts of land with limited time and budgets. Traditionally, tracking landscape recovery is a time intensive process. This research explored the effectiveness of using drones (UAVs), strategic soil sampling, and computer modeling as additional tools for land managers in order to monitor the recovery more efficiently.

By using drones to collect aerial imagery and using machine learning, plant regrowth was monitored in back-to-back years. The machine learning model performed well at telling broad groups apart (trees vs grass) but it struggled to identify differences between plant species. Because many plants look similar from above, camera resolution continues to be a limiting factor for species level identification.

Fire can change soil in a variety of ways, both chemically and physically. These changes, in theory, make soil easier to erode away. Using the most common method for calculating soil erodibility, it was found to not reflect fire-induced changes seen through soil sampling and laboratory testing. One site even had the opposite result then expected, where the burned soils were more stable. This was due to the wind removing the most unstable burned topsoil, leaving behind a hard-to-erode sand before researchers could study it.

Fires have noticeable impacts on the environment that can greatly increase erosion. By updating a standard erosion model (RUSLE) with post-fire inputs, year-to-year changes were observed. This model was then compared to changes in the terrain in order to see if they matched. Additionally, the RUSLE model was used to test an extreme erosion event. Where before the model performed well, under the extreme conditions, it failed.

So, while drones and computer models are excellent tools for tracking landscapes long-term, there is still room for improvement. Potential improvements can be using higher resolution cameras, changing how soil erodibility is calculated, and only using RUSLE for normal conditions.

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