Date of Award

6-2026

Degree Name

MS in Computer Science

Department/Program

Computer Science

College

College of Engineering

Advisor

Jonathan Ventura

Advisor Department

Computer Science

Advisor College

College of Engineering

Abstract

Accurate mapping of soil burn severity (SBS) is critical for post-fire watershed management, erosion risk assessment, and ecological recovery planning, yet traditional field-based approaches remain costly, time-intensive, and spatially limited. This thesis presents a machine learning pipeline for wall-to-wall SBS classification across California wildfires using multi-sensor satellite imagery, terrain derivatives, and bioclimatic covariates. Field-collected SBS observations (n = 2,180) from 52 wildfires occur- ring between 2013 and 2025, sourced from the U.S. Forest Service and CAL FIRE, were used to train and evaluate multiple classification architectures within a Google Earth Engine and Google Cloud-based prediction framework. After upsampling the unburned class to address class imbalance, the training dataset included n = 3,204 observations. Among the evaluated models, XGBoost trained on expert-engineered spectral change features achieved the highest cross-validated performance (Cohen’s Kappa = 0.61), outperforming neural architectures including a CNN, MLP, and hybrid CNN+MLP fusion model, suggesting that carefully designed spectral indices capture the dominant signal in soil burn severity classification more effectively than spatially learned representations at this dataset scale. The XGBoost model is also validated using a LOFO protocol where one fire is held out each train/test split for generalizable, ecologically valid results (Cohen’s Kappa = 0.44). The pipeline produces spatially continuous soil burn severity maps across all 52 study fires, with an interactive visualization tool under development at the University of California, Davis to provide land managers with accessible access to these outputs. Together, this thesis demonstrates that accurate landscape-scale soil burn severity mapping is achievable without expensive field campaigns, offering a reproducible framework and multi-fire benchmark dataset for future research in fire-prone regions.

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