Understanding the dynamic interplay between fire severity, topography, and tree mortality, is crucial for predicting future forest dynamics and enhancing resilience against climate change-induced wildfire regimes. This thesis develops a multi-sensor approach for automated estimation of tree mortality, then applies it to examine trends in tree mortality over a six-year period across a fire affected study site in the Trinity River basin in Northern California. The Random Forest model uses publicly available USGS 3D Elevation Program Lidar (3DEP) and NAIP imagery as inputs and is likely to be easily adaptable to other landscapes. The model had a Receiver Operating Characteristic Area Under the Curve (ROC AUC) score in training of 0.998. In multiple rounds of validation, using geographically distinct sets of holdout data, had mean accuracy of 0.998. The trained model was then used to assess tree mortality across a patchwork of different levels of burn severity at a site in Northern California. When applied to the study site significant variations were found in tree mortality across different fire severity treatments and landforms. This model shows potential for incorporation into predictive tree mortality models based on landform and climate.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-4435 |
| Date | 01 March 2024 |
| Creators | huggins, michael t |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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