At Bering-Alakaʻina, I supported the USDA Forest Service’s Forest Health Assessment and Applied Sciences Team by developing geospatial workflows for forest-condition analysis. I built and maintained GIS pipelines using Python, Google Earth Engine, and cloud platforms.
I contributed to tree-crown health models presented at the 2024 Esri User Conference plenary. Using multi-year NAIP imagery, I helped scale automated models that segment tree crowns and identify compromised canopies.
I also contributed to UAS-based forest structure mapping and drone-based forest recovery modeling for the Cameron Peak Wildfire. My photogrammetry methods and data processing helped isolate lodgepole pine seedlings for machine-learning training.
My work has included contributions to peer-reviewed and agency science products, including a Journal of Forestry study on detecting forest disturbance at the tree level. I’ve also processed diverse satellite imagery and lidar for forest-condition and disturbance analyses.
