About

I work at the intersection of agricultural and forest remote sensing and geospatial data science, with a focus on making lidar and satellite-derived structure data trustworthy enough to build decisions on. My background runs from a M.S. thesis assessing the impact of whole orchard recycling and almond biochar compost on the growth & performance of young almond trees, a project evaluating canopy-height products, and a NASA DEVELOP project fusing Sentinel-1/2, GEDI, and 3DEP for agroforestry biomass estimation using machine learning.

My current focus is lidar production capability — PDAL-based ground classification, height normalization, and DTM/DSM/CHM pipelines validated against the USGS Lidar Base Specification and ASPRS Positional Accuracy Standards (Ed. 2, v2) — built in an open-source stack (PDAL, GDAL, lidR) that maps onto proprietary tools like TerraScan and TerraSolid. I’m also developing spatial SQL and reproducible geospatial ML pipelines.