Development of improved methods to more accurately estimate spatial distributions of fuel loads in shrublands will allow for improved understanding of ecological processes such as wildfire behavior and postburn recovery. The goal of this study is to develop and test remote sensing methods to scale-up from field plot estimates of shrubland fuel over landscapes or to pixels of coarser spatial resolution data sets using ultra high spatial resolution imagery captured by a light-sport aircraft. The study is conducted on chaparral shrublands located in eastern San Diego County, California. We measured fuel load in the field using an allometric approach and estimated ground coverage of individual shrub species by using ultra-high spatial resolution imagery and image processing routines. Study results show a strong relationship between shrub coverage and fuel loads in all three stands (7, 28, and 68 years since last wildfire). Ordinary least squares analysis using ground coverage as the independent variable regressed against biomass was conducted. The analysis yielded R_ values ranging from 0.78 to 0.96 in the older stands for the live shrub species, while R_ values for species in the younger stands ranged from 0.03 to 0.8. Pooling species-based data into larger sample sizes consisting of functional and all-shrub classes while obtaining suitable linear regression models supports the potential for these methods to be used for scaling-up fuel estimates to broader areal extents, without having to classify and map shrubland vegetation at the species level.