Alvarado, ErnestoBarber, Nastassia2021-08-262021-08-262021Barber_washington_0250O_22817.pdfhttp://hdl.handle.net/1773/47555Thesis (Master's)--University of Washington, 2021Predicting wildfire behavior is a complex task which has historically relied on empirical models. Physics-based fire models could potentially improve predictions and have wide applicability, but these models require more detailed inputs, including spatially explicit estimates of fuel characteristics. One of the most critical of these characteristics is fuel moisture. Obtaining moisture measurements with traditional destructive sampling techniques can be prohibitively time-consuming and extremely limited in resolution. This study seeks to assess how effectively moisture in grasses can be estimated using reflectance in six wavelengths in the visible and infrared ranges. We collected 120 1 m-square field samples in a western Washington grassland and collected overhead imagery in six wavelengths for the same area. Predictive models of vegetation moisture using existing vegetation indices as well as components from principal component analysis of the wavelengths were generated and compared. The best model, a linear model based on principal components and biomass, showed modest predictive power (r²=.45). This model performed better for the plots with both dominant grass species pooled than it did for each individually. Given the limited scope of this study especially regarding moisture range, the presence of this correlation suggests that further research using samples across the entire fire season could potentially produce effective models for estimating moisture in this type of ecosystem using UAVs, even when more than one major species of grass is present. This approach would be a fast and flexible approach compared to traditional moisture measurements.application/pdfen-USCC BYfuel moistureremote sensingunmanned aerial vehiclesvegetation moisturewildfireForestryWildlife managementEnvironmental scienceForestryThesis