Estimating Fuel Moisture in Grasslands Using UAV-Mounted Infrared and Visible Light Sensors

Predicting wildfire behavior is a complex task that has historically relied on empirical models. Physics-based fire models could improve predictions and have broad 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 spatial 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. One hundred twenty 1 m-square field samples were collected in a western Washington grassland as well as overhead imagery in six wavelengths for the same area. Predictive models of vegetation moisture using existing vegetation indices and 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 = 0.45). This model performed better for the plots with both dominant grass species pooled than it did for each species individually. The presence of this correlation, especially given the limited moisture range of this study, 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 unmanned aerial vehicles, 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.