Fire is used extensively in prairie grassland management in the Flint Hills region of the midwestern United States, particularly at the end of the dormant season (March–April). A model is used to manage grassland fires in the region to avoid deterioration of air quality beyond acceptable standards. Dormant season dry biomass is an important parameter in the model. The commonly used method for producing high-quality biomass estimates relies on clipping, drying and weighing small biomass samples, which is tedious, expensive and does not scale efficiently to larger areas to provide regional estimates. Small unmanned aircraft systems (sUAS) were used to develop a reliable and more efficient method of biomass estimation based on the correlation between biomass and vegetation canopy height derived from digital surface models (DSMs). A linear regression model was developed from data collected at 11 representative sites in the Kansas Flint Hills region, and the model was validated at two sites. Biomass and canopy heights derived from DSMs were correlated, with a Pearson product moment correlation value of 0.881 (P-value <0.001). Biomass estimated from clipped vegetation at two validation sites positively correlated with model-derived biomass estimates, resulting in linear regression R2-values of 0.90 and 0.74 and Pearson moment correlation coefficients of 0.99 (P<0.001) and 0.86 (P=0.003). The described sUAS method has the potential to increase the efficiency and reliability of dormant season grassland biomass estimates.
An efficient method for estimating dormant season grass biomass in tallgrass prairie from ultra-high spatial resolution aerial imaging produced with small unmanned aircraft systems
