Accounting for woody vegetation aboveground biomass with uncrewed aerial vehicle laser scanning (ULS) in rangelands

Rangelands cover approximately 80% of Australia’s land surface and encompass savannas, woodlands, shrublands, and grasslands. Their sparse canopy cover, spatial heterogeneity, and high rainfall variability make aboveground biomass (AGB) difficult to measure, model and monitor, posing challenges for robust carbon accounting. Satellite remote sensing is essential for monitoring biomass over such vast and remote areas but cannot directly measure AGB and requires reliable calibration data. Here we assess the utility of uncrewed aerial vehicle laser scanning (ULS) for plot-scale assessment of AGB, as a key step toward scaling biomass estimates using satellite imagery. We used field-based estimates of AGB collected in 77 plots from 11 properties to test relationships with ULS derived structural metrics. Multiple combinations of woody canopy height and cover metrics were evaluated using Ordinary Least Squares (OLS) regression. A model based on a single ULS derived metric, percentage woody canopy cover taller than 3.5 m (𝑝𝑐𝑡_𝑔𝑡3𝑝5𝑚), could linearly account for 78% of the variability in AGB, with a Mean Absolute Error (MAE) of 7.19 Mg ha−1 and minimal bias (-0.10%). The moderate RMSE and unbiased nature of this relationship across diverse rangelands is encouraging for regional and continental scale mapping of AGB with airborne LiDAR systems. Predictions based on the same single metric had less uncertainty at individual property scales when local models were fitted. ULS is a practical pathway for wall-to-wall modelling of AGB estimates at 10–1000 ha scales and can bridge field measurements with satellite-based models, improving the rigour and transparency in carbon accounting in rangelands.