Optimization of the National Biomass Allometric Equation Using Remote Sensing Data

Abstract. Accurately estimating forest aboveground biomass is crucial for assessing its carbon sequestration and carbon emission capacity. However, the traditional approach to estimating biomass is time-consuming and labour-intensive. Therefore, remote sensing data are widely used to estimate forest biomass, with LiDAR being the most used. This study used available LiDAR and optical data to estimate aboveground biomass using an existing biomass allometric equation. The widely used allometric equations rely on DBH, height, and species information to estimate tree biomass. This study employed the DBH model and integrated LiDAR structural metrics and optical spectral bands to estimate aboveground biomass in a mixed-wood temperate forest. There was a moderate relationship between field-measured DBH and LiDAR estimated DBH, as indicated by a coefficient of determination of 0.52, the RMSE of 4.13 cm, and the MAE of 3.16. Additionally, combining LiDAR and optical data to estimate aboveground biomass yielded lower RMSE (115.4 Mg/ha) and MAE (96.8 Mg/ha) than using LiDAR alone, with 3.4% and 4.1% reductions, respectively. Overall, following the workflow presented in this study, the well-established allometric equation can be optimized for more scalable and larger extent forest biomass estimation.