Biomass Estimation of Apple and Citrus Trees Using Terrestrial Laser Scanning and Drone-Mounted RGB Sensor - Min-Ki Lee; Yong-Ju Lee; Dong-Yong Lee; Jee-Su Park; Chang-Bae Lee (Remote Sensing)
Developing accurate activity data on tree biomass using remote sensing tools such as LiDAR and drone-mounted sensors is essential for improving carbon accounting in the agricultural sector. However, direct biomass measurements of perennial fruit trees remain limited, especially for validating remote sensing estimates. This study evaluates the potential of terrestrial laser scanning (TLS) and drone-mounted RGB sensors (Drone_RGB) for estimating biomass in two major perennial crops in South Korea: apple (‘Fuji’/M.9) and citrus (‘Miyagawa-wase’). Trees of different ages were destructively sampled for biomass measurement, while volume, height, and crown area data were collected via TLS and Drone_RGB. Regression analyses were performed, and the model accuracy was assessed using R2, RMSE, and bias. The TLS-derived volume showed strong predictive power for biomass (R2 = 0.704 for apple, 0.865 for citrus), while the crown area obtained using both sensors showed poor fit (R2 ≤ 0.7). Aboveground biomass was reasonably estimated (R2 = 0.725–0.865), but belowground biomass showed very low predictability (R2 < 0.02). Although limited in scale, this study provides empirical evidence to support the development of remote sensing-based biomass estimation methods and may contribute to improving national greenhouse gas inventories by refining emission/removal factors for perennial fruit crops.