Accurate aboveground biomass (AGB) estimation of farmland shelterbelts is critical for evaluating shelterbelt degradation and guiding restoration in arid agricultural landscapes. However, satellite-based retrieval is challenging for narrow linear belts affected by strong edge effects and canopy gaps under degradation. Here we developed a plot-scale Unmanned Aerial Vehicle (UAV) workflow that fuses Light Detection and Ranging (LiDAR) structural metrics and multispectral vegetation indices to estimate individual-tree AGB for Populus euphratica Olivier (Xinjiang poplar) shelterbelts in Tiemenguan, Xinjiang, China. Field measurements were collected in October 2024 from three belts representing healthy, moderately degraded, and severely degraded conditions (n = 135 trees; 45/50/40). Because destructive sampling was infeasible, AGB was derived as allometry-based reference values, with a prior-constrained scale factor (ρ) used to ensure physically plausible ranges. We compared multiple linear regression, random forest, and Support vector regression (SVR) models under LiDAR-only, multispectral-only, and fused inputs. Fusion consistently improved agreement with reference AGB, and the fused SVR achieved the best performance (test R2= 0.846/0.848/0.718 for healthy/moderately/severely degraded belts). The workflow highlights spectral–structural complementarity for degraded shelterbelts, while broader deployment requires local calibration and independent biomass validation.
Wang et al. (Fri,) studied this question.