What question did this study set out to answer?

The study aims to accurately estimate aboveground biomass (AGB) of farmland shelterbelts to evaluate their degradation.

February 22, 2026Open Access

Aboveground Biomass Inversion of Farmland Shelterbelts Across Degradation Levels Using UAV LiDAR–Multispectral Fusion: A Case Study in Xinjiang, China

Puntos clave

The study aims to accurately estimate aboveground biomass (AGB) of farmland shelterbelts to evaluate their degradation.
Developed a UAV workflow fusing LiDAR metrics with multispectral vegetation indices.
Collected field measurements from three shelterbelt conditions: healthy, moderately degraded, and severely degraded.
Used allometry-based reference values for AGB estimation due to the infeasibility of destructive sampling.
Compared multiple regression models including random forest and support vector regression under different input conditions.
Fusion of LiDAR and multispectral data consistently improved the accuracy of AGB estimation.
The best performance was achieved with support vector regression on fused data, with test R2 values of 0.846, 0.848, and 0.718 for the three degradation levels.
The workflow demonstrates spectral–structural complementarity for the assessment of degraded shelterbelts.

Resumen

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.

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Cite This Study

Wang et al. (Fri,) studied this question.

synapsesocial.com/papers/699a9d27482488d673cd2def https://doi.org/https://doi.org/10.3390/drones10020148

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