Shrub encroachment in grasslands， intensified by climate change and anthropogenic activities， represents a substantial threat to the health of grassland ecosystems. This underscores the pressing need for effective and precise biomass monitoring methodologies. The present study was carried out in the shrub-encroached grasslands of the Xilingol League， to investigate optimal above ground biomass （AGB） estimation model by utilizing unmanned aerial vehicle-based LiDAR and hyperspectral data validated against field measurements. Stepwise multiple linear regression， support vector machine， and random forest algorithms were employed for analysis. The results demonstrated a robust correlation between AGB and both LiDAR-derived mean canopy height and hyperspectral reflectance within the red-edge spectral region. Hyperspectral data demonstrated superior accuracy to LiDAR for herbaceous AGB inversion （R²=0.87 vs. 0.24）， whereas LiDAR performed better than hyperspectral data for shrub AGB inversion （R²=0.58 vs. 0.47）. Data fusion significantly improved the model performance， with the random forest model integrating features from both LiDAR and hyperspectral data achieving R² values of 0.88 and 0.64 for herbaceous and shrub AGB， respectively. The related findings offer critical scientific support for the sustainable utilization and precise management of shrub-encroached grasslands.

Grassland above-ground biomass  /  LiDAR  /  Hyperspectral  /  Machine learning  /  Shrub-encroached grassland