Summary
Leaf angle distribution (LAD) impacts plant photosynthesis, water use efficiency, and ecosystem primary productivity, which are crucial for understanding surface energy balance and climate change responses. Traditional LAD measurement methods are time-consuming and often limited to individual sites, hindering effective data acquisition at the ecosystem scale and complicating the modeling of canopy LAD variations.
We present a deep learning approach that is more affordable, efficient, automated, and less labor-intensive than traditional methods for estimating LAD. The method uses unmanned aerial vehicle images processed with structure-from-motion point cloud algorithms and the Mask Region-based convolutional neural network.
Validation at the single-leaf scale using manual measurements across three plant species confirmed high accuracy of the proposed method (Pachira glabra: R
2 = 0.87, RMSE = 7.61°; Ficus elastica: R
2 = 0.91, RMSE = 6.72°; Schefflera macrostachya: R
2 = 0.85, RMSE = 5.67°). Employing this method, we efficiently measured leaf angles for 57 032 leaves within a 30 m × 30 m plot, revealing distinct LAD among four representative tree species: Melodinus suaveolens (mean inclination angle 34.79°), Daphniphyllum calycinum (31.22°), Endospermum chinense (25.40°), and Tetracera sarmentosa (30.37°).
The method can efficiently estimate LAD across scales, providing critical structural information of vegetation canopy for ecosystem modeling, including species-specific leaf strategies and their effects on light interception and photosynthesis in diverse forests.
