TRANSFORMING LEAF-OFF LIDAR TO LEAF-ON CANOPY HEIGHT MODELS USING DEEP LEARNING

<p dir="ltr">Accurate tree canopy metrics are essential for various ecological, environmental, and climate studies. Recent advancements in airborne LiDAR sensing technologies have rev- olutionized the acquisition of three-dimensional Earth observations. However, extensive validation across diverse ecological sites reveals that conventional methods significantly un- derestimate canopy height and coverage, underscoring the need for more accurate leaf-on canopy height model generation approaches. To overcome this limitation, we introduce a deep learning framework leveraging Pix2Pix GAN and U-Net architectures to transform leaf-off LiDAR data into accurate leaf-on canopy height models. Our proposed models sub- stantially enhance canopy height estimation, achieving robust performance (R2 = 0.85–0.92, MAE = 1.3–1.6 meters), clearly outperforming conventional approaches. Additionally, our method provides superior delineation of canopy details and consistently higher accuracy compared to recent optical imagery-based approaches. To facilitate widespread adoption, we developed an open-access web application that deploys our pre-trained models, enabling users to generate leaf-on canopy height models directly from leaf-off LiDAR datasets across the U.S. Our approach effectively bridges the seasonal data gap in LiDAR data collection, en- riching the ecological and environmental research community by providing accurate, reliable canopy metrics during critical vegetative periods.</p>