Super-Resolved Canopy Height Mapping from Sentinel-2 Time Series Using Airborne LiDAR HD Reference Data across Metropolitan France
Researchers have developed THREASURE-Net, a novel deep learning framework designed for high-resolution canopy height mapping using satellite imagery. This end-to-end model leverages Sentinel-2 time series data and is trained with reference height metrics from airborne LiDAR. THREASURE-Net achieves competitive accuracy, with mean absolute errors as low as 2.63 m at a 2.5 m resolution, and does not require pre-trained models or very high-resolution optical imagery for its super-resolution module. The framework aims to provide a scalable and cost-effective solution for structural monitoring of temperate forests using publicly available satellite data. AI
IMPACT Enables more precise and cost-effective forest monitoring using satellite data.