Super-Resolved Canopy Height Mapping from Sentinel-2 Time Series Using Airborne LiDAR HD Reference Data across Metropolitan France
arXiv:2512.11524v3 Announce Type: replace-cross Abstract: Fine-scale forest monitoring is essential for understanding canopy structure and its dynamics, which are key indicators of carbon stocks, biodiversity, and forest health. Deep learning is particularly effective for this task, as it integrates spectral, temporal, and spatial signals that jointly reflect the canopy structure. To address this need, we introduce THREASURE-Net, a novel end-to-end framework for Tree Height Regression And Super-Resolution. The model is trained on Sentinel-2 time series using reference height metrics derived fr
The proliferation of high-resolution satellite data and advancements in deep learning capabilities are converging to enable more precise environmental monitoring techniques.
This development allows for granular, continuous monitoring of forest health and carbon dynamics, crucial for climate change mitigation, biodiversity conservation, and resource management.
The ability to super-resolve canopy height from satellite time series data significantly enhances remote sensing capabilities for ecological and environmental applications.
- · Environmental monitoring agencies
- · Climate change researchers
- · Forestry sector
- · AI/Remote sensing companies
- · Traditional, less data-intensive forest survey methods
Improved accuracy in carbon sequestration estimates and better-informed conservation strategies.
Potential for new climate-related financial instruments based on verifiable, fine-scale forest data.
Enhanced global accountability for environmental policies and land-use changes.
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