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[2602.21421] ECHOSAT: Estimating Canopy Height Over Space And Time
Summary
ECHOSAT introduces a global tree height map that captures temporal forest dynamics, enhancing carbon monitoring and disturbance assessment through advanced satellite data analysis.
Why It Matters
This research addresses the critical need for accurate forest monitoring in the context of climate change. By providing a dynamic view of tree height over time, ECHOSAT aids in better carbon accounting and understanding forest disturbances, which are vital for environmental policy and conservation efforts.
Key Takeaways
- ECHOSAT offers a 10 m resolution global tree height map over multiple years.
- Utilizes multi-sensor satellite data and a specialized vision transformer model for pixel-level temporal regression.
- Incorporates a self-supervised growth loss to ensure predictions align with natural tree growth patterns.
- Demonstrates improved accuracy over state-of-the-art methods for single-year predictions.
- Facilitates enhanced carbon monitoring and assessment of forest disturbances.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.21421 (cs) [Submitted on 24 Feb 2026] Title:ECHOSAT: Estimating Canopy Height Over Space And Time Authors:Jan Pauls, Karsten Schrödter, Sven Ligensa, Martin Schwartz, Berkant Turan, Max Zimmer, Sassan Saatchi, Sebastian Pokutta, Philippe Ciais, Fabian Gieseke View a PDF of the paper titled ECHOSAT: Estimating Canopy Height Over Space And Time, by Jan Pauls and 9 other authors View PDF HTML (experimental) Abstract:Forest monitoring is critical for climate change mitigation. However, existing global tree height maps provide only static snapshots and do not capture temporal forest dynamics, which are essential for accurate carbon accounting. We introduce ECHOSAT, a global and temporally consistent tree height map at 10 m resolution spanning multiple years. To this end, we resort to multi-sensor satellite data to train a specialized vision transformer model, which performs pixel-level temporal regression. A self-supervised growth loss regularizes the predictions to follow growth curves that are in line with natural tree development, including gradual height increases over time, but also abrupt declines due to forest loss events such as fires. Our experimental evaluation shows that our model improves state-of-the-art accuracies in the context of single-year predictions. We also provide the first global-scale height map that accurately quantifies tree growth and disturbances over time. We expect ECHOSAT to adv...
