ECHOSAT: Estimating Canopy Height Over Space And Time

2026-02-24

AI Summary

This study addresses the limitation of existing global tree height products, which are typically static snapshots incapable of capturing forest dynamics, thereby hindering accurate carbon accounting. To overcome this, the authors integrate multi-source satellite data and develop a specialized Vision Transformer model to perform pixel-level, time-series tree height regression at 10-meter resolution across multiple years. A novel self-supervised growth loss function is introduced to enforce physically plausible predictions that align with natural forest growth patterns and disturbance processes such as fire. The resulting product constitutes the first globally consistent, high-resolution, time-series map of tree height dynamics, offering not only superior single-year prediction accuracy compared to current methods but also enabling, for the first time, quantification of annual forest growth and disturbance impacts at the global scale.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Unsupervised & Self-Supervised Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Data transparency and provenance

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 advance global efforts in carbon monitoring and disturbance assessment. The maps can be accessed at https://github.com/ai4forest/echosat.

Problem

• canopy height
• temporal dynamics
• forest monitoring
• carbon accounting
• global tree height maps

Innovation

• temporal tree height mapping
• vision transformer
• self-supervised growth loss
• multi-sensor satellite fusion
• forest disturbance monitoring