ECHOSAT: Estimating Canopy Height Over Space And Time

TL;DR

ECHOSAT introduces a global, temporally consistent tree height map at 10m resolution, capturing forest dynamics over multiple years using satellite data and a vision transformer model, enhancing carbon monitoring accuracy.

Contribution

This work presents the first global-scale, temporally consistent tree height map that models growth and disturbances over time using multi-sensor satellite data and a specialized vision transformer.

Findings

Improved accuracy over state-of-the-art single-year predictions.

First global-scale height map capturing growth and disturbances.

Model effectively detects forest loss events like fires.

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.