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ECHOSAT: Estimating Canopy Height Over Space And Time

Jan Pauls, Karsten Schrödter, Sven Ligensa, Martin Schwartz, Berkant Turan, Max Zimmer, Sassan Saatchi, Sebastian Pokutta, Philippe Ciais, Fabian Gieseke

TL;DR

This work introduces ECHOSAT, a global and temporally consistent tree height map at 10 m resolution spanning multiple years, using multi-sensor satellite data to train a specialized vision transformer model, which performs pixel-level temporal regression.

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.

ECHOSAT: Estimating Canopy Height Over Space And Time

TL;DR

This work introduces ECHOSAT, a global and temporally consistent tree height map at 10 m resolution spanning multiple years, using multi-sensor satellite data to train a specialized vision transformer model, which performs pixel-level temporal regression.

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.
Paper Structure (29 sections, 7 equations, 12 figures, 6 tables)

This paper contains 29 sections, 7 equations, 12 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Vision Architectures & Self-Supervised Learning
  4. Forest Height Prediction & Remote Sensing Data
  5. Approach
  6. Data
  7. Model Architecture
  8. Growth Loss
  9. Model Training
  10. Results
  11. Canopy Height Accuracy
  12. Canopy Height Growth/Decline
  13. Comparison Against Existing Maps
  14. Conclusion
  15. Limitations
  16. ...and 14 more sections

Figures (12)

  • Figure 1: Qualitative comparison across three geographically diverse locations. The first column shows Google Maps imagery for spatial context, while subsequent columns display predicted tree heights (0m$-$30m range) for each method. This visual assessment reveals differences in spatial detail, forest boundary detection, and height estimation accuracy across the various approaches.
  • Figure 2: Architecture of the Temporal-Swin-Unet. Skip connections are depicted as dashed arrows. The shape of the tensor in between a layer is shown in magenta: $C$ (channels), $T$ (timesteps), $H$ (height), $W$ (width), $Y$ (years), and $E$ (embedding dimension). In addition to the Video Swin Transformer Blocks liu2022videoswin, Encoder Layers have a Temporal Downsample (TD) layer at the end, and Decoder Layers a Temporal Skip Connection (TSC) at the beginning.
  • Figure 3: Left. Scatter plot showing the predicted height in 2018 against 2024. Disturbed pixels are identified by a decrease of more than 5m between 2018 and 2024, marked red and excluded from the median aggregation. Right. Median height difference from 2018 to each year, binned in 1m height classes. The right y-axis shows the height class distribution and area for these classes.
  • Figure 4: Examples of predicted tree height dynamics for two contrasting regions. Top: Le Landes (France) showing disturbance and regrowth patterns. Bottom: Amazonas (Brazil) with largely stable forest structure. Each block shows optical imagery (top row), predicted tree height (second row), and corresponding change maps from 2018 to 2024 (right column).
  • Figure 5: Left: Scattterplots showing the predicted height for 2020 vs GEDI labels with the correlation coefficient ($R^2$) and the correlation coefficient for labels exceeding 5m ($R^2_5$) indicated for each plot. Right: Histogram of the (predicted) values.
  • ...and 7 more figures