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Estimating Canopy Height at Scale [ICML2024]

Jan Pauls, Max Zimmer, Una M. Kelly, Martin Schwartz, Sassan Saatchi, Philippe Ciais, Sebastian Pokutta, Martin Brandt, Fabian Gieseke

[Paper] Google Earth Engine viewer] [BibTeX]

We propose a framework for global-scale canopy height estimation based on satellite data. Our model leverages advanced data preprocessing techniques, resorts to a novel loss function designed to counter geolocation inaccuracies inherent in the ground-truth height measurements, and employs data from the Shuttle Radar Topography Mission to effectively filter out erroneous labels in mountainous regions, enhancing the reliability of our predictions in those areas. A comparison between predictions and ground-truth labels yields an MAE / RMSE of 2.43 / 4.73 (meters) overall and 4.45 / 6.72 (meters) for trees taller than five meters, which depicts a substantial improvement compared to existing global-scale maps. The resulting height map as well as the underlying framework will facilitate and enhance ecological analyses at a global scale, including, but not limited to, large-scale forest and biomass monitoring.

A comparison between our map and two other existing global height maps (Lang et al., Potapov et al.), as well as a regional map for France reveals that the visual quality improved a lot. It closely matches the one from regional maps, albeit some regions with remaining quality differences (e.g. column 8)

Interactive Google Earth Engine viewer

We uploaded our produced canopy height map to Google Earth Engine and created a GEE app that allows users to visualize our map globally and compare it to other existing products. If you want to build your own app or download/use our map in another way, you can access the map under the following asset_id:

var canopy_height_2020 = ee.ImageCollection('projects/worldwidemap/assets/canopyheight2020')

# To display on the map, create the mosaic:
var canopy_height_2020 = ee.ImageCollection('projects/worldwidemap/assets/canopyheight2020').mosaic()

Acknowledgements

This paper is part of the project AI4Forest, which is funded by the German Aerospace Agency (DLR), the german federal ministry for education and research (BMBF) and the french national research agency (anr). Further, calculations (or parts of them) for this publication were performed on the HPC cluster PALMA II of the University of Münster, subsidised by the DFG (INST 211/667-1).

Citing the paper

If you use our map in your research, please cite using the following BibTex:

@inproceedings{pauls2024estimating,
title={Estimating Canopy Height at Scale},
author={Jan Pauls and Max Zimmer and Una M. Kelly and Martin Schwartz and Sassan Saatchi and Philippe CIAIS and Sebastian Pokutta and Martin Brandt and Fabian Gieseke},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=ZzCY0fRver}
}