CVAILGJun 3, 2024

Estimating Canopy Height at Scale

arXiv:2406.01076v127 citations
Originality Incremental advance
AI Analysis

This work provides a more reliable global canopy height map to facilitate ecological analyses like forest and biomass monitoring, representing a strong specific gain in this domain.

The paper tackles global-scale canopy height estimation from satellite data, achieving an MAE of 2.43 meters and RMSE of 4.73 meters overall, with improved accuracy in mountainous regions and for taller trees.

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.

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