Estimating Canopy Height at Scale

TL;DR

This paper introduces a novel framework for estimating global canopy height from satellite data, improving accuracy and reliability especially in mountainous regions, to aid ecological and biomass monitoring.

Contribution

The paper presents a new framework with advanced preprocessing, a novel loss function, and data filtering techniques to improve global canopy height estimation accuracy.

Findings

Achieved MAE of 2.43 meters overall

Reduced errors in mountainous regions

Significantly outperforms existing global maps

Abstract

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.