Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles

TL;DR

This paper introduces a deep ensemble neural network method to accurately estimate global canopy height from GEDI LIDAR waveforms, providing reliable uncertainty quantification and achieving an RMSE of 2.7 meters.

Contribution

It presents a novel probabilistic deep learning approach using ensemble CNNs for interpreting GEDI waveforms and estimating canopy height globally, with uncertainty estimation.

Findings

Achieved an RMSE of 2.7 meters in canopy height estimation.

Provided reliable uncertainty estimates for predictions.

Demonstrated generalization across diverse geographical regions.

Abstract

NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. While GEDI is the first space-based LIDAR explicitly optimized to measure vertical forest structure predictive of aboveground biomass, the accurate interpretation of this vast amount of waveform data across the broad range of observational and environmental conditions is challenging. Here, we present a novel supervised machine learning approach to interpret GEDI waveforms and regress canopy top height globally. We propose a probabilistic deep learning approach based on an ensemble of deep convolutional neural networks(CNN) to avoid the explicit modelling of unknown effects, such as atmospheric noise. The model learns to extract robust features that generalize to unseen geographical regions and, in addition, yields reliable estimates of predictive uncertainty. Ultimately, the global canopy top height estimates produced by our model have an expected RMSE of 2.7 m with low bias.