期刊
REMOTE SENSING OF ENVIRONMENT
卷 268, 期 -, 页码 -出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2021.112760
关键词
LIDAR; GEDI; Canopy height; Deep ensembles; Uncertainty; CNN; Bayesian deep learning
资金
- Royal Norwegian Embassy in Tanzania
- NASA [NNL 15AA03C]
NASA's GEDI mission aims to advance understanding of forests' role in the global carbon cycle. A novel supervised machine learning approach has been developed to interpret GEDI waveforms and regress canopy top height globally with reliable predictive uncertainty estimates.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据