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.
翻译:美国航天局的全球生态系统动态调查(GEDI)是一项关键的气候任务,其目标是增进我们对森林在全球碳循环中的作用的理解。虽然GEDI是第一个明确优化地测量地表生物量垂直森林结构预测的天基LIDAR,但准确解释在广泛的观测和环境条件下大量波形数据是具有挑战性的。在这里,我们提出了一个新型的受监督的机器学习方法,用以解释全球GEDI波形和回退波峰顶高。我们建议一种基于深相神经网络(CNN)的共合体的概率深刻学习方法,以避免对未知效应(如大气噪音)进行明确的建模。模型学会了提取强健的特征,这些特征可概括到看不见的地理区域,并产生预测不确定性的可靠估计值。最终,我们模型得出的全球可控顶高估计值预计将有2.7米且低偏差的 RMSE。