Quantification of forest biomass stocks and their dynamics is important for implementing effective climate change mitigation measures. The knowledge is needed, e.g., for local forest management, studying the processes driving af-, re-, and deforestation, and can improve the accuracy of carbon-accounting. Remote sensing using airborne LiDAR can be used to perform these measurements of vegetation structure at large scale. We present deep learning systems for predicting wood volume, above-ground biomass (AGB), and subsequently above-ground carbon stocks directly from airborne LiDAR point clouds. We devise different neural network architectures for point cloud regression and evaluate them on remote sensing data of areas for which AGB estimates have been obtained from field measurements in the Danish national forest inventory. Our adaptation of Minkowski convolutional neural networks for regression gave the best results. The deep neural networks produced significantly more accurate wood volume, AGB, and carbon stock estimates compared to state-of-the-art approaches operating on basic statistics of the point clouds. In contrast to other methods, the proposed deep learning approach does not require a digital terrain model. We expect this finding to have a strong impact on LiDAR-based analyses of biomass dynamics.

翻译：量化森林生物质储量及其动态变化对于实施有效的减缓气候变化措施至关重要。这些知识可用于地方森林管理、研究造林/再造林/毁林驱动过程，并能提升碳核算精度。利用机载LiDAR遥感技术可大规模测量植被结构。我们提出了深度学习系统，可直接从机载LiDAR点云预测木材体积、地上生物量（AGB）及相应地上碳储量。我们设计了多种用于点云回归的神经网络架构，并在已通过丹麦国家森林清查实测获得AGB估算值的遥感数据上进行评估。基于Minkowski卷积神经网络的回归改进模型取得了最佳效果。与基于点云基础统计量的现有最佳方法相比，深度神经网络能显著提升木材体积、AGB和碳储量的估算精度。不同于其他方法，所提出的深度学习方法无需数字地形模型。我们预期该发现将对基于LiDAR的生物量动态分析产生重要影响。