用机器学习从雷达数据中高精度估测树高，助力碳储量监测。
3D-SAR Tomography and Machine Learning for High-Resolution Tree Height Estimation
- 融合3D SAR数据与深度学习模型预测树高
- 最佳模型误差仅2.82米，适用于约30米林冠
- 为欧洲航天局生物量卫星任务提供技术准备
准确估算森林生物量对全球碳循环建模和气候减缓至关重要。树高是生物量计算的关键因素，可借助合成孔径雷达（SAR）技术测量。本研究将机器学习应用于两种SAR产品：单次复数图像（SLC）和层析立方体，为欧洲航天局（ESA）生物量卫星任务做准备。利用德国艾菲尔国家公园的TomoSense数据集（包含SAR与激光雷达数据），开发并评估了多种高度估计算法。方法涵盖经典算法、基于3D U-Net的深度学习模型及贝叶斯优化技术。通过测试不同SAR频率与极化方式，建立了未来高度与生物量建模的基准。表现最佳的模型在30米左右林冠条件下，平均绝对误差仅为2.82米，显著提升了全球碳储量测量能力，支持气候行动决策。
原文摘要 · Abstract (English)
Accurately estimating forest biomass is crucial for global carbon cycle modelling and climate change mitigation. Tree height, a key factor in biomass calculations, can be measured using Synthetic Aperture Radar (SAR) technology. This study applies machine learning to extract forest height data from two SAR products: Single Look Complex (SLC) images and tomographic cubes, in preparation for the ESA Biomass Satellite mission. We use the TomoSense dataset, containing SAR and LiDAR data from Germany's Eifel National Park, to develop and evaluate height estimation models. Our approach includes classical methods, deep learning with a 3D U-Net, and Bayesian-optimized techniques. By testing various SAR frequencies and polarimetries, we establish a baseline for future height and biomass modelling. Best-performing models predict forest height to be within 2.82m mean absolute error for canopies around 30m, advancing our ability to measure global carbon stocks and support climate action.
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