用卫星基础模型提升美国东北部森林生物量监测精度与覆盖范围
Foundation-Model Earth Representations Enable Regional-Scale Forest Aboveground Biomass Monitoring Across the Northeastern United States
- 结合卫星嵌入、激光雷达与林区调查数据，构建机器学习模型
- 模型预测准确率达R²=0.82，偏差降低70%以上
- 适合缺乏激光雷达覆盖区域的年度碳储量监测应用
森林地上生物量（AGB）是生态系统生产力和陆地碳储存的关键指标，但区域碳监测受限于野外调查和机载结构测量的空间与时间稀缺性。近期地球观测基础模型提供了来自多源异构数据的全球一致地理空间表征，为可扩展的生物量监测提供了新路径。本文评估了由AlphaEarth基础模型生成的谷歌卫星嵌入（GSE），用于美国东北部多样化温带森林生态系统的区域性生物量估算。通过将年度GSE观测、机载激光雷达（LiDAR）与东北部森林清查网络（NEFIN）连续清查数据整合至机器学习框架中，基于LiDAR-GSE联合模型实现了0.79的R²值。利用年度GSE进行时序生长校正，使训练数据量增加十倍以上，预测性能提升至R²=0.82，模型偏差降低超过70%。空间自相关分析显示，融合基础模型表征与结构变量显著降低了残差空间依赖性。蒙特卡洛模拟表明，超参数优化使模型性能波动减少27.9%。结果证明，基础模型地球表征能捕捉与森林生物量相关的生态学信息，为缺乏完整机载激光雷达覆盖区域的年度碳监测提供可扩展框架。研究为基于全球可用基础模型地球观测的下一代森林碳评估奠定了基础。
原文摘要 · Abstract (English)
Forest aboveground biomass (AGB) is a critical indicator of ecosystem productivity and terrestrial carbon storage, yet regional carbon monitoring remains constrained by the sparse spatial and temporal availability of field inventories and airborne structural measurements. Recent Earth observation foundation models provide globally consistent geospatial representations derived from diverse multimodal datasets, offering a potential pathway toward scalable biomass monitoring. Here, we evaluate Google Satellite Embeddings (GSE), generated by the AplphaEarth Foundation Model, for regional-scale AGB estimation across diverse temperate forest ecosystems in the northeastern United States. We integrated annual GSE observations, airborne LiDAR, and continuous forest inventory measurements from the Northeastern Forest Inventory Network (NEFIN) within a machine-learning framework. Combined LiDAR-GSE models achieved an R^2 of 0.79 for AGB estimation. Capitalizing on annual GSE observations expanded the training dataset by more than tenfold through temporal growth adjustment, increasing predictive performance to R^2 = 0.82 while reducing model bias by over 70%. Spatial autocorrelation analyses showed that integrating foundation-model representations and structural predictors substantially reduced residual spatial dependence. Monte Carlo simulations demonstrated that hyperparameter optimization reduced model-performance variability by 27.9%. Our findings demonstrate that foundation-model Earth representations capture ecologically meaningful information relevant to forest biomass and provide a scalable framework for annual carbon monitoring in regions with incomplete airborne LiDAR coverage. Our fundings establish a pathway toward next-generation forest carbon assessment based on globally available foundation-model Earth observations.
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