发表机构
University of Connecticut（康涅狄格大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究利用AlphaEarth基础模型生成的Google卫星嵌入，结合LiDAR数据与森林清查数据构建模型，实现美国东北部区域森林地上生物量的高精度监测，为规模化碳评估提供了新途径。
AI 中文摘要
森林地上生物量（AGB）是衡量生态系统生产力和陆地碳储存的关键指标，但区域碳监测受野外清查和机载结构测量时空分布稀疏的限制。近期，地球观测基础模型从多模态数据中生成全球一致的地理空间表征，为规模化生物量监测提供了潜在途径。本研究评估了由AlphaEarth基础模型生成的Google卫星嵌入（GSE）在美国东北部不同温带森林生态系统的区域尺度AGB估算中的应用效果。我们将年度GSE观测、机载激光雷达（LiDAR）数据与东北部森林清查网络（NEFIN）的连续森林清查数据整合到机器学习框架中。结合LiDAR与GSE的模型在AGB估算中达到了0.79的决定系数（R²）；利用年度GSE观测通过时间增长调整将训练数据集扩大了10倍以上，使预测性能提升至R²=0.82，同时模型偏差降低了70%以上。空间自相关分析显示，整合基础模型表征与结构预测因子显著减少了残差的空间依赖性；蒙特卡洛模拟表明，超参数优化使模型性能的变异性降低了27.9%。研究结果表明，基础模型地球表征能捕捉与森林生物量相关的生态意义信息，为机载LiDAR覆盖不完整的区域提供了可扩展的年度碳监测框架，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。
英文摘要
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.
