Earth Observation (EO) data can provide added value to nations’ assessments of vegetation aboveground biomass density (AGBD) with minimal additional costs. Yet, neither open access to global-scale EO datasets of vegetation heights or biomass, nor the availability of computational power, has proven sufficient for their wide uptake in climate policy-related assessments. Using Mexico as an example, one of the primary obstacles to enhancing their National Forest Inventory (NFI) with such global EO datasets is the lack of statistically defensible methodologies that do so, while addressing the nation’s existing reporting needs and gaps. In collaboration with the Comisión Nacional Forestal (CONAFOR), this study develops a geostatistical model that integrates vegetation height and AGBD estimates from NASA’s Global Ecosystem Dynamics Investigation (GEDI) and ESA’s Climate Change Initiative (CCI) with Mexico’s NFI to attain sub-national and geographically-explicit biomass predictions. The posited model includes spatially varying parameters, allowing flexibility to capture non-stationary relations between the EO-based covariates and NFI-estimated AGBD. Inference is conducted with Bayesian methods, allowing the computation of summary statistics, such as the standard deviations for single-location and area-wide predictions of AGBD. This enables the transparent disclosure and traceability of sources of uncertainty throughout the prediction approach. Results indicate strong model performance; the EO-based covariates explain 79% of the variance in NFI-estimated AGBD in a randomly withheld sample of 10% of observations and a heuristic root mean squared error (RMSE) of 21.55 Mg/ha. Approximately 96% of the observations falling within the 95% credible intervals of our predictions, with some systematic under-prediction observed at AGBD ranges of >100 Mg/ha. To ease the operational uptake of the model for policy purposes, source code based in the ‘R’ language with the optional use of urban and (non)forest masks for AGBD predictions is released. It includes demonstrations for predicting AGBD in Mexico’s Natural Protected Areas, terrestrial ecological strata, and community forest management or payment for environmental services projects, which are commonly used delineations in its climate policy reports. For other nations considering the presented approach for policy purposes, the study discusses challenges concerning the use of EO-based covariates and the limitations of the model. It concludes with a broader call toward ensuring consistency in EO data streams, and prioritizing the co-development of EO-NFI integration approaches with nations in the future, thereby directly addressing their long-term climate policy needs.

中文翻译：

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利用地球观测加强国家森林生物量评估的地统计方法，以满足气候政策需求


地球观测 （EO） 数据可以为各国评估植被地上生物量密度 （AGBD） 提供附加值，而额外成本最低。然而，无论是开放获取全球规模的植被高度或生物量 EO 数据集，还是计算能力的可用性，都不足以使其在气候政策相关评估中得到广泛采用。以墨西哥为例，使用此类全球 EO 数据集加强其国家森林清查 （NFI） 的主要障碍之一是缺乏统计上合理的方法，同时解决了该国现有的报告需求和差距。本研究与 Comisión Nacional Forestal （CONAFOR） 合作开发了一个地统计模型，该模型将 NASA 全球生态系统动力学调查 （GEDI） 和欧洲航天局气候变化倡议 （CCI） 的植被高度和 AGBD 估计与墨西哥的 NFI 相结合，以实现次国家和地理上明确的生物量预测。假设模型包括空间变化的参数，可以灵活地捕获基于 EO 的协变量和 NFI 估计的 AGBD 之间的非平稳关系。使用贝叶斯方法进行推理，允许计算汇总统计数据，例如 AGBD 的单一位置和区域范围预测的标准差。这使得在整个预测方法中可以透明地披露和追溯不确定性来源。结果表明模型性能强劲;基于 EO 的协变量解释了 NFI 估计的 AGBD 方差的 79%，在随机保留的 10% 的观察样本和 21.55 毫克/公顷的启发式均方根误差 （RMSE） 中。 大约 96% 的观测结果落在我们预测的 95% 可信区间内，在 >100 毫克/公顷的 AGBD 范围内观察到一些系统性的预测不足。为了简化模型在策略方面的操作采用，发布了基于“R”语言的源代码，并可选择使用城市和（非）森林掩码进行 AGBD 预测。它包括预测墨西哥自然保护区、陆地生态层和社区森林管理或环境服务项目付款的演示，这些都是其气候政策报告中常用的描述。对于出于政策目的考虑所提出的方法的其他国家，该研究讨论了有关使用基于 EO 的协变量的挑战和模型的局限性。最后，它更广泛地呼吁确保 EO 数据流的一致性，并优先考虑在未来与各国共同开发 EO-NFI 集成方法，从而直接解决其长期气候政策需求。