A geostatistical approach to enhancing national forest biomass assessments with Earth Observation to aid climate policy needs

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. • Develops a geostatistical model to integrate global EO datasets with Mexico’s NFI. • Achieves strong model performance for predictions of aboveground biomass density. • Uses Bayesian methods, with transparent and traceable sources of uncertainty. • Releases open-source ‘R’ code to facilitate operational use by in-country experts. • Emphasizes collaborative development of methods to meet global climate policy needs.