Global biomass maps can increase the precision of (sub)national aboveground biomass estimates: A comparison across tropical countries.

Countries within the tropics face ongoing challenges in completing or updating their national forest inventories (NFIs), critical for estimating aboveground biomass (AGB) and for forest-related greenhouse gas (GHG) accounting. While previous studies have explored the integration of map information with local reference data to fill in data gaps, limited attention has been given to the specific challenges presented by the clustered plot designs frequently employed by NFIs when combined with remote sensing-based biomass map units. This research addresses these complexities by conducting four country case-studies, encompassing a variety of NFI characteristics within a range of AGB densities. Examining four country case-studies (Peru, Guyana, Tanzania, Mozambique), we assess the potential of European Space Agency's Climate Change Initiative (CCI) global biomass maps to increase precision in (sub)national AGB estimates. We compare a baseline approach using NFI field-based data with a model-assisted scenario incorporating a locally calibrated CCI biomass map as auxiliary information. The original CCI biomass maps systematically underestimate AGB in three of the four countries at both the country and stratum level, with particularly weak agreement at finer map resolution. However, after calibration with country-specific NFI data, stratum and country-level AGB estimates from the model-assisted scenario align well with those obtained solely from field-based data and official country reports. Introducing maps as a source of auxiliary information fairly increased the precision of stratum and country-wise AGB estimates, offering greater confidence in estimating AGB for GHG reporting purposes. Considering the challenges tropical countries face with implementing their NFIs, it is sensible to explore the potential benefits of biomass maps for climate change reporting mechanisms across biomes. While country-specific NFI design assumptions guided our model-assisted inference strategies, this study also uncovers transferable insights from the application of global biomass maps with NFI data, providing valuable lessons for climate research and policy communities.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)