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Background: Information on above-ground biomass (AGB) is important for managing forest resource use at local levels, land management planning at regional levels, and carbon emissions reporting at national and international levels. In many tropical developing countries, this information may be unreliable or at a scale too coarse for use at local levels. There is a vital need to provide estimates of AGB with quantifiable uncertainty that can facilitate land use management and policy development improvements. Model-based methods provide an efficient framework to estimate AGB. Methods: Using National Forest Inventory (NFI) data for a~1,000,000 ha study area in the miombo ecoregion, Zambia, we estimated AGB using predicted canopy cover, environmental data, disturbance data, and Landsat 8 OLI satellite imagery. We assessed different combinations of these datasets using three models, a semiparametric generalized additive model (GAM) and two nonlinear models (sigmoidal and exponential), employing a genetic algorithm for variable selection that minimized root mean square prediction error (RMSPE), calculated through cross-validation. We compared model fit statistics to a null model as a baseline estimation method. Using bootstrap resampling methods, we calculated 95 % confidence intervals for each model and compared results to a simple estimate of mean AGB from the NFI ground plot data. Results: Canopy cover, soil moisture, and vegetation indices were consistently selected as predictor variables. The sigmoidal model and the GAM performed similarly; for both models the RMSPE was~36.8 tonnes per hectare (i.e., 57 % of the mean). However, the sigmoidal model was approximately 30 % more efficient than the GAM, assessed using bootstrapped variance estimates relative to a null model. After selecting the sigmoidal model, we estimated total AGB for the study area at 64,526,209 tonnes (+/− 477,730), with a confidence interval 20 times more precise than a simple designbased estimate. Conclusions: Our findings demonstrate that NFI data may be combined with freely available satellite imagery and soils data to estimate total AGB with quantifiable uncertainty, while also providing spatially explicit AGB maps useful for management, planning, and reporting purposes.

Spatially explicit wall-to-wall forest-attributes information is critically important for designing management strategies resilient to climate-induced uncertainties. Multivariate estimation methods that link forest attributes and auxiliary variables at full-information locations can be used to estimate the forest attributes for locations with only auxiliary variables information. However, trade-offs between estimation accuracies versus logical consistency among estimated attributes may occur. This is particularly likely for macroscales (i.e., ≥1 Mha) with large forest-attributes variances and wide spacing between full-information locations. We examined these trade-offs for ∼390 Mha of Canada’s boreal zone using variable-space nearest-neighbours imputation versus two modelling methods (i.e., a system of simultaneous nonlinear models and kriging with external drift). We found logical consistency among estimated forest attributes (i.e., crown closure, average height and age, volume per hectare, species percentages) using (i) k ≤ 2 nearest neighbours or (ii) careful model selection for the modelling methods. Of these logically consistent methods, kriging with external drift was the most accurate, but implementing this for a macroscale is computationally more difficult. This extra cost is justified given the importance of assessing strategies under expected climate changes in Canada’s boreal forest and in other forest regions.