The proper estimation of above-ground biomass (AGB) stocks of managed forests is a prerequisite to quantifying their role in climate change mitigation. The aim of this study was to analyze the spatial variability of AGB and its uncertainty between actively managed pine and unmanaged pine-oak reference forests in central Mexico. To investigate the determinants of AGB, we analyzed variables related to forest management, stand structure, topography, and climate. We developed linear (LM), generalized additive (GAM), and Random Forest (RF) empirical models to derive spatially explicit estimates and their uncertainty, and compared them. AGB was strongly influenced by forest management, as LiDAR-derived stand structure and stand age explained 80.9% to 89.8% of its spatial variability. The spatial heterogeneity of AGB varied positively with stand structural complexity and age in the managed forests. The type of predictive model had an impact on estimates of total AGB in our study site, which varied by as much as 19%. AGB densities varied from 0 to 492 ± 17 Mg ha⁻¹ and the highest values were predicted by GAM. Uncertainty was not spatially homogeneously distributed and was higher with higher AGB values. Spatially explicit AGB estimates and their association with management and other variables in the study site can assist forest managers in planning thinning and harvesting schedules that would maximize carbon stocks on the landscape while continuing to provide timber and other ecosystem services. Our study represents an advancement toward the development of efficient strategies to spatially estimate AGB stocks and their uncertainty, as the GAM approach was used for the first time with improved results for such a purpose.

Reliable land cover information is essential for scaling plot-based measurements in national forest inventories (NFIs). This study compared the precision of key forest indicators in the Bangladesh NFI using remote sensing (RS)-derived and field-assigned land cover data. Field data from 1781 plots, collected as part of the Bangladesh NFI (2015–2019), were integrated with a 2015 national land cover map produced from SPOT-6/7, Landsat, and Sentinel-2 imagery. The precision of forest indicator estimates was evaluated across land cover domains and ecological zones. Results show that, under an unchanged NFI field measurement and estimation framework, RS-derived land cover reduced the width of confidence intervals (i.e., improved statistical precision) of estimates for most biomass related indicators, including above- and below-ground biomass, tree volume, basal area, and carbon pools, by 15–20% on average, with some reductions exceeding 50%. Improvements were less consistent for regeneration-related indicators (saplings, seedlings). The insights from this study highlight the advantages of remote sensing-derived land cover for improving NFI indicator precision, while underscoring the continued need for advancing ontology-driven approaches with necessary strengthening of field crew capacity to ensure the consistent application of land cover standards.

Reliable land cover (LC) information is essential for scaling plot-based measurements in National Forest Inventories (NFIs). This study assessed the impact of using remote sensing (RS)-derived LC compared with field-assigned LC on the precision of key forest indicators in Bangladesh’s NFI (2015–2019). Field data from 1,781 plots were integrated with a 2015 national LC map produced from SPOT-6/7, Landsat, and Sentinel-2 imagery. Sampling errors of forest indicator estimates were evaluated across LC classes and ecological zones. Results show that RS derived LC reduced sampling errors for most biomass related indicators, including above- and below-ground biomass, tree volume, basal area, and carbon pools, by 15–20% on average, with some reductions exceeding 50%. Improvements were less consistent for regeneration related indicators (saplings, seedlings). These findings highlight the advantages of RS derived LC for improving NFI precision, while underscoring the continued need for advancing ontology driven approaches with necessary strengthening of field crew capacity to ensure consistent application of land cover standards.