The global forest diameter spectrum using a machine learning approach

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Abstract. Global forest assessments assist climate policy development, ecosystem science, and conservation planning, yet they rely on biomass and canopy data that do not explicitly represent the stand structural attributes derived from tree diameter measurements. This limits the ability to compare size-related structure and within-stand heterogeneity at large spatial scales. Here we present a global, spatially explicit dataset of stand-level tree diameter structure for forest cover in 2020 at 0.027° (~3 km) resolution, based on 1,203,524 georeferenced forest inventory plots comprising 54.6 million trees (≥10 cm DBH) integrated with more than 50 environmental and satellite-derived covariates into machine learning models. The dataset provides the first globally consistent maps of three complementary diameter-based metrics: arithmetic mean diameter (Dmean), quadratic mean diameter (Dqm), and the coefficient of variation of diameter (Dcv), representing average tree size, large-tree dominance, and within-stand size variability, respectively. Model performance of the ecozone-specific Random Forest framework ranged from R² = 0.41–0.82 (RMSE = 3.91–4.63 cm) for Dmean, R² = 0.43–0.83 (RMSE = 4.38–5.27 cm) for Dqm, and R² = 0.47–0.62 with (RMSE = 0.10–0.13) for Dcv across different forest ecozones. By jointly quantifying central tendency and variability in tree size, the dataset revealed spatial patterns of forest structural organization not captured by existing biomass or canopy-height products. It provides a consistent baseline for cross-biome comparison of forest structure, supporting parameterization and evaluation of vegetation and Earth system models, while offering an independent benchmark for remotely sensed structural proxies. Furthermore, it enables spatial assessment of stand structural attributes, including large-tree dominance and structural complexity, facilitating integration of diameter-based structure into global analyses of carbon dynamics and ecosystem functioning.