Forest biometrics in the 21 century special issue

Abstract

Abstract Forest biometrics has evolved from a measurement-driven discipline focused on field efficiency and statistical rigor to a data-rich, technology-enabled science integrating multisensor information and advanced modeling approaches. This special issue, inspired by the Second North American Forest Mensurationists Conference held in 2022, highlights this transformation through nine studies that collectively span scales from individual branches to regional forest dynamics. Together, they emphasize a shift from identifying single optimal models to developing integrated, uncertainty-aware model systems that support operational decision-making. At the finest scale, advances in terrestrial laser scanning enable improved characterization of branch geometry under challenging conditions, yielding robust taper and form factor estimates for volume. At the tree level, extensive benchmarking of height–diameter relationships demonstrates that model form and stand origin strongly influence predictive performance, with generalized additive models often outperforming traditional approaches. Complementary work shows that calibration strategies are not universally transferable across model forms, underscoring the need for careful alignment of function choice and calibration design. Addressing biases in young stands, Bayesian model averaging offers a practical interim solution where traditional volume models trained on mature cohorts fail. At broader scales, studies demonstrate the operational potential of integrating public and low-cost remote sensing data. Freely available USGS 3DEP LiDAR supports highly accurate dominant height and site index estimation, while bias-corrected digital aerial photogrammetry provides a viable alternative in areas lacking LiDAR coverage. Landscape-level analyses using Landsat time series and permanent plots enable mapping of basal area growth, revealing spatial variability and temporal trends driven largely by stand dynamics. Collectively, these studies define a cohesive framework for modern forest biometrics: combining multiple data sources, selecting model families deliberately, applying light but effective calibration, and explicitly quantifying uncertainty. This integrated approach supports scalable, reliable predictions tailored to the needs of forest managers and policymakers. The special issue thus outlines a forward-looking research agenda that prioritizes resilient modeling systems over isolated solutions, enabling forestry to meet contemporary challenges across scales from tree components to landscapes.

Plant phenotyping relevance

森林の枝形状、樹高、林分指標などの植物形質を、レーザースキャン、航空写真、LiDAR、時系列衛星データ、統計モデルで推定・検証する方法群を中心に扱う特集概説であり、測定・推定手法が中心である。

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