Wood Quality Assessment of Standing Tree Stems When Measurements Are Spatially Misaligned

Non-destructive assessment of wood quality in standing trees is increasingly important for value-based harvesting, precision forestry, and large-scale monitoring. Yet most operational approaches rely on destructive sampling or plot-level measurements that cannot be feasibly extended across extensive forest areas. A central barrier to scalable assessment is the spatial misalignment between external structural measurements and internal wood-quality responses which introduces systematic bias when conventional regression methods are applied. We introduce Regression based on Misaligned Covariates (RMC), a statistical framework that reconciles covariates and responses measured at different spatial locations by combining the Law of Total Expectation with kernel-based estimation of marginal relationships. RMC recovers height-dependent conditional means without requiring one-to-one spatial correspondence. RMC is demonstrated using two contrasting conifers, eastern white pine (Pinus strobus) and red pine (Pinus resinosa), with external covariates derived from high-precision manual measurements compatible with LiDAR-based structural characterization. Across leave-one-tree-out cross-validation, linear functional forms frequently outperformed the Normal model with a few exceptions, producing smoother and biologically interpretable knot-volume profiles aligned with known species-specific crown architecture. Model performance varied by stem zone and kernel choice when evaluated against the height-only baseline (m-hat). For white pine, the linear kernel achieved the lowest error in the clear stem (1.04 ± 0.04) and the Normal kernel performed best in the living crown (1.09 ± 0.08). For red pine, the linear kernel minimized error in the clear stem (1.28 ± 0.49), while the quadratic kernel yielded the lowest mean error in the living crown (1.24 ± 0.05). The resulting cumulative knot volume profiles captured species-specific differences in knot accumulation and aligned with known patterns of crown architecture and stem form. RMC establishes a mathematically formal foundation for estimating internal wood-quality attributes from external structural metrics for misaligned covariates. This framework offers a theoretical framework for future integration with automated LiDAR-derived datasets and provides a conceptual step toward non-destructive, stand-level wood-quality assessment.