Comparing Two Novel LiDAR ‐Based Indices for Quantifying Forest Structural Complexity

ABSTRACT Forest structural complexity is critical for ecosystem functions, yet standardized metrics for its quantification remain elusive. This study compares two LiDAR‐derived three‐dimensional indices, the box dimension () as a fractal‐based measure, and canopy entropy (), an entropy‐based metric, to evaluate their methodological, computational, and conceptual differences. Using mobile LiDAR scans from forest plots in Maine, USA, and Nova Scotia and New Brunswick, Canada, we analyzed 170 point clouds to assess correlation, computation time, and theoretical underpinnings. Statistical analysis revealed a strong linear relationship between and (Pearson's ), with Deming regression indicating . Also, computation averaged 40 times slower than , scaling roughly linearly with point cloud size. Conceptually, reflects fractal dimensionality linked to physiological process optimization, while quantifies biomass distribution homogeneity. s unit dependence on plot size limits cross‐study comparability, whereas s dimensionless fractal interpretation offers broader intuitiveness. Both indices address sampling density bias but differ in parameterization and data efficiency. Despite s theoretical novelty, it does not surpass in interpretability, precision, or speed, and its proposed advantage in capturing higher complexity remains unsubstantiated. Despite their conceptual distinctions, their strong correlation suggests competitive rather than complementary roles. Future research should explore biome‐specific variability and physiological links to ecosystem functions to refine their utility in forest management under climate change.