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In structurally complex subtropical evergreen broad-leaved forests with dense understories, conventional remote sensing approaches are often limited by spectral saturation and insufficient structural characterization. This study developed a multi-source data fusion framework integrating airborne laser scanning (ALS), terrestrial laser scanning (TLS), and hyperspectral imagery (HSI), using ground truth data from 34 permanent plots in southern China subtropical evergreen broad-leaved forests. Six key structural parameters from ALS/TLS and six spectral indices from HSI were integrated as input features for adaptive fuzzy C-means clustering to estimate tree species diversity. Variance decomposition was conducted to quantify the independent and interactive contributions of ALS- and TLS-derived parameters. The results showed that: (1) ALS-based multi-scale watershed segmentation achieved high individual-tree segmentation accuracy (R2 = 0.873); (2) ALS-derived structural parameters exhibited significant correlations with plot-level species diversity (R2 = 0.385–0.824); (3) inter-crown standard deviations of six vegetation indices showed consistent associations with species diversity (R2 = 0.361–0.479), capturing interspecific spectral and functional variation; (4) combined ALS, HSI, and TLS predictors explained approximately 83% of diversity variation, with TLS contributing minimal unique information beyond ALS; (5) adaptive fuzzy C-means clustering estimated Shannon–Wiener indices with high accuracy (R2 = 0.725), though plot-level aggregated metrics outperformed individual-tree aggregates; (6) TLS inclusion reduced estimation accuracy (R2 = 0.653), likely due to understory liana interference, while silhouette analysis confirmed that clustering stability remained unchanged. These findings demonstrate that ALS–HSI fusion enables robust regional-scale tree species diversity estimation, while TLS may introduce confounding structural signals rather than complementary information in dense understory conditions.
Che et al. (Wed,) studied this question.