Digital twin–based voxel-scale clumping index (CI) improves leaf area density (LAD) retrieval from simulated terrestrial laser scanning (TLS)

Accurately representing three-dimensional (3D) canopy structure is essential for Earth System Models (ESMs) and radiative transfer schemes that link vegetation to climate–carbon feedback. Leaf area density (LAD) and related structural metrics are widely retrieved from remote sensing using Beer–Lambert (BL) transmittance inversions, yet these approaches commonly assume randomly distributed foliage and woody material. In real canopies, plant material is spatially aggregated (clumped), violating random mixing and introducing systematic LAD bias. Although clumping has been corrected using canopy or crown scale clumping indices (CI), voxel-based LAD retrievals from terrestrial laser scanning (TLS) and other 3D sensing approaches require clumping information that is defined at the same spatial scale as the inversion. The lack of a physically grounded voxel-resolved CI remains a key methodological gap, particularly for dense and heterogeneous canopy regions.

 

Here, we develop a voxel-scale effective reference clumping index (CI_ref) retrieval method that is structurally consistent with voxel-based BL retrievals. We used digital twin 3D tree meshes from the RAMI-V benchmark forest scenes, spanning six contrasting crown forms and six leaf inclination angle distribution (LIAD) variants (36 canopy geometries). Each tree was partitioned into regular voxel grids at four sizes (0.2, 0.5, 1.0, and 2.0 m). Within each voxel, we performed multi-directional (18 bin viewing angle) ray tracing on every voxel-clipped mesh to directly quantify within-voxel gap probability, leaf projection function G(θ), and path-length statistics required for transmittance-based LAD inference. Directional CI estimates were derived for each viewing angle and then aggregated through a hierarchical pooling strategy that reduces sampling noise and directional variability (all angles → azimuth pooled → zenith-pooled). This procedure yields a single, robust CI_ref per voxel that is independent of viewing angle and suitable as a reference label for operational LAD retrieval algorithm development from LiDAR data.

We then quantified the practical impact of voxel-scale clumping correction on BL LAD retrieval using simulated TLS point clouds. LAD was estimated per voxel under two assumptions: (i) the conventional random-foliage case (CI = 1) and (ii) clumping-corrected inversion using CI_ref. Across all crown forms, LIAD variants, and voxel sizes, the CI = 1 assumption produced predominantly negative LAD errors relative to mesh-derived reference LAD, consistent with systematic underestimation when clumping is ignored. Incorporating CI_ref shifted LAD errors toward zero and improved agreement, evidenced by reduced bias and normalized RMSE. Improvements were most pronounced for planophile canopies, where directional foliage aggregation is strongest and for coarser voxel sizes (1.0–2.0 m), where greater within-voxel heterogeneity amplifies departures from random mixing, demonstrating that clumping-induced bias is strongly scale dependent.

These results provide practical recommendations for 3D canopy modelling: specifically, that voxel-scale clumping correction becomes increasingly essential as voxel size increases, especially when within-voxel heterogeneity grows. The proposed CI_ref framework strengthens scale consistency between local canopy structure and voxel-based radiative transfer, enabling unbiased LAD retrievals and providing physically grounded labels for future deep learning model-based CI prediction from TLS point clouds.