Quantifying canopy-clumping effects on LiDAR detectability in heterogeneous forests using an effective detection factor

Quantifying the detectability of understorey and ground targets is a key methodological problem in forest LiDAR remote sensing, because photon penetration is jointly controlled by canopy density, foliage spatial organization, and sensor detection statistics. Although canopy clumping has been widely considered in vegetation radiative transfer and gap-fraction modelling, its role in LiDAR target detectability remains insufficiently represented in existing analytical frameworks. In this study, we develop a clumping-aware physical–statistical framework to estimate LiDAR detectability beneath heterogeneous forest canopies. The framework incorporates the canopy clumping factor into modified gap-fraction and bidirectional transmission formulations, links the expected returned photon count to detection probability through a Poisson photon-counting model, and defines an effective detection factor (EDF) as a scene-scale metric integrating gap-dominated and cover-dominated detection pathways. Controlled synthetic scenes and Monte Carlo photon-tracing simulations serve as numerical benchmarks, and the predictions are further compared with real airborne LiDAR observations from the National Ecological Observatory Network (NEON). The results show that foliage clumping can substantially alter LiDAR detectability under the same canopy density by modifying connected gap pathways and bidirectional photon attenuation, and this effect becomes more pronounced under moderate-to-high leaf area index, where homogeneous canopy assumptions may misrepresent penetration-related detection opportunities. Gap pathways remain the dominant detection mechanism across the parameter space, while attenuated cover-region penetration plays a secondary but growing role as the canopy becomes denser and more homogeneous; the modelled decline of detectability with increasing leaf area index agrees with the ground-return ratio observed in the real LiDAR data. The proposed framework provides a physically interpretable tool for analysing LiDAR penetration sensitivity in heterogeneous canopies, offering a basis for future forest LiDAR simulation, sensor performance assessment, and empirical validation.