Estimation of forest stand density profiles using a voxel-based modeling approach combining airborne and terrestrial LiDAR data

Forest density is a key parameter for sustainable forest management and planning of silvicultural operations (e.g. thinning, regeneration), biomass energy estimation, or biodiversity and ecological monitoring. The vertical distribution of vegetation material inside forest canopies is poorly k nown due to a lack of field measurements, which are difficult to acquire. The development of Airborne Laser Scanning (ALS) provides new metrics for characterizing forest 3D structure. However, ALS has limited capabilities for assessing the understory layer , especially in dense canopies. The ability to identify the vertical profile of vegetation is affected by object occlusion as the ALS signal penetrates deeper into the canopy. Interestingly , Terrestrial Laser Scanning (TLS) provides complementary and detai led information about the vertical distribution of woody and leafy components in the lower canopy. The aim of this study is to compare ALS and TLS vertical profiles of forest density at plot level, in order to quantify the importance of occlusion effect fr om ALS and to propose a correction model. The study site is in Mal ahat located in s outhern Vancouver Island (B.C.), Canada , and is a 0.4 ha retention bloc k of a coniferous stand composed of d ouglas fir ( Pseudotsuga menziesii ), western red cedar ( Thuja plic ata ) , and western hemlock ( Tsuga heterophylla ). The methodology can be summarized by a 5 - step procedure : (i) extraction of ALS vegetation and single returns probability density function according to height, (ii) use of voxel geometry to transform georefere nced multiple TLS scans into normalized forest density index values, then aggregated into vertical profiles, (iii) comparison of ALS and TLS forest density vertical profiles, (iv) validation with a normalized surface index from a tree architecture model, ( v) fitting of a correction model between ALS returns and TLS density voxels. Preliminary results show that ALS and TLS forest density vertical profiles can be approximated by a Weibull function. As expected, the ALS function shows a narrow peak of forest d ensity in the upper part of the canopy. The TLS function reveals a higher degree of skewness and kurtosis, which seems to confirm that TLS detects a higher range of forest density among the height strata, especially in the lower canopy. The fitting of the TLS function to the ALS data allowed us to identify the equation parameters that need to be corrected in order to overcome the occlusion effect in the ALS signal. However, it remains to be determined whether the differences observed between the ALS and TLS are site - dependent or whether they are predictable characteristics specific to each LiDAR system. The applicability of this method to various forest environments, such as deciduous and mixed heterogeneous stands, is currently being investigate.

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