Self-adaptive individual tree modeling based on skeleton graph optimization and fractal self-similarity

Three-dimensional tree modeling is crucial for forest ecological applications. However, building accurate individual tree models still faces unresolved challenges, such as wrongly connected branches within the canopy and poor quality modeling results when dealing with tree points containing data gaps. To address these issues, this paper proposes an in-novation method for individual tree modeling based on skeleton graph optimization and fractal self-similarity. In this paper, the skeleton points are initially extracted through the Laplacian-based contraction and the farthest distance spherical sampling. To centralize the extracted skeleton points within each point set, a method for skeleton points adjusting and optimization is presented, which helps achieve centralized skeleton points, particularly in cases with incomplete branch points. Additionally, instead of using Euclidean distance or its square as edge weight, the paper proposes a novel edge weight definition, which ensures the construction of correctly connected skeleton lines, especially for branches within the canopy. To improve fidelity and robustness against outliers, fractal self-similarity is first applied in this paper to refine individual tree models and achieve better modeling results. The effectiveness of the pro-posed method is evaluated using 29 individual trees of different structure characteristics with known harvest volumes. Experimental results demonstrate that this method achieves tree volumes closest to the referenced values, with a relative mean deviation of 0.01 ​% and a relative root mean square error of 0.09 ​%. Moreover, the concordance correlation coefficient of the proposed method is 0.994, outperforming two classical individual tree modeling methods, TreeQSM (Quantitative Structure Model) and AdQSM, based on five accuracy indicators.