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Shortest-Path Decomposition for Foliage-Robust 3D Tree Modeling and Above-Ground Biomass Estimation from Point Clouds

Estimating above-ground biomass (AGB) from terrestrial laser scanning (TLS) via quantitative structural models (QSMs) is highly accurate under leaf-off conditions, yet major QSM paradigms degrade sharply when foliage is present. The standard remedy, leaf-wood separation, introduces its own errors and complexity. We present topology-driven foliage suppression that replaces explicit leaf--wood classification inside the reconstruction pipeline. On the shortest-path tree of a point-cloud graph, the number of root-to-node paths traversing a node equals the size of its rooted subtree, so path traversal frequency is an exact topological descriptor of branching hierarchy. Three decompositions of the path structure, plus an adaptive frequency threshold at the elbow of frequency growth along each branch, recover the branching hierarchy from a single Dijkstra run. We evaluate it on 90 trees under exclusively leaf-on conditions across tropical TLS and temperate UAV laser scanning (ULS) platforms. On dense tropical TLS it achieves a mean absolute percentage deviation (MAPD) of 17.2% with RMSE 2.37~Mg (RMSE% 34.8%) and an R^2 of 0.956, outperforming the best separation-assisted TreeQSM pipeline (21.2%). Structural analysis confirms that foliage introduces 2.6~cm of mean geometric error (3.8~cm RMS) and preserves the hierarchical volume distribution. On low-density ULS data, MAPD reaches 6.4% with field-measured diameter at breast height (DBH) and 32.3% with DBH inferred allometrically from tree height. All AGB experiments use identical parameter settings. Directly recovering branching hierarchy from shortest-path structure removes a barrier to operational QSM-based forest inventory under leaf-on conditions.

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