UAV-Spherical Data Fusion Approach to Estimate Individual Tree Carbon Stock for Urban Green Planning and Management

Abstract

Due to ever-accelerating urbanization in recent decades, exploring the contributions of trees in mitigating atmospheric carbon in urban areas has become one of the paramount concerns. Remote sensing-based approaches have been primarily implemented to estimate the tree-stand atmospheric carbon stock (CS) for the trees in parks and streets. However, a convenient yet high-accuracy computation methodology is hardly available. This study introduces an approach that has been tested for a small urban area. A data fusion approach based on a three-dimensional (3D) computation methodology was applied to calibrate the individual tree CS. This photogrammetry-based technique employed an unmanned aerial vehicle (UAV) and spherical image data to compute the total height (H) and diameter at breast height (DBH) for each tree, consequently estimating the tree-stand CS. A regression analysis was conducted to compare the results with the ones obtained with high-cost laser scanner data. Our study demonstrates the applicability of this method, highlighting its advantages even for large city areas in contrast to other approaches that are often more expensive. This approach could serve as an efficient tool for assisting urban planners in ensuring the proper utilization of the available green space, especially in a complex urban environment.

Plant phenotyping relevance

UAVと球面画像の融合・3D計算法により、個体樹木の樹高と胸高直径を推定し、炭素蓄積量を算出する手法が中心であり、植物個体の形態形質測定として実質的な応用・検証を行っている。

Code and data availability

The supplied blocks describe UAV/spherical image acquisition and SfM processing for 20 urban trees, but contain no public phenotype dataset, image deposit, author code, or model release. The only URL mentioned (Agisoft Metashape) is commercial software, not a paper-specific asset. No data availability statement appears

No evidence-backed public reproduction asset is currently recorded.