2026-08-07 パシフィック・ノースウェスト国立研究所（PNNL）
＜関連情報＞
- https://www.pnnl.gov/publications/mapping-intermittently-dry-headwaters-drones-and-phones
- https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2026.1725258/full
非恒常河川の河床における水生生物地球化学プロセスを対象とした、費用対効果の高い3D再構築手法の精度評価 Accuracy evaluation of cost-effective 3D reconstruction approaches for hydrobiogeochemical processes in non-perennial stream riverbeds
Jie Bao,Yunxiang Chen,Vanessa A. Garayburu-Caruso,Etienne Fluet-Chouinard,Maggi Laan, John Smart,Kameron E. Markham,Lupita Renteria,+2,James C. Stegen
Frontiers in Environmental Science Published:23 March 2026
DOI:https://doi.org/10.3389/fenvs.2026.1725258
Abstract
Non-perennial streams, characterized by intermittent or episodic flows, comprise over half of global river networks and play an essential role in several ecosystem functions. Accurate stream channel topography is critical for representing flow, hyporheic exchange, and nutrient transport. Although many studies have applied Unmanned Aerial Vehicle (UAV)-based Structure-from-Motion (SfM) to reconstruct river and terrain topography, they have focused more on larger rivers or steep terrain and often relied on RTK-GNSS and ground control points (GCPs), leaving the performance of low-cost workflows for small non-perennial streams lacking evaluations. This study quantitatively evaluates the accuracy of multiple cost-efficient approaches for reconstructing 3-dimensional (3D) stream riverbeds: (1) a UAV imagery-based SfM approach, machine learning-based 3D reconstruction model, (2) Visual Geometry Grounded Deep Structure from Motion (VGGSfM), and (3) Visual Geometry Grounded Transformer for long sequence of images (VGGT-Long), and (4) handheld smartphone LiDAR scanning. The accuracy of the reconstructed topography was assessed against field measurements from a tripod optical level and GCPs GPS positions. UAV-based SfM emerges as the most effective and accessible method for accurately mapping non-perennial streambeds. Its planimetric error is around 1 m, and the ground elevation error is around 0.04 m. Although machine-learning based reconstructions substantially reduce computation time, they do not achieve comparable accuracy. Their planimetric error is over 5 m, and the ground elevation error is above 0.18 m. Likewise, iPhone LiDAR is not suitable for long reaches because cumulative sensor drift degrades positional and vertical precision, compromising the final reconstruction. Propagating these geometric errors into hydraulic and biogeochemical calculations showed that SfM yields relatively modest uncertainty in inferred water depth, velocity, nitrate uptake velocity, and reaeration, whereas the other methods introduce substantially larger uncertainty. This work exemplifies the significant potential for UAV-based surveys in characterizing stream habitats and conditions and in supporting reliable estimates of hydrobiogeochemical processes.
