Low-Cost MLS-Based Forest Plot Mapping via Feature Graph Registration

Forest plot mapping is a significant task in forest inventories by providing accurate structural parameters. However, understory mapping still predominantly relies on terrestrial laser scanning (TLS), which is time-consuming and labor-intensive. Moreover, existing mobile laser scanning (MLS)-based methods either require expensive high-beam LiDAR or struggle with feature extraction and registration accuracy. To address these issues, we propose a novel low-cost MLS-based forest plot mapping method utilizing feature graph registration. Local submaps are first constructed via tree stem extraction and scan-to-scan graph-based registration, followed by global alignment to generate the final forest plot map. Experiments on three forest plots with varying structures and species demonstrate that our method achieves an average mapping accuracy of approximately 10 cm, even without loop closure optimization. Comparative results further demonstrate our effectiveness and efficiency for practical forest surveys.