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Advancing terrestrial laser scanning for 3D classification of Spanish moss (Tillandsia usneoides L.) using unsupervised, machine learning, and deep learning methods

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Abstract

Quantifying the distribution of Spanish moss (Tillandsia usneoides L.) is challenging because it grows suspended from high tree branches, limiting manual sampling. Terrestrial laser scanning (TLS) provides a non-destructive means of capturing vegetation structure in three dimensions. However, no established methods exist for identifying Spanish moss from TLS data. We evaluated five classification methods for distinguishing Spanish moss in TLS-derived point cloud data: Graph, DBSCAN, Random Forest (RF), Kernel Point Convolution (KPConv), and PointNet++. PointNet++ achieved the highest accuracy (81%), followed by DBSCAN (70%), KPConv (61%), RF (54%), and Graph (52%). Unsupervised methods required minimal computational resources (2–3 min, 8–16 GB memory) without training. RF required 3 h for training, 8 for prediction with 1024 GB memory. Deep learning methods required substantially more: KPConv needed 60 h for training, 4 for prediction (256 GB), while PointNet++ required 48 h for training, 1 for prediction (128 GB). Agreement was lowest in the central and upper canopy due to occlusion. Surface variation, PCA1, and verticality contributed most to accurate predictions. These results demonstrate the feasibility of using TLS and advanced classification methods for non-destructive Spanish moss mapping and highlight the accurate classification ability of PointNet++ for future biomass estimation at landscape scales.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

We thank Cesar Alvites, Adithi Anugu, Nadeem Fareed, Kody Brock, Nikhitha Nagalla, Naga Vincata Siva Reddy, and Ana Terra from the Forest Biometrics, Remote Sensing and Artificial Intelligence Laboratory at the University of Florida. We thank Ana Paula Dalla Corte and Alan Sulato de Andrade at the Universidade Federal do Paraná. Carlos A. Silva has been funded through NASA’s grants (ICESat-2, 80NSSC23K0941), Carbon Monitoring System (CMS, grant 80NSSC23K1257) and Commercial Smallsat Data Scientific Analysis (CSDSA, grant 80NSSC24K0055).

Funding

This work was supported by NASA through the ICESat-2 program (grant 80NSSC23K0941), the Carbon Monitoring System (CMS, grant 80NSSC23K1257), and the Commercial Smallsat Data Scientific Analysis program (CSDSA, grant 80NSSC24K0055).

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Authors and Affiliations

  1. Forest Biometrics, Remote Sensing and Artificial Intelligence Laboratory (Silva Lab), School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL, 32611, USA

    Alexander J. Gaskins, Jinyi Xia, Inacio T. Bueno & Carlos A. Silva

Authors

  1. Alexander J. Gaskins
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  2. Jinyi Xia
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  3. Inacio T. Bueno
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  4. Carlos A. Silva
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Contributions

Conceptualization: A.J.G., C.A.S; Methodology: A.J.G., J.X., C.A.S; Software: A.J.G, J.X; Validation: A.J.G.; Formal analysis: A.J.G., J.X; Investigation: A.J.G.; Resources: C.A.S.; Data curation: A.J.G., J.X., C.A.S.; Writing – Original Draft: A.J.G.; Writing – Review and editing: A.J.G., J.X, I.T.B, C.A.S.; Visualization: A.J.G., J.X., I.T.B., C.A.S.; Supervision: C.A.S.; Project administration: C.A.S.; Funding acquisition: C.A.S.

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Correspondence to
Alexander J. Gaskins.

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Gaskins, A.J., Xia, J., Bueno, I.T. et al. Advancing terrestrial laser scanning for 3D classification of Spanish moss (Tillandsia usneoides L.) using unsupervised, machine learning, and deep learning methods.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-49230-7

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  • DOI: https://doi.org/10.1038/s41598-026-49230-7

Keywords

  • Point cloud
  • LiDAR
  • Graph
  • DBSCAN
  • Random forest
  • KPConv
  • PointNet++ 

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