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Combining UAV-SfM, SAR, MSI and field surveys for estimation of above ground biomass in mangrove forest of Chonburi, Thailand

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Abstract

Mangrove biomass is a key indicator for quantifying carbon cycling in blue-carbon ecosystems, yet conventional approaches face significant challenges. To improve large-scale mangrove biomass assessment and provide a baseline for targeted conservation, present study proposes a Single-tree–Plot–Community–Region (AGBT/F~U~S) upscaling method that integrates UAV-SfM, SAR, MSI, and field surveys, and applies it to Chonburi, Thailand. In 2023, total mangrove aboveground biomass in Chonburi Province was 145.24 kt, with a mean AGB density of 101.61 Mg/ha, slightly below the global mangrove average. Long-term records reveal an initial decline followed by post-2015 recovery to about 85% of the 1996 level. Relative to the conventional plot–satellite model, the AGBT/F~U~S framework substantially improves estimation performance and reduces prediction error (ΔR²≈0.47; ΔRMSE ≈ 66.03 Mg/ha), and remains robust under limited training data, with accuracy gains saturating once plot numbers exceed a moderate threshold. These results demonstrate that multi-scale upscaling provides a transferable pathway for mangrove biomass mapping in data-scarce regions and offers a practical baseline for blue-carbon accounting and targeted restoration planning.

Data availability

Data will be made available on request; you can access the data in the study through the DOI: https://doi.org/10.6084/m9.figshare.28639718.v1 or by contacting the following email address: [email protected] (Zhen Guo).

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant Nos. 42171292, 42376228), the Special Fund for Asian Regional Cooperation from the China Ministry of Foreign Affairs (Grant No. WJ0922011), and the China Oceanic Development Foundation (Grant No. B222023017). We extend our sincere gratitude to the Thailand Department of Marine and Coastal Resources (DMCR) and the Intergovernmental Oceanographic Commission Sub-Commission for the Western Pacific (IOC-WESTPAC) for their invaluable support in facilitating this research.

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Authors and Affiliations

  1. School of GeoAI and Hindon STAI Institute, East China Normal University, Shanghai, 200241, China

    Jinchao Ma

  2. Research Center of Coastal Science and Marine Planning, Ministry of Natural Resources, First Institute of Oceanography, Qingdao, 266061, China

    Jinchao Ma, Zhen Guo, Zhiwei Zhang, Wenxue Xu, Huanshan Ning & Jiawei Shen

  3. Department of Wood Science, The University of British Columbia, Vancouver, Canada

    Haibo Feng

  4. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, 266590, China

    Huanshan Ning

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  1. Jinchao Ma
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  2. Zhen Guo
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  3. Haibo Feng
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  4. Zhiwei Zhang
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  5. Wenxue Xu
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  6. Huanshan Ning
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Contributions

Conceptualization, Z.G. and H.F.; methodology, Z.G. and Z.Z.; investigation, W.X. and H.N.; resources, W.X. and H.N.; data curation, J.M. and J.S.; writing—original draft preparation, J.M.; writing—review and editing, Z.G.; supervision, H.F.; funding acquisition, Z.G. All authors have read and agreed to the published version of the manuscript.

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Zhen Guo or Haibo Feng.

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Ma, J., Guo, Z., Feng, H. et al. Combining UAV-SfM, SAR, MSI and field surveys for estimation of above ground biomass in mangrove forest of Chonburi, Thailand.
Sci Rep (2026). https://doi.org/10.1038/s41598-025-34281-z

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  • DOI: https://doi.org/10.1038/s41598-025-34281-z

Keywords

  • Mangrove
  • Aboveground biomass
  • Upscaling method
  • Structure from motion
  • Single-tree segmentation

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