Integrating Satellite and UAV Imagery for Mangrove Aboveground Biomass and Carbon Stock Modeling

Sinlapachat Pungpa; Krisanadej Jaroensutasinee; Mullica Jaroensutasinee; Wacharapong Srisang; Sirilak Chumkiew; Elena B. Sparrow

doi:10.28991/HEF-2025-06-02-014

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Sinlapachat Pungpa School of Biology, Institute of Science, Suranaree University of Technology, Muang Nakhon Ratchasima, 30000, Thailand https://orcid.org/0000-0001-5068-3768
Krisanadej Jaroensutasinee Center of Excellence for Ecoinformatics, Walailak University, Nakhon Si Thammarat, 80160, Thailand https://orcid.org/0000-0001-6532-8830
Mullica Jaroensutasinee Center of Excellence for Ecoinformatics, Walailak University, Nakhon Si Thammarat, 80160, Thailand https://orcid.org/0000-0003-2309-7493
Wacharapong Srisang Faculty of Science and Agricultural Technology, Rajamangala University of Technology Lanna, 52000, Lampang, Thailand https://orcid.org/0000-0002-6058-1498
Sirilak Chumkiew
s.chumkiew@g.sut.ac.th
School of Biology, Institute of Science, Suranaree University of Technology, Muang Nakhon Ratchasima, 30000, Thailand https://orcid.org/0000-0003-0871-588X
Elena B. Sparrow Department of Natural Resources and Environment, University of Alaska Fairbanks, AK, United States https://orcid.org/0000-0003-1864-6494

Vol. 6 No. 2 (2025): June

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This study aimed to: (1) quantify aboveground biomass (AGB) and carbon (AGC) stocks in the Banlaem mangrove forest, Nakhon Si Thammarat, Thailand; and (2) construct an AGB estimation model using vegetation indices (VIs) derived from Sentinel-2, Landsat-8, and unmanned aerial vehicle (UAV) imagery. On-the-ground measurements were carried out to evaluate the AGB and AGC stocks of the mangrove forest. VIs were then calculated using passive remote sensing data, including satellite and UAV imagery. These indices were compared through multiple regression analysis with the ground-truthed AGB for evaluation. Three mangrove species were found: Rhizophora mucronata, R. apiculata, and Avicennia Marina. Overall, the AGB and AGC stocks ranged from 0 to 179.78 tons•ha¹ (56.30 ± 51.81 tons•ha¹) and 0 to 89.89 tons•ha¹ (28.15 ± 25.90 tons•ha¹), respectively. The best AGB model exhibited an R² of 0.73 and an RMSE of 22.0 tons•ha¹. This study presents a novel approach for estimating AGB and AGC stocks in the Thai mangrove ecosystem by integrating a UAV with two open-access satellite imagery sources. Combining multiple VIs (NDVI, SAVI, and GNDVI) with CHM provides better accuracy for the mangrove AGB estimation model than using a single variable.

Pungpa, S., Jaroensutasinee, K., Jaroensutasinee, M., Srisang, W., Chumkiew, S., & Sparrow, E. B. (2025). Integrating Satellite and UAV Imagery for Mangrove Aboveground Biomass and Carbon Stock Modeling. Journal of Human, Earth, and Future, 6(2), 474–487. https://doi.org/10.28991/HEF-2025-06-02-014

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Integrating Satellite and UAV Imagery for Mangrove Aboveground Biomass and Carbon Stock Modeling

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Integrating Satellite and UAV Imagery for Mangrove Aboveground Biomass and Carbon Stock Modeling

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