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

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. O...

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Main Authors: Sinlapachat Pungpa, Krisanadej Jaroensutasinee, Mullica Jaroensutasinee, Wacharapong Srisang, Sirilak Chumkiew, Elena B. Sparrow
Format: Article
Language:English
Published: Ital Publication 2025-06-01
Series:Journal of Human, Earth, and Future
Subjects:
Online Access:https://hefjournal.org/index.php/HEF/article/view/620
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author Sinlapachat Pungpa
Krisanadej Jaroensutasinee
Mullica Jaroensutasinee
Wacharapong Srisang
Sirilak Chumkiew
Elena B. Sparrow
author_facet Sinlapachat Pungpa
Krisanadej Jaroensutasinee
Mullica Jaroensutasinee
Wacharapong Srisang
Sirilak Chumkiew
Elena B. Sparrow
author_sort Sinlapachat Pungpa
collection DOAJ
description 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.
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institution Kabale University
issn 2785-2997
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publishDate 2025-06-01
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series Journal of Human, Earth, and Future
spelling doaj-art-919e43bcfab54b73836f1385ef009e0b2025-08-25T09:29:58ZengItal PublicationJournal of Human, Earth, and Future2785-29972025-06-016247448710.28991/HEF-2025-06-02-014568Integrating Satellite and UAV Imagery for Mangrove Aboveground Biomass and Carbon Stock ModelingSinlapachat Pungpa0https://orcid.org/0000-0001-5068-3768Krisanadej Jaroensutasinee1https://orcid.org/0000-0001-6532-8830Mullica Jaroensutasinee2https://orcid.org/0000-0003-2309-7493Wacharapong Srisang3https://orcid.org/0000-0002-6058-1498Sirilak Chumkiew4https://orcid.org/0000-0003-0871-588XElena B. Sparrow5https://orcid.org/0000-0003-1864-6494School of Biology, Institute of Science, Suranaree University of Technology, Muang Nakhon Ratchasima, 30000Center of Excellence for Ecoinformatics, Walailak University, Nakhon Si Thammarat, 80160Center of Excellence for Ecoinformatics, Walailak University, Nakhon Si Thammarat, 80160Faculty of Science and Agricultural Technology, Rajamangala University of Technology Lanna, 52000, LampangSchool of Biology, Institute of Science, Suranaree University of Technology, Muang Nakhon Ratchasima, 30000Department of Natural Resources and Environment, University of Alaska Fairbanks, AKThis 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.https://hefjournal.org/index.php/HEF/article/view/620aboveground biomasscarbon stockmangroveremote sensingvegetation index
spellingShingle Sinlapachat Pungpa
Krisanadej Jaroensutasinee
Mullica Jaroensutasinee
Wacharapong Srisang
Sirilak Chumkiew
Elena B. Sparrow
Integrating Satellite and UAV Imagery for Mangrove Aboveground Biomass and Carbon Stock Modeling
Journal of Human, Earth, and Future
aboveground biomass
carbon stock
mangrove
remote sensing
vegetation index
title Integrating Satellite and UAV Imagery for Mangrove Aboveground Biomass and Carbon Stock Modeling
title_full Integrating Satellite and UAV Imagery for Mangrove Aboveground Biomass and Carbon Stock Modeling
title_fullStr Integrating Satellite and UAV Imagery for Mangrove Aboveground Biomass and Carbon Stock Modeling
title_full_unstemmed Integrating Satellite and UAV Imagery for Mangrove Aboveground Biomass and Carbon Stock Modeling
title_short Integrating Satellite and UAV Imagery for Mangrove Aboveground Biomass and Carbon Stock Modeling
title_sort integrating satellite and uav imagery for mangrove aboveground biomass and carbon stock modeling
topic aboveground biomass
carbon stock
mangrove
remote sensing
vegetation index
url https://hefjournal.org/index.php/HEF/article/view/620
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AT mullicajaroensutasinee integratingsatelliteanduavimageryformangroveabovegroundbiomassandcarbonstockmodeling
AT wacharapongsrisang integratingsatelliteanduavimageryformangroveabovegroundbiomassandcarbonstockmodeling
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