Assessing above ground biomass of Wunbaik Mangrove Forest in Myanmar using machine learning and remote sensing data
Abstract Estimating Above Ground Biomass (AGB) of mangroves provides crucial information for regional and global blue carbon strategies. Despite growing interest in mangrove AGB estimation using a remote sensing approach, the effectiveness of this advanced method has not been evaluated for mangrove...
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Springer
2025-03-01
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| Series: | Discover Conservation |
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| Online Access: | https://doi.org/10.1007/s44353-025-00025-3 |
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| author | Win Sithu Maung Satoshi Tsuyuki Takuya Hiroshima San San Htay |
| author_facet | Win Sithu Maung Satoshi Tsuyuki Takuya Hiroshima San San Htay |
| author_sort | Win Sithu Maung |
| collection | DOAJ |
| description | Abstract Estimating Above Ground Biomass (AGB) of mangroves provides crucial information for regional and global blue carbon strategies. Despite growing interest in mangrove AGB estimation using a remote sensing approach, the effectiveness of this advanced method has not been evaluated for mangrove forests in Myanmar. This study estimated the AGB of Wunbaik Mangrove Forest (WMF) in Myanmar using machine learning models with data from Sentinel-2, Sentinel-1, and Advanced Land Observing Satellite Phased Arrayed L-band Synthetic Aperture Radar (ALOS-PALSAR) HH and HV polarizations and Canopy Height Model. Reference AGB information was derived from field inventory plots via an allometric equation. Initially, we tested machine learning models such as Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boost (XGBoost). The results revealed that the RF model outperformed the other models, yielding a higher coefficient of determination (R2) of 0.33 and a Root Mean Square Error (RMSE) of 32.05 Mg ha−1. We then set up different scenarios to improve the performance of the RF model for AGB estimation. Through different feature selection approaches, we identified features highly correlated with the field AGB, enhancing the RF model’s performance and resulting in the highest improvement in R2: 0.48 and RMSE: 28.12 Mg ha−1. In this study, an AGB map of the WMF was generated using the best RF model. The predicted AGB distribution ranged from 22.266 Mg ha−1 to 181.948 Mg ha−1 in 2019. Compared with global datasets such as GEDI L4B and ESA CCI predictions, the proposed method provides a more accurate prediction of AGB for Wunbaik Mangrove, enhancing blue carbon information in Myanmar. |
| format | Article |
| id | doaj-art-0dd798fca18045bf9b93abe4f63720ac |
| institution | OA Journals |
| issn | 3004-9784 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Conservation |
| spelling | doaj-art-0dd798fca18045bf9b93abe4f63720ac2025-08-20T01:57:47ZengSpringerDiscover Conservation3004-97842025-03-012112010.1007/s44353-025-00025-3Assessing above ground biomass of Wunbaik Mangrove Forest in Myanmar using machine learning and remote sensing dataWin Sithu Maung0Satoshi Tsuyuki1Takuya Hiroshima2San San Htay3Social Sciences Division, Japan International Research Center for Agricultural Sciences (JIRCAS)Department of Global Agricultural Sciences, Graduate School of Agriculture and Life Sciences, The University of TokyoDepartment of Global Agricultural Sciences, Graduate School of Agriculture and Life Sciences, The University of TokyoFreelance ResearcherAbstract Estimating Above Ground Biomass (AGB) of mangroves provides crucial information for regional and global blue carbon strategies. Despite growing interest in mangrove AGB estimation using a remote sensing approach, the effectiveness of this advanced method has not been evaluated for mangrove forests in Myanmar. This study estimated the AGB of Wunbaik Mangrove Forest (WMF) in Myanmar using machine learning models with data from Sentinel-2, Sentinel-1, and Advanced Land Observing Satellite Phased Arrayed L-band Synthetic Aperture Radar (ALOS-PALSAR) HH and HV polarizations and Canopy Height Model. Reference AGB information was derived from field inventory plots via an allometric equation. Initially, we tested machine learning models such as Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boost (XGBoost). The results revealed that the RF model outperformed the other models, yielding a higher coefficient of determination (R2) of 0.33 and a Root Mean Square Error (RMSE) of 32.05 Mg ha−1. We then set up different scenarios to improve the performance of the RF model for AGB estimation. Through different feature selection approaches, we identified features highly correlated with the field AGB, enhancing the RF model’s performance and resulting in the highest improvement in R2: 0.48 and RMSE: 28.12 Mg ha−1. In this study, an AGB map of the WMF was generated using the best RF model. The predicted AGB distribution ranged from 22.266 Mg ha−1 to 181.948 Mg ha−1 in 2019. Compared with global datasets such as GEDI L4B and ESA CCI predictions, the proposed method provides a more accurate prediction of AGB for Wunbaik Mangrove, enhancing blue carbon information in Myanmar.https://doi.org/10.1007/s44353-025-00025-3Mangrove forestAbove ground biomassAbove ground carbonRemote sensing approachMachine learning modelsFeature selection |
| spellingShingle | Win Sithu Maung Satoshi Tsuyuki Takuya Hiroshima San San Htay Assessing above ground biomass of Wunbaik Mangrove Forest in Myanmar using machine learning and remote sensing data Discover Conservation Mangrove forest Above ground biomass Above ground carbon Remote sensing approach Machine learning models Feature selection |
| title | Assessing above ground biomass of Wunbaik Mangrove Forest in Myanmar using machine learning and remote sensing data |
| title_full | Assessing above ground biomass of Wunbaik Mangrove Forest in Myanmar using machine learning and remote sensing data |
| title_fullStr | Assessing above ground biomass of Wunbaik Mangrove Forest in Myanmar using machine learning and remote sensing data |
| title_full_unstemmed | Assessing above ground biomass of Wunbaik Mangrove Forest in Myanmar using machine learning and remote sensing data |
| title_short | Assessing above ground biomass of Wunbaik Mangrove Forest in Myanmar using machine learning and remote sensing data |
| title_sort | assessing above ground biomass of wunbaik mangrove forest in myanmar using machine learning and remote sensing data |
| topic | Mangrove forest Above ground biomass Above ground carbon Remote sensing approach Machine learning models Feature selection |
| url | https://doi.org/10.1007/s44353-025-00025-3 |
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