September 5, 2025Open Access

Integrating Remote Sensing, Field-Measured Tree Heights, and Machine Learning to Enhance Mangrove Above-Ground Carbon Estimation in Baluran National Park, Indonesia

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Authors

SRSeftiawan Samsu RijalUniversity of Brawijaya NSNeil SaintilanMacquarie University

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Implication

Observational analysis improves mangrove above-ground carbon estimation in Baluran National Park, suggesting effective integration of remote sensing and field data.

Key Points

Combining field measurements and remote sensing significantly enhances mangrove above-ground carbon estimation.
The Random Forest model achieved the best accuracy in estimating above-ground carbon, with R² testing at 0.86.
Field data included 60 plots measuring tree height, canopy cover, and tree density, ensuring comprehensive data.
Results indicate mangrove AGC values offer more accuracy than global datasets, emphasizing the need for local assessments.

Cite This Study

Rijal et al. (2025) studied this question.

synapsesocial.com/papers/68c23922b210217d6477b126 https://doi.org/10.21203/rs.3.rs-7359399/v1

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Implication

Observational analysis improves mangrove above-ground carbon estimation in Baluran National Park, suggesting effective integration of remote sensing and field data.

Integrating Remote Sensing, Field-Measured Tree Heights, and Machine Learning to Enhance Mangrove Above-Ground Carbon Estimation in Baluran National Park, Indonesia

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Implication

Key Points

Cite This Study

Integrating Remote Sensing, Field-Measured Tree Heights, and Machine Learning to Enhance Mangrove Above-Ground Carbon Estimation in Baluran National Park, Indonesia

Discussion

Member takes

Implication

Key Points

Cite This Study

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