Mangrove biomass estimation through remote sensing and machine learning based approaches

Zhang, Kangyong (2025) Mangrove biomass estimation through remote sensing and machine learning based approaches. PhD thesis, University of Glasgow.

Abstract

Mangroves play a crucial role in providing valuable ecosystem services, particularly as highly efficient carbon sinks that mitigate climate change impacts Understanding their contribution to the global carbon cycle requires accurate assessment of carbon stocks, which typically depends on the estimation of biomass, especially aboveground biomass (AGB). Existing studies on accurate estimation of mangrove AGB have been constrained by uncertainties in modelling efforts, limited field data and methodological challenges in integrating multisource remote sensing datasets. This research develops improved methodologies of mangrove AGB estimation by addressing these challenges. First, two local mangrove forests in Mexico were used to evaluate the feasibility and performance of open access global digital elevation models (NASADEM, ALOS DSM and Copernicus GLO-30 DEM) for AGB estimation. After calibration with spaceborne LiDAR (Light Detection and Ranging) datasets, the DEMs produced comparable and spatially consistent AGB estimates. For stands with a mean canopy height of 15 m, the standard error was ~30% of the estimated AGB. Second, an approach was developed to upscale localised field inventory to a continental level (the Americas), by incorporating spaceborne LiDAR data. Third, a novel data fusion framework was introduced using extensive spaceborne LiDAR derived AGB estimates to train high-resolution optical mosaics and rasterised environmental variables through a machine learning algorithm. This integration produced wall-to-wall mangrove AGB estimates across the Americas, achieving a validation accuracy of R 2 = 0.72 and root mean square error (RMSE) = 37.24 Mg/ha. Ultimately, applying the improved methodologies of mangrove AGB estimation to the Americas revealed not only high agreements in AGB estimates across country-level undisturbed mangrove forests but also 5.10 million Mg AGB gains in regrowing mangroves between 2000 and 2020. The findings underscore the resilience of mangroves and their capacity to recover as significant carbon sinks, which is particularly relevant to climate change adaptation and conservation efforts. Overall, this research provides improved methodologies in mangrove AGB estimation by integrating multisource datasets at a local and a continental scale, which is transferable and valuable to other tropical coastal ecosystems, offering researchers and practitioners an effective means to better integrate mangrove carbon dynamics into global climate mitigation frameworks. Additionally, spatially explicit mangrove AGB estimates derived from the improved methodologies can inform conservation priorities, restoration strategies and national carbon accounting efforts.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Additional Information: Supported by funding from the China Scholarship Council (CSC), Mobility Funding from the School of Geographical and Earth Sciences, University of Glasgow, and the Volkswagen foundation.
• Colleges/Schools: College of Science and Engineering > School of Geographical and Earth Sciences
• Supervisor's Name: Barrett, Professor Brian, Balke, Dr. Thorsten and Vovides, Dr. Alejandra
• Date of Award: 2025
• Depositing User: Theses Team
• Unique ID: glathesis:2025-85530
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 22 Oct 2025 15:06
• Last Modified: 24 Oct 2025 09:32
• Thesis DOI: 10.5525/gla.thesis.85530
• URI: https://theses.gla.ac.uk/id/eprint/85530