Date of Defense
12-6-2025 10:00 AM
Location
F1, 2007
Document Type
Dissertation Defense
Degree Name
Doctor of Philosophy in Ecology and Environmental Sciences
College
College of Science
Department
Biology
First Advisor
Prof. Taoufik Ksiksi
Keywords
Mangrove, Biomass, Carbon Stock, Litterfall, ML modeling, Remote Sensing
Abstract
Mangrove ecosystems play a vital role in carbon sequestration by capturing atmospheric CO₂, storing it in aboveground and belowground biomass, and enhancing soil organic carbon (SOC) through litterfall. Arid mangrove ecosystems, such as those in the UAE, experience extreme environmental stressors, leading to distinct variations in biomass allocation and carbon stock estimates compared to tropical regions. This study aims to evaluate biomass accumulation, litterfall production, and SOC storage in selected UAE mangroves. It analyses seasonal litterfall dynamics and assesses the influence of bulk density, soil moisture, and tidal regimes on SOC and aboveground biomass (AGB) across distinct mangrove zones. Field-based measurements, remote sensing techniques, and machine learning models were integrated to estimate mangrove tree biomass, litterfall production, and SOC across selected UAE mangrove systems. Litterfall was monitored monthly and analyzed in relation to vegetative indices, SOC was estimated across mangrove zones using ML models, and AGB was estimated through allometric equations with field data and used to find a relationship with remote sensing variables using ML models. Results revealed that remote sensed vegetation indices emerged as strong predictors of AGB. Litterfall production peaked during the summer, with predictive estimation achieved using remotely sensed vegetation indices and random forest regression models. SOC storage showed spatial variability, with inner mangrove zones exhibiting higher SOC content than water-edge mangrove zones and landward edge mangrove zones, highlighting the influence of hydrological and sedimentary processes on carbon sequestration. This study provides a region-specific approach for monitoring carbon stock dynamics in arid ecosystems, improving mangrove biomass and SOC estimations. The study enhances predictive capabilities for carbon sequestration in extreme environments by leveraging remote sensing and machine learning techniques. The findings directly impact mangrove conservation, afforestation programs, and climate mitigation strategies in arid coastal regions. The study addresses a critical gap in understanding the carbon sequestration potential of arid mangrove ecosystems, offering a replicable approach for estimating biomass and SOC under extreme climatic conditions. It highlights the necessity of tailored models for assessing carbon storage and informs sustainable management practices essential for preserving mangrove-based carbon sinks.
Included in
DYNAMICS OF MANGROVE CARBON STOCK IN SELECTED UAE COASTLINE: A REMOTE SENSING APPROACH
F1, 2007
Mangrove ecosystems play a vital role in carbon sequestration by capturing atmospheric CO₂, storing it in aboveground and belowground biomass, and enhancing soil organic carbon (SOC) through litterfall. Arid mangrove ecosystems, such as those in the UAE, experience extreme environmental stressors, leading to distinct variations in biomass allocation and carbon stock estimates compared to tropical regions. This study aims to evaluate biomass accumulation, litterfall production, and SOC storage in selected UAE mangroves. It analyses seasonal litterfall dynamics and assesses the influence of bulk density, soil moisture, and tidal regimes on SOC and aboveground biomass (AGB) across distinct mangrove zones. Field-based measurements, remote sensing techniques, and machine learning models were integrated to estimate mangrove tree biomass, litterfall production, and SOC across selected UAE mangrove systems. Litterfall was monitored monthly and analyzed in relation to vegetative indices, SOC was estimated across mangrove zones using ML models, and AGB was estimated through allometric equations with field data and used to find a relationship with remote sensing variables using ML models. Results revealed that remote sensed vegetation indices emerged as strong predictors of AGB. Litterfall production peaked during the summer, with predictive estimation achieved using remotely sensed vegetation indices and random forest regression models. SOC storage showed spatial variability, with inner mangrove zones exhibiting higher SOC content than water-edge mangrove zones and landward edge mangrove zones, highlighting the influence of hydrological and sedimentary processes on carbon sequestration. This study provides a region-specific approach for monitoring carbon stock dynamics in arid ecosystems, improving mangrove biomass and SOC estimations. The study enhances predictive capabilities for carbon sequestration in extreme environments by leveraging remote sensing and machine learning techniques. The findings directly impact mangrove conservation, afforestation programs, and climate mitigation strategies in arid coastal regions. The study addresses a critical gap in understanding the carbon sequestration potential of arid mangrove ecosystems, offering a replicable approach for estimating biomass and SOC under extreme climatic conditions. It highlights the necessity of tailored models for assessing carbon storage and informs sustainable management practices essential for preserving mangrove-based carbon sinks.