Date of Defense
29-10-2025 11:00 AM
Location
F3-037
Document Type
Thesis Defense
Degree Name
Master of Science in Geosciences
College
COS
Department
Biology
First Advisor
Dr. Sunil Mundra
Keywords
Mangroves, Avicennia marina, Bacterial community, Blue carbon, Remote sensing, Soil carbon
Abstract
Mangrove ecosystems are vital blue carbon sinks increasingly threatened by human activities and climate change, especially in arid, hypersaline regions. This study examines Avicennia marina, the dominant UAE mangrove species, recognized for its significant carbon retention capability0. Its biomass and stability are influenced by sediment chemistry, and bacterial diversity and community structure which drive nutrient cycling and organic matter decomposition—key processes sustaining soil fertility and carbon sequestration. Remote sensing enables large-scale assessment of mangrove distribution and density, but single spectral indices often miss ecological variability in the fragmented UAE mangrove habitats. Combining multiple spectral indices with field data improves detection, monitoring, and understanding of mangrove resilience in these arid habitats. Nevertheless, few studies have integrated remote sensing with field and microbial data, a connection essential for comprehending the ecological and carbon dynamics of UAE mangroves. This study aimed to (1) develop high-accuracy digital maps of UAE mangroves using multispectral vegetation, soil, and water indices validated through field observations, and (2) explore links between below-ground bacterial communities, mangrove growth, sediment chemistry, and remote sensing indices. Landsat 8 imagery was processed to derive spectral indices, which were verified with field data from 15 mangrove sites. Tree height and diameter were measured to estimate age and biomass using allometric equations. Sediment properties, including pH, salinity, nutrients, and organic matter, were analyzed using standard methods. DNA extracted from sediments underwent 16S rRNA sequencing on the MGI platform, and microbial data were processed using QIIME2, DADA2, and VSEARCH, with statistical analyses performed in R. Remote sensing maps showed that UAE mangroves were mainly distributed in sheltered intertidal zones with carbonate-rich sediments. Multispectral indices effectively distinguished vegetation density, with moderate vegetation index values overall and the lowest in sparse stands. Water and soil indices exhibited strong inverse relationships with vegetation indices, highlighting their complementary sensitivities. Mangrove growth parameters were strongly correlated with age and biomass, indicating stands dominated by middle-aged trees—ranging from just over 2 years in Ras Al Khaimah to nearly 29 years in Abu Dhabi. Consequently, carbon storage was uneven, highest in Abu Dhabi, intermediate in Umm Al Quwain, and lowest in Ras Al Khaimah. Sediment salinity ranged from 5.96–31.1 dS/m, while both phosphorus and organic matter (0.18–2.37%) were consistently low. Alpha diversity was unrelated to mangrove growth parameters but showed a moderate positive correlation with soil organic matter, while the Scaled Shadow Index exhibited a weak positive association with Shannon diversity. Bacterial community mangrove sediments was primarily affected by soil organic matter, with vegetation structure affecting it through canopy density. Salinity, pH, and tree size showed no relationships with bacterial communities. Actinomycetota and Pseudomonadota dominated across sites, while sulfate-reducing and organic-matter-degrading families were most abundant in carbon-rich sediments. Overall, organic matter was identified as the key determinant of below-ground bacterial diversity and composition in arid mangrove ecosystems. Data also suggested a link between bacterial diversity and vegetation canopy cover derived from remote sensing. The multi-index approach demonstrated in this study provided a scalable framework for satellite-based monitoring and aligned with the objectives of the UAE-led Arab Satellite 813 project, a hyperspectral mission designed to support environmental mapping, land-cover analysis, and natural-resource monitoring across the Arab region.
Included in
INTEGRATING MICROBIAL COMMUNITIES WITH ABOVE- AND BELOW-GROUND CARBON AND MULTISPECTRAL SATELLITE DATA IN MANGROVE FORESTS
F3-037
Mangrove ecosystems are vital blue carbon sinks increasingly threatened by human activities and climate change, especially in arid, hypersaline regions. This study examines Avicennia marina, the dominant UAE mangrove species, recognized for its significant carbon retention capability0. Its biomass and stability are influenced by sediment chemistry, and bacterial diversity and community structure which drive nutrient cycling and organic matter decomposition—key processes sustaining soil fertility and carbon sequestration. Remote sensing enables large-scale assessment of mangrove distribution and density, but single spectral indices often miss ecological variability in the fragmented UAE mangrove habitats. Combining multiple spectral indices with field data improves detection, monitoring, and understanding of mangrove resilience in these arid habitats. Nevertheless, few studies have integrated remote sensing with field and microbial data, a connection essential for comprehending the ecological and carbon dynamics of UAE mangroves. This study aimed to (1) develop high-accuracy digital maps of UAE mangroves using multispectral vegetation, soil, and water indices validated through field observations, and (2) explore links between below-ground bacterial communities, mangrove growth, sediment chemistry, and remote sensing indices. Landsat 8 imagery was processed to derive spectral indices, which were verified with field data from 15 mangrove sites. Tree height and diameter were measured to estimate age and biomass using allometric equations. Sediment properties, including pH, salinity, nutrients, and organic matter, were analyzed using standard methods. DNA extracted from sediments underwent 16S rRNA sequencing on the MGI platform, and microbial data were processed using QIIME2, DADA2, and VSEARCH, with statistical analyses performed in R. Remote sensing maps showed that UAE mangroves were mainly distributed in sheltered intertidal zones with carbonate-rich sediments. Multispectral indices effectively distinguished vegetation density, with moderate vegetation index values overall and the lowest in sparse stands. Water and soil indices exhibited strong inverse relationships with vegetation indices, highlighting their complementary sensitivities. Mangrove growth parameters were strongly correlated with age and biomass, indicating stands dominated by middle-aged trees—ranging from just over 2 years in Ras Al Khaimah to nearly 29 years in Abu Dhabi. Consequently, carbon storage was uneven, highest in Abu Dhabi, intermediate in Umm Al Quwain, and lowest in Ras Al Khaimah. Sediment salinity ranged from 5.96–31.1 dS/m, while both phosphorus and organic matter (0.18–2.37%) were consistently low. Alpha diversity was unrelated to mangrove growth parameters but showed a moderate positive correlation with soil organic matter, while the Scaled Shadow Index exhibited a weak positive association with Shannon diversity. Bacterial community mangrove sediments was primarily affected by soil organic matter, with vegetation structure affecting it through canopy density. Salinity, pH, and tree size showed no relationships with bacterial communities. Actinomycetota and Pseudomonadota dominated across sites, while sulfate-reducing and organic-matter-degrading families were most abundant in carbon-rich sediments. Overall, organic matter was identified as the key determinant of below-ground bacterial diversity and composition in arid mangrove ecosystems. Data also suggested a link between bacterial diversity and vegetation canopy cover derived from remote sensing. The multi-index approach demonstrated in this study provided a scalable framework for satellite-based monitoring and aligned with the objectives of the UAE-led Arab Satellite 813 project, a hyperspectral mission designed to support environmental mapping, land-cover analysis, and natural-resource monitoring across the Arab region.