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24 October 2018 Remote sensing-based estimation of seagrass percent cover and LAI for above ground carbon sequestration mapping
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Abstract
Seagrasses are distinct flowering plants which thrive underwater. They are part of a complex ecosystem that supports different forms of life. Recent studies found out that coastal wetlands – mangroves, saltmarshes, and seagrass, are far more proficient in sequestering and storing carbon than terrestrial ecosystems. Although seagrasses occupy only 0.2% of the area of the oceans, they sequester approximately 15% of total carbon storage in the ocean. Several remote sensing techniques are available to map and monitor seagrasses but most of them focus only on extent and area coverage. To estimate the carbon sequestration of seagrass beds, aside from extent, other parameters are needed such as leaf area index, percent cover, density, biomass etc., However, there are limits in mapping seagrass parameters using remote sensing. The reflectance measured by sensors is affected by other factors such as water absorption, turbidity, dissolved organic matter, depth and phytoplankton which affects the backscattering of energy. In this study, different remotely sensed datasets and field data were used to measure the parameters needed to estimate the carbon sequestration. Multispectral satellite images such as Sentinel-2 and PlanetScope were utilized to map the distribution and percent cover. High-resolution RGB images obtained by unmanned aerial vehicle (UAV) were also utilized to correlate field data gathered parameters. Field data such as species, percent cover, leaf area index, canopy height and above ground biomass were gathered in situ. Data extracted from different remote sensing technologies were put together to support the estimation of carbon sequestration of seagrass beds.
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Ayin Tamondong, Charmaine Cruz, Rey Rusty Quides, Mikko Garcia, John Andrew Cruz, Jaime Guihawan, and Ariel Blanco "Remote sensing-based estimation of seagrass percent cover and LAI for above ground carbon sequestration mapping", Proc. SPIE 10778, Remote Sensing of the Open and Coastal Ocean and Inland Waters, 1077803 (24 October 2018); https://doi.org/10.1117/12.2324695
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