Coastal Benthic Optical Properties (CoBOP) of Coral Reef Environments: Small Scale Fluorescent Optical Signatures and Hyperspectral Remote Sensing of Coral Reef Habitats

2002 ◽  
Author(s):  
Michael P. Lesser
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Coral Reef ◽  
Hyperspectral Remote Sensing ◽  
Small Scale ◽  
Reef Environments

The contribution of acoustic seafloor mapping techniques to outlining coral reef geomorphology: A case study in the Republic of Maldives (Magoodhoo Reef – Maldivian Archipelago)

2021 ◽  
Author(s):  
Alessandra Savini ◽  
Fabio Marchese ◽  
Luca Fallati ◽  
Sebastian Krastel ◽  
Aaron Micallef ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Coral Reef ◽  
Morphological Changes ◽  
Remote Sensing Data ◽  
Circulation Pattern ◽  
Small Scale ◽  
Optical Remote Sensing ◽  
Patch Reefs ◽  
Reef Environments ◽  
Geomorphological Map

<p>Optical remote sensing data coupled with a dense network of field surveys have historically played a crucial role in geomorphological mapping of coral reef environments. Recently this field has undergone a major upgrade thanks to the integration of new advanced methods such as LiDAR, AUV-based and close-range digital photogrammetry and acoustic remote sensing techniques, which are able to investigate the deeper components of this complex geomorphic system. The new detailed maps can produce seamless digital elevation model (DEM) of coral reef environments, by integrating the elevation datasets acquired by the combination of the mentioned survey techniques.</p><p>In our work, a harmonised geomorphological map is generated for the Magoodhoo reef, which borders the southwestern discontinuous marginal rim of a subcircular atoll (i.e. Faafu Atoll) of the Maldivian archipelago. In its north-eastern sector the reef consists of a cuspate reef joined to an almost closed ring reef to the south-west, where Magoodhoo Island is located. The map was generated from the analysis of Sentinel data, orthomosaics and 3D optical models generated by the application of SfM techniques to UAV images, as well as bathymetry and backscatter intensity measurements. The latter were collected down to a depth of up to 120 m along the oceanward margin of the atoll's rim, and to a depth of roughly 60 m along the lagoonward margin. Direct observations were also performed using an observational ROV on the forereef and within the lagoon, and video-transects on the reef flat.</p><p>The oceanward margin shows steep terraced slopes that reveal a complex history of late Pleistocene/Holocene sea level oscillations, while the backreef slopes (toward the lagoon) are generally more gentle, although at places can show abrupt escarpments and overhangs. The lagoon submarine landscape is distinctly featured by patch reefs of variable shapes (from circular to sub-elongated) and dimensions (from few meters to 30m high). Their distribution is clearly controlled by the surface circulation pattern, regulated by the pass that borders the reef to the west. Towards the deeper edge of the mapped sector of the lagoon floor, where patch reefs are totally absent, intriguing small-scale depressions have been detected instead. The regular circular and concave shape calls for their interpretation as pockmarks, but their origin is still unknow due to the  lack of core samples and geochemical analysis in the area. New data are actually needed to precisely outline the sedimentary environments that feature Faafu Atoll and its inner lagoon. Nevertheless, the obtained geomorphological map and the mapped landforms shed new light and a more complete understanding on the processes that drive morphological changes of the entire Magoodhoo reef.</p>

Hyperspectral Remote Sensing and Radiative Transfer Simulation as a Tool for Monitoring Coral Reef Health

2002 ◽  
Vol 36 (1) ◽  
pp. 4-13 ◽  
Author(s):  
Hiroya Yamano ◽  
Masayuki Tamura ◽  
Yoshimitsu Kunii ◽  
Michio Hidaka
Keyword(s):  
Remote Sensing ◽  
Coral Reef ◽  
Radiative Transfer ◽  
Feasibility Studies ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Dead Coral ◽  
Hyperspectral Sensors ◽  
Reef Health ◽  
Radiative Transfer Modeling

Recent advances in the remote sensing of coral reefs include hyperspectral remote sensing and radiative transfer modeling. Hyperspectral data can be regarded as continuous and the derivative spectroscopy is effective for extracting coral reef components, including sand, macroalgae, and healthy, bleached, recently dead, and old dead coral. Radiative transfer models are effective for feasibility studies of satellite or airborne remote sensing. Using these techniques, we simulate and analyze the apparent reflectance of coral reef benthic features associated with bleaching events, obtained by hyperspectral sensors on various platforms (ROV, boat, airplane, and satellite), and suggest that the coral reef health on reef flats can be discriminated precisely. Remote sensing using hyperspectral sensors should significantly contribute to mapping and monitoring coral reef health.

Predicting the Optical Properties of Arbitrarily Shaped Black Carbon Aerosols with Graph Neural Networks

2021 ◽  
Author(s):  
Kara D. Lamb ◽  
Pierre Gentine
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Black Carbon ◽  
Radiative Forcing ◽  
Training Data ◽  
Radiative Properties ◽  
Training Dataset ◽  
Small Scale ◽  
Atmospheric Models ◽  
Fractal Aggregates

<p>Aerosols sourced from combustion such as black carbon (BC) are important short-lived climate forcers whose direct radiative forcing and atmospheric lifetime depend on their morphology. These aerosols are typically fractal aggregates consisting of ~20-80 nm spheres. This complex morphology makes modeling their optical properties difficult, contributing to uncertainty in both their direct and indirect climate effects. Accurate and fast calculations of BC optical properties are needed for remote sensing inversions and for radiative forcing calculations in atmospheric models, but current methods to accurately calculate the optical properties of these aerosols such as the multi-sphere T-matrix method or generalized multiple-particle Mie Theory are computationally expensive and must be compiled in extensive data-bases off-line and then used as a look-up table. Recent advances in machine learning approaches have applied the graph convolutional neural network (GCN) to various physical science applications, demonstrating skill in generalizing beyond initial training data by exploiting and learning internal properties and interactions inherent to the larger system. Here we demonstrate for the first time that a GCN trained to predict the optical properties of numerically-generated BC fractal aggregates can accurately generalize to arbitrarily shaped aerosol particles, even over much larger aggregates than in the training dataset, providing a fast and accurate method to calculate aerosol optical properties in atmospheric models and for observational retrievals. This approach could be integrated into atmospheric models or remote sensing inversions to more realistically predict the physical properties of arbitrarily-shaped aerosol and cloud particles. In addition, GCN’s can be used to gain physical intuition on the relationship between large-scale properties (here of the radiative properties of aerosols) and small-scale interactions (here of the spheres’ positions and their interactions).</p>

Progress in the use of remote sensing for coral reef biodiversity studies

2007 ◽  
Vol 31 (4) ◽  
pp. 421-434 ◽  
Author(s):  
Anders Knudby ◽  
Ellsworth LeDrew ◽  
Candace Newman
Keyword(s):  
Remote Sensing ◽  
Coral Reef ◽  
Information Needs ◽  
Ecosystem Health ◽  
Conservation Management ◽  
Cost Effective ◽  
Marine Biodiversity ◽  
Reef Environments ◽  
New Sensors ◽  
Habitat Variables

Coral reefs are hotspots of marine biodiversity, and their global decline is a threat to our natural heritage. Conservation management of these precious ecosystems relies on accurate and up-to-date information about ecosystem health and the distribution of species and habitats, but such information can be costly to gather and interpret in the field. Remote sensing has proven capable of collecting information on geomorphologic zones and substrate types for coral reef environments, and is cost-effective when information is needed for large areas. Remote sensing-based mapping of coral habitat variables known to influence biodiversity has only recently been undertaken and new sensors and improved data processing show great potential in this area. This paper reviews coral reef biodiversity, the influence of habitat variables on its local spatial distribution, and the potential for remote sensing to produce maps of these habitat variables, thus indirectly mapping coral reef biodiversity and fulfilling information needs of coral reef managers.

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