Challenges to identify phytoplankton species in coastal waters by remote sensing

2005 ◽  
Author(s):  
B. Lubac ◽  
H. Loisel ◽  
A. Poteau ◽  
X. Meriaux
Keyword(s):  
Remote Sensing ◽  
Coastal Waters ◽  
Phytoplankton Species

Inverse Problems in Optical Remote Sensing of Coastal Waters

2006 ◽  
Author(s):  
Irina Dolina ◽  
Lev Dolin ◽  
Alexander Luchinin ◽  
Iosif Levin ◽  
Liza Levina
Keyword(s):  
Remote Sensing ◽  
Inverse Problems ◽  
Coastal Waters ◽  
Optical Remote Sensing

scholarly journals Remote sensing of sun-induced chlorophyll-a fluorescence in inland and coastal waters: Current state and future prospects

2021 ◽  
Vol 262 ◽  
pp. 112482
Author(s):  
Remika S. Gupana ◽  
Daniel Odermatt ◽  
Ilaria Cesana ◽  
Claudia Giardino ◽  
Ladislav Nedbal ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Coastal Waters ◽  
Chlorophyll A Fluorescence ◽  
Future Prospects ◽  
Current State

scholarly journals Monitoring oyster culture rafts and seagrass meadows in Nagatsura-ura Lagoon, Sanriku Coast, Japan before and after the 2011 tsunami by remote sensing: their recoveries implying the sustainable development of coastal waters

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10727
Author(s):  
Hiroki Murata ◽  
Motoyuki Hara ◽  
Chinatsu Yonezawa ◽  
Teruhisa Komatsu
Keyword(s):  
Remote Sensing ◽  
Sustainable Development ◽  
Coastal Waters ◽  
Coastal Ecosystems ◽  
Oyster Culture ◽  
Sanriku Coast ◽  
The Sustainable Development ◽  
Seagrass Meadows ◽  
Before And After ◽  
Aquaculture Management

Background Coastal ecosystems are blue infrastructures that support coastal resources and also aquaculture. Seagrass meadows, one of coastal ecosystems, provide substrates for epiphytic diatoms, which are food resources for cultured filter feeder organisms. Highly intensive coastal aquaculture degrades coastal environments to decrease seagrass meadows. Therefore, efficient aquaculture management and conservation of seagrass meadows are necessary for the sustainable development of coastal waters. In ria-type bays, non-feeding aquaculture of filter feeders such as oysters, scallops, and ascidians are actively practiced along the Sanriku Coast, Japan. Before the 2011 Great East Japan Earthquake, the over-deployment of oyster culture facilities polluted the bottom environment and formed an hypoxic bottom water layer due to the organic excrements from cultured oysters. The tsunami in 2011 devastated the aquaculture facilities and seagrass meadows along the Sanriku Coast. We mapped the oyster culture rafts and seagrass meadows in Nagatsura-ura Lagoon, Sanriku Coast before and after the tsunami and monitored those and environments after the tsunami by field surveys. Methods We conducted field surveys and monitored the environmental parameters in Nagatsura-ura Lagoon every month since 2014. We used high-resolution satellite remote sensing images to map oyster culture rafts and seagrass meadows at irregular time intervals from 2006 to 2019 in order to assess their distribution. In 2019, we also used an unmanned aerial vehicle to analyze the spatial variability of the position and the number of ropes suspending oyster clumps beneath the rafts. Results In 2013, the number and distribution of the oyster culture rafts had been completely restored to the pre-tsunami conditions. The mean area of culture raft increased after the tsunami, and ropes suspending oyster clumps attached to a raft in wider space. Experienced local fishermen also developed a method to attach less ropes to a raft, which was applied to half of the oyster culture rafts to improve oyster growth. The area of seagrass meadows has been expanding since 2013. Although the lagoon had experienced frequent oyster mass mortality events in summer before the tsunami, these events have not occurred since 2011. The 2011 earthquake and tsunami deepened the sill depth and widened the entrance to enhance water exchange and improve water quality in the lagoon. These changes brought the expansion of seagrass meadows and reduction of mass mortality events to allow sustainable oyster culture in the lagoon. Mapping and monitoring of seagrass meadows and aquaculture facilities via satellite remote sensing can provide clear visualization of their temporal changes. This can in turn facilitate effective aquaculture management and conservation of coastal ecosystems, which are crucial for the sustainable development of coastal waters.

scholarly journals Hyperspectral Remote Sensing of Phytoplankton Species Composition Based on Transfer Learning

2019 ◽  
Vol 11 (17) ◽  
pp. 2001 ◽  
Author(s):  
Qing Zhu ◽  
Fang Shen ◽  
Pei Shang ◽  
Yanqun Pan ◽  
Mengyu Li
Keyword(s):  
Remote Sensing ◽  
Species Composition ◽  
Transfer Learning ◽  
Natural Waters ◽  
Hyperspectral Remote Sensing ◽  
Sensor Technology ◽  
Percentage Error ◽  
Phytoplankton Species ◽  
Phytoplankton Species Composition

Phytoplankton species composition research is key to understanding phytoplankton ecological and biogeochemical functions. Hyperspectral optical sensor technology allows us to obtain detailed information about phytoplankton species composition. In the present study, a transfer learning method to inverse phytoplankton species composition using in situ hyperspectral remote sensing reflectance and hyperspectral satellite imagery was presented. By transferring the general knowledge learned from the first few layers of a deep neural network (DNN) trained by a general simulation dataset, and updating the last few layers with an in situ dataset, the requirement for large numbers of in situ samples for training the DNN to predict phytoplankton species composition in natural waters was lowered. This method was established from in situ datasets and validated with datasets collected in different ocean regions in China with considerable accuracy (R2 = 0.88, mean absolute percentage error (MAPE) = 26.08%). Application of the method to Hyperspectral Imager for the Coastal Ocean (HICO) imagery showed that spatial distributions of dominant phytoplankton species and associated compositions could be derived. These results indicated the feasibility of species composition inversion from hyperspectral remote sensing, highlighting the advantages of transfer learning algorithms, which can bring broader application prospects for phytoplankton species composition and phytoplankton functional type research.

scholarly journals Using Optical Water Types to Monitor Changes in Optically Complex Inland and Coastal Waters

2019 ◽  
Vol 11 (19) ◽  
pp. 2297 ◽  
Author(s):  
Kristi Uudeberg ◽  
Ilmar Ansko ◽  
Getter Põru ◽  
Ave Ansper ◽  
Anu Reinart
Keyword(s):  
Remote Sensing ◽  
Coastal Waters ◽  
Atmospheric Correction ◽  
Suspended Sediments ◽  
Reflectance Spectra ◽  
Water Type ◽  
Colored Dissolved Organic Matter ◽  
Water Color ◽  
Sentinel 2

The European Space Agency’s Copernicus satellites Sentinel-2 and Sentinel-3 provide observations with high spectral, spatial, and temporal resolution which can be used to monitor inland and coastal waters. Such waters are optically complex, and the water color may vary from completely clear to dark brown. The main factors influencing water color are colored dissolved organic matter, phytoplankton, and suspended sediments. Recently, there has been a growing interest in the use of the optical water type (OWT) classification in the remote sensing of ocean color. Such classification helps to clarify relationships between different properties inside a certain class and quantify variation between classes. In this study, we present a new OWT classification based on the in situ measurements of reflectance spectra for boreal region lakes and coastal areas without extreme optical conditions. This classification divides waters into five OWT (Clear, Moderate, Turbid, Very Turbid, and Brown) and shows that different OWTs have different remote sensing reflectance spectra and that each OWT is associated with a specific bio-optical condition. Developed OWTs are distinguishable by both the MultiSpectral Instrument (MSI) and the Ocean and Land Color Instrument (OLCI) sensors, and the accuracy of the OWT assignment was 95% for both the MSI and OLCI bands. To determine OWT from MSI images, we tested different atmospheric correction (AC) processors, namely ACOLITE, C2RCC, POLYMER, and Sen2Cor and for OLCI images, we tested AC processors ALTNNA, C2RCC, and L2. The C2RCC AC processor was the most accurate and reliable for use with MSI and OLCI images to estimate OWTs.

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