Predicting <i>Sargassum</i> blooms in the Caribbean Sea from MODIS observations

Wang, Mengqiu; Hu, Chuanmin

doi:10.1002/2017gl072932

Cited by 88 publications

(50 citation statements)

References 21 publications

(31 reference statements)

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“…As has been established since the beginning of this article, the Sargasso has been increasing since 2011 and 2012, with a significant decrease in 2013. From 2014 to 2019, the increase has continued with notable peaks in 2018 and 2019, which can be classified as historical (Wang et al, 2017(Wang et al, , 2019.…”

Section: Discussionmentioning

confidence: 99%

Influence of climatic factors on Sargassum arrivals to the coasts of the Dominican Republic

Méndez‐Tejeda¹,

JimÃ©nez²

2019

J. Oceanogr. Mar. Sci.

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Since 2011, the coasts of North America, Mexico, and the Caribbean have experienced an unusual increase in the arrival of Sargassum. A large amount of Sargassum has caused significant economic losses in the tourism sector of the Dominican Republic as well as the entire Caribbean region. The present article discusses the possible factors contributing toward this unusual increase. Large Sargassum masses are generated in the Sargasso Sea. In this region, several current systems converge with the North Atlantic Subtropical Anticyclone (NASH), which has an area of displacement extending from Brazil to Africa. Sargassum is transported toward Africa, where it meets the Northern Equatorial Recirculation Region (NERR) before recirculating back toward the Caribbean in a clockwise pattern (Putman et al., 2018; Wang, 2007). Upon returning to the Caribbean and Gulf of Mexico, Sargasso grows flowers and distributes across the region. The effect of the North Atlantic Oscillation (NAO) is also analyzed as well as the possible influence of Sahara dust, which acts as a source of nutrients for the growth and development of Sargassum. In the Dominican Republic, the species found in the current study included Sargassum fluitans and Sargassum natans. As for the species Sargassum polyceratium var. ovatum, the variety ovatum has not been reported before on the coasts of the Dominican Republic.

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Section: Discussionmentioning

confidence: 99%

Influence of climatic factors on Sargassum arrivals to the coasts of the Dominican Republic

Méndez‐Tejeda¹,

JimÃ©nez²

2019

J. Oceanogr. Mar. Sci.

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“…Specific models developed to analyze satellite imagery and detect floating algae-the Floating Algae Index (FAI) [34] or the Alternate Floating Algae Index (AFAI) [35]-reveal that only in recent years was the area subject to mass proliferation of Sargassum: satellite imagery prior 2011 shows the area to be "largely free from seaweed". Unfortunately, operational warning devices able to anticipate algae washing ashore still have disadvantages related to the inadequate sampling and temporal frequency (MODIS observations, e.g., [36]) and the interposing obstacles such as cloud shadows and sun glint constitute very important issues. Actually, there is little space for improvement, as these are natural phenomena.…”

Section: Sargassum Monitoring By Imagerymentioning

confidence: 99%

Assessment of a Smartphone-Based Camera System for Coastal Image Segmentation and Sargassum monitoring

Valentini

Balouin

2020

JMSE

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Coastal video monitoring has proven to be a valuable ground-based technique to investigate ocean processes. Presently, there is a growing need for automatic, technically efficient, and inexpensive solutions for image processing. Moreover, beach and coastal water quality problems are becoming significant and need attention. This study employs a methodological approach to exploit low-cost smartphone-based images for coastal image classification. The objective of this paper is to present a methodology useful for supervised classification for image semantic segmentation and its application for the development of an automatic warning system for Sargassum algae detection and monitoring.A pixel-wise convolutional neural network (CNN) has demonstrated optimal performance in the classification of natural images by using abstracted deep features. Conventional CNNs demand a great deal of resources in terms of processing time and disk space. Therefore, CNN classification with superpixels has recently become a field of interest. In this work, a CNN-based deep learning framework is proposed that combines sticky-edge adhesive superpixels. The results indicate that a cheap camera-based video monitoring system is a suitable data source for coastal image classification, with optimal accuracy in the range between 75% and 96%. Furthermore, an application of the method for an ongoing case study related to Sargassum monitoring in the French Antilles proved to be very effective for developing a warning system, aiming at evaluating floating algae and algae that had washed ashore, supporting municipalities in beach management.

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“…Early advisory systems have been developed since 2013 (Webster and Linton, 2013), and advanced prediction through analysis of the Sargassum loop system using remote sensing technology has been developed (Dierssen et al, 2015). Additionally, a monthly Sargassum prediction outlook bulletin is already available for the WCR from MODIS observations (Hu et al, 2016;Wang and Hu, 2017). Consultancy agencies like Fulcrum and CLS have also developed early warning systems for some of the Caribbean islands.…”

Section: Sargassum Influx: Impacts Monitoring and Trendsmentioning

confidence: 99%

Future Ocean Observations to Connect Climate, Fisheries and Marine Ecosystems

et al. 2019

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Advances in ocean observing technologies and modeling provide the capacity to revolutionize the management of living marine resources. While traditional fisheries management approaches like single-species stock assessments are still common, a global effort is underway to adopt ecosystem-based fisheries management (EBFM) approaches. These approaches consider changes in the physical environment and interactions between ecosystem elements, including human uses, holistically. For example, integrated ecosystem assessments aim to synthesize a suite of observations (physical, biological, socioeconomic) and modeling platforms [ocean circulation models, ecological models, short-term forecasts, management strategy evaluations (MSEs)] to assess the current status and recent and future trends of ecosystem components. This information provides guidance for better management strategies. A common thread in EBFM approaches is the need for high-quality observations of ocean conditions, at Frontiers in Marine Science | www.frontiersin.org 1 September 2019 | Volume 6 | Article 550 Schmidt et al. Ocean Observation Futures for Fisheriesscales that resolve critical physical-biological processes and are timely for management needs. Here we explore options for a future observing system that meets the needs of EBFM by (i) identifying observing needs for different user groups, (ii) reviewing relevant datasets and existing technologies, (iii) showcasing regional case studies, and (iv) recommending observational approaches required to implement EBFM. We recommend linking ocean observing within the context of Global Ocean Observing System (GOOS) and other regional ocean observing efforts with fisheries observations, new forecasting methods, and capacity development, in a comprehensive ocean observing framework.

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Predicting Sargassum blooms in the Caribbean Sea from MODIS observations

Cited by 88 publications

References 21 publications

Influence of climatic factors on Sargassum arrivals to the coasts of the Dominican Republic

Influence of climatic factors on Sargassum arrivals to the coasts of the Dominican Republic

Assessment of a Smartphone-Based Camera System for Coastal Image Segmentation and Sargassum monitoring

Future Ocean Observations to Connect Climate, Fisheries and Marine Ecosystems

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