Search

In this paper, a two-stage method is proposed for predicting the catch of skipjack tuna (Katsuwonus pelamis) from a 2D sea environmental pattern. Following the assumption that sea water temperature and sea surface height (SSH) which fishermen often use for finding fishing spots has a correlation with the skipjack tuna catch, a new approach of using Faster R-CNN in object detection is proposed. The proposed method consists of two part. In the first part, taking a sea temperature map as input, Faster R-CNN extracts the candidates of where skipjack tuna would be on the map in order to imitate the behaviors of fishers. In the second part, Support Vector Regression (SVR) estimates the catch amount in each candidate. Fater R-CNN is applied to several sea environmental patterns with three different loss functions and compares each performance. The proposed model is evaluated by comparing the result with average fishers’ ability on the skipjack tuna catches and several criteria for evaluating the proposed model. The results show that the proposed method is able to outperform the average fishers’ ability by an average of 3%.

This is a set of matlab .mat files along with a readme file that describes the various variables. These data are described in the paper, "Influence of kelp forest biomass on nearshore currents," by Monismith et al. submitted to JGR Oceans in December 2021, and in revised form in April 2022.

IEEE OCEANS 2017 ABERDEEN conference and workshop, Aberdeen, Scotland,19th - 22nd June 2017. -- http://www.nexosproject.eu/node/180, Measurement of the seawater temperature at different depths to get a vertical temperature profile on the water column is presented in this paper: The acquisition system based on optical fiber Bragg gratings (FBGs) and the subsequent data processed is also presented. The temperature measurements are taken in distributed points along the fiber where the Bragg gratings are located. The paper reports on the robustness of the new technique.

Nearshore coastal waters are among the most dynamic regions on the planet and difficult to sample from conventional oceanographic platforms. It has been suggested that environmental sampling of the nearshore could be improved by mobilising vast numbers of citizens who partake in marine recreational sports, like surfing. In this paper, we compared two approaches for measuring sea surface temperature (SST), an Essential Climate Variable, when surfing. One technique involved attaching a commercially-available miniature temperature logger (Onset UTBI-001 TidbiT v2) to the leash of the surfboard (tether connecting surfer and surfboard) and the second, attaching a surfboard fin (Smartfin) that contained an environmental sensor package. Between July 2017 and July 2018, 148 surfing sessions took place, 90 in the southwest UK and 58 in San Diego, California, USA. During these sessions, both Smartfin and leash sensors were deployed simultaneously. On the leash, two TidbiT v2 sensors were attached, one with (denoted LP) and one without (denoted LU) a protective boot, designed to shield the sensor from sunlight. The median temperature from each technique, during each surfing session, was extracted and compared along with independent water temperature data from a nearby pier and benthic logger, and matched with photosynthetically available radiation (PAR) data from satellite observations (used as a proxy for solar radiation during each surf). Results indicate a mean difference ( &delta, ) of 0.13 ∘ C and mean absolute difference ( ϵ ) of 0.14 ∘ C between Smartfin and LU, and a &delta, of 0.04 ∘ C and an ϵ of 0.06 ∘ C between Smartfin and LP. For UK measurements, we observed better agreement between methods ( &delta, = 0 . 07 ∘ C and ϵ = 0 . 08 ∘ C between Smartfin and LU, and &delta, = 0 . 00 ∘ C and ϵ = 0 . 03 ∘ C between Smartfin and LP) when compared with measurements in San Diego ( &delta, = 0 . 22 ∘ C and ϵ = 0 . 23 ∘ C between Smartfin and LU, and &delta, = 0 . 08 ∘ C and ϵ = 0 . 11 ∘ C between Smartfin and LP). Surfing SST data were found to agree well, in general, with independent temperature data from a nearby pier and benthic logger. Differences in SST between leash and Smartfin were found to correlate with PAR, both for the unprotected (LU) and protected (LP) TidbiT v2 sensors, explaining the regional differences in the comparison (PAR generally higher during US surfing sessions than UK sessions). Considering that the Smartfin is sheltered from ambient light by the surfboard, unlike the leash, results indicate the leash TidbiT v2 sensors warm with exposure to sunlight biasing the SST data positively, a result consistent with published tests on similar sensors in shallow waters. We matched all LU data collected prior to this study with satellite PAR products and corrected for solar heating. Results highlight the need to design temperature sensor packages that minimise exposure from solar heating when towed in the surface ocean.

As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed. © 2022 by the authors.

In this paper, we examine sea surface temperatures (SSTs) and sea ice conditions in the Hudson Bay Complex as a baseline evaluation for the BaySys 2016-2018 field program time frame. Investigated in particular are spatiotemporal patterns in SST and sea ice state and dynamics, with rankings of the latter to highlight extreme conditions relative to the examined 1981-2010 climatology. Results from this study show that SSTs in northwestern Hudson Bay from May to July, 2016-2018, are high relative to the climatology for SST (1982-2010). SSTs are also warmer in 2016 and 2017 than in 2018 relative to their climatology. Similarly, unusually low sea ice cover existed from August to December of 2016 and July to September of 2017, while unusually high sea ice cover existed in January, February, and October of 2018. The ice-free season was approximately 20 days longer in 2016 than in 2018. Unusually high ice-drift speeds occurred in April of 2016 and 2017 and in May of 2018, coinciding with strong winds in 2016 and 2018 and following strong winds in March 2017. Strong meridional circulation was observed in spring of 2016 and winter of 2017, while weak meridional circulation existed in 2018. In a case study of an extreme event, a blizzard from 7 to 9 March 2017, evaluated using Lagrangian dispersion statistics, is shown to have suppressed sea ice deformation off the coast of Churchill. These results are relevant to describing and planning for possible future pathways and scenarios under continued climate change and river regulation.

The importance of coastal upwelling systems is widely recognized. However, several aspects of the current and future behaviors of these systems remain uncertain. Fluctuations in temperature because of anthropogenic climate change are hypothesized to affect upwelling-favorable winds and coastal upwelling is expected to intensify across all Eastern Boundary Upwelling Systems. To better understand how upwelling may change in the future, it is necessary to develop a more rigorous method of quantifying this phenomenon. In this paper, we use SST data and wind data in a novel method of detecting upwelling signals and quantifying metrics of upwelling intensity, duration, and frequency at four sites within the Benguela Upwelling System. We found that indicators of upwelling are uniformly detected across five SST products for each of the four sites and that the duration of those signals is longer in SST products with higher spatial resolutions. Moreover, the high-resolution SST products are significantly more likely to display upwelling signals at 25 km away from the coast when signals were also detected at the coast. Our findings promote the viability of using SST and wind time series data to detect upwelling signals within coastal upwelling systems. We highlight the importance of high-resolution data products to improve the reliability of such estimates. This study represents an important step towards the development of an objective method for describing the behavior of coastal upwelling systems.

International audience; The increasing volume of data generated by earth observation programs such as Copernicus, NOAA, and NASA Earth Data, is overwhelming. Although these programs are very costly, data usage remains limited due to lack of interoperability and data linking. In fact, multi-source and heterogeneous data exploitation could be significantly improved in different domains especially in the natural disaster prediction one. To deal with this issue, we introduce the PREDICAT project that aims at providing a semantic service-oriented platform to PREDIct natural CATastrophes. The PREDICAT platform considers (1) data access based on web service technology; (2) ontology-based interoperability for the environmental monitoring domain; (3) data integration and linking via big data techniques; (4) a prediction approach based on semantic machine learning mechanisms. The focus in this paper is to provide an overview of the PREDICAT platform architecture. A scenario explaining the operation of the platform is presented based on data provided by our collaborators, including the international intergovernmental Sahara and Sahel Observatory (OSS).

A type of extreme disastrous floods are associated with a sequence of prior heavy precipitation events occurring frequently from over several days to several weeks. Transitional methods for precipitation clusters prediction usually rely on the measurement and analyses of meteorological variables. However while a short-term prediction of certain location depends only on variables in near spatial and temporal neighborhood, predictions with long lead time must consider variables in a long time window and large spatial neighborhoods, this means an enormous amount of potentially influencing variables and only a subset of them strongly relate to prediction. Processing a deluge of variables and discovering strongly relevant features pose a significant challenge for big data analytics. Finding influencing variables calls for automated methods of strongly relevant feature selection, which is what online streaming feature selection provides. In particular, online streaming feature selection, which deals with the stream of features sequentially added while the total data observations are fixed, aims to select a subset of strongly relevant features from the original feature set. In this paper, we apply four state-of-the-art online streaming feature selection methods for building long-lead extreme floods forecasting models, which identify optimal size of strongly relevant meteorological variables and confine learning the prediction model on the relevant feature set instead of the original entire feature set. The prediction models are evaluated and compared systematically on the historical precipitation and associated meteorological data collected in the State of Iowa. Refereed/Peer-reviewed

The paper is focus in the use the optical fiber to measure the temperature in various heigh at the same time to get a temperature vertical variation. The temperature measurements are puntual while the Bragg gratings located in the fiber.