6 research outputs found

    Satellite-derived bathymetry of the Achziv coastal area, northern Israel

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    The International Bathymetric Chart of the Arctic Ocean Version 4.0

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    Funder: The Nippon Foundation of Japan, grant Seabed 2030Funder: Open access funding provided by Stockholm UniversityAbstract: Bathymetry (seafloor depth), is a critical parameter providing the geospatial context for a multitude of marine scientific studies. Since 1997, the International Bathymetric Chart of the Arctic Ocean (IBCAO) has been the authoritative source of bathymetry for the Arctic Ocean. IBCAO has merged its efforts with the Nippon Foundation-GEBCO-Seabed 2030 Project, with the goal of mapping all of the oceans by 2030. Here we present the latest version (IBCAO Ver. 4.0), with more than twice the resolution (200 × 200 m versus 500 × 500 m) and with individual depth soundings constraining three times more area of the Arctic Ocean (∼19.8% versus 6.7%), than the previous IBCAO Ver. 3.0 released in 2012. Modern multibeam bathymetry comprises ∼14.3% in Ver. 4.0 compared to ∼5.4% in Ver. 3.0. Thus, the new IBCAO Ver. 4.0 has substantially more seafloor morphological information that offers new insights into a range of submarine features and processes; for example, the improved portrayal of Greenland fjords better serves predictive modelling of the fate of the Greenland Ice Sheet

    Seafloor characterization and monitoring of the inner shelf in northern Israel using remote sensing

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    Coastal marine systems are experiencing rapid transformations as a result of multiple human-induced stressors. Many of these transformations occur at the level of a landscape and as such it is increasingly important to detect changes at these appropriate spatial scales. Remote sensing can be a powerful tool to investigate landscape level changes, but it still requires calibration and ground-truth via field observations. In this research, classification schemes using multispectral and hyperspectral imagery were developed in order to map and monitor temporal changes in the benthos over a fifteen-year period (1999-2014) along the Levant Mediterranean rocky reefs that are made of sandstone (known as Kurkar) and are found along the northern coast of Israel. The Levant Mediterranean rocky reef has been experiencing major temporal shifts in marine biodiversity. These shifts include, but are not limited to, the creation of reefs by large oysters that invaded from the Red Sea, the addition of non-native seaweed species, and over-grazing of seaweed beds by non-native herbivorous fish. Such changes can likely be observed at the landscape level. The bathymetry of the reef is characterized by sandstone ridges that are parallel to the shoreline with emerging islands at their top and a long-shallow channel between the ridge and the shore. Using bathymetric models, a correction for the water attenuation was applied to the spectral dataset and an optical extinction depth was calculated. A recent field campaign (May, 2014) in the study area using acoustic (side scan sonar) and underwater optical measurements provided a ground truth dataset for the study. Decision trees used for the classification were developed based on in situ spectral measurements and underwater video imagery. A time series of seafloor characterization maps was derived from imagery acquired using Itres CASI, Landsat 7 and Landsat 8

    The Autonomous Underwater Vehicle Integrated with the Unmanned Surface Vessel Mapping the Southern Ionian Sea. The Winning Technology Solution of the Shell Ocean Discovery XPRIZE

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    The methods of data collection, processing, and assessment of the quality of the results of a survey conducted at the Southern Ionian Sea off the Messinian Peninsula, Greece are presented. Data were collected by the GEBCO-Nippon Foundation Alumni Team, competing in the Shell Ocean Discovery XPRIZE, during the Final Round of the competition. Data acquisition was conducted by the means of unmanned vehicles only. The mapping system was composed of a single deep water AUV (Autonomous Underwater Vehicle), equipped with a high-resolution synthetic aperture sonar HISAS 1032 and multibeam echosounder EM 2040, partnered with a USV (Unmanned Surface Vessel). The USV provided positioning data as well as mapping the seafloor from the surface, using a hull-mounted multibeam echosounder EM 304. Bathymetry and imagery data were collected for 24 h and then processed for 48 h, with the extensive use of cloud technology and automatic data processing. Finally, all datasets were combined to generate a 5-m resolution bathymetric surface, as an example of the deep-water mapping capabilities of the unmanned vehicles’ cooperation and their sensors’ integration
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