6 resultados para Topographic maps

em Aquatic Commons


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A limnological and fish survey program was developed on 112 lakes and reservoirs of Argentina during the summers of 1984 to 1987. Bathymetric surveys with a SIMRAD Skipper 411 model echosounder and line and lead were conducted on more than 40 lakes. This report presents bathymetric maps for seventeen lakes and reservoirs situated in Patagonian Andes Region and Patagonian Plateau betweem 38°53'S and 45°30'S. The bathymetric maps for two reservoirs were made from topographic maps before impoundment. Hypsographic and depth-area curves, and some morphometric parameters are presented for twenty one Patagonian lakes. Mean depth ranged from 2.0 to 111 m. The deepest lakes are situated in Patagonian Andes Region. Colhue Huapi Lake on Patagonian Plateau, is very shallow, having a mean depth of 2.0 m and being 810 km. in surface area.

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Without knowledge of basic seafloor characteristics, the ability to address any number of critical marine and/or coastal management issues is diminished. For example, management and conservation of essential fish habitat (EFH), a requirement mandated by federally guided fishery management plans (FMPs), requires among other things a description of habitats for federally managed species. Although the list of attributes important to habitat are numerous, the ability to efficiently and effectively describe many, and especially at the scales required, does not exist with the tools currently available. However, several characteristics of seafloor morphology are readily obtainable at multiple scales and can serve as useful descriptors of habitat. Recent advancements in acoustic technology, such as multibeam echosounding (MBES), can provide remote indication of surficial sediment properties such as texture, hardness, or roughness, and further permit highly detailed renderings of seafloor morphology. With acoustic-based surveys providing a relatively efficient method for data acquisition, there exists a need for efficient and reproducible automated segmentation routines to process the data. Using MBES data collected by the Olympic Coast National Marine Sanctuary (OCNMS), and through a contracted seafloor survey, we expanded on the techniques of Cutter et al. (2003) to describe an objective repeatable process that uses parameterized local Fourier histogram (LFH) texture features to automate segmentation of surficial sediments from acoustic imagery using a maximum likelihood decision rule. Sonar signatures and classification performance were evaluated using video imagery obtained from a towed camera sled. Segmented raster images were converted to polygon features and attributed using a hierarchical deep-water marine benthic classification scheme (Greene et al. 1999) for use in a geographical information system (GIS). (PDF contains 41 pages.)

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In April 2005, a SHOALS 1000T LIDAR system was used as an efficient alternative for safely acquiring data to describe the existing conditions of nearshore bathymetry and the intertidal zone over an approximately 40.7 km2 (11.8 nm2) portion of hazardous coastline within the Olympic Coast National Marine Sanctuary (OCNMS). Data were logged from 1,593 km (860 nm) of track lines in just over 21 hours of flight time. Several islands and offshore rocks were also surveyed, and over 24,000 geo-referenced digital still photos were captured to assist with data cleaning and QA/QC. The 1 kHz bathymetry laser obtained a maximum water depth of 22.2 meters. Floating kelp beds, breaking surf lines and turbid water were all challenges to the survey. Although sea state was favorable for this time of the year, recent heavy rainfall and a persistent low-lying layer of fog reduced acquisition productivity. The existence of a completed VDatum model covering this same geographic region permitted the LIDAR data to be vertically transformed and merged with existing shallow water multibeam data and referenced to the mean lower low water (MLLW) tidal datum. Analysis of a multibeam bathymetry-LIDAR difference surface containing over 44,000 samples indicated surface deviations from –24.3 to 8.48 meters, with a mean difference of –0.967 meters, and standard deviation of 1.762 meters. Errors in data cleaning and false detections due to interference from surf, kelp, and turbidity likely account for the larger surface separations, while the remaining general surface difference trend could partially be attributed to a more dense data set, and shoal-biased cleaning, binning and gridding associated with the multibeam data for maintaining conservative least depths important for charting dangers to navigation. (PDF contains 27 pages.)

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One of the most popular sections of the ReefBase website is the online GIS that enables users to create an almost unlimited range of maps showing a wide variety of features. A new version of the GIS, offering an even larger array of facilities and data layers, is online as of early September. In this article, we will provide an introduction to the ReefBase GIS and also highlight some of the powerful but lesser used features of the system.

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In this study a total of 75 species were identified, from which 17 species, 9 genes and 6 families; belonged to Green Algae, 18 species, 7 genes and 4 families; belonged to Brown Algae, and 40 species, 18 genes and 11 families; belonged to Red Algae. From total times spent for sampling, it was determined that at lengeh harbor with 6 species, had the lowest diversity of green algae. The species diversity of brown algae at Michael location with 10 species each; had the highest, and Tahooneh location with 5 species; had the lowest species diversity. Species diversity of red algae at Michael location with 28 species; had the highest, and Sayeh Khosh location with 13 species; had the lowest diversity. From all locations where sampling took place, the highest species diversity regarding Time and Space for all three groups of algae; were associated to Late February (20th. Feb. ), and late March(20th. March). Coverage data of macroalgae and Ecological Evaluation Index indicate a high level of eutrophication for the Saieh khosh, and Bostaneh, They are classified as zones with a bad and poor ecological status. It has been proved that concentrations of biogenic elements and phytoplankton blooming are higher in these zones. The best values of the estimated metrics at Tahooneh and Michaeil could be explained with the good ecological conditions in that zone and the absence of pollution sources close to that transect . The values of abundance of macroalgae and Ecological Evaluation Index indicate a moderate ecological conditions for the Koohin, Lengeh and Chirooieh.