Copano Bay is a shallow (< 2-3 m), microtidal estuary in south central Texas. In an effort to both determine the distribution as well as investigate the controls on the distribution of oyster reefs, a geophysical survey of Copano Bay was conducted in June and July 2007. Surficial sediment analysis confirms that the recent sedimentation in Copano Bay is comprised of mostly estuarine mud with little sand or shell, large extents of oyster reefs and smaller areas of sand. Seismic stratigraphy analyses verify that the first oyster reefs in Copano Bay formed atop topographic highs in the Pleistocene surface. About 6 ka, sea level rise slowed to near its present rate and sediment supply decreased tremendously to Copano Bay decreasing the amount of suspended sediment. The first oyster reefs began forming around this time using these fluvial terraces as suitable substrate. Once the initial reefs were established, additional reefs began forming atop these initial reefs, or on the eroded shell hash material from the initial reefs. During this time of slow sea level rise and low sediment input to the bay, oyster reefs thrived and reef and shell hash material covered a majority of the bay surface. Once climate change increased sediment input to the bay, the reefs began to decrease in size due to siltation. The reefs have continued to decrease in size causing a 64 percent reduction in oyster reef and shell hash area from approximately 4.8 ka to today.

Sjöfartsverket har i sitt arkiv en stor mängd backscatterdata, insamlat med multibeamekolod, som ännu inte har använts till sin fulla potential. Backscatterdata innehåller information om den reflekterade signalens styrka, även kallad amplitud. Stora mängder backscatterdata kan användas för att beskriva den akustiska bottenreflektionen. Syftet med denna undersökning är att beskriva hur variationen för amplituden varierar beroende på vilken bottentyp den reflekteras ifrån. En metod för att skapa rasterdataset med bottenhårdhet och bottentyp baserat på amplituddata ska utvecklas. Resultaten från denna metod ska sedan jämföras med kartdata från Sveriges Geologiska Undersökning (SGU). Totalt användes cirka 45 miljoner bottenpunkter i studieområdet. Varje punkt innehåller information om amplitud som systemet har registrerat från det reflekterade ekot. Dessa data behövde genomgå databehandlingar, bl.a. en vinkelkorrigering som ger ett mer trovärdigt värde av amplitud. Med hjälp av befintlig information om studieområdets sjöbotten i form av en maringeologisk karta från SGU, kunde amplitud från ett antal specifika uppskattade bottentyper studeras direkt. Resultatet uppvisar stora skillnader i amplitudens variationer. Specifika medelvärden och standardavvikelser kan urskiljas beroende av vilken specifik uppskattad bottentyp som studerades. ”Mjuk lera” gav en svagare signal med relativt låg standardavvikelse. ”Häll” och ”sten och block” reflekterade en liknade men starkare signal. Amplitudata från backscatter-informationen i hela datamängden utnyttjades för att skapa raster vars syfte var att beskriva den uppskattade bottenhårdheten. Olika raster skapades med olika parametrar beroende på ändamål. Gemensamt för alla skapade raster är att de är uppbyggda med metoden ”flytande beräkning” som möjliggjorde en mer utjämning. Resultatet av medelvärde och standardavvikelse från varje enskild bottentyp utnyttjades för att utföra en klassning av bottentyper på ett skapat raster lämpad för just bottentypsklassificering. För att få ett mer noggrannare medelvärde och standardavvikelse studerades ett 68 % konfidensintervall för de olika bottentyperna. De bottentyper som valdes för klassningen var ”mjuk lera”, ”sand, grus och sten”, ”häll”, ”sten och block” och även ”lägre amplituder”. ”Häll” och ”sten och block” klassades samma eftersom deras fysikaliska egenskaper gör att deras värden ligger nära varandra vilket gjorde det svårt att urskilja dem.”Lägre amplituder” utnyttjades för att identifiera områden som har lägre reflektionsförmåga än mjuk lera. Vilken bottentyp det är kan endast provtagning ge svar på. Med hjälp av tolkning av skapade raster och den maringeologiska kartan så korrigerades intervallen och användes som klassning. Resultatet från klassningen visar tydligt att områden kan urskiljas i kartbilden. Majoriteten av klassningarna resulterade i typen mjuk lera. En jämförelse av klassningen med den maringeologiska kartan visar att stora skillnader finns mellan dem. Mjuk lera gav en överensstämmelse på 86 %, sand, grus och sten 30 % och häll, sten och block 52,5 %, vilket gav en total överenstämmelse på 56,2 %. Jämförelse utfördes även med 9 provtagningspunkter som fanns tillgängliga i området. Det visade en total överenstämmelse på 89 %. Undersökningen visar att amplitud från havsbottnen korrelerar med bottentypen det är. Noterbart är att metoden för bottentypsklassificering som utvecklats i denna studie inte har kunnat kvalitetsgranskas med ett trovärdigt resultat, p.g.a. av statistiskt för få provtagningspunkter att jämföra mot. Studien visar dock att med mer data och noggrannare referensdata kan en mer automatisk klassningsmetod utvecklas. / The Swedish Maritime Administration (Sjöfartsverket) has a large amount of backscatter data collected with multibeam echosounder in their archive that has not been fully used despite its great potential. Backscatter data contains information about the strength of the reflected signal, often called amplitude strength. Large amounts of backscatter data could be used to describe the acoustic bottom reflection. The purpose of this study is to describe how the variation of the amplitude strength varies dependent on which estimated bottom types the data reflects from. Also a method will be produced which purpose is to create gridded dataset of estimated bottom hardness and bottom type based on amplitude data and compare this method with official data from the Geological Survey of Sweden (SGU).A total number of 45 million depths (data points) were used in the study area. Every data point contains information about the amplitude strength that the system has recovered from the reflective echo. This data needed to be preprocessed, including an angle correction that produces a more reliable value of the amplitude strength. With existing information about the bottom from the study area, in this case a marine geological map from SGU, the amplitude from some estimated specified bottom types could be studied. The result shows differences in their variation. Specific values of mean and standard deviation could be distinguished by which estimated specific bottom types that were studied.The amplitude strength from the backscatter information of the complete data set was used to create a raster that describes the estimated bottom-hardness. Different raster were created with various parameters dependent on the purpose. All of the created raster data had in common that it was created using a technique called “flow calculation” which result in more equalization.The mean and standard deviation for every individual estimated bottom type were used to create interval for classification of the bottom types. To achieve a more accurate estimation of the mean and standard deviation for the bottom types, a 68 % confidence interval were used. The classes that were chosen for classification was “soft clay”, “sand, gravel and stone”, “solid rock”, “stone and block” and “lower amplitudes”. “Solid rock” and “stone and block” were combined in the same class because of their similar physical properties. “Lower amplitudes” were chosen in order to indicate areas where the amplitude strength from the reflective echo was lower than “soft clay”.The result of the intervals was adjusted by an examination of the raster data and the marine geological map and was then used for classification.The result from the classification shows that areas of different bottom types could be distinguished in the map. The majority of the classification was of the type “soft clay”. A comparison between the classification and the marine geological map showed some differences. “Soft clay” matched with 86 %, “sand, gravel and stone” 30 %, “solid rock, stone and block” 52,5 % and the total matched with 56,2 %. Comparisons between 9 samplings in the area were made. The result shows that the classification-accuracy is 89 %.The study shows that the amplitude strength correlates to the bottom type. Note that too few samplings for bottom classification were used in the study and thus the results are not fully reliable. The study, however, shows that with larger amount of data and more accurate reference data a better automatic classification method could be developed.

The fundamental components of a coral reef are coral, algae, and sand. At its simplest assessing the status of a coral reef may be reduced to quantifying the relative benthic cover of these three bottom-types. While in situ surveys can provide an accurate census on an individual reef scale (10s of meters), the only feasible method to surveys coral reefs on a reef tract (10-100s of kilometers) or worldwide scale is through the use of remote sensing. Remote sensing is a means of surveying entire ecosystems. A major issue in remote sensing of coastal environments is the confounding effects of the water column on the signal emerging from the water column. We used a simulation method to model differing levels of environmental parameters, which occur in marine ecosystems, with HydrolightEcolight 5. Simulated data were interpolated with actual bottom; type spectra to determine the accuracy of a classification function developed in MATLAB. The aim was to distinguish bottom-types as well as predict levels of water column parameters. The results of this study demonstrate that bottom-type (78% algae, 84% coral, and 94% sand) and chlorophyll concentration (85-90% across range) are well determined, while depth and suspended sediment load are not as well predicted (<70%) and has a tendency to slightly over predict depth.