GIS USE IN THE STUDY OF ESTUARINE SOILS AND SEDIMENTS Margot K. Payne NRS 509 November 30, 2005

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1 GIS USE IN THE STUDY OF ESTUARINE SOILS AND SEDIMENTS Margot K. Payne NRS 509 November 30, 2005 Throughout the world, estuaries are not only some of the most ecologically productive environments, but are centers for human populations and industry. Because of the importance of estuaries to humans as well as to the environment, it is vital for us to thoroughly study and understand these areas. Soils have long been mapped on land, mainly for agricultural purposes, and sediments have been mapped in deep ocean habitats in order to determine geologic histories. Soil and sediment studies in shallow estuaries, however, have only recently become a concern for habitat mapping, contaminant studies and water quality surveys. Geographic Information Systems (GIS) are gradually becoming an invaluable part of mapping soils and sediments in estuaries. Estuarine systems are extremely variable both spatially and temporally. In traditional sediment mapping, spatial variability can become a problem because of the large number of individual samples that must be taken in order to determine boundaries between sediment types. Spatial variability can, in part, be determined by taking factors into account that are important in soil development. In mapping soils, topography or bathymetry as well as surficial geology are very important to the type of soil that will develop. Subaqueous soils have the additional influence of prevailing wind speed and direction as well as water currents and inputs (Van Wijnegaarden et al., 2002). Using GIS and overlaying procedures that enable us to take all of these factors into account can help in determining how soils developed and therefore what types exist in certain areas. Estuary qualities not only change quickly through space, but can also change very rapidly over time. Daily tidal fluctuations change water quality and salinity and sediment exposure to air. Water quality and salinity changes can have an impact on soil and sediment development as dissolved oxygen and suspended matter levels change the environment that the soil develops in. Yearly temperature changes also impact plant and animal activity which will have an effect on the sediments. Field sampling alone would be very extensive to cover all of these differences in estuarine water and sediment quality. GIS can be very useful in building predictive models using current conditions as well as predicted changes over time to determine the impact on sediment quality and development. Satellite imagery has also been used to attempt to get a picture of estuarine water quality at a single moment in time, rather than point samples over several days or weeks (Baban, 1997). Knowledge of the topography of the land is important in predicting where different soil or sediment types will be found. Detailed topographic maps are readily available for terrestrial mapping, but bathymetric maps often need to be created for an estuary of interest before sediment mapping can be carried out. There are many methods of collecting bathymetric data from physically measuring the water depth at certain locations (Bradley, 2001) to multibeam sonar (Nitsche et al., 2004) to satellite imagery interpretation (Lafon et al., 2002). All of these methods are greatly enhanced by the use of GIS. The most common and cost-efficient method of measuring bathymetry is done by taking depth readings at individual locations. Using GIS, it is possible to interpolate between the points to result in an accurate map of bathymetry. Multibeam sonar can also be used to gather data points that cover the entire surface of the sediment-water interface. This data is also mapped using GIS. Finally, many researchers are now attempting to use detailed satellite imagery to determine depth. These images are interpreted using the degree of reflectance of certain spectral bands which represent depths. This method is only accurate for shallow water areas.

2 Satellite and aerial imagery is also becoming very important in direct soil and sediment mapping (Rainey et al., 2003, Donoghue and Mironnet, 2002). Rather then simply gathering point data and interpolating between the points, these researchers want to get the full picture of soil and sediment distribution. Using remotely sensed images and functions of the relative intensities of reflected spectral bands, this type of mapping is able to determine the difference between coarse-grained sediment and fine-grained mud. The limitation to the research in this field is that subtidal sediments are not mapped because the reflectance of the overlying water gives a spectral value that is not comparable to dewatered sediment. For this reason, this remotely sensed data is gathered at extremely low tide in areas where tidal fluctuation is large enough to expose a large expanse of sediment that can be imaged. Sidescan sonar is another method of seamlessly mapping sediments that is becoming very popular (Wewetzer et al., 1999; Nitsch et al., 2004). The sidescan sonar device sends out acoustic signals that bounce off of the sediment-water interface and return to the receiver where the data are collected. Sidescan sonar can map water depth as well as sediment type and surface texture based on the type of return from the sonar. Using this mapping technique, the entire bottom of a water-body is imaged and imported to a GIS. The imagery shows differences in bottom type but does not explicitly determine what type of image refers to a certain sediment type. Researchers must conduct field sampling of the sediments within the mapped units to determine grain size and type. Most of the published research on estuarine soils describes methods of gathering data, as this is the first step in any type of research. Once sediment data is collected, however, GIS can be very useful in analyzing this data along with other important data about the estuary. Estuarine soils are important for shellfish and eelgrass, two valuable species in many areas. Using GIS, sediment maps can be used, along with spatial distribution of other factors such as water quality or light attenuation, to determine ideal habitats for these species. For this type of analysis, raster datasets are mapped together to create new maps of habitat or areas of special concern (Urbanski and Szymelfenig, 2003). Mapping sediments in estuaries can be very challenging because of the variability of the soils and the difficulty in accessing and imaging the data. It is very time consuming to gather sediment samples over a large area at a density that is needed to create an accurate and seamless map of sediment types. For this reason, a lot of research is being conducted to explore more efficient ways to map bottom sediments. Remotely sensed data and technologies such as sidescan sonar can be very effective and, with the integration of GIS and scaled back field sampling, can create accurate maps of intertidal and subtidal soils. The increasing availability and quality of satellite data and aerial photography and the improved methods for photo interpretation is very promising for the future of subaqueous soil mapping. The dynamic nature of estuaries and estuarine soils make them ideal for using GIS to overlay different datasets that will assist in determining changes over time and over the area of a water body. Although GIS has been slow in reaching estuarine science, I believe that it has great potential in the mapping and study of estuarine soils and sediments. Literature Cited Bradley, M Subaqueous soils and subtidal wetlands in Rhode Island. University of Rhode Island Master s Thesis.

3 Van Wijnegaarden, M., L.B. Venema and R.J. De Meijer Radiometric sand mud characterization in the Rhine-Meuse Estuary Part B. In situ mapping. Geomorphology 43: Annotated Bibliography Baban, S.M.J Environmental monitoring of estuaries; estimating and mapping various environmental indicators in Breydon Water Estuary, U.K. using Landsat TM imagery. Estuarine, Coastal and Shelf Science 44: Baban has utilized satellite imagery to determine water quality parameters, including suspended solids, turbidity, temperature, salinity, chlorophyll a and total phosphorous, in an estuary in the United Kingdom. These water quality indicators are usually recorded by spot sampling and therefore we never get a complete measure of these parameters for an entire estuary all at the same time. Additionally, these water quality measures change rapidly in an estuary and are difficult to monitor over a period of time without extensive field work. Baban used Landsat TM imagery in order to determine these measurements by observing the relative strengths of the TM1, TM2, TM3, TM4 and TM6 wavelength bands and applying them to various algorithms. The data were field validated and it was determined that all results based on the satellite imagery were within reasonable levels except for the chlorophyll a concentrations. The bluegreen wavelengths were believed to have been masked by other agents in the water column. Using algorithms based off of satellite imagery to determine water quality parameters is a promising way to track spatial and temporal changes within a constantly fluctuating estuarine habitat. Donoghue, D.N.M. and N. Mironnet Development of an integrated geographical information system prototype for coastal habitat monitoring. Computers & Geosciences 28: This paper describes the development and functionality of an Integrated Geographical Information System that would use remotely sensed imagery for mapping of coastal habitats including tidal marsh and intertidal mud and sand flats. Donoghue and Mironnet s goal was to develop a Coastal Habitat Information Monitoring System (CHIMS) that could be used by coastal ecologists who are not specialists in remote sensing and do not have access to specialized software. Their system utilized Landsat TM satellite imagery that was rectified using ground control points and classified the images based on a maximum likelihood algorithm that coded each pixel as upper marsh, middle marsh, pioneer marsh, sand or mud. The system was tested on the Wash estuary of eastern England and was found to be very effective in determining different vegetation communities. In order to differentiate the unvegetated sediment types Donoghue and Mironnet used spectral unmixing of the satellite data, which quantified abundance at a sub-pixel level. Once satellite imagery is run through CHIMS, it can be made part of a GIS and can be viewed as vector or raster maps of the habitat types. Lafon, V., J.M. Froidefond, F. Lahet and P. Castaing SPOT shallow water bathymetry of a moderately turbid tidal inlet based on field measurements. Remote Sensing and the Environment 81: In this paper Lafon et al. describe a method for determining bathymetry of a tidal inlet using SPOT satellite imagery and an equation based on a set of field measurements. Because

4 bathymetry can change quickly and depth soundings can be difficult in high-energy areas such as tidal inlets, the authors sought to develop a fast and accurate way to measure bottom contours using satellite imagery. Field measurements were taken including bottom color, total suspended matter, chlorophyll a and pheopigments in the water column. These measurements were factored into an equation to determine the water reflectance. Because reflectance just above the water column changes with water depth, the authors were able to determine depth based on the strength of the various wavebands in the SPOT imagery. The authors determined that their equation was able to determine realistic depths down to 6 meters for waters with low suspended sediment. Compared with field depth measurements, the mean error of the depth determination by equation was 20%. This method proved to be effective within the tidal inlet but needs further testing within shallow estuaries or bays where water parameters may differ. Nitsche, F.O., R. Bell, S.M. Carbotte, W.B.F. Ryan and R. Flood Process-related classification of acoustic data from the Hudson River Estuary. Marine Geology 209: This paper describes the mapping and classification of habitats in the Haverstraw Bay section of the Hudson River Estuary. The researchers used sidescan sonar, sub-bottom profiling and multibeam bathymetry as well as grab samples and gravity cores in order to determine differences in the sediment type and process. The data gathered from these various methods were integrated using a GIS in order to determine the sediment processes. Sidescan sonar is often used on its own to map differences in sediment size, however, the Haverstraw Bay section of the Hudson River has very little variability in sediment size. For this reason it was important to integrate all three mapping methods using a GIS in order to determine the reasons for differences in the backscatter received from the sonar signal. Using multi-beam bathymetry the entire study area was mapped and digital elevation models (DEM) were created. The sidescan sonar and sub-bottom profiling were added to determine the surface texture and density and the sub-bottom sediment structure. Based on these three mapping techniques and ground truth samples, the researchers identified eight different acoustic facies that represented different sedimentary processes such as depositional, erosional and dynamic. A complete map of the study area was created displaying these eight sedimentary processes. Rainey, M.P., A.N. Tyler, D.J. Gilvear, R.G. Bryant and P. McDonald Mapping intertidal estuarine sediment grain size distributions through airborne remote sensing. Remote Sensing and the Environment 86: Rainey et al. attempted to map the intertidal sediments of the Ribble Estuary in Lancashire, UK using Airborne Thematic Mapper (ATM) data gathered at extreme low tide from a small aircraft. Imagery was gathered over several hours and five flight lines to include wet imagery, in which the area was just recently dewatered and dry imagery that showed the same areas after approximately 3 hours of drying time. The radiation data gathered from the ATM is separated into 11 spectral bands similar to Landsat TM data that include visible, near-infrared and shortwave infrared bands. The authors interpreted the relative intensities of these different bands to determine the percent clay, sand and microphytobenthos throughout the exposed intertidal area in the imagery. These image interpretations were ground truthed by sampling the sediments using grab samples. The authors found that using the dry images, they could very accurately determine the percent clay in the sediment. Sand abundance was also estimated very accurately using the dry imagery. The presence of interstitial water significantly interfered with the interpretation of the spectral bands in the imagery so that the wet imagery was very inaccurate in determining percent clay and sand. This study was limited to the intertidal

5 sediments as the presence of any surface water pools masked the spectral signal of the sediments below. Urbanski, J.A. and M. Szymelfenig GIS-based mapping of benthic habitats. Estuarine, Coastal and Shelf Science 56: Urbanski and Szymelfenig mapped benthic habitats of the Polish zone of the Baltic Sea based on the position relative to the euphotic zone and the bottom sediment types using GIS. An analog map of the sediment types was imported as a raster dataset into a GIS. The euphotic zone was determined based on water clarity. These two datasets were mapped together using cross-classification in the GIS. Cross-classification is described as creating a new raster map by ascribing each cell a value based on a formula using variables from cells values in multiple input maps. Because of the gradation of change between units in sediment photic zone maps, this study also uses fuzzy sets to map the uncertainty of the boundaries between different sediment types and the euphotic/ photic boundary. This approach allows a cell to be a partial member of a set by assigning numbers between 0 and 1 to cells in between two zones rather than making hard lines between the map units. This paper is a good summary of some of the analyses that can be done using GIS that would be very difficult to do by hand. Wewetzer, S.F.K., R.W. Duck, and J. McManus Side-scan sonar mapping of bedforms in the middle Tay Estuary, Scotland. International Journal of Remote Sensing 20(3): Wewetzer et al. describe their work using sidescan sonar as well as echo-sounding and sediment grab sampling to map the sediment types in a section of the Tay Estuary in Scotland. Sidescan sonar is often used to determine larger subaqueous landforms, but this paper focuses on the more detailed mapping of different sediment grain-sizes. Sidescan sonar data was compared with sediment samples in order to identify the backscatter images by sediment type. The data gathered was compared with bathymetric data for the study area in order to determine if sediment type changed with depth. Some of the textural breaks followed bathymetric boundaries very closely while other texture changes appeared to be controlled by something other than bathymetry such as energy in the water column. This research shows that sidescan sonar can be very effective in mapping surface sediment types and boundaries seamlessly over a large area.