NGDC Coastal Relief Model Development
The database is being assembled by gridding the NOS sounding data at the same 3 arc-second (~90 m) resolution and registration as the USGS 3-arc-second DEMs and splicing the two data sets at the NOS medium-resolution vector shoreline. The principal component of the database is 3-arc-second elevation grids, of areas 1° in longitude by 1° in latitude, in which elevations are resolved to 1/10 of a meter. The database also includes grids containing the number of soundings enclosed by each cell in the offshore areas of the elevation grids; radius grids, that are equivalent in size and resolution to the elevation and data-density grids, and indicate the distance to the nearest cell in the data-density grids that includes a sounding; and images of both the elevation and sounding density grids.
Grids that integrate land and seafloor elevations are needed by planners using LIS/GIS software to manage the US coastal zone, which encompasses the coastal states out to the country's 200-mile offshore limit. The National Geophysical Data Center is addressing this need by assembling a gridded database that merges the US Geological Survey 3-arc-second DEMs with a vast compilation of hydrographic soundings collected by the National Ocean Service and various academic institutions. The principal component of the database is 3-arc-second elevation grids, of areas 1° in longitude by 1° in latitude, in which elevations are resolved to 1/10 of a meter. The database also includes grids containing the number of soundings enclosed by each cell in the offshore areas of the elevation grids, and images of both the elevation and sounding density grids. The latter are in common graphic formats that can be displayed by a range of UNIX-based and personal computer software. This paper describes how the database is being constructed and the novel software that accompanies the grids on CD-ROM. The software allows the user to navigate the CD-ROM, view grid images, and modify the grids for importation into GIS/LIS applications.
While the purposes for land and marine elevation surveys are similar (e.g., national defense, finding routes of passage, resource exploration, engineering applications, etc.), the surveys are carried out independently of one another and mutually stop at the shoreline. Despite this division, integration of the results of the two types of surveys is of increasing interest. Population growth in the US has been fastest in the coastal states where many of the country's largest cities and most popular recreational beaches are located. With this growth have come a variety of new environmental pressures such as over development of beaches and wetlands, contamination of estuaries, increased economic costs associated with storm damage and flooding, dredging, oil and gas exploration/production, and over fishing to name but a few.
These environmental pressures are prompting federal, state and local government agencies to be increasingly pro-active in sustaining the robust and attractive environment of the US coastal zone-- defined here as extending from the coastal states out to the country's 200-mile offshore limit. These agencies are attempting to manage growth within the coastal zone and its usage through a variety of means, one of the most important being Geographic and Land Information Systems. Coastal planners are using these systems to map out future land development, mitigate pollution, prepare for emergencies due to natural hazards, monitor environmental change within the coastal zone over time, and assess offshore resources.
A fundamental database for such GIS/LIS applications is gridded elevations, upon which digital maps of rivers, infrastructure and other geographic information can be overlain. While digital elevation models (DEMs) of the coastal states are available through the US Geological Survey (USGS), until now there has been no comparable product for offshore elevations aside from custom grids generated by individual investigators. The National Geophysical Data Center is taking advantage of both an extensive national hydrographic database and the maturation of software for constructing, managing, viewing and accessing gridded geophysical data, to assemble a new, gridded data base of coastal zone elevations that complements and enhances the USGS 3-arc-second DEMs. This database, which merges the hydrographic soundings with the USGS/NIMA DEMs in a common grid format, will provide the first comprehensive view of the US Coastal Zone; one that extends from the coastal states across the shoreline into water depths as deep as the hydrographic data will support a continuous view of the seafloor.
The gridded database will encompass the coastal zone of the continental US, Alaska, Hawaii and Puerto Rico. The US coastal zone is being gridded in sections due to the immense volume of data involved in this project. In this paper, we describe how the database is being assembled. We also describe the novel software we have developed for accessing the database, which greatly facilitates the selection and preparation of an elevation grid for importation into GIS/LIS applications.
Land elevations within the gridded dataset come from the United States Geological Survey/ National Image Mapping Agency (USGS/NIMA) 1:250,000 or 1° DEMs of the states. A description of the USGS/NIMA DEMs and how they were derived can be accessed on the World Wide Web at http://edcwww.cr.usgs.gov/glis/hyper/guide/1_dgr_dem. Our focus, however, is on the bathymetric soundings that are used in constructing the offshore regions of the grids.
Soundings for each volume of the Coastal Relief model series are compiled from hydrographic surveys conducted by the National Ocean Service (NOS) and from various academic institutions. The surveys were carried out using a variety of sounding methods including lead-line soundings (from the late 1800's until the 1930's), single beam echosounder (1930's - 1990's) and multibeam sonar (1980's to present). The sonar systems utilized a wide-range of frequencies with varying beam widths.
A wide range of navigation methods are also associated with the surveys. Visual navigation (three-point sextant fixes to objects on shore) was the most common method of survey positioning (navigation) until the 1930's and continued to be used for nearshore positioning until the 1980's. Radio waves were first used for offshore positioning in the 1930's and electronic positioning evolved over the years becoming more accurate and reliable until being replaced by GPS in the mid 1990's.
Sounding and navigation techniques have changed over the more than 100 years of NOS hydrographic data collection. As a consequence, the required horizontal and vertical accuracy standards for the resulting data have also changed over time (Table 1). Differential GPS has improved the level of accuracy considerably for the most recent survey data. NOS surveys are plotted at map scales that range from 1:5,000 for harbors and channels to 1:80,000 for open ocean surveys, with 1:20,000 being the most commonly used scale.
While the NOS soundings collected since 1965 have been recorded digitally, those collected prior to this time were recorded manually and then used to make hand-drawn bathymetric maps. Approximately 1,550 of these hand-drawn bathymetric maps have been digitized and assimilated into the NOS Hydrographic Database and, subsequently, were used in constructing the many volumes of the gridded dataset.
Gridding of the NOS and NECOR soundings is accomplished using a combination of Generic Mapping Tool algorithms (Wessel and Smith, 1995), UNIX commands, and programs developed at NGDC specifically for merging gridded bathymetry with the USGS DEMs. The programs are linked together in a single Perl script command so that large volumes of digital bathymetric data can be gridded with minimal assistance from an operator. The script command is also versatile enough that it can be used to update individual grids as new data becomes available.
Each grid produced by the script encompasses an area of one degree of latitude by one degree of longitude. The horizontal resolution of the grid cells is identical to that of the USGS/NIMA DEMs; 3-arc-seconds, which is nominally 90 meters. The vertical resolution of the grid cells, on the other hand, is bimodal. Grid elevations of the onshore areas of the coastal zone are taken directly from the USGS/NIMA DEMs, which are resolved to one meter. But in the offshore areas, the grid elevations are resolved to one tenth of a meter.
This higher resolution was chosen at the urging of present users of the NOS hydrographic database who have found that the higher level of detail is not only supported by the sounding data, but reveals valuable morphologic information in near-shore areas and estuaries. To allow for these two vertical resolutions, the unit of elevation in the grids is one-tenth of a meter. Figure 1 is a flow chart of the procedure used to produce the grids. It diagrams the inputs to the Perl script and the various stages of the gridding process carried out by the script. These inputs and gridding stages are described in the sections that follow.
Five input datasets must be assembled for each region of the US coastal zone to be gridded (Fig. 1). The first of these inputs is the compilation of all USGS/NIMA DEMs within the region. The USGS DEMs are acquired remotely via ftp from http://edcftp.cr.usgs.gov. The DEMs are then converted from their USGS ASCII format into a binary raster grid using a program called GS3 (written at NGDC). The final step is to convert this binary raster grid into a GMT grid using the GMT command grdraster. The second input dataset is the NOS medium-resolution (1:80,000) vector shoreline. The vector shoreline is referenced within a 3-arc-second resolution grid of the 1° x 1° region. The shoreline is then rasterized by flagging the grid cells that encompass the vector shoreline points as well as those cells that fall along straight lines connecting the points.
The third input dataset is an ASCII list containing the longitudes and latitudes of an arbitrary number of points that lie within the offshore area of the region to be gridded. These points are "seed" locations, which are used along with the rasterized shoreline to create a binary grid in which land areas have one value and ocean areas another. This land-sea mask and its role in the gridding process are described in detail below.
The fourth input dataset is a compilation of all the digital bathymetric soundings collected within the region to be gridded. These soundings come from three sources of data: (i) the NOS Hydrographic Soundings CD-ROM, (ii) the NGDC Multibeam database and (iii) recently digitized NOS soundings that have not yet been released on CD-ROM. The NOS Hydrographic Database CD-ROM does contain NOS multibeam data, but only five of the sixteen possible beams in a survey are included in this dataset. The Multibeam database contains not only the full resolution (i.e., all 16 beams) NOS multibeam surveys, but also the full resolution NECOR surveys archived at NGDC. As a result, all the multibeam bathymetry in the gridding process comes from this second database.
Soundings are extracted from all three datasets using the Search and Download software components of the NGDC GEODAS system (Sharman et al., 1998). Prior to gridding, as surveys are assimilated into the NOS Hydrographic Database, the soundings in each survey are manually checked against the soundings of adjacent and overlapping surveys to ensure depth consistency. In the future, the soundings will also all be corrected to the same vertical and horizontal datum (this issue was not addressed in constructing this first version of the gridded database). Currently, the soundings used to create the East Coast grids are referenced to two vertical datums and various horizontal datums. The vertical datums are mean low water (89% of the surveys) and mean lower low water (11%) of the surveys). The primary horizontal datums used were the NAD27 ellipsoid for soundings collected up until 1987-88, and the NAD83 ellipsoid for soundings collected since then. Despite their lack of uniformity, the different horizontal and vertical datums do not significantly alter the accuracy of the East Coast grids. This is because there is little difference in elevation between mean low water and mean lower low water in the gridding region, and the horizontal differences between the ellipsoids is less than the horizontal resolution of the cells within the grids (i.e.,<90m).
After being quality checked, all of the soundings are entered into a single, master sounding file. This file is an ASCII list of sounding longitude, latitude and depth. The fifth and final dataset is a list of all the 1° x 1° areas of longitude and latitude within the gridding region that contain bathymetric data.
Gridding Methodology and Outputs
The actual gridding is begun by entering into the Perl script command the paths of the directories to the five datasets above and the boundaries of the gridding region (Fig. 1). The first input dataset utilized by the Perl script is the list of 1° x 1° areas that contain bathymetric data. Using this list as a reference, every 1° x 1° area in the gridding region is classified as one of three grid types: land topography only, seafloor bathymetry only, or topography and bathymetry. Each area then undergoes a particular processing sequence based on its classification.
The simplest sequence is that for constructing grids of the areas of only land topography. In fact, the topography only grids are simply the USGS/NIMA DEMs converted to an internal format.
Slightly more complex is the sequence used to construct the seafloor bathymetry-only grids. In this sequence, two grids for each 1° x 1° area are produced. The first of these is the bathymetry-only elevation grid. A Perl script called trimXYZ is used to cull from the master sounding file all the soundings within an initial area of 1.2° x 1.2° (e.g., 67.9° - 69.1° W lon. by 37.9° - 39.1° N lat.), which is centered over the 1° x 1° area (68° - 69° W lon. by 38° - 39° N lat.). The culled soundings are input into the GMT program surface (Smith and Wessel, 1990) to create a grid of this larger. The final 1° x 1° grid is then extracted from this larger grid using the GMT program grdcut. The purpose of this process of gridding a larger area and then trimming the grid down to size is to ensure that the elevations along the boundaries of adjacent grids are identical. (See Fig. 2A).
The second grid produced for the bathymetry-only areas is a sounding-density grid (Fig. 2B). This grid has the same dimensions as the bathymetry grid, but instead contains the number of soundings in each cell of the bathymetry grid. Thus, the sounding-density grid provides the locations, spatial coverage and number of soundings upon which the depths of the bathymetry grids are derived. It also serves as an excellent reference for planning future bathymetric surveys, both to collect new soundings where data is lacking, and to document potential changes in areas where soundings have already been collected.
The sounding-density grid is created using the NGDC program grdDataLoc, which reads in all of the soundings within the data file created by trimXYZ, determines the grid cell within which each sounding falls, and then increments the value of that grid cell. Hence, cells containing for example three soundings have a value of three, while those with no soundings have a value of zero.
An additional step to constructing the bathymetry-only grids is performed for those grids that encompass the seaward limit of the hydrographic/bathymetric surveys. To prevent meaningless extrapolation of bathymetry into unsurveyed waters, an arbitrary limit is placed on the number of grid cells that the gridding process projects depths beyond a sounding location. Based on a trial-and-error evaluation of the maximum distance of extrapolation that preserves morphologic trends in the sounding data, a value of 110 grid cells was chosen.
The gridding sequence for areas encompassing both land and sea is the most complex. Again, both an elevation grid and a sounding-density grid are produced for each 1° x 1° area. The basic procedure for creating these grids is as follows:
The final sounding-density and elevation grids are stored in an internal format designed to minimize the size of the grids and make them compatible for use with viewing software described below. The NGDC grid format consists of a 128-byte header followed by the grid values, which are stored in rows that proceed from left to right and which are arranged from top to bottom (i.e., the grid origin is the upper left corner). The header contains 32 4-byte descriptors of the grid, which occur in the following order: version number, header length, data type (elevation grid, data-density grid, etc.), degrees of northernmost latitude, minutes of northernmost latitude, seconds of northernmost latitude, latitude dimension of grid cell (in arc-seconds), number of cells per grid row, degrees of westernmost longitude, minutes of westernmost longitude, seconds of westernmost longitude, longitude dimension of grid cell (in arc-seconds), number of cells per grid column, minimum grid elevation, maximum grid elevation, grid radius, elevation precision (meters or tenths of meters), empty grid-cell value (e.g., NaN), value type (how the data are stored, i.e. floating point, 2-byte integer, etc.), water datum (mean sea level or local datum), data limit ( maximum calculated value in grid cell), and 11 unused fields.
Grid Images and Radius Grids
At the same time that the grids are converted to an NGDC format, two other types of data are generated as well. The first of these is a shaded-relief JPEG image of each elevation grid (Fig. 2A). An equivalent size GIF image is made of each sounding-density grid; one in which the number of soundings in each grid cell is color-coded (Fig. 2B). Both types of images provide ready-made figures of the grids in commonly-used computer graphic formats. Consequently, the images can be easily viewed using a range of UNIX and personal computer software. A number of these graphic programs can also be used to quickly convert the images into other formats that can be directly inserted into text documents.
The second type of data generated during this stage of the processing are radius grids. These grids are equivalent in size and resolution to the elevation and data-density grids, and indicate the distance to the nearest cell in the data-density grids that includes a sounding. These distances are stored in terms of grid cells. For example, if a cell in the data-density grid encompasses an area in which one or more bathymetric soundings are located, the same cell in the corresponding radius grid has a value of zero, which means its distance from a sounding in terms of grid cells is zero. If a cell in the data-density grid does not encompass any soundings, then the corresponding cell in the radius grid has a value equivalent to the number of grid cells between it and the closest sounding location. The maximum distance to a sounding that is stored in the radius grids is 110 cells, the same maximum distance to which bathymetry is extrapolated beyond a sounding in the elevation grids. A radius of -1 identifies those cells that encompass land areas. The radius grid can be used to modify the distances over which depths are projected beyond sounding locations in the elevation grids. (To see how Grid-Radius is applied to user-generated output grids see Figure 5A and 5B.)
Development of a Seamless Multisource Topographic/Bathymetric Elevation Model Dean Gesch and Robert Wilson
Many applications of geospatial data in coastal environments require knowledge of the near-shore topography and bathymetry. However, because existing topographic and bathymetric data have been collected independently for different purposes, it has been difficult to use them together at the land/water interface owing to differences in format, projection, resolution, accuracy, and datums. As a first step toward solving the problems of integrating diverse coastal datasets, the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA) are collaborating on a joint demonstration project to merge their data for the Tampa Bay region of Florida. The best available topographic and bathymetric data were extracted from the USGS National Elevation Dataset and the NOAA hydrographic survey database, respectively. Before being merged, the topographic and bathymetric datasets were processed with standard GIS tools to place them in a common horizontal reference frame. Also, a key part of the preprocessing was transformation to a common vertical reference through the use of VDatum, a new tool created by NOAA's National Geodetic Survey for vertical datum conversions. The final merged product is a seamless topographic/bathymetric model covering the Tampa Bay region at a grid spacing of 1 arc-second. Topographic LIDAR data were processed and merged with the bathymetry to demonstrate the incorporation of recent third party data sources for several test areas. A primary application of a merged topographic/bathymetric elevation model is for user-defined shoreline delineation, in which the user decides on the tidal condition (for example, low or high water) to be superimposed on the elevation data to determine the spatial position of the water line. Such a use of merged topographic/bathymetric data could lead to the development of a shoreline zone, which could reduce redundant mapping efforts by Federal, State, and local agencies by allowing them to customize their portrayals of the shoreline using a standard baseline elevation dataset. -------------------------------------------------------------------------------- Introduction It is widely recognized that coastal environments, where the ocean meets the land, contain some of the most dynamic ecosystems. These ecosystems at the land/water interface experience significant change owing to both natural and human-induced factors. One-half of the U.S. population lives within a 1-hour drive of the coast, and this portion is predicted to rise to 75% within 25 years. Because of the growing population and increasing commercial and residential development in coastal areas, the impacts and costs of natural hazards continue to climb each year. Many of the physical processes at the land/water interface are controlled by the shape and configuration of the adjacent land (topography) and underwater surface (bathymetry). In low-relief coastal areas, the topographic and bathymetric gradients can be very gentle, and the associated landforms are susceptible to significant changes, from both erosion and accretion. Studies of such a dynamic environment require high-resolution, up-to-date measurements of near-shore topography and bathymetry. In a recent survey of coastal resource managers (NOAA Coastal Services Center, 1999), the following datasets were identified as being "very useful" or "moderately useful" by at least two-thirds of the respondents: near-shore bathymetry, estuarine and bay bathymetry, and coastal topography. These datasets were also listed in the top 10 (out of 29) data needs identified by the coastal managers. Unfortunately, the required data do not exist for many locations along the coast. Even where they do exist, it can be very difficult to integrate the topographic and bathymetric data because of different projections, datums, and data formats. In most cases, the topographic and bathymetric data were collected and processed independently by different agencies for specific purposes, so seamless integration across the land/water interface was not a requirement. As studies in the coastal environment increasingly take a more holistic approach to understanding the physical processes at work, they require seamless integration of recent, high-resolution topographic and bathymetric data. Because topographic data produced by the U.S. Geological Survey (USGS) and bathymetry data produced by the National Oceanic and Atmospheric Administration (NOAA) were collected at different times and to different specifications, especially the vertical datum, it is difficult for geospatial data users in the coastal resource management community to use USGS and NOAA data together for shoreline applications. State and local agencies also collect extensive spatial datasets in coastal areas, and these data are often difficult to integrate effectively with Federal data because of the inconsistent geospatial framework. With over 95,000 miles of U.S. coastline, the significant challenge of maintaining up-to-date mapping requires that all data sources (Federal, State, and local) be used together to meet the information requirements for scientists, engineers, and resource managers working in coastal environments. As a first step toward solving the problems of integrating diverse coastal datasets, the USGS's National Mapping Discipline (NMD) and NOAA's National Ocean Service (NOS) are collaborating on a joint demonstration project to merge their data for the Tampa Bay region of Florida. The goal of the project is to develop tools and techniques to facilitate the integration of the best available USGS topographic data and NOAA hydrographic survey data. The project start included a users workshop in December 1999 at which local coastal managers in the Tampa Bay region identified the difficulty in using USGS and NOAA data together. They expressed the view that data consistency was more important than data accuracy for many of their applications (in which many different demographic and environmental data types must be georeferenced). They also cited the resolution, accuracy, and age of both the topographic and the bathymetric data as areas where significant improvements were needed. Data Sources and Processing The primary data sources merged for Tampa Bay are the currently best available USGS topography and NOAA bathymetry datasets. Another aspect of the Tampa Bay project is to demonstrate the integration of third party data into the geospatial framework, and this has been accomplished by processing high-resolution topographic LIDAR data for several test areas in the Tampa Bay region. The key requirement for creating a seamless, merged product is that each of the input datasets must be in a common geospatial framework, consisting of a coordinate system, horizontal datum, and vertical datum. The geographic coordinate system (decimal degrees of latitude and longitude) referenced to the North American Datum of 1983 (NAD 83) was used for horizontal coordinates, and decimal feet were used for vertical coordinates (referenced vertically to the NAD 83 ellipsoid). Details on the characteristics and processing approach for each of the input data sources are given below. USGS Topography Data The best available topographic data for the Tampa Bay region (Figure 1) were extracted from the USGS National Elevation Dataset (NED). The NED (Osborn and others, 2001) is an implementation of the National Spatial Data Infrastructure (NSDI) concept of framework data. Framework data are defined as those spatial datasets that are fundamental to many applications. In this case, land elevation is a basic layer of information upon which other data layers can be overlaid. The NED is a seamless raster elevation dataset that provides national U.S. coverage at a grid spacing of 1 arc-second (approximately 30 meters). It is derived from USGS map-based digital elevation models (DEM) that have a resolution of either 10 meters or 30 meters. NED production includes the following processing steps performed on the individual source 7.5-minute DEM files: datum and coordinate unit conversion (horizontal and vertical), projection transformation and resampling, filtering (for removal of production artifacts), mosaicking, edge matching, and metadata generation. The resulting 50-gigabyte dataset includes an elevation value posted every 1 arc-second on a latitude/longitude grid (referenced to the NAD 83 horizontal datum). Elevations are expressed in decimal meters referenced to the North American Vertical Datum of 1988 (NAVD 88). Because the native horizontal coordinate system and datum of the NED are the same as those chosen for the merged dataset to be produced in the demonstration project, no transformation was required to place the topographic data into the common horizontal geospatial reference frame. After elevation units were converted from decimal meters to decimal feet, the NED elevations were transformed to the common ellipsoid vertical reference frame using industry standard tools and datasets (VERTCON and GEOID99) from the National Geodetic Survey (NGS). NOAA Bathymetry Data The best available bathymetric data for the Tampa Bay region were extracted from NOAA's hydrographic survey database. The hydrographic surveys are accessed through the Geophysical Data System (GEODAS) at NOAA's National Geophysical Data Center. For the Tampa Bay region, data were available from 47 hydrographic surveys conducted from 1950 to 1996 (Figure 2). Approximately 800,000 soundings were extracted from GEODAS and loaded into ArcView* for processing. An Avenue script facilitated conversion of the ASCII XYZ formatted data from GEODAS into a shapefile format. Because the hydrographic surveys overlap in many areas, the soundings were subjected to a spatial-temporal filtering process to select the most recent bathymetric data for all areas in the Tampa Bay region. Spatial survey polygons, or indexes, were created for each of the 47 hydrographic surveys. The survey polygons were sorted by date, merged with other survey polygons for the same year, and clipped on the basis of the survey date. Another Avenue script was developed to test for topological consistency and to remove polygon slivers for surveys that overlapped. As a result of this process, 15 new master spatial-temporal polygons were produced. Each new polygon represents the spatial location of the most current NOAA soundings in Tampa Bay. Approximately 99% of the project area is covered by digital sounding data at variable data densities. The soundings identified as the most recent from the polygon processing were merged into a single file and sorted on the basis of the vertical datum. For Tampa Bay, approximately half of the soundings were collected in reference to a mean low water (MLW) vertical datum, and the other half were collected for surveys using a mean lower low water (MLLW) vertical datum. A vertical datum transformation was required to place the soundings into the required common vertical reference frame for merging with the topographic data. The transformation was accomplished with VDatum, a tool developed by the NGS specifically for the conversion of elevation data from one datum to another datum. VDatum converts elevation data among 26 different vertical datums that may be categorized into three general classes: orthometric datums, tidal datums, and three-dimensional datums. Orthometric datums, such as NAVD 88, are based on a form of mean sea level (MSL). Tidal datums, including MLW, MLLW, and mean high water (MHW) often used on nautical charts, are tidally derived surfaces of low or high water. Three-dimensional datums are Earth-centered datums realized through the use of space-based geodetic methods, including the Global Positioning System. For conversions involving one of the tidal datums, VDatum uses the geographic distribution of tidal surfaces calculated either from a fully calibrated numerical hydrodynamic model of Tampa Bay or from spatial interpolation (Hess, 2001). The inputs to the hydrodynamic model include coastal water levels (measured at tide gages), inflow from seven rivers into the bay, winds, air temperature, and water salinity and temperature. The outputs from the model incorporated into VDatum include a set of grids representing the vertical differences among MLLW, MSL, and the other tidal datums for geographic locations throughout Tampa Bay. For the selected set of NOAA soundings, VDatum was used to transform the depth values from their tidal datum reference to values referenced to the three-dimensional datum of the NAD 83 ellipsoid. Topography/Bathymetry Merge Processing Before the merged topographic/bathymetric elevation model was generated, the digital soundings (point data) were processed to create a bathymetric grid at the same resolution as the extracted NED topographic data. The NED "shoreline" (interface of zero/nonzero elevations) was used to make the final selection of bathymetry and topography points for merging. All land elevations within 600 meters of the shoreline were converted from raster format to XYZ point data. All bathymetry points coinciding with areas of zero elevation in the NED were selected. Because of the age of some of the hydrographic surveys, some of the soundings were located on areas that had been filled and are now represented as land in the DEM. These points were withheld from further processing. The selected topography points within the shoreline buffer zone and the bathymetry points were gridded to produce a raster surface model with 1-arc-second grid spacing to match the resolution of NED. The points were processed in ArcInfo with TOPOGRID, an implementation of the ANUDEM thin plate spline interpolation algorithm that is optimized for the generation of topographic surfaces (Hutchinson, 1989). The bathymetry points could have been gridded independently of the topographic data, but the shoreline zone land elevations were included in the interpolation to ensure a better match of the bathymetric and topographic surfaces in the subsequent mosaicking step. To avoid the introduction of any interpolation edge effects into the merged elevation model, the output grid from the interpolation was clipped to include land elevations within 300 meters of the shoreline (half of the original 600-meter buffer). The final processing step involved mosaicking the bathymetry grid and the NED elevation grid. The values in the 300-meter overlap area were blended using the ArcGrid MOSAIC function, which performs weighted averaging on a cell-by-cell basis according to the cell's proximity to the edges of the overlap area. The resulting final merged product is a seamless topographic/bathymetric model covering the Tampa Bay region at a grid spacing of 1 arc-second (Figure 3). Topographic LIDAR Topographic data derived from an airborne LIDAR survey conducted by the University of Florida were processed for several test areas to demonstrate the usefulness of incorporating recent, high-resolution, high-accuracy data. The LIDAR survey covered the Pinellas County part of the Tampa Bay region. LIDAR XYZ point data representing "bare earth" elevations were gridded in TOPOGRID to produce a DEM with a 1/9th-arc-second posting (approximately 3 meters). A merge of this high-resolution DEM and gridded bathymetry was done using the same methodology as described earlier for the 1-arc-second product (summarized in Figure 4). The results of the high-resolution topographic/bathymetric merge are seen in Figure 5. Results, Conclusions, and Future Directions The seamless topographic/bathymetric elevation model for the Tampa Bay region demonstrates the successful integration of disparate USGS and NOAA data sources that previously have been difficult for GIS users to integrate. The key to effectively combining USGS topographic data and NOAA bathymetric data for applications in the dynamic coastal zone is the availability of tools developed by the NGS for transformation to a common vertical reference frame. Standard GIS processing and analysis tools were used for the other steps of the data merging, including data reformatting and subsetting, transformation to a common horizontal reference frame, surface interpolation, and mosaicking. Because the source bathymetric and topographic data vary in density and accuracy, users need to be made aware of the spatially varying quality of the merged model. In some areas, the spacing of the soundings would support gridding at a much higher resolution than 1 arc-second, but in other areas the values of the 1-arc-second grid were interpolated only on the basis of distant points. Likewise, the vertical accuracy of the model varies spatially, owing mainly to the wide variety of dates and data collection technologies used for source data acquisition. A merged raster model at a uniform grid cell spacing was produced because most users require such a product for their computer mapping systems. Current work involves generating spatial indices of data quality and accuracy that are coregistered with the model to help users better judge the applicability of the model for their application in a specific location. One index will be a representation of the density (point spacing) of the input sounding data. Another index will portray the estimated vertical accuracy of the bathymetric and topographic data. This index will be helpful for indicating to users the inherent accuracy of the source data and, thus, the derived merged model. Without such labeling, users may assume more accuracy than is actually present, especially because the data are presented in a seamless fashion where discontinuities among data sources have been intentionally minimized, and the vertical units are expressed to a precision of decimal feet. The generation of a gridded bathymetric surface from NOAA digital sounding data points provides a useful data layer for GIS users working in coastal areas. The raster bathymetric data work well in integration with other raster data and visualization of the near-shore marine environment. For many users, it will be easier to work with a raster grid derived from the best available archived soundings, rather than the voluminous point data from multiple overlapping hydrographic surveys. The bathymetric grid generated for this project was produced from historical data collected at various times over 45 years. In order to assess the accuracy of the bathymetric grid derived from the diverse soundings, the grid was compared with recent (1999) high-accuracy bathymetric transect data collected by a NOAA hydrographic survey team at several locations in Tampa Bay. Figure 6 shows the transect locations and the statistics of the differences between the bathymetric grid and the reference transect data. An overall root mean square error of 1.4 feet was calculated for the bathymetric grid. A primary application of a merged topographic/bathymetric dataset is for shoreline definition. USGS topographic products and NOAA nautical charts have different delineations of the shoreline because of different criteria used to define the land/water interface. In many cases, both sources are most likely out-of-date in areas where shorelines have changed because of natural and human influences. Up-to-date, high-resolution, high-accuracy topographic and bathymetric data that have been effectively merged to create a "shoreline zone" will allow users to define their own criteria for shoreline portrayal without having to choose among static representations from differing spatial data sources. A number of "shorelines" could be generated by moving the water level on the merged DEM to the desired tidal datum heights. This would be especially effective in areas of broad sloping beaches where low-water and high-water shorelines differ significantly. In such areas, use of a combined bathymetric/topographic LIDAR data source, such as the U.S. Army Corps of Engineers SHOALS system, would provide the best quality data where the need is the greatest, at the land/water interface. High-resolution and high-accuracy data that cover both near-shore bathymetry and near-shore elevations would be ideal because it would serve as the reference dataset to which the inland topographic data and the offshore bathymetric data would be matched. The merging process could be the same as that used for the current model; surface interpolation across the overlap area by including points from all three data sources, followed by raster mosaicking with weighted average data blending to minimize discontinuities at data source transition zones. The advantages of integrating USGS topographic data and NOAA hydrographic data to create a seamless elevation model for the coastal zone have been recognized by other groups using spatial data for coastal applications. Preliminary work has begun to produce integrated models for study areas along the New York/New Jersey coast and in the Gulf of Mexico along the coast in southeastern Louisiana. The Tampa Bay elevation model is currently being used as a key baseline geospatial dataset for the various geologic, biologic, and hydrologic studies that are part of the USGS integrated science initiative for Gulf of Mexico estuaries.