noaa / NATIONAL OCEANIC AND

ATMOSPHERIC ADMINISTRATION

National Environmental Satellite,

Data, and Information Service

NOAA Technical Memorandum NESDIS NGDC-19

DIGITAL ELEVATION MODEL OF DAYTONA BEACH, FLORIDA:

PROCEDURES, DATA SOURCES AND ANALYSIS 

B.W. Eakins

L.A.Taylor

K.S. Carignan

R.R. Warnken

T. Sazonova 

D.C. Schoolcraft

National Geophysical Data Center

Marine Geology and Geophysics Division

Boulder, Colorado

March 2009

UNITED STATES

DEPARTMENT OF COMMERCE

Gary Locke

Secretary

NATIONAL OCEANIC AND

ATMOSPHERIC ADMINISTRATION

Dr. Jane Lubchenco

Under Secretary for Oceans

and Atmosphere/Administrator

National Environmental Satellite,

Data, and Information Service

Mary E. Kicza

Assistant Administrator

NOAA Technical Memorandum NESDIS NGDC-19

DIGITAL ELEVATION MODEL OF DAYTONA BEACH, FLORIDA:

PROCEDURES, DATA SOURCES AND ANALYSIS

Barry W. Eakins1

Lisa A. Taylor2

Kelly S. Carignan1

Robin R. Warnken2

Tatiana Sazonova1

David C. Schoolcraft2

1

Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder

2NOAA, National Geophysical Data Center, Boulder, Colorado 

National Geophysical Data Center

Marine Geology and Geophysics Division 

Boulder, Colorado

March 2009
NOTICE

Mention of a commercial company or product does not constitute an endorsement by the 

NOAA National Environmental Satellite, Data, and Information Service. Use of information 

from this publication concerning proprietary products or the test of such products for publicity

or advertising purposes is not authorized. 

Corresponding project contact:

Lisa A. Taylor

NOAA, National Geophysical Data Center

Marine Geology and Geophysics Division

325 Broadway, E/GC 3

Boulder, Colorado 80305

Phone: 303-497-6767

Fax: 303-497-6513

E-mail: Lisa.A.Taylor@noaa.gov

http://www.ngdc.noaa.gov/mgg/inundation/tsunami/inundation.html

Also available from the National Technical Information Service (NTIS)

(http://www.ntis.gov)
Digital Elevation Model of Daytona Beach, Florida

iii

Contents

1. Introduction........................................................................................................................................1

2. Study Area..........................................................................................................................................2

3. Methodology ......................................................................................................................................2

3.1 Data Sources and Processing ......................................................................................................3

3.1.1 Shoreline................................................................................................................4

3.1.2 Bathymetry ............................................................................................................6

3.1.3 Topography..........................................................................................................19

3.1.4 Bathymetry–Topography.....................................................................................13

3.2 Establishing Common Datums .................................................................................................14

3.2.1 Vertical datum transformations ...........................................................................14

3.2.2 Horizontal datum transformations.......................................................................14

3.3 Digital Elevation Model Development .....................................................................................14

3.3.1 Verifying consistency between datasets..............................................................14

3.3.2 Gridding of Volusia County LiDAR data............................................................15

3.3.3 Smoothing of bathymetric data ...........................................................................15

3.3.4 Gridding the data with MB-System.....................................................................15

3.4 Quality Assessment of the DEM...............................................................................................16

3.4.1 Horizontal accuracy.............................................................................................16

3.4.2 Vertical accuracy .................................................................................................16

3.4.3 Slope maps and 3-D perspectives........................................................................16

3.4.4	 Comparison with source data files.......................................................................18

3.4.5 Comparison with NGS geodetic monuments......................................................18

4. Summary and Conclusions...............................................................................................................20

5. Acknowledgments............................................................................................................................20

6. References........................................................................................................................................20

7. Data Processing Software ................................................................................................................20

List of Figures

Figure 1. Shaded-relief image of the Daytona Beach, Florida DEM.........................................................1

Figure 2. Ponce de Leon Inlet ....................................................................................................................2

Figure 3. Source and coverage of datasets used to compile the Daytona Beach DEM..............................3

Figure 4. Digital coastline datasets used in building a ‘combined coastline’.............................................4

Figure 5. Spatial coverage of bathymetric datasets used to compile the Daytona Beach DEM ................6

Figure 6. Digital NOS hydrographic survey coverage in the Daytona Beach region ................................8

Figure 7. Source and coverage of topographic datasets used to compile the Daytona Beach DEM .........9

Figure 8. Gridded relief image of the Volusia County topographic LiDAR data used in 

compiling the Daytona Beach DEM..................................................................................10

Figure 9. Example of St. Johns County topographic data ........................................................................11

Figure 10. Gridded relief image of USGS NED DEM...............................................................................12

Figure 11. Spatial coverage of JALBTCX coastal bathymetric–topographic LiDAR survey ...................13

Figure 12. Histogram of the differences between NOS hydrographic survey H09371 and the 

1 arc-second pre-surfaced bathymetric grid ......................................................................15

Figure 13. Slope map of the Daytona Beach DEM....................................................................................17

Figure 14. Perspective view from the southeast of the Daytona Beach DEM...........................................17

Figure 15.	 Histogram of the differences between one St Johns County LiDAR file and the

Daytona Beach DEM.........................................................................................................18

Figure 16.	 Histogram of the differences between one JALBTCX LiDAR file and the Daytona

Beach DEM. ......................................................................................................................18

Figure 17. Histogram of the differences between NGS geodetic monument elevations and the 

Daytona Beach DEM.........................................................................................................19

Figure 18. Location of NGS geodetic monuments and NOAA tide stations .............................................19
Eakins et al., 2009

iv

List of Tables

Table 1.	 PMEL specifications for the Daytona Beach, Florida DEM.......................................................2

Table 2. Shoreline datasets available in the Daytona Beach, Florida region............................................4

Table 3. Electronic navigational charts available in the Daytona Beach, Florida region.........................5

Table 4. NOAA Raster Nautical Charts in the Daytona Beach, Florida region........................................5

Table 5. Bathymetric datasets used in compiling the Daytona Beach DEM............................................6

Table 6. Digital NOS hydrographic surveys used in compiling the Daytona Beach DEM......................7

Table 7. Topographic datasets used in compiling the Daytona Beach DEM............................................9

Table 8. Bathymetric–Topographic dataset used in compiling the Daytona Beach DEM......................13

Table 9. Relationship between Mean High Water and other vertical datums at the Daytona 

Beach Shores tide station, #8721120.................................................................................14

Table 10. Data hierarchy used to assign gridding weight in MB-System.................................................16
Digital Elevation Model of Daytona Beach, Florida:

Procedures, Data Sources and Analysis

1. Introduction

In October 2007, the National Geophysical Data Center (NGDC), an office of the National Oceanic and

Atmospheric Administration (NOAA), developed a topographic–bathymetric digital elevation model (DEM) of 

Daytona Beach, Florida (Fig. 1) for the Pacific Marine Environmental Laboratory (PMEL) NOAA Center for Tsunami

Research (http://nctr.pmel.noaa.gov/). The 1/3 arc-second1 coastal DEM will be used as input for the Method of 

Splitting Tsunami (MOST) model developed by PMEL to simulate tsunami generation, propagation and inundation. 

The DEM was generated from diverse digital datasets in the region (grid boundary and sources shown in Fig. 3) and 

will be used for tsunami inundation modeling, as part of the tsunami forecast system SIFT (Short-term Inundation 

Forecasting for Tsunamis) currently being developed by PMEL for the NOAA Tsunami Warning Centers. This report 

provides a summary of the data sources and methodology used in developing the Daytona Beach DEM. 

Figure 1. Shaded-relief image of the Daytona Beach, Florida DEM.

1. The Daytona Beach DEM is built upon a grid of cells that are square in geographic coordinates (latitude and longitude), however, the cells are 

not square when converted to projected coordinate systems, such as UTM zones (in meters). At the latitude of Daytona Beach, Florida (29°13′ N, 

81°0′ W) 1/3 arc-second of latitude is equivalent to 10.263 meters; 1/3 arc-second of longitude equals 9.004 meters.
Eakins et al., 2009

2

2. Study Area

The Daytona Beach DEM covers the coastal region surrounding the town of Daytona Beach, Florida (Fig. 1), a 

popular tourist destination spot. Daytona Beach is bordered to the north by Palm Coast, Flagler Beach, and Ormond 

Beach and to the south by Daytona Beach Shores and New Smyrna Beach. The region has a long, heavily populated 

sandbar that is largely isolated from mainland Florida by the Halifax River lagoon. Entrance to the lagoon, whose 

banks provide anchorage for numerous ships and sailing vessels, is through the mouth of Ponce de Leon Inlet (Fig. 2). 

The Atlantic Intracoastal Waterway, which stretches from Miami to Maine, passes through the Halifax River lagoon, 

and is maintained by the U.S. Army Corps of Engineers.

The Atlantic shoreline near Daytona Beach is a dynamic system, though development has attempted to stabilize 

it. Sand is constantly being moved by wind and waves. Shorelines, islands, spits, bars, and dunes change shape, move, 

grow, or diminish in size or even disappear as part of the dynamic natural process. 

Figure 2. Ponce de Leon Inlet. A) Satellite image of the mouth of Ponce de Leon Inlet. B) Photo of Ponce de Leon Inlet 

looking south from the Ponce de Leon Lighthouse. (photo from http://ponceinletfirefighter.com/)

3. Methodology

The Daytona Beach, Florida DEM was constructed to meet PMEL specifications (Table 1), based on input

requirements for the development of Reference Inundation Models (RIMs) and Standby Inundation Models (SIMs) 

(V. Titov, pers. comm.) in support of NOAA’s Tsunami Warning Centers use of SIFT to provide real-time tsunami 

forecasts in an operational environment. The best available digital data were obtained by NGDC and shifted to common 

horizontal and vertical datums: North America Datum 1983 (NAD 83) and Mean High Water (MHW), for modeling of 

maximum flooding, respectively2. Data processing and evaluation, and DEM assembly and assessment are described 

in the following subsections.

Table 1: PMEL specifications for the Daytona Beach, Florida DEM.

Grid Area Daytona Beach, Florida

Coverage Area 81.40o to 80.50o W; 28.85o to 29.70o N

Coordinate System Geographic decimal degrees

Horizontal Datum World Geodetic System 1984 (WGS 84)

Vertical Datum Mean High Water (MHW)

Vertical Units Meters

Grid Spacing 1/3 arc-second

Grid Format ESRI Arc ASCII grid

2. The horizontal difference between the North American Datum of 1983 (NAD 83) and World Geodetic System of 1984 (WGS 84) geographic 

horizontal datums is approximately one meter across the contiguous U.S., which is significantly less than the cell size of the DEM. Most GIS applications treat the two datums as identical, so do not actually transform data between them, and the error introduced by not converting between 

the datums is insignificant for our purposes. NAD 83 is restricted to North America, while WGS 84 is a global datum. As tsunamis may originate

most anywhere around the world, tsunami modelers require a global datum, such as WGS 84 geographic, for their DEMs so that they can model the 

wave’s passage across ocean basins. This DEM is identified as having a WGS 84 geographic horizontal datum even though the underlying elevation

data were typically transformed to NAD 83 geographic. At the scale of the DEM, WGS 84 and NAD 83 geographic are identical and may be used 

interchangeably.
Digital Elevation Model of Daytona Beach, Florida

3

3.1 Data Sources and Processing

Shoreline, bathymetric, topographic, and topographic–bathymetric digital datasets (Fig. 3) were obtained from 

several U.S. federal, state and local agencies, and a private company, including: NOAA’s National Ocean Service 

(NOS), Office of Coast Survey (OCS) and Coastal Services Center (CSC); the U.S. Geological Survey (USGS);

the U.S. Army Corps of Engineers (USACE); the Florida Fish and Wildlife Research Institute (FWRI); Lake, St. 

Johns, and Volusia Counties; and Jones Edmunds & Associates, Inc. Safe Software’s (http://www.safe.com/) FME 

data translation tool package was used to shift datasets to WGS 84 horizontal datum and to convert them into ESRI 

(http://www.esri.com/) ArcGIS shape files3. The shape files were then displayed with ArcGIS to assess data quality and

manually edit datasets. Vertical datum transformations to MHW were accomplished using FME, based upon the offset 

grids compiled by PMEL and the data from the NOAA tide stations. VDatum model software (http://nauticalcharts.

noaa.gov/csdl/vdatum.htm) was not available for this area. Applied Imagery’s Quick Terrain Modeler software (http://

www.appliedimagery.com/) was used to edit and assess the quality of the LiDAR data. GEODAS (http://www.ngdc.

noaa.gov/mgg/geodas/) was used to convert topographic contours into point data. GMT (http://gmt.soest.hawaii.edu/) 

and MB-System (http://www.ldeo.columbia.edu/res/pi/MB-System/) were used to grid the data and build the DEM.

Figure 3. Source and coverage of datasets used to compile the Daytona Beach DEM. DEM 

boundary in purple.

3. FME uses the North American Datum Conversion Utility (NADCON; http://www.ngs.noaa.gov/TOOLS/Nadcon/Nadcon.html) developed by 

NOAA’s National Geodetic Survey (NGS) to convert data from NAD 27 to NAD 83. NADCON is the U.S. Federal Standard for NAD 27 to NAD 

83 datum transformations.
Eakins et al., 2009

4

3.1.1 Shoreline

Coastline datasets of the Daytona Beach region were obtained from the Florida Fish and Wildlife Research 

Institute (FWRI), St. Johns County GIS Division, and NOAA’s Office of Coast Survey (OCS; Table 2; Fig. 4).

Table 2: Shoreline datasets available in the Daytona Beach, Florida region.

Source Year Data Type Spatial 

Resolution

Original Horizontal 

Datum/Coordinate System

Original 

Vertical Datum URL

Florida Fish and 

Wildlife Research 

Institute

1995 Digitized 

coastline 1:40,000 NAD 83 Albers Conical Equal Area (meters) Inferred MHW http://research.myfwc.

com/

St. Johns County 2003–

2004

LiDAR 

hydrography 1:24,000 NAD 83 State Plane, Florida East (survey feet) NAVD88

OCS ENCs 2006–

2007

Digitized 

Hydrography

1:80,000–

1:449,659 WGS 84 geographic MHW http://nauticalcharts.noaa.

gov/mcd/enc/index.htm

NGDC 2006 Gridded coastal 

LiDAR WGS 84 geographic MHW

Figure 4. Digital coastline datasets used in building a ‘combined coastline’ 

for the Daytona Beach region. DEM boundary in purple.
Digital Elevation Model of Daytona Beach, Florida

5

1) Florida Fish and Wildlife Research Institute Digitized Coastline

The Florida Fish and Wildlife Research Institute (FWRI; http://research.myfwc.com/) contracted the U.S. 

Fish and Wildlife Service to digitize the Florida coast in 1990. The data set was created from the most current 

National Oceanic and Atmospheric Administration (NOAA) Nautical Charts available at the time. The scale 

of the source charts varied from 1:10,000 in some harbors to 1:80,000 in the Big Bend area. However, most 

of the source scale is 1:40,000. The current, 2000, data set is the result of revisions to the 1990 version. Some 

areas, including inland areas where there is no chart coverage and areas that have needed more accuracy for 

individual projects, have been digitized from USGS 7.5-minute Quadrangles and Digital Orthophoto Quarter 

Quadrangles (DOQQs). This GIS data set represents the Florida shoreline as polygons and lines, in Albers 

Conical Equal Area projection. Vertical datum is undefined but is inferred to be MHW.

2) St. Johns County LiDAR-derived Hydrography

The St. Johns County, Florida Board of County Commissioners contracted out for a high-resolution 

LiDAR survey of the entire county, which was performed in 2003 and 2004. The survey was designed to 

support the development of a digital terrain modelsufficient to derive 1-foot contours. The work also included

aerial photography, ground control survey points, and derivation of local hydrographic features for use as 

“breaklines” in modeling the terrain. Results of this work were provided to NGDC by Michael Campbell, St. 

Johns County GIS Division. Data were originally in NAD 83 Florida East State Plane coordinates (survey 

feet) and NAVD88 vertical datum.

3) OCS electronic navigational chart

Three electronic navigational charts (ENCs) were available for the Daytona Beach area (Table 3) and 

were downloaded from NOAA’s Office of Coast Survey website (http://nauticalcharts.noaa.gov/mcd/enc/

index.htm). The ENCs are available in S-57 format and include coastline data files referenced to Mean High

Water. Other nautical charts (Table 4) are available as georeferenced raster nautical charts (RNCs; digital 

images of the charts) and were used to QC bathymetric, bathymetric–topographic, and topographic datasets. 

Table 3: Electronic navigational charts available in the Daytona Beach, Florida region.

Chart Title Edition Issue Date Scale

11480 Charleston Light to Cape Canaveral 10 2007-03-22 1:449,659

11484 Ponce de Leon Inlet to Cape Canaveral 3 2007-07-10 1:80,000

11486 St. Augustine Light to Ponce to Leon Inlet 1 2006-10-23 1:80,000

Table 4. NOAA Raster Nautical Charts in the Daytona Beach, Florida region.

Nautical Chart # Region Scale Ed. Date

11485 Intracoastal Waterway Tolmato River to Palm Shores 1:40,000 2007-08-01

4) NGDC LiDAR-derived MHW contour

In order to define the current coastline, NGDC processed the most recent JALBTCX high-resolution

bathymetric–topographic LiDAR dataset along the Atlantic coast, available from NOAA’s Coastal Services 

Center, to create a zero-elevation coastline at Mean High Water vertical datum.

The FWRI 1:40,000 scale coastline of Florida was merged with the St. Johns County hydrographic coastline and 

the MHW contour from the JALBTCX coastal LiDAR survey to create a ‘combined coastline’ of the Daytona Beach 

area; the ENC coastlines were not used due to their low resolution compared to the other datasets. River inlets were 

included in the ‘combined coastline’ where digital bathymetric data were present. Modifications to the combined

coastline included adjustments to fit the most recent topographic LiDAR data. In addition, the jetty at the mouth of

Ponce de Leon Inlet was added, as it was not well represented in the coastlines. All modifications were made using

ArcMap editing tools.
Eakins et al., 2009

6

3.1.2 Bathymetry

Bathymetric datasets used in the compilation of the Daytona Beach DEM include 41 NOS hydrographic surveys, 

and 6 USACE bathymetric surveys (Table 5; Fig. 5).

Table 5: Bathymetric datasets used in compiling the Daytona Beach DEM.

Source Year Data Type Spatial Resolution

Original Horizontal 

Datum/Coordinate 

System

Original 

Vertical Datum URL

 NOS 

1870 

to 

1983

Hydrographic 

survey 

soundings

Ranges from 10 m to 1 

km (varies with scale 

of survey, depth, traffic,

and probability of 

obstructions)

NAD 27 geographic

Mean Low 

Water or Mean 

Lower Low 

Water (meters)

http://www.ngdc.noaa.gov/

mgg/bathymetry/hydro.html

USACE 2006 Hydrographic 

survey profiles

Profiles 50 to 200

meters long, spaced 50 

meters apart, with point 

spacing < 1 meter 

NAD 83 Florida 

State Plane East

(survey feet)

Mean Lower 

Low Water 

(feet)

http://www.saj.usace.army.

mil/hydroSurvey/hydro.htm

Figure 5. Spatial coverage of bathymetric datasets used to compile the Daytona Beach DEM. DEM boundary in purple.
Digital Elevation Model of Daytona Beach, Florida

7

1) NOS hydrographic survey data

A total of 41 NOS hydrographic surveys conducted between 1870 and 1983 were utilized in developing 

the Daytona Beach DEM (Table 6; Fig. 6). The hydrographic survey data were originally horizontally 

referenced to NAD 27 or NAD 83 geographic datum, and vertically referenced to Mean Lower Low Water 

(MLLW), Mean Low Water (MLW), “average water level during low river stages,” or “local low water” 

datum. The last two vertical datums were inferred to be equivalent to MLW.

Data point spacing for the NOS surveys varied by collection date. In general, earlier surveys had greater 

point spacing than more recent surveys. All surveys were extracted from NGDC’s online NOS hydrographic 

database (http://www.ngdc.noaa.gov/mgg/bathymetry/hydro.html). The data were then converted to NAD 

83 and MHW using FME software, an integrated collection of spatial extract, transform, and load tools for 

data transformation (http://www.safe.com). The surveys were subsequently clipped to a polygon 0.05 degree 

(~5%) larger than the Daytona Beach DEM area to support data interpolation along grid edges. 

After converting all NOS survey data to MHW (see Section 3.2.1), the data were displayed in ESRI 

ArcMap and reviewed for digitizing errors against scanned original survey smooth sheets and edited as 

necessary. The surveys were also compared to the topographic, bathymetric, and topographic–bathymetric 

datasets, the combined coastline, and NOS raster nautical charts (RNCs). The surveys were clipped to remove 

soundings that overlap the more recent USACE bathymetric surveys. 

Table 6: Digital NOS hydrographic surveys used in compiling the Daytona Beach DEM.

NOS Survey ID Year of 

Survey Survey Scale Original Vertical Datum Horizontal Datum of digital records

H01047 1870 10,000 mean low water NAD 27

H01148A 1872 5,000 mean low water NAD 27

H01148B 1872 5,000 mean low water NAD 27

H01232A 1874 5,000 mean low water NAD 27

H01232B 1874 5,000 mean low water NAD 27

H01232C 1874 5,000 mean low water NAD 27

H01233A 1874 5,000 mean low water NAD 27

H01233B 1874 5,000 mean low water NAD 27

H01234A 1874 5,000 mean low water NAD 27

H01234B 1874 5,000 mean low water NAD 27

H01289B 1874 5,000 mean low water NAD 27

H01289C 1874 5,000 mean low water NAD 27

H01290 1874/75 10,000 mean low water NAD 27

H01291 1875 20,000 mean low water NAD 27

H04478 1925 10,000 mean low water NAD 27

H06132 1935/37 10,000 mean lower low water NAD 27

H06263 1937 10,000 mean lower low water NAD 27

H06302 1938 10,000 mean lower low water NAD 27

H06306 1938 5,000 mean low water NAD 27

H06307 1938 10,000 mean low water NAD 27

H06308 1938 5,000 mean low water NAD 27

H06310 1938 5,000 avg water level during low river stages NAD 27

H06311 1938 5,000 mean low water NAD 27

H06312 1938 5,000 mean low water NAD 27

H06313 1938 5,000 avg water level during low river stages NAD 27

H06431 1939 5,000 mean low water NAD 27

H06432 1939 5,000 mean low water NAD 27

H06433 1939 5,000 mean low water NAD 27

H06434 1939 5,000 mean low water NAD 27

H06435 1939 10,000 mean low water NAD 27
Eakins et al., 2009

8

H06676 1941 10,000 mean lower low water NAD 27

H08840 1965 80,000 mean low water NAD 27

H08879 1966 80,000 mean low water NAD 27

H08937 1966 80,000 mean low water NAD 27

H09344 1973 40,000 mean low water NAD 27

H09367 1973 80,000 mean low water NAD 27

H09358 1973/74 40,000 mean low water NAD 27

H09371 1974 40,000 mean low water NAD 27

H09455 1974 40,000 mean low water NAD 27

D00082 1983 25,000 local low water NAD 83

H10071 1983 10,000 local low water NAD 83

Figure 6. Digital NOS hydrographic survey coverage in the Daytona Beach region. Some older surveys were not utilized 

as they have been superseded by more recent surveys. DEM boundary in red, land areas in brown.

2) U.S. Army Corps of Engineers hydrographic surveys

The USACE, Daytona Beach District provided NGDC with six recent bathymetric surveys located in 

Daytona Beach, Ponce Inlet, St. John River, and the Atlantic Intracoastal Waterway (Fig. 5). The surveys 

were collected in 2006, and referenced to NAD 83 Florida State Plane East (survey feet) and MLLW (feet) 

datums. The files were converted to NAD 83 and MHW using FME. The surveys consist of profiles 50 to 200

meters long, averaging 50 meters apart. Point spacing along the profiles is generally less than 1 meter.
Digital Elevation Model of Daytona Beach, Florida

9

3.1.3 Topography

Topographic datasets in the Daytona Beach region were obtained from Volusia County Special Projects, St. Johns 

County GIS Division, Lake County GIS Division, Jones Edmunds & Associates, Inc., and the U.S. Geological Survey 

(USGS; Table 7; Fig. 7). The Lake County data were outside the boundaries of the Daytona Beach DEM and not used. 

The LiDAR data from Jones Edmunds and Associates covered Flagler County, in the central part of the DEM region, 

however, the data were not processed to bare earth and vegetation and buildings were significantly represented in the

data. NGDC therefore chose not to use the Flagler County LiDAR data.

Table 7: Topographic datasets used in compiling the Daytona Beach DEM.

Source Year Data Type Spatial 

Resolution

Original Horizontal 

Datum/Coordinate System

Original 

Vertical Datum URL

Volusia County 2006 Topographic 

LiDAR 1 meter NAD 83 Florida East Sate Plane, (survey feet) NAVD88 (feet)

St Johns 

County

2003–

2004

Topographic 

LiDAR 1 meter NAD 83 Florida East Sate Plane, (survey feet) NAVD88 (feet)

USGS 1999-

2000 NED DEM 1/3 arcsecond NAD 83 geographic NAVD88

(meters) http://ned.usgs.gov/

Figure 7. Source and coverage of topographic datasets used to compile the Daytona Beach DEM. 

DEM boundary in purple.
Eakins et al., 2009

10

1) Volusia County topographic LiDAR

Volusia County Special Projects provided NGDC with topographic LiDAR data4, in NAD 83 Florida East 

State Plane (survey feet) datum and NAVD88 vertical datum, for Volusia County, Florida from a survey flight

conducted in March 2006. These data have elevation points spaced approximately 1 meter apart covering the 

southern part of the Daytona Beach DEM region. NGDC processed the raw LAS files with Quick Terrain

Modeler (http://www.appliedimagery.com/) to extract the second return, which roughly corresponds to bare 

earth. The data were transformed to NAD 83 geographic coordinates and gridded with MB-system’s (http://

www.ldeo.columbia.edu/res/pi/MB-System/) ‘mbgrid’ tool to resample the dense bare-earth LiDAR data 

to 1/3 arc second cell size (see Section 3.3.2). The Volusia County LiDAR grid was brought into ArcMap 

(Fig. 8), where it was evaluated, clipped to the coastline to remove false returns from water bodies, and 

transformed to MHW. Gaps within the grid are the result of QT Modeler’s inability to read some of the files

provided to NGDC. The Volusia LiDAR data provided greater resolution of morphologic features compared 

with USGS NED topography, though on average the NED elevations were close to the Volusia County 

LiDAR elevations.

Figure 8. Gridded relief image of the Volusia County topographic LiDAR data used in compiling the Daytona Beach DEM. 

DEM boundary in purple.

4. The LiDAR data were acquired using a Leica ALS50 from an altitude of 3,281’ above ground level to provide a nominal ground sample distance 

of 3.3-feet. The scanner field of view was 42-degrees, and the scan rate was 33-hertz. First and last return data were collected along with the signal

return intensity. A total of 141 flights of LiDAR data were acquired. Flight lines were flown with a 30% sidelap, providing a line-to-line swath

width of 1,763-feet. Ten (10) airborne GPS base station locations were utilized across the county with as minimum of two redundant airborne GPS 

bases stations in operation during any data acquisition with maximum line-of-sight distance between the base station and aircraft of 30-km. The 

LiDAR data were reduced using Grafnav (Waypoint Consulting) for GPS post-processing, PosProc (Applanix Crop) for IMU processing, ALS50 

Post Processor (LH Systems) to initial LiDAR processing, TerraScan (Terrasolid) for initial point classification, and proprietary Woolpert developed

software for refining the point classification and QC. The LiDAR points within this file were derived from a full resolution LiDAR dataset using

Keypoint identification. This results in a reduciton of the total number of points and retains only those points necessary to define the ground surface

at the specified accuracies and tolerances. For a complete description of survey methods, processing methods, software, system parameters, and

accuracy analysis, see the Florida MTS Report of Specific Purpose Survey, 2006/2007 Volusia County LiDAR DTM Project, dated June 2007.

[Extracted form metadata.]
Digital Elevation Model of Daytona Beach, Florida

11

2) St. Johns County topographic LiDAR

The St. Johns County GIS Division provided NGDC with topographic datasets, in NAD 83 Florida East 

State Plane (survey feet) datum and NAVD88 vertical datum, for St. Johns County, Florida (Fig. 7). The 

datasets included LiDAR-derived elevation points (processed to bare earth), 1-foot bare-earth topographic 

contours derived from the LiDAR data5

 using aerial photographs as an interpolation aid, and hydrographic 

lines, also derived from the LiDAR data and aerial photographs (e.g., Fig. 9). NGDC transformed the datasets 

to WGS 84 geographic horizontal and MHW vertical datums using FME. The hydrographic lines near the 

Atlantic Coast were inferred to be at MHW and were used in building the combined coastline for Daytona 

Beach, however, interior lines defining rivers and lakes were clearly not at MHW and were not used in

building the Daytona Beach DEM. NGDC extracted points sampled every 10 meters along the topographic 

contours, using GEODAS, which, along with the LiDAR elevations, were used in building the DEM.

Figure 9. Example of St. Johns County topographic data. Point elevations are from a 2003–2004 LiDAR survey. Hydrographic lines 

and topographic contours were derived from the LiDAR data and aerial photographs. Note the contours defining the ridge (highway), 

which were drawn using aerial photographs as a guide. No gridding algorithm is capable of properly representing such a linear 

morphologic feature from point elevation data alone. Contour interval: 1 foot.

5. The LiDAR data were acquired using a Leica ALS40. Ninety-three (93) flight lines of LiDAR data were acquired in 6 sessions across the County

between January 17 and February 7, 2004. The average GSD of the raw LiDAR data were 3.3-feet. Three (3) concurrent airborne GPS base stations 

were used for each acquisition session. The ABGPS data were reduced using the GrafNav software package by Waypoint Consulting, Incorporated. 

The IMU data were reduced using the PosProc software package by Applanix Corporation. The initial LiDAR “point cloud” was derived through 

the ALS Post Processor software package by Leica Geosystems. The aircraft, LiDAR system, and associated computer hardware and software are 

owned and operated by Woolpert LLP. Once the initial LiDAR “point cloud” was derived, Woolpert performed QC to look for any systematic error 

within the LiDAR flights using proprietary software. After systematic error was identified and removed, the individual LiDAR flights were clipped

to remove overlap between adjacent flight lines and provide a homogeneous coverage over the project extents. Using the homogeneous coverage,

above ground features were classified and removed using proprietary software to produce the bare-earth coverage. [Extracted from survey report.]
Eakins et al., 2009

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3) USGS NED topographic DEM

The U.S. Geological Survey (USGS) National Elevation Dataset (NED; http://ned.usgs.gov/) provides 

complete 1/3 arc-second coverage of the Daytona Beach region6. Data are in NAD 83 geographic coordinates 

and NAVD88 vertical datum (meters), and are available for download as raster DEMs. The bare-earth 

elevations have a vertical accuracy of +/- 7 to 15 meters depending on source data resolution. See the USGS 

Seamless web site for specific source information (http://seamless.usgs.gov/). The dataset was derived from 

USGS quadrangle maps and aerial photographs based on topographic surveys; it has been revised using data 

collected in 1999 and 2000. The NED DEM included “zero” elevation values over the open ocean (Fig. 10), 

which were removed from the dataset by clipping to the combined coastline. The NED DEM was also clipped 

to the extents of the Volusia and St Johns county LiDAR datasets to prevent degradation of those two higherresolution datasets in the Daytona Beach DEM.

Figure 10. Gridded relief image of USGS NED DEM. Blue is zero elevation over water, which had to be clipped from the 

NED DEM. DEM boundary in purple.

6. The USGS National Elevation Dataset (NED) has been developed by merging the highest-resolution, best quality elevation data available across 

the United States into a seamless raster format. NED is the result of the maturation of the USGS effort to provide 1:24,000-scale Digital Elevation 

Model (DEM) data for the conterminous U.S. and 1:63,360-scale DEM data for Georgia. The dataset provides seamless coverage of the United 

States, HI, AK, and the island territories. NED has a consistent projection (Geographic), resolution (1 arc second), and elevation units (meters). The 

horizontal datum is NAD 83, except for AK, which is NAD 27. The vertical datum is NAVD88, except for AK, which is NGVD29. NED is a living 

dataset that is updated bimonthly to incorporate the “best available” DEM data. As more 1/3 arc second (10 m) data covers the U.S., then this will 

also be a seamless dataset. [Extracted from USGS NED website]
Digital Elevation Model of Daytona Beach, Florida

13

3.1.4 Bathymetry–Topography

One bathymetric–topographic LiDAR dataset was available from NOAA’s Coastal Services Center (CSC) Coastal 

Remote Sensing Program, covering the Atlantic coastal region near Daytona Beach (Fig. 11, Table 8).

Table 8: Bathymetric–topographic dataset used in compiling the Daytona Beach DEM.

Source Year Data Type Spatial 

Resolution

Original Horizontal Datum/

Coordinate System

Original Vertical 

Datum

JALBTCX/CSC 2006 Coastal LiDAR < 5 meters NAD 83 geographic NAVD88 (meters)

1) JALBTCX coastal LiDAR

The Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX) conducted a 

bathymetric–topographic LiDAR survey of the Florida east coast in 2006 using the Compact Hydrographic 

Airborne Rapid Total Survey (CHARTS) system7. These data are published on the NOAA CSC web site 

(http://maps.csc.noaa.gov/TCM/) and were downloaded at 5-meter point spacing in NAD 83 geographic 

and NAVD88 datums. Horizontal accuracy is 3 m, and vertical accuracy is estimated at 30 cm. These data 

have not been processed to bare earth, so buildings and trees are represented in the data. Because of this, 

the coastal LiDAR data were given lower weighting in the gridding process than the Volusia and St. Johns 

County topographic LiDAR data (see Section 3.3.4; Table 10). 

Figure 11. Spatial coverage of JALBTCX coastal bathymetric–topographic LiDAR survey. Combined coastline in red, 

DEM boundary in purple.

7. These data were collected using a SHOALS-1000T system. It is owned and operated by Fugro Pelagos performing contract survey services for 

the US Army Corps of Engineers. The system collects topographic lidar data at 10kHz and hydrographic data at 1kHz. The system also collects 

RGB imagery at 1Hz. Aircraft position, velocity and acceleration information are collected through a combination of Novatel and POS A/V equipment. Raw data are collected and transferred to the office for downloading and processing in SHOALS GCS software. GPS data are processed using

POSPac software and the results are combined with the lidar data to produce 3-D positions for each lidar shot. These data are edited using Fledermaus software where anomalous data are removed from the dataset. The edited data are unloaded from SHOALS GCS, converted from ellipsoid to 

orthometric heights, based on the GEOID03 model, and split into geographic tiles covering approximately 5km each. [Extracted from metadata.]
Eakins et al., 2009

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3.2 Establishing Common Datums

3.2.1 Vertical datum transformations

Datasets used in the compilation and evaluation of the Daytona Beach DEM were originally referenced to a number 

of vertical datums including Mean Lower Low Water (MLLW), Mean Low Water (MLW), and North American Vertical 

Datum of 1988 (NAVD88). All datasets were transformed to MHW to provide the maximum flooding for inundation

modeling. Units were first converted from feet to meters as appropriate. PMEL provided NGDC with conversion grids

for MLLW to MHW, and NAVD88 to MHW for the Daytona Beach region. These grids were derived from vertical 

datum relationships measured at tide stations in the Daytona Beach region (http://tidesandcurrents.noaa.gov/).

1) Bathymetric data

NOS hydrographic surveys in MLW were transformed to MLLW, using FME software, by adding a 

constant of 0.047, as measured at Daytona Beach Shores tide station, #8721120 (Table 9). Conversion of 

these soundings to MHW, along with NOS and USACE surveys in MLLW, was accomplished using the 

MLLW-to-MHW conversion grid.

2) Topographic data

The USGS NED 1/3 arc-second DEM and the Volusia County and St Johns County LiDAR data were 

originally referenced to NAVD88. Conversion to MHW, using FME software, was accomplished by using the 

NAVD88-to-MHW grid provided by the PMEL. 

Table 9. Relationship between Mean High Water and other vertical datums at the Daytona Beach Shores tide 

 station, #8721120.

Vertical datum Difference to MHW

NAVD88 -0.364 meters

MSL -0.606 meters

MLW -1.190 meters

MLLW -1.237 meters

3.2.2 Horizontal datum transformations

Datasets used to compile the Daytona Beach DEM were originally referenced to NAD 83 Florida East State Plane 

(survey feet), NAD 83 geographic, NAD 27 geographic, or WGS 84 geographic horizontal datums. The relationships 

and transformational equations between these horizontal datums are well established. All data were converted to a 

horizontal datum of NAD 83 geographic using FME and GMT software.

3.3 Digital Elevation Model Development

3.3.1 Verifying consistency between datasets

After horizontal and vertical transformations were made, the resulting ESRI shape files were checked in ArcMap

for consistency between datasets. Problems and errors were identified and resolved before proceeding with subsequent

gridding steps. The evaluated and edited ESRI shape files were then converted to xyz files in preparation for gridding.

Problems included:

•	 Data values were present over the Atlantic Ocean and rivers in the NED and Volusia County LiDAR 

topographic data. Each dataset required automated clipping to the combined coastline.

•	 Digital, measured bathymetric values from NOS surveys date back over 100 years. More recent data, such 

as the USACE hydrographic surveys differed from older NOS data by as much as 10 meters. The older NOS 

survey data were excised where more recent bathymetric data exists.

•	 The JALBTCX coastal bathymetric–topographic LiDAR dataset had not been processed to bare earth.
Digital Elevation Model of Daytona Beach, Florida

15

3.3.2 Gridding of Volusia County topographic LiDAR data

The high density and large volume of the Volusia County topographic LiDAR data necessitated gridding of this 

dataset for visualization and evaluation purposes. A 1/3 arc-second grid of the LiDAR data—0.05 degrees larger 

than the Daytona Beach DEM gridding region—was generated using MB-System (http://www.ldeo.columbia.edu/

res/pi/MB-System/), an NSF-funded share-ware software application specifically designed to manipulate submarine

multibeam sonar data, though it can utilize a wide variety of data types, including generic xyz data. The tool ‘mbgrid’ 

was used to create the grid, using low tension to ensure that the grid accurately represented the underlying data. This 

grid was evaluated in ArcMap, clipped to the extent of the LiDAR data—to limit interpolation into areas without 

data—then exported as an xyz file for use in the final gridding process (see Table 10).

3.3.3 Smoothing of bathymetric data

The NOS hydrographic surveys are generally sparse at the resolution of the 1/3 arc-second Daytona Beach DEM: 

in both deep water and in some areas close to shore, the NOS survey data have point spacing up to 1900 m apart. In 

order to reduce the effect of artifacts in the form of lines of ‘pimples’ in the DEM due to this low resolution dataset, 

and to provide effective interpolation into the coastal zone, a 1 arc-second-spacing ‘pre-surface’ bathymetric grid 

was generated using GMT, an NSF-funded share-ware software application designed to manipulate data for mapping 

purposes (http://gmt.soest.hawaii.edu/).

The point data were median-averaged using the GMT tool ‘blockmedian’ to create a 1 arc-second grid 0.05 

degrees (~5%) larger than the Daytona Beach DEM gridding region. The GMT tool ‘surface’ was then used to fill in

the data gaps. The GMT grid created by ‘surface’ was converted into an ESRI Arc ASCII grid file, and clipped to the

combined coastline (to eliminate data interpolation into land areas). The resulting surface was compared with original 

soundings to ensure grid accuracy (e.g., Fig. 12), converted to a raster and used in the final gridding process (see Table

10).

Figure 12. Histogram of the differences between NOS hydrographic survey H09371 and the 1 arc-second pre-surfaced 

bathymetric grid.

3.3.4 Gridding the data with MB-System

MB-System (http://www.ldeo.columbia.edu/res/pi/MB-System/) was used to create the 1/3 arc-second Daytona 

Beach DEM. The MB-System tool ‘mbgrid’ was used to apply a tight spline tension to the xyz data, and interpolate 

values for cells without data. The data hierarchy used in the ‘mbgrid’ gridding algorithm, as relative gridding weights, 

is listed in Table 10. Greatest weight was given to the Volusia and St. Johns counties bare-earth LiDAR data. Least 

weight was given to the pre-surfaced 1 arc-second bathymetric grid. Gridding was performed in quadrants that were 

merged using GMT’s ‘grdblend’ tool to create the final 1/3 arc-second Daytona Beach DEM.
Eakins et al., 2009

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Table 10. Data hierarchy used to assign gridding weight in MB-System.

Dataset Relative Gridding Weight

Volusia County topographic LiDAR 1000

St. Johns County topographic LiDAR 1000

USACE bathymetric surveys 100

JALBTCX bathymetric–topographic LiDAR 100

USGS NED topographic DEM 10

NOS hydrographic surveys: bathymetric soundings 10

Combined coastline 10

Pre-surfaced bathymetric grid 1

3.4 Quality Assessment of the DEM

3.4.1. Horizontal accuracy

The horizontal accuracy of topographic and bathymetric features in the Daytona Beach DEM is dependent upon 

the datasets used to determine corresponding DEM cell values. Topographic features have an estimated accuracy of 

1 to 10 meters: Volusia County and St. Johns County topographic LiDAR data and JALBTCX coastal LiDAR data 

have accuracies between 1 and 3 meters; NED topography is accurate to within about 10 meters. Bathymetric features 

are resolved only to within a few tens of meters in deep-water areas. Shallow, near-coastal regions, rivers, and harbor 

surveys have an accuracy approaching that of subaerial topographic features. Positional accuracy is limited by: the 

sparseness of deep-water soundings; potentially large positional uncertainty of pre-satellite navigated (e.g., GPS) NOS 

hydrographic surveys; and by the morphologic change that occurs in this dynamic region.

3.4.2 Vertical accuracy

Vertical accuracy of elevation values for the Daytona Beach DEM is also highly dependent upon the source 

datasets contributing to DEM cell values. Topographic areas have an estimated vertical accuracy between 0.1 to 0.3 

meters for Volusia County and St. Johns County topographic LiDAR data and JALBTCX coastal LiDAR data, and 

up to 7 meters for NED topography. Bathymetric areas have an estimated accuracy of between 0.1 meters and a few 

meters. Those values were derived from the wide range of input data sounding measurements from the early 20th

century to recent, GPS-navigated USACE bathymetric surveys. Gridding interpolation to determine values between 

sparse, poorly-located NOS soundings degrades the vertical accuracy of elevations in deep water.

3.4.3 Slope maps and 3-D perspectives

ESRI ArcCatalog was used to generate a slope grid from the Daytona Beach DEM to allow for visual inspection and 

identification of artificial slopes along boundaries between datasets (e.g., Fig. 13). The DEM was transformed to UTM

Zone 9 coordinates (horizontal units in meters) in ArcCatalog for derivation of the slope grid; equivalent horizontal 

and vertical units are required for effective slope analysis. Three-dimensional viewing of the UTM-transformed DEM 

was accomplished using ESRI ArcScene (e.g., Fig. 14). Analysis of preliminary grids revealed suspect data points, 

which were corrected before recompiling the DEM. Figure 1 shows a color image of the 1/3 arc-second Daytona 

Beach DEM in its final version.
Digital Elevation Model of Daytona Beach, Florida

17

Figure 13. Slope map of the Daytona Beach DEM. Flat-lying slopes are white; dark shading denotes 

steep slopes; combined coastline in red.

Figure 14. Perspective view from the southeast of the Daytona Beach DEM. The red isolated peak to the east of 

Ponce de Leon Inlet is the Tomoka landfill. Vertical exaggeration–times 50.
Eakins et al., 2009

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3.4.4	 Comparison with source data files

To ensure grid accuracy, the Daytona Beach DEM was compared to select source data files. Files were chosen on

the basis of their contribution to the grid-cell values in their coverage areas (i.e., had the greatest weight and did not 

significantly overlap other data files with comparable weight). Histogram of the differences between one St. Johns

County topographic LiDAR file, and one JALBTCX coastal LiDAR file with the Daytona Beach DEM are shown in

Figures 15 and 16. Differences cluster around zero, with only a handful of soundings exceeding 0.5-meter discrepancy 

from the DEM.

Figure 15. Histogram of the differences between one St Johns County LiDAR file and the Daytona Beach DEM.

Figure 16. Histogram of the differences between one JALBTCX LiDAR file and the Daytona Beach DEM.

3.4.5 Comparison with NGS geodetic monuments

The elevations of 1637 NOAA NGS geodetic monuments were extracted from online shape files of monument

datasheets (http://www.ngs.noaa.gov/cgi-bin/datasheet.prl), which give monument positions in NAD 83 (typically submm accuracy) and elevations in NAVD88 (in meters). Elevations were shifted to MHW vertical datum for comparison 

with the Daytona Beach DEM (see Fig. 18 for monument locations). Differences between the Daytona Beach DEM 

and the NGS geodetic monument elevations range from -11 to 14 meters, with the majority within + 2 meters. Positive 

values indicate that the DEM is greater than the monument elevation (Fig. 17). The largest discrepancies between the 

monument elevations and the DEM were caused by monuments mounted on bridges and other man-made structures 

or by monument locations only known to within 6 arc-seconds (~180 meters). At least one monument has been lost 

and presumed destroyed.
Digital Elevation Model of Daytona Beach, Florida

19

Figure 17. Histogram of the differences between NGS geodetic monument elevations and the Daytona Beach DEM. 

Figure 18. Location of NGS geodetic monuments and NOAA tide stations. 

NGS monument elevations were used to evaluate the DEM. Combined coastline in red.
Eakins et al., 2009

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4. Summary and Conclusions

A topographic–bathymetric digital elevation model of the Daytona Beach, Florida region, with cell spacing of 

1/3 arc-second, was developed for the Pacific Marine Environmental Laboratory (PMEL) NOAA Center for Tsunami

Research. The best available digital data from U.S. federal, state and local agencies were obtained by NGDC, shifted 

to common horizontal and vertical datums, and evaluated and edited before DEM generation. The data were quality 

checked, processed and gridded using ESRI ArcGIS, FME, GMT, MB-System, GEODAS, and Quick Terrain Modeler 

software. 

Recommendations to improve the Daytona Beach DEM, based on NGDC’s research and analysis, are listed below:

•	 Jones Edmunds & Associates, Inc. graciously provided NGDC with topographic LiDAR data for all of Flagler 

County, which had not been processed to bare earth. The significant amount of vegetation and buildings

represented in the data precluded their use in the Daytona Beach DEM. Processing of these data to bare earth 

would result in nearly complete topographic lidar coverage for the region.

•	 Process JALBTCX coastal LiDAR data to bare earth.

5. Acknowledgments

The creation of the Daytona Beach DEM was funded by the NOAA Pacific Marine Environmental Laboratory.

The authors thank Chris Chamberlin and Vasily Titov (PMEL), Lisa Holland (USACE, Jacksonville District), Kitty 

Cooper (Lake County GIS Division), Arden Fontaine (Volusia County Special Projects), Mark Nelson and Bill Millinor 

(Jones Edmunds & Associates, Inc.), and Michael Campbell (St. Johns County GIS Division).

6. References

Nautical Chart #11480, 40th Edition, 2007. Charleston Light to Cape Canaveral. Scale 1:449,659. U.S. Department of 

Commerce, NOAA, National Ocean Service, Office of Coast Survey.

Nautical Chart #11484, 23rd Edition, 2007. Ponce de Leon Inlet to Cape Canaveral. Scale 1:80,000. U.S. Department 

of Commerce, NOAA, National Ocean Service, Office of Coast Survey.

Nautical Chart #11485, 35th Edition, 2007. Intracoastal Waterway Tolmato River to Palm Shores. Scale 1:40,000. U.S. 

Department of Commerce, NOAA, National Ocean Service, Office of Coast Survey.

Nautical Chart #11486, 15th Edition, 2007. St. Augustine Light to Ponce de Leon Inlet. Scale 1:80,000. U.S. Department 

of Commerce, NOAA, National Ocean Service, Office of Coast Survey.

7. Data Processing Software

ArcGIS v. 9.2, developed and licensed by ESRI, Redlands, California, http://www.esri.com/

FME 2007 – Feature Manipulation Engine, developed and licensed by Safe Software, Vancouver, BC, Canada, http://

www.safe.com/

GEODAS v. 5 – Geophysical Data System, shareware developed and maintained by Dan Metzger, NOAA National 

Geophysical Data Center, http://www.ngdc.noaa.gov/mgg/geodas/

GMT v. 4.1.4 – Generic Mapping Tools, shareware developed and maintained by Paul Wessel and Walter Smith, 

funded by the National Science Foundation, http://gmt.soest.hawaii.edu/

MB-System v. 5.1.0, shareware developed and maintained by David W. Caress and Dale N. Chayes, funded by the 

National Science Foundation, http://www.ldeo.columbia.edu/res/pi/MB-System/

Quick Terrain Modeler v. 6.0.1, LiDAR processing software developed by John Hopkins University’s Applied Physics 

Laboratory (APL) and maintained and licensed by Applied Imagery, http://www.appliedimagery.com/