U.S. Department of the Interior

U.S. Geological Survey

Fact Sheet 2008–3088

December 2008

In cooperation with the Delaware Geological Survey and the U.S. Department of Agriculture Natural Resources Conservation Service

Use of Light Detection and Ranging (LiDAR) to Obtain HighResolution Elevation Data for Sussex County, Delaware

Printed on recycled paper

Need for High-Resolution 

Elevation Data

Sussex County, Delaware, occupies 

a 938-square-mile area of low relief near 

sea level in the Atlantic Coastal Plain 

(fig. 1). The county is bounded on the 

east by the Delaware Bay and the Atlantic 

Ocean, including a barrier-island system, 

and inland bays that provide habitat for 

valuable living resources. Eastern Sussex 

County is an area of rapid population 

growth with a long-established beachresort community, where land elevation 

is a key factor in determining areas 

that are appropriate for development. 

Of concern to State and local planners 

are evacuation routes inland to escape 

flooding from severe coastal storms, as 

most major transportation routes traverse 

areas of low elevation that are subject 

to inundation. The western half of the 

county is typically rural in character, 

and land use is largely agricultural with 

some scattered forest land cover. Western 

Sussex County has several low-relief 

river flood-prone areas, where accurate 

high-resolution elevation data are needed 

for Federal Emergency Management 

Agency (FEMA) Digital Flood Insurance 

Rate Map (DFIRM) studies.

In 2004, available U.S. Geological 

Survey (USGS) and State of Delaware 

elevation data for Sussex County 

consisted of topographic maps depicting 

5-foot and 10-foot contours shown in 

graphic form and 30-meter digital elevation models (DEMs). These data were 

inadequate in resolution and detail for 

DFIRM studies and local planning needs. 

The USGS elevation data for this area 

were not standardized or integrated with 

any other elevation data sets and were 

too coarse to be integrated with other 

large-scale, high-resolution framework 

geospatial data used by government 

agencies or the private sector in new 

Figure 1. LiDAR collection area showing 10-kilometer by 10-kilometer tiles for which digital 

elevation model and 2-foot topographic contours were produced. Parsing data into tiles 

enhances data display and facilitates data analysis by minimizing computer hardware and 

software response time.

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10 MILES

10 KILOMETERS

EXPLANATION

10-KILOMETER GRID TILE AND IDENTIFICATION

 NUMBER—First three digits are the western boundary of

 the tile; last four digits are the southern boundary of the

 tile. Digits are coordinates in Universal Transverse

 Mercator Projection, Zone 18

4404250

High

Low

75°30' 75°

38°

30'

39°

PENNSYLVANIA NEW

MARYLAND JERSEY

Atlantic Ocean

Delaware

Bay

Chesapeake Bay

New

Castle

Kent

Sussex

DELAWARE RELATIVE ELEVATION

regulatory and scientific studies. The 

most immediate need for high-resolution 

elevation data was in the updating 

of FEMA DFIRMs by the Delaware 

Department of Natural Resources and 

Environmental Control (DNREC). Other 

needs for high-resolution elevation 

data included National Oceanic and 

Atmospheric Administration (NOAA) 

and USGS sea-level-rise projections; 
storm-surge-prediction mapping by 

the U.S. Army Corps of Engineers; 

predicting habitat change and updating 

maps of ditches and ditch flow regimes 

by DNREC; soil-resource studies by the 

U.S. Department of Agriculture Natural 

Resources Conservation Service (NRCS); 

and revision of geologic base maps by the 

Delaware Geological Survey (DGS).

What is LiDAR?

Light Detection And Ranging 

(LiDAR) is an optical remote-sensing 

technology that is used to determine the 

distance of a target from the sensor by 

measuring some of the basic properties of 

a reflected laser light pulse, such as intensity and timing. The principle of airborne 

LiDAR is that laser pulses aimed toward 

the ground are emitted from an aircraft 

at known times. When light strikes a 

feature (referred to as a target) on the 

ground, some of it is scattered while the 

remaining light energy is reflected back 

to an aircraft-mounted receiver. Target 

coordinates x, y, and z are calculated 

using on-board and base-station Global 

Positioning System- (GPS) derived aircraft location, the speed-of-light constant, 

and laser-pulse geometry (distance and 

angle of the pulse). Rapid laser pulsing 

increases the light’s ability to penetrate 

vegetation and reach the Earth’s surface. 

The collected set of target points for an 

area is known as “mass points.” From 

selected mass points, derivative products 

such as topographic contours and DEM 

data are produced. The maximum vertical 

resolution of most commercial LiDAR 

instruments in 2004–05 was about 15 cm 

(centimeters) when standard groundcontrol procedures were used to ensure 

accurate data-point location.

LiDAR is especially useful in lowelevation and low-relief areas because of 

its ability to detect small changes in land 

elevation. It can also be used to collect 

elevation data over large areas in remote 

locations more affordably than equivalent 

ground surveys, to provide elevation 

data for modeling of inundation due to 

precipitation runoff or coastal storm 

flooding, and to provide data that can 

be readily combined with other data in 

geographic information systems (GIS) for 

analysis. LiDAR data can be integrated 

easily with the high-resolution orthoimagery acquired by the State of Delaware 

in 2002 and 2007.

Elevation is one of the primary 

data themes of The National Map 

(http://nationalmap.gov/). The National 

Elevation Dataset (http://ned.usgs.gov/) 

provides access to DEM data. Contours 

are a Delaware spatial data framework 

layer to be provided from the Delaware 

DataMIL (http://datamil.delaware.gov/), 

an Internet mapping server that provides 

the most current base map of Delaware. 

The USGS, DGS, and NRCS were 

interested in bringing accurate, highresolution data into the public domain for 

use in planning and decisionmaking at all 

governmental levels by providing access 

to 2-foot contour data to update the elevation framework layer for use in Delaware 

DataMIL-generated topographic maps 

and future USGS topographic map products. With these requirements known, 

the USGS, in cooperation with the DGS 

and NRCS, initiated a project designed 

to produce bare-earth mass points; a 

tiled, bare-earth, 2-meter DEM; 2-foot 

topographic contours, edited and filtered; 

and Federal Geographic Data Committeecompliant (Federal Geographic Data 

Committee, 2006) project metadata for 

Sussex County.

This fact sheet describes the 

methods and techniques used to collect 

and process LiDAR elevation data, the 

generation of the DEM and the 2-foot 

contours, and the quality-assurance 

procedures and results. It indicates where 

to view metadata on the data sets and 

where to acquire bare-earth mass points, 

DEM data, and contour data.

Data Collection and Processing

Several options for collecting 

statewide high-resolution elevation data 

were examined. LiDAR was selected 

because of the following capabilities: 

ability to collect high-resolution data in 

a relatively large area in a short time, 

using aircraft; cost effectiveness when 

compared to detailed topographic field 

surveys of the same vertical resolution 

and areal extent; ease of data collection 

in remote and inaccessible areas; ability 

to collect and process data for more than 

one elevation product (bare-earth, tree 

canopy) from one data set; and ability to 

integrate with other data layers, such as 

orthoimagery.

The selection of the National 

Aeronautics and Space Administration 

(NASA) Experimental Advanced 

Airborne Research LiDAR (EAARL) to 

collect high-resolution elevation data for 

Sussex County was based on several key 

factors (U.S. Geological Survey, 2007a):

•	 Geographic proximity (the NASA 

EAARL aircraft was based 10 miles 

south of Sussex County, Delaware).

•	 Operating altitude of 300 meters above 

ground level (lower than normal commercial LiDAR collection operation).

•	 Dual green laser wavelengths (532 and 

1,064 nanometers), which have the 

ability to penetrate non-turbid water to 

produce subsurface contours.

•	 Simultaneous multi-channel waveform 

digitizer.

•	 Real-time LiDAR waveform processing, allowing potential data users to 

process data in multiple ways to examine different levels of vegetation and 

produce a bare-earth DEM.

•	 Proven capability for vertical resolution of features exceeding FEMA’s 

18.5-cm standard.

NASA EAARL data for Sussex 

County were collected during leaf-off 

foliage condition from late February 2005 

to early April 2005. Elevation data were 

collected with sufficient point density 

to create accurate data values for a grid 

of points arrayed in squares 2 meters 

by 2 meters, a DEM that represents a 

sampling of the mass points collected 

at the surface of the Earth. Data were 

denser in areas of flight-line overlap and 

also where collected in cross flight paths 

during each collection flight for data-verification and quality-assurance purposes. 

A GPS base station, a station that is 

stationary at or near ground level over 

a monumented position while continuously receiving data from satellites the 

instrument is tracking, was established 

in the scheduled LiDAR acquisition 

area. Locational data referenced among 

three or more GPS satellites, the base 

station, and the aircraft were collected 

at 1-Hertz intervals at least 1 hour prior 

to data-collection flights, during flights, 

and for at least 30 minutes after flights to 

correct aircraft location with respect to 

the ground.

The software used to process the 

collected EAARL LiDAR data is the 
Airborne LiDAR Processing Software 

(ALPS), developed collaboratively by the 

USGS and NASA. The ALPS software 

was installed in new Linux hardware 

and tested with a sample data set, then 

verified with NASA oversight before the 

processing of the Sussex County data 

began.

ALPS characteristics include (U.S. 

Geological Survey, 2007b):

•	 Open source code and freely available 

processing algorithms (such as bareearth routines and filters).

•	 Cost-free distribution.

•	 Ability to operate in a Linux environment.

•	 Data-viewing software that processes 

raw EAARL data into various products.

•	 Collegial development environment in 

which users are encouraged to submit 

enhancements to existing code for 

NASA review and acceptance.

The LiDAR data can be processed 

in different ways, resulting in various 

products. When initially processed from 

the on-board aircraft computing system, 

LiDAR data consist of many thousands 

of irregularly spaced points, each with 

horizontal coordinates and an elevation 

value. In this project the mass points 

were processed to yield a bare-earth data 

set. All mass points determined to be 

from manmade features on or near the 

land surface were removed. The removal 

of mass points to create the bare-earth 

data set is to a large extent an automated 

process, and the ALPS software was used 

to delete an estimated 97 percent of data 

returns from buildings and other features. 

Almost all data points determined to be 

from vegetation were also removed using 

the ALPS software. The 3 percent of 

mass points not removed by ALPS were 

anomalies, such as birds or aircraft in 

flight, high-voltage electrical wires, the 

tops of telephone poles, and especially 

buildings that were not recognized by 

ALPS. These remaining mass points 

were removed by manual inspection and 

comparison of mass points to feature 

identification using orthoimagery. 

ArcMap software was used to create 

shape-files of the bare-earth mass points 

data set (see figure 2), which in turn were 

used to produce a DEM.

Digital Elevation Model 

Generation

DEMs are simple data sets that are 

easy to store and manipulate. A DEM 

surface is typically displayed as a uniform set of interpolated bare-earth mass 

points in a gridded array. Because a DEM 

surface is interpolated, it is slightly less 

accurate than the mass points (Bales and 

others, 2007) or a Triangulated Irregular 

Network (TIN). Because LiDAR data 

points are randomly placed and the DEM 

is a gridded version of these points, the 

DEM surface may omit small details 

associated with larger features, such 

as roadside ditches or the location of a 

streambank lip. Break-line information 

can be added to a DEM to restore some 

omitted small features and details, but the 

resulting improvement is often considered small when compared with the time 

and cost required to integrate such data.

The shape-files of the bare-earth 

mass points were transferred to the 

appropriate 10-kilometer by 10-kilometer 

tiles with sufficient overlap data to 

ensure proper tile joining. A 2-meter 

Figure 2. Bare-earth LiDAR mass points superimposed on corresponding 1-foot-resolution 

orthoimage of Sussex County, Delaware. This image illustrates the density and distribution 

of bare-earth LiDAR mass points. Mass points have been removed for buildings and some 

vegetation where the LiDAR did not reach ground level.

75°14'50" 75°14'40"

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50 METERS
noise-reduction filter was applied to the 

data points to reduce the irregularity of 

the model surface while preserving data 

accuracy and subtle terrain features. The 

data set required 40 tiles to cover the 

938-square-mile area of Sussex County. 

Quick Terrain Modeler, a software 

application tool for visualization and 

feature extraction from large digital 

elevation surface data sets, was applied to 

the variably spaced noise-filtered points 

to create an interpolated 2-meter DEM 

for each tile.

A second filtering process was 

applied to the DEM surface data. It was 

weighted specifically to reduce small, 

sharper irregularities in the DEM surface, 

especially in lower relief, low-elevation 

areas. This second filtering process 

produced a relatively smooth elevationmodel surface by removing small highrelief spikes and pits, which tend to be 

uncharacteristic in low-lying, low-relief 

areas, while having a minimal effect 

on data accuracy. This second filtering 

process also applied little smoothing in 

higher elevation, greater relief terrain. 

The DEM was then hydrographically 

enforced, so that streams and lakes are 

connected where the DEM data are 

not distinct and so that water will flow 

downslope when modeled to do so. To 

accomplish the hydrographic enforcement, hydrography vector data from the 

National Hydrographic Dataset (NHD) 

(http://nhd.usgs.gov/) was overlaid on the 

DEM and used as a guide to manually 

connect lakes and streams on the DEM. 

Next, the open-water surface areas within 

water-feature polygons were flattened 

to a uniform height to eliminate waves 

sensed by the LiDAR. The flattened open 

water enhances the visual recognition 

and differentiation of the lakes, bays, 

and streams from land areas. The DEM 

was then given a final inspection and 

edited to eliminate remnants of manmade 

structures or any remaining vegetation 

responses from the LiDAR. The DEM 

data were then converted to Arc ASCII 

Grid file format. The example of DEM 

data overlaid with orthoimagery shown 

in figure 3 is indicative of the detail that 

each provides. When georegistered to 

each other, orthoimagery and DEM data 

create a powerful information data set 

that can be used to visualize events and 

support decisionmaking.

Generating Topographic Contours

Global Mapper software was used 

to generate contour data from the DEM 

data. A low simplification factor was 

used in Global Mapper to increase the 

number of vertices per unit length of 

contour, resulting in a smoother contour 

than would be generated if no vertices 

were added to the DEM data. The 

contour data were then filtered to remove 

numerous small, closed contour tops and 

depressions within a contour interval 

that had a linear on-the-ground length 

of less than 80 feet. Any contour with 

only two vertices that was less than 20 

feet in length also was deleted. Elevation 

values, in feet, are embedded with each 

contour line. The contour files were then 

converted to shape-files. Contour lines 

that that continue between adjacent tiles 

(depicted in figure 1) share a common 

vertex on the tile boundary, so that 

contours appear continuous when tiles are 

joined together.

For additional detail and description 

of the process and software used in 

producing DEM data and contours for 

this project, refer to the contour metadata 

files posted on the Delaware DataMIL 

(http://datamil.delaware.gov/) and the 

DEM metadata posted on the National 

Elevation Database web site (http://ned.

usgs.gov/). An example of the final result 

of the contour generation specifications 

and process is shown in figure 4.

Quality Assurance

Guidance

The American Society of Photogrammetry and Remote Sensing (ASPRS) 

Guidelines For Vertical Accuracy 

Reporting For LiDAR Data, version 1.0 

(American Society of Photogrammetry 

and Remote Sensing, 2004), were used 

for reference on vertical-accuracy evaluation. Vertical accuracy is the principal 

criterion used to evaluate the quality of 

the elevation data produced, and therefore 

the degree of confidence the data user 

can have in the data. Vertical-accuracy 

requirements depend on the intended 

user applications. There are four primary 

applications of digital-elevation data 

sets for which high vertical accuracy is 

typically required:

•	 Infrastructure management of dense 

urban areas where planimetric maps 

are typically required at scales of 

1 inch:100 feet or larger.

•	 Stormwater and floodplain management in flat terrain.

•	 Management of wetlands and other 

ecologically sensitive low-lying areas.

•	 Marine navigation and safety.

Figure 3. LiDAR digital elevation model of Sussex County, Delaware, integrated with 2007 

1-foot color-infrared orthoimagery (rectified digital aerial photography) draped over it. Twofoot contours (yellow lines) have been overlaid on the orthoimagery. This figure illustrates 

that different imaging techniques and linear data can complement each other.
Many applications not listed above 

do not require the highest accuracy 

achievable. Users of elevation data need 

to balance the requirement for accuracy 

with the increased cost of obtaining 

higher accuracy elevation data; other 

processing options, such as hydrographic enforcement (which ensures 

that streams are connected and flow 

downslope); or product choices, such 

as topographic contours. The capability 

of the NASA-EAARL data to produce 

data that exceeded the FEMA Appendix 

A (Federal Emergency Management 

Agency, 2003) specification to resolve 

18.5-cm vertical features was a primary 

benefit for DNREC, which is responsible 

for DFIRM mapping in Sussex County, 

which has many inhabited low-lying, 

low-relief areas. The National Standard 

for Spatial Data Accuracy (National Spatial Data Infrastructure, 1998) was used 

as guidance to report vertical accuracy of 

the NASA-EAARL processed bare-earth 

mass points for this project. ASPRS 

Guidelines for Vertical Accuracy Reporting for LiDAR Data state that a minimum 

of 20 checkpoints for each major landcover type identified in the acquisition 

area shall be tested and must provide 

a 95-percent confidence level to meet 

the standard. The checkpoints shall be 

distributed to reflect areas of geographic 

interest and the distribution of error in 

the data set. When 20 points are tested 

for each land-cover type, the 95-percent 

confidence level allows 1 point to fail 

in order to meet product specifications. 

It is not acceptable practice to collect 

an abundance of checkpoints and 

retain only points that meet or exceed 

vertical-accuracy reporting criteria. 

These guidelines were followed for two 

land-cover categories in Sussex County 

as a quality-assurance test of the LiDAR 

mass points.

Procedures

To test the accuracy of selected bareearth LiDAR points, 913 GPS-surveyed 

ground-control points in Sussex County 

were obtained from NRCS. These 913 

control points were divided between two 

major land-cover categories: agricultural 

fields, and farm lanes and roads. A total 

of 913 LiDAR bare-earth points closely 

corresponding to the GPS-surveyed 

ground-control points were selected using 

GIS methods to compare vertical height 

readings. Of the 913 NRCS control 

points, 144 were located along farm 

lanes and roads and 769 were located in 

agricultural fields. The locations of the 

GPS-surveyed points and the selected 

bare-earth LiDAR mass points were 

required to be within 1 meter surface 

distance from each other for point-topoint comparison. The graph shown in 

figure 5 is a quantitative comparison of 

elevation between points surveyed by 

traditional GPS methods and the Sussex 

County LiDAR data set for the two 

land-cover classes specified.

In figure 5, red symbols represent 

the farm lanes and roads land-cover 

category and black symbols represent the 

agricultural fields land-cover category. 

The regression line indicates a strong 

relation (R2 = 0.7757) between the 

LiDAR mass points and the corresponding GPS control points.

Results

Results of the quality-assurance 

analysis indicate an aggregate average 

vertical accuracy for both land-cover 

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75°7'30" 75°07'

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20 18 16 14 12 10 8 6 4 2 0 -2

ALTITUDE, IN FEET ABOVE NORTH

AMERICAN VERTICAL DATUM OF 1988

Figure 4. Two-foot topographic contours derived from 2-meter gridded elevation model of 

the area near Wolfe Neck, Delaware. Color has been added to water areas (blue) and to 

the land area (grading from green (low elevation) to gray (highest elevation shown)). These 

bare-earth (buildings and vegetation removed) elevation data depict part of a subdivision, 

wetland areas, and the Lewes and Rehoboth Canal. 
classes tested of 14.8 cms, which is more 

accurate than the established 18.5-cm 

FEMA standard. The root mean square 

error (RMSE) value for each individual 

land-cover class also surpassed the 

RMSE established by FEMA. The 

2-meter gridded DEM meets or exceeds 

±18.5-cm vertical-accuracy standard for 

FEMA DFIRM products. The contours 

generated from the DEM were compiled 

to meet a standard of 15 cms vertical 

accuracy at a 95-percent confidence level.

Disclaimer

Any use of trade, product, or firm 

names in this report is for descriptive 

purposes only and does not imply 

endorsement by the U.S. Government.

—by Roger A. Barlow, Mark R. Nardi, 

and Betzaida Reyes Figure 5. Relation of predicted LiDAR elevation to measured GPS elevation for 

roads and agricultural fields land-cover categories, Sussex County, Delaware. PREDICTED LiDAR ELEVATION (Z), IN METERS

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10.75

10.50

11.00 11.25 11.50 11.75 12.00 12.25 12.50

GLOBAL POSITIONING SYSTEM (GPS) ELEVATION (Z), IN METERS

ROADS

AGRICULTURAL FIELDS

ZLiDAR = 0.3779 + 0.9454 (ZGPS)

Equation of regression line:

R = 0.7757 2

N = 913

RMSE = 0.1487

For more information, contact:

Roger A. Barlow

National Spatial Data Infrastructure Partnership,

 Eastern Region Liaison

U.S. Geological Survey

563 National Center

Reston, VA 20192

E-mail: rbarlow@usgs.gov

Web: http://www.usgs.gov/ngpo/

References Cited

American Society of Photogrammetry and Remote Sensing, 2004, 

ASPRS guidelines, Vertical accuracy reporting for LiDAR data, 

Version 1.0, May 24, 2004, accessed January 17, 2008, at http://www.

asprs.org/society/committees/lidar/downloads/Vertical_Accuracy_

Reporting_for_Lidar_Data.pdf

Bales, J.D., Wagner, C.R., Tighe, K.C., and Terziotti, S.E., 2007, 

LiDAR-derived flood-inundation maps for real-time flood-mapping 

applications, Tar River Basin, North Carolina: U.S. Geological Survey 

Scientific Investigations Report 2007–5032, 42 p.

Federal Emergency Management Agency, 2003, Guidelines and 

specifications for flood hazard mapping partners—Appendix A: 

Guidance for aerial mapping and surveying, p. A10–A14, A39–A47, 

and A49–A53, accessed January 18, 2008, at http://www.fema.gov/

library/viewRecord.do?id=2206

Federal Geographic Data Committee, 2006, Geospatial metadata: 

Accessed June 18, 2008, at http://www.fgdc.gov/metadata/

National Spatial Data Infrastructure, Federal Geographic Data Committee, Subcommittee for Base Cartographic Data, 1998, Geospatial 

positioning accuracy standards, Part 3: National standards for spatial 

data accuracy, FGDC-STD-007.3–1998, p. 3-1–3-25, accessed June 

18, 2008, at http://www.fgdc.gov/standards/projects/FGDC-standardsprojects/accuracy/part3/chapter3

U.S. Geological Survey, 2007a, Center for Coastal and Watershed 

Studies, Integrated Remote Sensing and Modeling Group, Advanced 

methods, EAARL—Airborne LiDAR system for high-resolution 

submerged and sub-aerial topography, accessed January 19, 2008, at 

http://coastal.er.usgs.gov/remote-sensing/advancedmethods/eaarl.html

U.S. Geological Survey, 2007b, Center for Coastal and Watershed 

Studies, Integrated Remote Sensing and Modeling Group, Advanced 

methods, LiDAR processing systems, accessed January 19, 2008, at 

http://coastal.er.usgs.gov/remote-sensing/advancedmethods/lidar.html

Data Availability

•	 The Sussex County DEM data will be made 

available on the USGS National Elevation 

Dataset (NED) web site for downloading at 

http://seamless.usgs.gov/website/seamless/

products/9arc.asp. The DEM data will be 

offered in four data formats: ArcGrid, bil, 

TIFF, and GridFloat.

•	 The contours will be available for downloading from the Delaware DataMIL web site at 

http://datamil.delaware.gov/.

•	 Metadata for the DEM and contour data will 

be available from the Delaware DataMIL web 

site and the Geospatial One-Stop web site at 

http://gos2.geodata.gov/wps/portal/gos/.

•	 Mass points will be distributed from the USGS 

Center for LiDAR Information Coordination 

and Knowledge (CLICK) at http://gisdata.

usgs.net/website/lidar/.