U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2009–5065

Comparison of Surface Flow Features from Lidar-Derived 

Digital Elevation Models with Historical Elevation and 

Hydrography Data for Minnehaha County, South Dakota
Front cover. Upper left: Clip of lidar-derived surface flow features draped over a three-dimensional lidar-derived digital elevation 

model (DEM) of Minnehaha County, South Dakota.

Center: Aerial photograph of South Dakota topography by Darrell Napton, South Dakota State University and Visiting 

Scientist, U.S. Geological Survey. 

Lower right: Lidar-derived DEM of Minnehaha County, South Dakota.

Back cover. Lidar-derived DEM of Minnehaha County, South Dakota.
Comparison of Surface Flow Features from 

Lidar-Derived Digital Elevation Models 

with Historical Elevation and Hydrography 

Data for Minnehaha County, South Dakota

By Sandra K. Poppenga, Bruce B. Worstell, Jason M. Stoker, and 

Susan K. Greenlee

Scientific Investigations Report 2009–5065

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2009

For more information on the USGS—the Federal source for science about the Earth, its natural and living resources, 

natural hazards, and the environment, visit http://www.usgs.gov or call 1-888-ASK-USGS

For an overview of USGS information products, including maps, imagery, and publications, 

visit http://www.usgs.gov/pubprod

To order this and other USGS information products, visit http://store.usgs.gov

Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the 

U.S. Government.

Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report.

Suggested citation:

Poppenga, S.K., Worstell, B.B., Stoker, J.M., and Greenlee, S.K., 2009, Comparison of surface flow features from lidarderived digital elevation models with historical elevation and hydrography data for Minnehaha County, South Dakota: 

U.S. Geological Survey Scientific Investigations Report 2009–5065, 24 p.
iii

Contents

Abstract...........................................................................................................................................................1

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

Comparisons of Minnehaha County 1-Meter Lidar-Derived Digital Elevation 

Model with Ancillary Datasets.......................................................................................................4

Surface Flow Features Derived from Lidar..............................................................................................19

Implications for Further Study and Additional Capabilities ..................................................................20

Conclusion.....................................................................................................................................................20

References Cited..........................................................................................................................................24

Figures

1–10. Maps showing:

1. The Elevation, Topographic Science, and Lidar Branch at the 

U.S. Geological Survey (USGS) Earth Resources Observation and 

Science (EROS) Center Science Division........................................................................2

2. National Elevation Dataset source information .............................................................3

3. Minnehaha County physical geography..........................................................................4

4. Minnehaha County lidar-derived elevation.....................................................................5

5. 1-meter lidar-derived elevation and 10-meter data incorporated 

into the National Elevation Dataset..................................................................................6

6. National Hydrography Datatset flowlines overlain on 1-meter lidarderived elevation data in the Sioux Falls Regional Airport area..................................7

7. Elevation Derivatives for National Applications overlain on 1-meter 

lidar-derived elevation data in the Sioux Falls Regional Airport area ........................8

8. National Hydrography Dataset flowlines overlain on 1-meter lidarderived elevation data in rural Minnehaha County showing 

differences in channel and bridge location ....................................................................9

9. Elevation Derivatives for National Applications overlain on 1-meter 

lidar-derived elevation data in rural Minnehaha County showing 

differences in channel and bridge location ..................................................................10

10. Data from Elevation Derivatives for National Applications, National 

Hydrography Dataset, and National Bridges Inventory overlain on 

1-meter lidar-derived elevation data in rural Minnehaha County 

showing differences in channel and bridge location..................................................11

11. U.S. Geological Survey high-resolution scan of aerial photograph taken 

in 1958, rural Minnehaha County..............................................................................................12

12. Aerial imagery draped over lidar-derived elevation data in rural 

Minnehaha County .....................................................................................................................13

13. Map showing National Hydrography Dataset “Lake or Pond(s)” overlain 

on 1-meter lidar-derived elevation data located near the intersection of 

West 12th Street and Interstate 29, Sioux Falls, South Dakota...........................................14
iv

14. Aerial imagery draped over lidar-derived elevation data in a location 

that no longer contains National Hydrography Dataset water bodies 

near the intersection of West 12th Street and Interstate 29, Sioux Falls, 

South Dakota ...............................................................................................................................15

15–16. Maps showing:

15. Contour lines from National Elevation Dataset 1/3-arc-second 

digital elevation model and from Digital Line Graphs overlain on 

1-meter lidar-derived elevation data located near the intersection 

of West 12th Street and Interstate 29, Sioux Falls, South Dakota .............................16

16. Contour lines from 1-meter lidar-derived digital elevation model 

and from Digital Line Graphs overlain on 1-meter lidar-derived 

elevation data located near the intersection of West 12th Street 

and Interstate 29, Sioux Falls, South Dakota.................................................................17

17. Aerial imagery draped over lidar-derived elevation data in a location 

where National Hydrography Dataset “Lake or Pond(s)” once existed 

near the intersection of West 12th Street and Interstate 29, Sioux Falls, 

South Dakota ...............................................................................................................................18

18–21. Maps showing:

18. Lidar-derived surface flow features overlain on 1-meter lidarderived elevation data in rural Minnehaha County......................................................19

19. Lidar-derived surface flow from hydrologically conditioned 

digital elevation model and downstream trace surface flow 

derived from National Hydrography Dataset headwater points................................21

20. Surface flow from downstream trace (raindrop) method, derived 

from National Hydrography Dataset headwater points, and the 

National Hydrography Dataset flowlines.......................................................................22

21. Difference grid resulting from subtracting values of the original lidarderived digital elevation model from the filled depressionless DEM........................23
v

Conversion Factors and Datums

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft) 

meter (m) 1.094 yard (yd) 

kilometer (km) 0.6214 mile (mi)

kilometer (km) 0.5400 mile, nautical (nmi) 

Area

square kilometer (km2) 247.1 acre

square kilometer (km2) 0.3861 square mile (mi2)

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88).

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

Acronyms

DEM digital elevation model

EDNA Elevation Derivatives for National Applications

EROS Earth Resources Observation and Science (Center)

lidar Light detection and ranging

NED National Elevation Dataset

NHD National Hydrography Dataset

RMSE root mean square error

USGS U.S. Geological Survey

UTM Universal Transverse Mercator

Comparison of Surface Flow Features from Lidar-Derived 

Digital Elevation Models with Historical Elevation and 

Hydrography Data for Minnehaha County, South Dakota

By Sandra K. Poppenga1, Bruce B. Worstell2, Jason M. Stoker1, and Susan K. Greenlee1

Abstract

The U.S. Geological Survey (USGS) has taken the lead in 

the creation of a valuable remote sensing product by incorporating digital elevation models (DEMs) derived from Light 

Detection and Ranging (lidar) into the National Elevation 

Dataset (NED), the elevation layer of “The National Map.” 

High-resolution lidar-derived DEMs provide the accuracy 

needed to systematically quantify and fully integrate surface 

flow including flow direction, flow accumulation, sinks, slope, 

and a dense drainage network. In 2008, 1-meter resolution 

lidar data were acquired in Minnehaha County, South Dakota. 

The acquisition was a collaborative effort between Minnehaha 

County, the city of Sioux Falls, and the USGS Earth Resources 

Observation and Science (EROS) Center. With the newly 

acquired lidar data, USGS scientists generated high-resolution 

DEMs and surface flow features. This report compares lidarderived surface flow features in Minnehaha County to 30- and 

10-meter elevation data previously incorporated in the NED 

and ancillary hydrography datasets. Surface flow features 

generated from lidar-derived DEMs are consistently integrated 

with elevation and are important in understanding surfacewater movement to better detect surface-water runoff, flood 

inundation, and erosion. Many topographic and hydrologic 

applications will benefit from the increased availability of 

accurate, high-quality, and high-resolution surface-water data. 

The remotely sensed data provide topographic information and 

data integration capabilities needed for meeting current and 

future human and environmental needs.

Introduction

The U.S. Geological Survey (USGS) is a primary source 

for seamless elevation data at the global, national, and local 

scales (Gesch, 2007). Since the early 1990s, the USGS has 

been developing seamless elevation datasets to incorporate the best publicly available elevation data for research, 

applications, and distribution (fig. 1). At the global scale, the 

GTOPO30 elevation model, developed with a 30-arc-second 

resolution (about 1 kilometer; Gesch and others, 1999), was 

the framework for a global HYDRO1k hydrologic application (Verdin and Jenson, 1996). At the national scale, the 

National Elevation Dataset (NED) was developed in 1999 

as the primary source for 30-meter resolution elevation data 

in the contiguous United States (Gesch and others, 2002). In 

2001, nationwide hydrologic derivatives were derived from 

the NED, integrating surface flow characteristics as the Elevation Derivatives for National Applications (EDNA) database 

(Franken and others, 2001). At the local scale, the USGS is 

incorporating lidar-derived digital elevation models (DEMs) 

into the NED, the elevation layer of “The National Map” 

(Stoker and others, 2008; http://www.nationalmap.gov). As 

higher resolution source data (10- and 3-meter resolution or 

better) are incorporated into the NED, the national elevation 

model is evolving from a static 30-meter elevation model to a 

multiresolution model consisting of the best publicly available 

elevation data (fig. 2). Development of hydrologic derivatives 

needs to progress at the same pace, thereby providing the best 

publicly available surface flow features for current and future 

applications of surface-water data.

The USGS has identified water availability as a high 

priority and highlights the need for a Water Census of the 

United States to meet the need for a comprehensive, scientific 

accounting of the status and trends in freshwater quantity and 

quality for human and environmental needs (U.S. Geological 

Survey, 2007). Over the past decade, the USGS has facilitated development of the highest resolution and best publicly 

available elevation models (fig. 1). To continue that course of 

action, the USGS has recently taken the lead in the creation 

of a valuable remote sensing product by incorporating lidarderived DEMs into the NED. Because elevation is a key factor 

in deriving surface flow features, high-resolution lidar-derived 

DEMs provide the detail needed to systematically quantify and 

fully integrate surface flow features and supply topographic 

spatial information and data integration capabilities needed for 

the Water Census of the United States.

1

U.S. Geological Survey.

2

Stinger Ghaffarian Technologies (SGT), Inc., contractor to the U.S. Geological Survey. Work performed under USGS contract O8HQN0005.
Figure 1. The Elevation, Topographic Science, and Lidar Branch at the U.S. Geological Survey (USGS) Earth Resources Observation 

and Science (EROS) Center Science Division.

2 Surface Flow Features from Lidar-Derived Digital Elevation Models for Minnehaha County, South Dakota

In an effort to support the USGS Water Census science 

directive, scientists of the Elevation, Topographic Science, 

and Lidar Branch at the USGS Earth Resources Observation 

and Science (EROS) Center Science Division are developing surface flow features from high-resolution DEMs derived 

from remotely sensed bare earth lidar data. In 2008, 1-meter 

lidar data were acquired in Minnehaha County, South Dakota. 

The acquisition was a collaborative effort between Minnehaha 

County, the city of Sioux Falls, and the USGS EROS Center. 

Using the newly acquired lidar-derived DEMs, scientists 

generated, analyzed, and compared Minnehaha County surface 

flow features with historical 30- and 10-meter elevation data 

previously incorporated in the NED and ancillary hydrography datasets including EDNA and the National Hydrography 

Dataset (NHD; U.S. Geological Survey, 2008). The comparisons were conducted to incorporate the highest resolution, best 

publicly available elevation data for hydrologic efficacy.

Lidar is being used as a remote sensing tool in many 

USGS applications (Queija and others, 2005). From climate 

change studies of coastal lands vulnerable to sea level rise 

(Gesch, in press) to debris flow modeling, feature extraction, 

or hydrologic studies, researchers are frequently using lidar 

and lidar derivatives to improve the understanding of our 

planet.
Figure 2. National Elevation Dataset source information. The multiresolution dataset consists of 30-meter, 10-meter, and 3-meter elevation data.

Alaska

December 2008

Hawaii

National Elevation Dataset Source Information

FY09 Quarter 1

NED RESOLUTION

1/9 arc-second (~3 meter or lidar source)1/3 arc-second (~10 meter or better source)1 arc-second (~30 meter source)2 arc-second - Alaska only
Figure 3. Minnehaha County physical geography.

4 Surface Flow Features from Lidar-Derived Digital Elevation Models for Minnehaha County, South Dakota

Comparisons of Minnehaha County 

1-Meter Lidar-Derived Digital Elevation 

Model with Ancillary Datasets

Minnehaha County, in southeastern South Dakota, has 

a total area of about 2,108 square kilometers. The county is 

the most populous in South Dakota with an estimated population of 171,316 (U.S. Census Bureau, 2008). The county seat 

is Sioux Falls, which is on the southern edge of Minnehaha 

County and extends into Lincoln County. The physical landscape of Minnehaha County consists of rolling plains on the 

southern edge of the Coteau des Prairies (Hogan and Fouberg, 

2001; fig. 3). Watershed drainage flows generally southward 

through the county into the Big Sioux River, a tributary of the 

Missouri and Mississippi Rivers.

Lidar data were collected for Minnehaha County in May 

2008 by an airborne platform with a combination of laser 

range finding, global positioning system, and inertial measurement technologies by Sanborn Map Company Inc. Lidar data 

accuracy determination uses the National Standard for Spatial 

Data Accuracy at 18.5 centimeter root mean square error 

(RMSE) on open bare terrain and 37.0 centimeter RMSE in 

obscured “vegetative” areas.

The Minnehaha County bare earth lidar data were used to 

produce 1-meter DEMs with a Universal Transverse Mercator 

(UTM) Zone 14 projection and an North American Datum of 

1983 (NAD83) horizontal datum (fig. 4). At that resolution, 

the DEM is a detailed representation of the earth’s topography and drainage networks. Artificial structures (Sioux 

Falls Regional Airport, north of Sioux Falls), transportation 

networks, (Interstates 29 and 90) and drainage networks (Big 

Sioux River) can be identified in figure 4. In the smaller area 

shown in figure 5, the Big Sioux River, channelized throughout the years, flows to the west of the airport, and a diversion 

channel drains to the east of the airport. The current lidar 

(1-meter) elevation data more accurately represent surface 

flow in low relief areas than the historical 10-meter elevation data (fig. 5), which were previously incorporated into the 

NED. This temporal elevation comparison emphasizes the 

need for elevation data currency to provide the best publicly 

available elevation framework for precise representation of 

surface flow.

Historically, surface flow features have been either 

cartographically derived from aerial photography or digitally 

developed from moderate resolution (30-meter) elevation 

data. Because different types of source data were used to 

create each dataset (cartographically or digitally developed), 
Figure 4. Minnehaha County lidar-derived elevation.

Comparisons of Minnehaha County 1-Meter Lidar-Derived Digital Elevation Model with Ancillary Datasets 5

integration can be problematic. Thus, comparisons were 

conducted to determine the efficacy of integrating ancillary 

hydrography data with 1-meter lidar-derived elevation data for 

Minnehaha County.

In figure 6, the lidar-derived shaded relief is overlain 

with NHD flowlines. At this location, the airport runways are 

surrounded by the Big Sioux River where the natural channel 

flows to the west and a diversion channel drains to the east. 

There is an estimated difference of 609.6 meters (2,000 feet) 

between the Big Sioux River channel and NHD flowlines. 

A comparison of the same area with the 30-meter EDNA 

synthetic streamlines overlain on lidar-derived shaded relief 

shows substantial deviation of surface flow (fig. 7). Although 

nationwide EDNA hydrologic derivatives were created in 

2001, the surface flow for this geographic area was derived 

from DEM source data within the NED that, in 2001, had not 

yet been updated to 10-meter or better resolution. Representation of the flat area to the west of the airport is inaccurate 

because of old elevation source data used when the 30-meter 

hydrologic derivatives were created in 2001.

Comparisons conducted in rural areas of Minnehaha 

County resulted in similar issues. In figure 8, NHD flowlines 

overlain on lidar-derived shaded relief portray an estimated 

152.4-meter (500-foot) difference between NHD flowlines and 

the current location of the channel. An analysis of the EDNA 

synthetic streamlines overlain on the lidar-derived shaded 

relief resulted in the same discrepancy (fig. 9). Such variances 

exist in other ancillary datasets including the U.S. Department 

of Transportation Federal Highway Administration National 

Bridges Inventory (NBI; fig. 10; U.S. Department of Transportation Federal Highway Administration, 2007). Although the 

present bridge location is accurately represented in the 1-meter 

lidar-derived DEM, the bridge was in a different location prior 

to the 1970s. An aerial photograph of the area in 1958 shows 

flow meandering west of the present bridge location (fig. 11). 

The stream was channelized and the bridge was moved to its 

present location in the early 1970s because of the construction 

of a highway that would provide access to the USGS EROS 

Center (fig. 12).

Comparing NHD water bodies with the lidar-derived 

DEM in Minnehaha County revealed that some water bodies 

no longer exist. In figure 13, NHD “Lake or Pond(s)” are 

overlain on the lidar-derived shaded relief. This area currently 

contains parking with railroad access and tank storage where 

three NHD “Lake or Pond(s)” are represented in the NHD 

dataset (fig. 14). Contour lines from the 1/3-arc-second 

(10-meter) NED (fig. 15) and from lidar (fig. 16) go through 

the NHD “Lake or Pond(s),” which is an indicator that a 

water body no longer exists. However, contour lines from the 

older Digital Line Graphs (DLGs) still encircle the “Lake or 

Pond(s).” The top left NHD “Lake or Pond(s)” in figure 13 

also no longer exists: overland surface flow has been routed 
Figure 5. 1-meter lidar-derived elevation (top) and 10-meter data (bottom) 

incorporated into the National Elevation Dataset.

6 Surface Flow Features from Lidar-Derived Digital Elevation Models for Minnehaha County, South Dakota
Figure 6. National Hydrography Datatset flowlines overlain on 1-meter lidar-derived elevation data in the Sioux Falls 

Regional Airport area.

Comparisons of Minnehaha County 1-Meter Lidar-Derived Digital Elevation Model with Ancillary Datasets 7
Figure 7. Elevation Derivatives for National Applications overlain on 1-meter lidar-derived elevation data in the Sioux 

Falls Regional Airport area.

8 Surface Flow Features from Lidar-Derived Digital Elevation Models for Minnehaha County, South Dakota
Figure 8. National Hydrography Dataset flowlines overlain on 1-meter lidar-derived elevation data in rural Minnehaha 

County showing differences in channel and bridge location.

Comparisons of Minnehaha County 1-Meter Lidar-Derived Digital Elevation Model with Ancillary Datasets 9
Figure 9. Elevation Derivatives for National Applications overlain on 1-meter lidar-derived elevation data in rural 

Minnehaha County showing differences in channel and bridge location.

10 Surface Flow Features from Lidar-Derived Digital Elevation Models for Minnehaha County, South Dakota
Figure 10. Data from Elevation Derivatives for National Applications, National Hydrography Dataset, and National 

Bridges Inventory overlain on 1-meter lidar-derived elevation data in rural Minnehaha County showing differences in 

channel and bridge location.

Comparisons of Minnehaha County 1-Meter Lidar-Derived Digital Elevation Model with Ancillary Datasets 11
Figure 11. U.S. Geological Survey high-resolution scan of aerial photograph taken in 1958, rural 

Minnehaha County.

12 Surface Flow Features from Lidar-Derived Digital Elevation Models for Minnehaha County, South Dakota
Figure 12. Aerial imagery draped over lidar-derived elevation data in rural Minnehaha County. Aerial imagery courtesy of 

the U.S. Geological Survey, city of Sioux Falls, Minnehaha County, and Sanborn Map Company Inc.

Comparisons of Minnehaha County 1-Meter Lidar-Derived Digital Elevation Model with Ancillary Datasets 13
Figure 13. National Hydrography Dataset “Lake or Pond(s)” overlain on 1-meter lidar-derived elevation data located near 

the intersection of West 12th Street and Interstate 29, Sioux Falls, South Dakota.

14 Surface Flow Features from Lidar-Derived Digital Elevation Models for Minnehaha County, South Dakota
Figure 14. Aerial imagery draped over lidar-derived elevation data in a location that no longer contains National Hydrography 

Dataset water bodies near the intersection of West 12th Street and Interstate 29, Sioux Falls, South Dakota. Aerial imagery 

courtesy of the U.S. Geological Survey, city of Sioux Falls, Minnehaha County, and Sanborn Map Company Inc.

Comparisons of Minnehaha County 1-Meter Lidar-Derived Digital Elevation Model with Ancillary Datasets 15
Figure 15. Contour lines from National Elevation Dataset 1/3-arc-second (10-meter) digital elevation model and from 

Digital Line Graphs overlain on 1-meter lidar-derived elevation data located near the intersection of West 12th Street and 

Interstate 29, Sioux Falls, South Dakota.

16 Surface Flow Features from Lidar-Derived Digital Elevation Models for Minnehaha County, South Dakota
Figure 16. Contour lines from 1-meter lidar-derived digital elevation model and from Digital Line Graphs overlain on 

1-meter lidar-derived elevation data located near the intersection of West 12th Street and Interstate 29, Sioux Falls, South 

Dakota.

Comparisons of Minnehaha County 1-Meter Lidar-Derived Digital Elevation Model with Ancillary Datasets 17
Figure 17. Aerial imagery draped over lidar-derived elevation data in a location where National 

Hydrography Dataset “Lake or Pond(s)” once existed near the intersection of West 12th Street and 

Interstate 29, Sioux Falls, South Dakota. Aerial imagery courtesy of the U.S. Geological Survey, city of 

Sioux Falls, Minnehaha County, and Sanborn Map Company Inc.

18 Surface Flow Features from Lidar-Derived Digital Elevation Models for Minnehaha County, South Dakota

through several culverts (fig. 17). More current contour lines 

now represent topography in areas that were previously lakes 

or ponds (figs. 15 and 16). To that end, using contour lines 

from DLGs to hydrologically enforce elevation will produce 

results that mimic older inaccurate map data.

These comparisons exemplify the currency issues that 

arise when attempting to integrate geographic data developed 

at various time intervals. USGS National Map Accuracy 

Standards indicate horizontal accuracy needs to be within 

12.19 meters (40 feet) at a 1:24,000 scale map (U.S. Geological Survey, 1999). In keeping with the USGS goal of providing the best publicly available elevation data, the Minnehaha 

County 1-meter lidar-derived elevation data meet the National 

Map Accuracy Standards and are currently the best publicly 

available elevation data to represent topography and applications of surface-water data. Therefore, surface flow features 

were developed from the Minnehaha County lidar data.
Figure 18. Lidar-derived surface flow features overlain on 1-meter lidar-derived elevation data in rural Minnehaha 

County. Note: In this bare earth lidar-derived elevation surface, the bridge (left side of image) was removed from the 

elevation data by the lidar vendor when the lidar point cloud data were processed.

Surface Flow Features Derived from Lidar 19

Surface Flow Features Derived from 

Lidar

Surface flow features derived from lidar are high resolution, hydrologically integrated, and consistently generated. 

The elevation data were hydrologically conditioned using 

techniques developed by Jenson and Domingue (1988). This 

method is a conditioning phase that generates three datasets: 

the original DEM with depressions filled, a dataset indicating the flow direction that represents the steepest down slope 

direction for each cell, and a flow accumulation dataset in 

which each cell receives a value equal to the number of 

cells that drain to it. Sinks are identified by subtracting the 

original DEM from the filled depressionless DEM. The filling 

of depressions allows surface routing across all cells in the 

dataset. Because of the detailed nature of lidar data, more 

research is needed to develop methods to systematically 

remove flow obstructions, such as bridges, from lidar-derived 

surface flow (fig. 18). This need is described in more detail in 

the section of this report titled “Implications for Further Study 

and Additional Capabilities.” 

Two methods were used to conduct comparisons between 

surface flow features derived from a hydrologically conditioned lidar-derived DEM and ancillary hydrography datasets 

including EDNA and NHD: a contributing area threshold and 

a downstream trace (raindrop analysis) technique. The first 

generation of surface flow features (channels) developed from 
20 Surface Flow Features from Lidar-Derived Digital Elevation Models for Minnehaha County, South Dakota

lidar was based on a contributing area threshold using the flow 

accumulation raster, which creates a channel network with 

a uniform drainage density and some small short headwater 

channels (fig. 19A). Although different drainage network 

densities may be needed for various applications, using 

contributing area threshold channel networks with existing 

cartographically derived channels can become challenging.

The second generation of surface flow features (channels) 

was based upon a downstream trace (raindrop) method where 

cartographically derived headwater reaches (NHD) were 

processed to extract the uppermost point on the reach. These 

points were used to “seed” a downstream trace or “raindrop” 

trace by using the filled DEM and related flow direction. 

Unlike the contributing area threshold, the downstream trace 

method uses predetermined points to define headwaters to 

delineate downstream channels, and the result is a drainage 

density that more closely mimics the cartographically derived 

hydrography (fig. 19B). 

Comparing lidar-derived surface flow generated from a 

hydrologically conditioned DEM with the downstream trace 

(raindrop) surface flow derived from NHD headwater reaches 

reveals some of the issues regarding integration and mapping 

of flow channels on the landscape. Figure 19 contains a 

headwater location in an agricultural field. The flow channels derived by using a contributing area threshold (fig. 19A) 

contain a more dense drainage network than the downstream 

trace channel (fig. 19B). Additional channels depicted in the 

lidar elevation data were not captured in the cartographically 

controlled downstream trace. When rendering a map at a given 

scale, it is necessary to control the number of features (detail) 

in a way that is appropriate for that scale. In this case, the 

cartographically based dataset was developed at a set scale, 

and the inclusion and exclusion of some features are somewhat subjective.

Downstream trace (raindrop) surface flow and the 

NHD flowlines were compared for the same agricultural 

field location (fig. 20). There is some deviation between the 

DEM-derived downstream trace channel and the cartographic 

channel. The largest disagreement between the two channels is 

about 36.5 meters (120 feet).

Implications for Further Study and 

Additional Capabilities

During the generation of lidar-derived surface flow 

features, several issues became evident regarding the integration and mapping of flow channels on the landscape. The 

following are some examples of methods, technologies, and 

models that are being refined and tested to enhance the use of 

lidar in deriving surface flow features.

Because of lidar’s fine-scale ability to represent the topographic surface of the earth, a vast amount of detail is captured 

in the bare earth lidar data. Using a depressionless DEM to 

create overland surface flow provides more accurate surface 

channels when obstructions, such as bridges, are removed. 

However, if such obstructions are not removed, the filling of 

sinks to create overland flow tends to either unnecessarily spill 

the flow over the obstruction in the wrong location or drain the 

flow in the opposite direction (fig. 18). Ongoing research is 

testing a selective draining method that will be implemented in 

future development of surface flow features from lidar.

In the process of identifying sinks in the hydrologic 

conditioning phase, a difference grid was created by subtracting the values of the original lidar DEM from the filled depressionless DEM. The deepest and shallowest cells could be 

identified in the sink(s) by using the highest and lowest values 

of the difference grid (fig. 21). In many cases, the deepest 

point in the sink could be used to drain through or under an 

obstruction, such as a bridge. Knowing the shallowest point 

is valuable in determining where water would overflow from 

the sink in the event of a flood when the drainage channels 

(through a culvert or bridge) could not accommodate the 

floodwaters. Additional research is being conducted to use 

these points to create overland flow as opposed to removing 

elevation data from the bare earth lidar, such as the location 

of bridges or culverts, where valuable transportation network 

information is lost by doing so.

One of the many benefits of deriving surface flow 

features from lidar is the ability to delineate watersheds. 

Elevation-derived watersheds provide a framework to quantify hydrologic footprint characteristics so that researchers 

can evaluate the health of the Nation’s watersheds (Poppenga 

and Worstell, 2008). Both the contributing area threshold and 

downstream trace methods were digitally created using the 

flow direction matrix (steepest slope) from the lidar-source 

DEM. The flow direction matrix provides the necessary data to 

support watershed delineation tools, which is a capability that 

is not an option when using traditional cartographic datasets. 

Future lidar applications will include the development 

of a water dataset (mask) derived from bare earth lidar data 

and spectral imagery. During the processing of the Minnehaha 

County lidar, ground points were classified by using TerraScan 

software by Terrasolid Limited of Helsinki, Finland. In the 

filtering process, points were classified as “default,” “ground,” 

or “water.” This information is useful for research of extracting water body features.

Conclusion

The USGS is the primary source for seamless elevation 

models at the global, national, and local scales (Gesch, 2007). 

By implementing remotely sensed lidar-derived DEMs into 

the NED, the elevation layer for “The National Map,” the 

USGS has developed a multiresolution, evolving elevation 

dataset that provides the best publicly available elevation data 

for research, applications, and data distribution. This perpetual development method needs to be applied to hydrologic 

derivatives to provide valuable surface flow information for 
Figure 19. (A) Lidar-derived surface flow from hydrologically conditioned digital elevation model and (B) downstream 

trace (raindrop) surface flow derived from National Hydrography Dataset headwater points.

Implications for Further Study and Additional Capabilities 21
Figure 20. Surface flow from downstream trace (raindrop) method, derived from National Hydrography Dataset 

headwater points, and the National Hydrography Dataset flowlines.

22 Surface Flow Features from Lidar-Derived Digital Elevation Models for Minnehaha County, South Dakota

surface-water applications. Because the USGS has identified 

water availability as a high priority and highlights the need for 

a Water Census of the United States, further research is needed 

to develop accurate surface flow to contribute to the determination of the status and trends in freshwater quantity and 

quality for human and environmental needs (U.S. Geological 

Survey, 2007). Because elevation is a key factor in deriving 

surface flow features, the best publicly available elevation 

data (high-resolution 1-meter lidar-derived DEMs) were used 

to create surface flow features in Minnehaha County, South 

Dakota. Lidar-derived surface flow, historical elevation data 

(30- and 10-meter resolution), and ancillary hydrography 

datasets were compared to determine the efficacy of elevation 

and hydrologic integration. Surface flow features generated 

from lidar-derived DEMs are of high resolution and produce 

highly integrated hydrologic data. These remotely sensed data 

and their derivatives provide topographic information and data 

integration capabilities needed for meeting current and future 

human and environmental needs.
Figure 21. Difference grid resulting from subtracting values of the original lidar-derived digital elevation model (DEM) from the filled 

depressionless DEM.

Conclusion 23
24 Surface Flow Features from Lidar-Derived Digital Elevation Models for Minnehaha County, South Dakota

References Cited

Franken, S.K., Tyler, D.J., and Verdin, K.L, 2001, Development of a National seamless database of topography and 

hydrologic derivatives, Proceedings of the 21st ESRI User’s 

Conference, July 9–13, 2001, San Diego. (Also available online at http://gis.esri.com/library/userconf/proc01/

professional/papers/pap730/p730.htm and on CD–ROM.)

Gesch, D.B., in press, Analysis of lidar elevation data for 

improved identification and delineation of lands vulnerable 

to sea level rise: Journal of Coastal Research.

Gesch, D.B., 2007, The National Elevation Dataset, in Maune, 

D., ed., Digital elevation model technologies and applications—the DEM users manual (2d ed.): Bethesda, Md., 

American Society for Photogrammetry and Remote Sensing, p. 99–118.

Gesch, D., Oimoen, M., Greenlee, S., Nelson, C., Steuck, M., 

and Tyler, D., 2002, The National Elevation Dataset: Photogrammetric Engineering & Remote Sensing, v. 68, no. 1, 

p. 5–11. (Also available online at: http://www.asprs.org/

publications/pers/2002journal/january/highlight.html)

Gesch, D.B., Verdin, K.L., and Greenlee, S.K., 1999, New 

land surface digital elevation model covers the earth: Eos, 

Transactions, American Geophysical Union, v. 80, no. 6, 

p. 69–70.

Hogan, E.P., and Fouberg, E.H., 2001, The geography of 

South Dakota (3d ed.): Sioux Falls, S. Dak., Center for 

Western Studies, 190 p.

Jenson, S.K., and Domingue, J.O., 1988, Extracting topographic structure from digital elevation data for geographic 

information system analysis: Photogrammetric Engineering 

and Remote Sensing, v. 54, p. 1,593–1,600.

Poppenga, S.K., and Worstell, B.B., 2008, Elevation-derived 

watershed basins and characteristics for major rivers of the 

conterminous United States, U.S. Geological Survey Scientific Investigations Report, 2008–5153, 27 p. (Also available 

online at http://pubs.usgs.gov/sir/2008/5153/.)

Queija, V.R., Stoker, J.M., and Kosovich, J.J., 2005, Recent 

U.S. Geological Survey applications of lidar: Photogrammetric Engineering & Remote Sensing, v. 71, no. 1, p. 5–9. 

(Also available online at http://www.asprs.org/publications/

pers/2005journal/january/highlight.pdf)

Stoker, J., Harding, D., and Parrish, J., 2008, The need for 

a National Lidar Dataset: Photogrammetric Engineering & Remote Sensing, v. 74, no. 9, p. 1066–1068. (Also 

available online at http://www.asprs.org/publications/

pers/2008journal/september/index.html)

U.S. Census Bureau, 2008, 2005–2007 American community survey 3-year estimates, Minnehaha County, South 

Dakota, accessed February 6, 2009, at http://factfinder.

census.gov/servlet/ACSSAFFFacts?_event=Search&geo_

id=&_geoContext=&_street=&_county=Minnehaha&_

cityTown=Minnehaha&_state=04000US46&_zip=&_

lang=en&_sse=on&pctxt=fph&pgsl=010

U.S. Department of Transportation Federal Highway Administration, 2007, National Bridges Inventory, accessed December 17, 2008, at http://www.fhwa.dot.gov/bridge/nbi.htm

U.S. Geological Survey, 1999, Map Accuracy Standards: U.S. 

Geological Survey Fact Sheet 171–99, 2 p. (Also available 

online at http://egsc.usgs.gov/isb/pubs/factsheets/fs17199.

html) 

U.S. Geological Survey, 2007, Facing tomorrow’s challenges—U.S. Geological Survey science in the decade 

2007–2017: U.S. Geological Survey Circular 1309, 

70 p. (Also available online at http://pubs.usgs.gov/

circ/2007/1309/.)

U.S. Geological Survey, 2008, National Hydrography Dataset: 

U.S. Geological Survey dataset available at http://nhd.usgs.

gov

Verdin, K.L., and Jenson, S.K., 1996, Development of continental scale digital elevation models and extraction of 

hydrographic features, in Proceedings, Third International 

Conference/Workshop on Integrating GIS and Environmental Modeling: Santa Fe, N. Mex., January 21–26, 1996, 

National Center for Geographic Information and Analysis, 

Santa Barbara, Calif. (Also available online at http://www.

ncgia.ucsb.edu/conf/SANTA_FE_CD-ROM/sf_papers/

verdin_kristine/santafe2.html)
Publishing support provided by:

Helena Publishing Service Center

For more information concerning this publication, contact:

U.S. Geological Survey Earth Resources Observation and 

Science (EROS) Center

Sioux Falls, South Dakota

(605) 594-6151

Or visit the EROS Center Web site at:

World Wide Web: http://eros.usgs.gov/
Poppenga and others—Surface Flow Features from Lidar-Derived Digital Elevation Models for Minnehaha County, South Dakota—SIR 2009–5065