U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2010–5059

Using Selective Drainage Methods to Extract Continuous 

Surface Flow from 1-Meter Lidar-Derived Digital Elevation 

Data 
Cover photograph. A water body located in Minnehaha County, 2009. Photograph taken by 

Jason M. Stoker, Physical Scientist, U.S. Geological Survey.
Using Selective Drainage Methods to 

Extract Continuous Surface Flow from 

1-Meter Lidar-Derived Digital Elevation 

Data 

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

Greenlee

Scientific Investigations Report 2010–5059

U.S. Department of the Interior

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

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2010

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Suggested citation:

Poppenga, S.K., Worstell, B.B., Stoker, J.M., and Greenlee, S.K., 2010, Using selective drainage methods to extract 

continuous surface flow from 1-meter lidar-derived digital elevation data: U.S. Geological Survey Scientific Investigations Report 2010–5059, 12 p.

ISBN 978-1-4113-2777-1
iii

Contents

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

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

Background.....................................................................................................................................................2

Study Area.......................................................................................................................................................5

Selective Drainage Methods .......................................................................................................................5

Identifying and Classifying Depressions....................................................................................................7

Discussion.......................................................................................................................................................9

Summary and Conclusion...........................................................................................................................10

Acknowledgments.......................................................................................................................................11

References Cited..........................................................................................................................................11

Figures

1–3. Maps showing:

 1. Lidar-derived surface flow before (A) and after (B) selective draining 

of a difference grid depression that is overlain on aerial imagery of 

Minnehaha County,

South Dakota..........................................................................................................................3

 2. Lidar-derived surface flow before (A) and after (B) selective draining 

of a difference grid depression that is overlain on 1-meter lidar-derived 

elevation data in Minnehaha County, South Dakota .......................................................4

 3. Research area located in the U.S. Geological Survey Sioux Falls West 

Quadrangle N4330—W9645/7.5 overlain on 1-meter lidar-derived 

elevation data in Minnehaha County, South Dakota .......................................................5

4. Flow chart showing selective drainage methods showing (A) filling process

to create a depressionless DEM, (B)contributing area threshold processes 

that results in obstructed lidar-derived surface flow, and (C) selective 

drainage methods that drains surface flow through obstructions to create 

a fully connected drainage network..........................................................................................6

5–7. Maps showing:

 5. Closed drainage basins identified as Sioux Quartzite mines that do not 

contribute to downstream flow with (A) the difference grid depression 

depth at the deepest cell is 64.1578 meters, and (B) aerial imagery of 

the Sioux Quartzite mines ....................................................................................................8

 6. The tinning of water surfaces in the 1-meter lidar-derived elevation 

data ..........................................................................................................................................9

 7. Water bodies extracted from the absence of lidar returns over water 

surfaces in 1-meter lidar point cloud data overlain on aerial imagery 

of Minnehaha County, South Dakota................................................................................10
iv

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft) 

meter (m) 1.094 yard (yd) 

square meter (m2) 10.76 square foot (ft2)

Conversion Factors and Datums

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).
Abstract 

Digital elevation data commonly are used to extract 

surface flow features. One source for high-resolution elevation 

data is light detection and ranging (lidar). Lidar can capture 

a vast amount of topographic detail because of its fine-scale 

ability to digitally capture the surface of the earth. Because 

elevation is a key factor in extracting surface flow features, 

high-resolution lidar-derived digital elevation models (DEMs) 

provide the detail needed to consistently integrate hydrography with elevation, land cover, structures, and other geospatial 

features. The U.S. Geological Survey has developed selective 

drainage methods to extract continuous surface flow from 

high-resolution lidar-derived digital elevation data. The lidarderived continuous surface flow network contains valuable 

information for water resource management involving flood 

hazard mapping, flood inundation, and coastal erosion. 

DEMs used in hydrologic applications typically are processed to remove depressions by filling them. High-resolution 

DEMs derived from lidar can capture much more detail of the 

land surface than courser elevation data. Therefore, high-resolution DEMs contain more depressions because of obstructions such as roads, railroads, and other elevated structures. 

The filling of these depressions can significantly affect the 

DEM-derived surface flow routing and terrain characteristics 

in an adverse way. In this report, selective draining methods 

that modify the elevation surface to drain a depression through 

an obstruction are presented. If such obstructions are not 

removed from the elevation data, the filling of depressions 

to create continuous surface flow can cause the flow to spill 

over an obstruction in the wrong location. Using this modified 

elevation surface improves the quality of derived surface flow 

and retains more of the true surface characteristics by correcting large filled depressions.

A reliable flow surface is necessary for deriving a consistently connected drainage network, which is important in 

understanding surface water movement and developing applications for surface water runoff, flood inundation, and erosion. 

Improved methods are needed to extract continuous surface 

flow features from high-resolution elevation data based on 

lidar.

Introduction

There is an increasing demand for continuous surface 

flow features extracted from lidar-derived bare earth elevation 

data (Stoker and others, 2008; Kenny and others, 2008; Liu 

and others, 2005; Snyder, 2009). Because topography is an 

important factor in deriving hydrologic features, the vertical 

accuracy and high-resolution characteristics of lidar-derived 

elevation greatly improve the quality of extracted hydrologic 

features, such as surface flow and drainage channels. As 

the need for flood hazard mapping and emergency response 

intensifies, continuous surface flow modeling improvements 

are needed to meet this obligation. Additionally, researchers 

studying hydrology have determined lidar’s bare earth resolving properties, high-resolution, and accuracy to be helpful in 

improving their understanding of surface water movements, 

drainage patterns, flood inundation and coastal erosion (Stoker 

and others, 2008).

Lidar has been increasingly accepted as an effective and 

accurate technology for acquiring high-resolution elevation 

data for bare earth, vegetation, and structures (Stoker and 

others, 2006). Because of lidar’s fine-scale ability to represent 

topography, a vast amount of detail is captured in the bare 

earth lidar elevation data. Although this detail is desirable 

for incorporating the best available elevation data into the 

National Elevation Dataset (NED) (Gesch and others, 2002), 

extracting continuous surface flow from high-resolution 

digital elevation models (DEMs) presents some challenges 

because obstructions, such as elevation data representing 

bridges or roads, are introduced (Poppenga and others, 2009). 

Using Selective Drainage Methods to Extract Continuous 

Surface Flow from 1-Meter Lidar-Derived Digital Elevation 

Data

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

1

 U.S. Geological Survey.

2

 Stinger Ghaffarian Technologies (SGT), Inc., contractor to the U.S. 

Geological Survey Earth Resources Observation and Science (EROS) Center. 

Work performed under USGS contract 08HQCN0005.
2 Using Selective Drainage Methods to Extract Continuous Surface Flow from Digital Elevation Data

Vegetation also may cause problems if the bare earth surface 

model erroneously contains vegetation return signatures.

Concerns regarding lidar-derived hydrologic modeling 

that address surface flow obstructions have been presented in 

the scientific literature (Barber and Shortridge, 2005; Kenny 

and others, 2008; Wang and Liu, 2006). Barber and Shortridge 

(2005) state there are limits to the practical use of lidar that 

vary with the intended application, and that a high-resolution, 

high-accuracy DEM does not necessarily produce a highly 

reliable model of surface flow. Other researchers have stated 

that lidar-derived DEMs gave a detailed delineation of subcatchments and offered the capability of improving the quality 

of lidar-derived hydrological features (Liu and others, 2005; 

Snyder, 2009). However, the accuracy of hydrological derivatives (surface flow) is affected by the DEM quality and resolution (Zhang and Montgomery, 1994, Wolock and McCabe, 

2000) as well as the type of source data the derivatives are 

based upon. Moore and others (1991) state that the deficiency 

of many hydrological and water quality models is their inability to account for the effects of terrain on flow processes.

In response to the need for improved elevation-derived 

continuous surface flow modeling, this report describes selective drainage methods that are incorporated into a hydrologic 

conditioning phase where surface flow is drained through 

obstructions in the lidar-DEM. The selective draining methodology enhances lidar-derived feature extraction by providing 

an improved representation of continuous surface flow. 

Background

Elevation data are fundamentally important for hydrologic feature extraction and analysis and are being used 

routinely as a source for deriving hydrologic features such as 

drainage basins and drainage channel networks (Gesch, 1999; 

Moore and others, 1993; Band, 1999; Garbrecht and Martz, 

2000; Wang and Liu, 2006). Elevation data are critically 

important for many hydrologic studies (Gesch, 2007) and are 

useful for deriving parameters that describe the local drainage 

conditions such as watersheds and stream channels (Gesch, 

2005; Poppenga and Worstell, 2008; Kost and Kelly, 2001). 

Additionally, elevation data are used to extract flow direction, 

upslope contributing area, aspect, and surface flow features. 

These parameters are important for many topographically 

based hydrologic modeling applications. 

All hydrologic models ultimately rely on some form 

of overland flow simulation to define drainage courses and 

watershed structure (Garbrecht and Martz, 2000). Various 

methods have been proposed for routing overland flow in the 

gridded elevation surface. Attempts have been made to remove 

depressions by smoothing the DEM data (O’Callaghan and 

Mark, 1984; Mark, 1983). The smoothing approach removes 

shallow depressions, but deeper depressions remain (Jenson 

and Domingue, 1988). Duke and others (2003) used a road 

enforcement algorithm to improve the accuracy of grid-based 

overland flow routing through the use of ancillary road, ditch, 

and culvert data. Others have proposed imposing relief across 

flats (Martz and Garbrecht, 1993, Garbrecht and Martz, 2000), 

network seeding (Kenny and others, 2001, 2008; Matthews 

and Kuyvenhoven, 2002), and outlet breaching (Martz and 

Garbrecht, 1999). The most common methods involve determining for each cell in a raster DEM the flow direction by 

reference to the elevation of the cell in comparison to its eight 

adjacent neighbors (Marks and others, 1984; O’Callaghan and 

Mark, 1984; Band, 1986; Jenson and Domingue, 1988; Jenson, 1991). At present (2010), the contributing area threshold 

model (Jenson and Domingue, 1988; Jenson, 1991) has been 

implemented by nearly all geographic information systems 

(GIS) and hydrologic software packages (Gesch, 1999, 2005; 

Wang and Liu, 2006), and it uses a method of filling spurious 

depressions to create a depressionless DEM.

 The process of filling depressions in lower resolution 

elevation data (30-m) to derive surface flow is a common 

technique; however, the actual filling process causes problems 

in hydrological modeling (Band, 1999; Hutchinson and Gallant, 1999; Kenny and others, 2008; Wang and Liu, 2006). The 

identification and classification process for filling depressions 

creates challenges when attempting to automatically derive 

fully connected drainage networks. Because bare earth elevation data derived from lidar are complex topographic surfaces, 

geographic features are more prominent, thereby causing 

obstructions in the contributing area threshold model (Jenson 

and Domingue, 1988). In other words, lidar returns capture 

details of the landscape, such as bridges and roads, which are 

higher than the surrounding terrain. The lidar returns also capture more detailed depressions that result in impediments to 

extracting continuous surface flow from lidar-derived DEMs. 

If the obstructions are removed, a more fully connected drainage network can be derived from a depressionless DEM. The 

current (2010) practice of the lidar vendor is to remove the 

lidar points associated with the bridges or culverts for water to 

travel downstream (Stoker, 2009). However, if the lidar points 

associated with such obstructions are not removed, the filling 

of depressions to create continuous surface flow tends to spill 

the surface flow over the obstruction in the wrong location 

(figs. 1 and 2) (Poppenga and others, 2009).

 Identification of surface flow depressions is a critical 

step for deriving a fully connected drainage network from 

high-resolution elevation data. With the growing availability 

of lidar-derived DEMs, there is a need to develop methods to 

systematically identify and classify surface flow depressions 

that form behind flow obstructions. Because of the complexity of high-resolution elevation data, methods are described 

herein to classify depressions and drain surface flow through 

obstructions from 1-meter lidar-derived DEMs using selective 

drainage methods. 
Background 3

96°49'50" 96°49'40" 96°49'30"

43°36'20"

43°36'10"

43°36'20"

43°36'10"

Deepest

elevation value

Shallowest

elevation value

0 50 100 200 METERS

0 190 380 760 FEET

Deepest

elevation value

Shallowest

elevation value

Base from U.S. Geological Survey, City of Sioux Falls, Minnehaha County, and 

Sanborn Map Company Inc., 2008

Universal Transverse Mercator projection

Zone 14N

Horizonal coordinate information referenced to the 

North American Datum of 1983 (NAD 83)

A

B

EXPLANATION

Difference of unfilled and filled 

 digital elevation model

Depth in meters

High: 3.41006

Low: 0.000092

EXPLANATION

Difference of unfilled and filled 

 digital elevation model

Depth in meters

High: 3.41006

Low: 0.000092

Lidar-derived surface flow 

 before draining

Lidar-derived surface flow 

 after draining

Figure 1. Lidar-derived surface flow before (A) and after (B) selective draining of a difference grid 

depression that is overlain on aerial imagery of Minnehaha County, South Dakota.
4 Using Selective Drainage Methods to Extract Continuous Surface Flow from Digital Elevation Data

96°49'50" 96°49'40" 96°49'30"

43°36'20"

43°36'10" Deepest

elevation value

Shallowest

elevation value

Obstruction

EXPLANATION

Difference of unfilled and filled 

 digital elevation model

Depth in meters

High: 3.41006

Low: 0.000092

Lidar-derived surface flow 

 before draining

43°36'20"

43°36'10"

0 50 100 200 METERS

0 190 380 760 FEET

Surface flow drained

through obstruction

Shallowest

elevation value Deepest

elevation value

Base from U.S. Geological Survey, City of Sioux Falls, Minnehaha County, and 

Sanborn Map Company Inc., 2008

Universal Transverse Mercator projection

Zone 14N

Horizonal coordinate information referenced to the 

North American Datum of 1983 (NAD 83)

A

B

EXPLANATION

Difference of unfilled and filled 

 digital elevation model

Depth in meters

High: 3.41006

Low: 0.000092

Lidar-derived surface flow 

 after draining

Figure 2. Lidar-derived surface flow before (A) and after (B) selective draining of a difference grid 

depression that is overlain on 1-meter lidar-derived elevation data in Minnehaha County, South Dakota.
Selective Drainage Methods 5

Study Area

The U.S. Geological Survey (USGS) Sioux Falls West 

Quadrangle (U.S. Geological Survey, 1962), located in Minnehaha County in southeastern South Dakota, was selected 

to research and test the selective draining methods. The area 

consists of agricultural land interspersed and flanking the 

Sioux Falls metropolitan area (fig. 3). The physical landscape 

consists of rolling plains with watershed drainage flowing 

generally southeastward into the Big Sioux River, a tributary 

of the Missouri River. The gridded rectangular pattern of the 

rural transportation network and denser metropolitan street 

pattern is visible in the lidar elevation data (fig. 3). The denser 

network of metropolitan streets served as an example of a 

challenging elevation surface to extract continuous surface 

flow from lidar-derived DEMs.

Lidar point cloud data were collected with an airborne 

platform for Minnehaha County in May 2008 by Sanborn 

Map Company Inc. The data met 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 (Poppenga and others, 2009). 

The lidar vendor processed the data to bare earth elevation 

before the USGS incorporated the elevation data into the 

NED, the elevation layer of “The National Map” (http://www.

nationalmap.gov).

Selective Drainage Methods

Spurious depressions in the DEM are identified and filled 

by raising the values of cells in depressions to the value of the 

96°50' 96°45' 96°40'

43°35'

43°30'

0 5,500 11,000 22,000 FEET

0 1,550 3,100 6,200 METERS

Big

Sioux

River

Big

Sioux

River

Sioux FallsSioux Falls

U.S. Geological Survey

Sioux Falls West Quadrangle

EXPLANATION

Minnehaha County

Figure 3. Research area located in the U.S. Geological Survey Sioux Falls West Quadrangle N4330—W9645/7.5 overlain on 1-meter 

lidar-derived elevation data in Minnehaha County, South Dakota. 
6 Using Selective Drainage Methods to Extract Continuous Surface Flow from Digital Elevation Data

spill point of the depression using the standard filling techniques (Jenson and Domingue, 1988; Jenson, 1991) described 

previously. The output of the filling process is a new, depressionless DEM. The depressionless DEM is used in the second 

preprocessing step to compute flow direction representing the 

steepest down slope direction for each cell. The third conditioning step is the computation of the flow accumulation in 

which each cell receives a value equal to the number of cells 

that drain to it (Jenson, 1991) (fig. 4A and 4B). 

Because of the complexity of the lidar-derived DEM, 

extracting continuous surface flow using only the three-step 

hydrologic conditioning process can result in a drainage 

network that misrepresents the true flow on the landscape (fig. 

4B). For example, geographic features such as elevation data 

representing road base or grade overlying hydraulic structures 

(bridges or culverts) are more prominent in high-resolution 

elevation data. Consequently, these features become surface 

flow obstructions that cause depressions to form upstream 

from the structures. Additional processes are needed to derive 

a fully connected drainage network. To that end, semi-automated techniques and metrics were developed for classifying 

depressions to drain surface flow by cutting through the 

obstructions (fig. 4C).

The method for identifying and classifying depressions 

begins by subtracting the unfilled lidar DEM from the filled 

lidar DEM. The output is a mask, or “difference grid,” containing depth values for all cells in each depression (figs. 1, 2, 

and 4C). The difference grid contains large numbers of small, 

insignificant depressions, many of which are interpolation artifacts of the DEM. Thus, the number of depressions that needs 

to be drained can be decreased by classifying depressions 

based upon parameters, such as area or depth. The depressions that meet all parameters are eligible for draining surface 

flow through obstructions. The depressions that are selectively 

classified and extracted from the lidar difference grid contain 

depth values that are important in identifying the location of 

surface flow obstructions, such as elevation data representing 

bridges or roads. In many cases, the deepest cell in a depression is the location of a surface flow obstruction and surface 

flow can be drained at that location (Poppenga and others, 

2009) (figs. 1 and 2).

Lidar-derived

unfilled digital 

elevation model (DEM)

Lidar-derived

depressionless

 DEM

Fill

depression

Minus

Euclidian

distance

LACP values set to

deepest value outside

of “difference grid”

depression

Copy of lidarderived unfilled

DEM with LACP

Fill

depression

Drainage enforced

lidar-derived

depressionless DEM

Mosaic

Lidar-derived

“difference

grid”

Classify “difference

grid” depressions

Flow

direction

Flow

direction

Flow

accumulation

Unobstructed

lidar-derived

surface flow

Flow

accumulation

Obstucted

lidar-derived

surface flow

Data

Process

Parameter

EXPLANATION

“Difference grid”

depressions

extracted for

draining

Deepest values

within “difference 

grid” depressions

Least 

accumulative

cost path (LACP)

LACP new 

values

Deepest values outside of

 “difference grid” depressions

and within buffer

Buffer

by 200

meters

Area Depth

Cost path

analysis

A Filling process C Processes to derive unobstructed surface flow

B Processes resulting

in obstructed lidarderived surface flow

Figure 4. Selective drainage methods showing (A) filling process to create a depressionless DEM, (B) contributing area 

threshold processes that results in obstructed lidar-derived surface flow, and (C) selective drainage methods that drains 

surface flow through obstructions to create a fully connected drainage network.
Identifying and Classifying Depressions 7

The selective draining method (fig. 4C) for draining surface flow through an obstruction depends upon two variables: 

identification of the lowest elevation value in a depression that 

is upstream from the flow obstruction, and identification of 

nearest cells outside the depression with elevations lower than 

the lowest elevation inside the depression. 

Each depression is evaluated independently to identify 

the deepest location or cell (source) within the depression. 

This cell is used as a starting location for delineating a path 

through the obstruction. Candidate target cells (destination) 

are locations outside the depression that have elevation values 

less than or equal to the lowest cell within the depression. A 

200-meter buffer is used to limit the search distance. Euclidian 

distance and cost path functions are used to choose the closest 

of the candidate cells to delineate a path connecting the two 

cells (source and destination). To derive connected drainage, 

the elevation value of the lowest cell outside of the depression is used as the elevation values along the path connecting 

the two cells (source and destination). This path is the least 

accumulative cost path whereby a few elevation values are 

modified to allow a depression to properly drain at the location 

of a conduit.

The least accumulative cost path adjusted elevation 

values are incorporated into a copy of the unfilled lidar DEM, 

and the three-step hydrologic conditioning phase processes 

(filling, computing flow direction, and computing flow accumulation) are re-calculated with the drained elevation values 

(fig. 4C). By minimally modifying elevation values at conduit 

locations, surface flow drains through depressions, thereby 

creating a fully connected lidar-derived surface flow network. 

By draining surface flow through obstructions using these 

selective draining methods, the lidar-surface flow more closely 

resembles what is occurring on the landscape with minimal 

modifications to the DEM data. 

Identifying and Classifying 

Depressions

Minnehaha County bare earth elevation data derived from 

lidar were used to produce 1-meter DEMs with a Universal 

Transverse Mercator Zone 14 projection and a North American 

Datum of 1983 horizontal datum. A shaded relief image of the 

1-meter lidar-derived DEM is shown in figure 3. A buffered 

USGS Sioux Falls West Quadrangle DEM was extracted from 

the bare earth elevation data and filled to create a depressionless DEM. To identify and classify depressions, a difference 

grid was produced by subtracting the unfilled DEM from the 

depressionless (filled) DEM (fig. 4C). 

In the study area, the difference grid depressions 

exceeded 60,000 polygons. Therefore, to efficiently extract 

continuous lidar-derived surface flow, the number of depressions was decreased to 227 polygons by classifying them 

based upon the following parameters: 

• area greater than or equal to 1,000 square meters; and

• depth greater than or equal to 1 meter and greater than 

0.5 standard deviation of the difference grid.

The depressions that met these parameters were used 

to drain surface flow through obstructions. Surface flow was 

drained through obstructions by calculating the least accumulated cost path for each obstructed area, and the breached 

elevation values were incorporated into a copy of the lidarderived DEM. Finally, the three-step hydrologic conditioning 

phase was re-calculated with the new lidar elevation values 

resulting in lidar-derived continuous surface flow (fig. 4C).

Lidar-derived elevation data present complex and challenging scenarios for generating continuous surface flow 

features. Nonetheless, the results of the selective draining 

methods provide fully integrated geospatial data that contain 

important information for research applications. The following paragraphs include examples of methods and technologies 

that are being refined and tested to enhance the use of lidar in 

deriving continuous surface flow features.

The value of the selective draining methods is the ability to automatically identify most surface flow obstructions 

by using the deepest cell(s) of difference grid depressions to 

drain surface flow through an obstruction. However, considering the hydrologic complexity of the surface of the earth, 

some depressions are actual closed drainage basins that do not 

contribute to downstream flow. Therefore, closed basins were 

identified by classifying them based on location and depth values of the deepest cells within the difference grid depressions. 

For example, in the Sioux Falls West Quadrangle, several of 

the filled depressions had a depth greater than 60 meters with 

their deepest cell(s) in their centroid. They were identified as 

operative Sioux Quartzite mines and were classified as closed 

drainage basins that were excluded from the selective draining 

methods (fig. 5). 

Although there is value in identifying the deepest cells(s) 

of filled depressions, identification of the shallowest cell is 

important for determining where water would overflow in a 

flood. If a culvert or bridge could not accommodate the floodwaters, the surface flow would back up and spill out of the 

depression at the shallowest cell. In other words, in figures 1 

and 2, the deepest cell or highest elevation value of the depression is indicative of a surface flow obstruction representing a 

culvert or bridge. If this conduit was blocked, the depression 

would become inundated and the surface flow would spill out 

of the depression at the shallowest cell or lowest elevation 

value (Poppenga and others, 2009). This is important geospatial information for land use planners, flood hazard management, or insurance entities interested in identifying geographic 

areas prone to risks of flooding.
8 Using Selective Drainage Methods to Extract Continuous Surface Flow from Digital Elevation Data

96°47'30" 96°47' 96°46'30" 96°46' 96°45'30"

43°33'30"

43°33'

43°32'30"

0 1,050 2,100 4,200 FEET

0 312.5 625 1,250 METERS

EXPLANATION

Depth in meters

High: 64.1578

Low: 0.000031

Difference of unfilled and filled 

 digital elevation model

43°33'30"

43°33'

43°32'30"

Sioux Quartzite mines

Base from U.S. Geological Survey, City of Sioux Falls, Minnehaha County, and 

Sanborn Map Company Inc., 2008

Universal Transverse Mercator projection

Zone 14N

Horizonal coordinate information referenced to the 

North American Datum of 1983 (NAD 83)

A

B

Figure 5. Closed drainage basins identified as Sioux Quartzite mines that do not contribute to downstream 

flow with (A) the difference grid depression depth at the deepest cell is 64.1578 meters, and (B) aerial 

imagery of the Sioux Quartzite mines.
Discussion 9

Discussion

Lidar data, or point cloud returns, are an effective remote 

sensing technology capable of delivering highly accurate 

topographic surface representations (Stoker and others, 2005). 

However, lidar returns are sparsely recorded in areas of larger 

stream channels or water bodies because lidar laser pulses are 

not reflected by water. Because of the decreased point cloud 

return density, water surfaces in a lidar-derived gridded DEM 

typically inherit a triangulated irregular network (TIN), or 

tinning pattern (fig. 6). In the selective draining methods, the 

tinning pattern associated with water caused the surface flow 

to drain unevenly down the elevation slope. Although USGS 

specifications for lidar data acquisition and derivative products are in the process of being defined, additional research 

is needed to alleviate surface flow problems caused by voids 

over water in the lidar point cloud. 

Even though selective draining methods are affected 

negatively by the absence of lidar returns over water surfaces, 

ongoing analysis of lidar voids indicates potential for thresholding and extracting potential water bodies (fig. 7) and double 

line streams from lidar return voids using density analysis and 

return intensity methods. This research could provide the capability to correct the tinning of water bodies in lidar-derived 

gridded elevation data so that the best available elevation data 

can be incorporated into the NED.

96°50' 96°49'30"

43°33'

43°32'30"

Tinning of water surfaces

0 462.5 925 1,850 FEET

0 137.5 275 550 METERS

Base from U.S. Geological Survey, City of Sioux Falls, Minnehaha County, and 

Sanborn Map Company Inc., 2008

Universal Transverse Mercator projection

Zone 14N

Horizonal coordinate information referenced to the 

North American Datum of 1983 (NAD 83)

Figure 6. The tinning of water surfaces in the 1-meter lidar-derived elevation data.
10 Using Selective Drainage Methods to Extract Continuous Surface Flow from Digital Elevation Data

Summary and Conclusion

There is an increasing demand for continuous surface 

flow extracted from light detection and ranging (lidar)-derived 

bare earth digital elevation models (DEMs). Contributing area 

threshold models that previously have been used to extract 

continuous surface flow, or hydrologic derivatives, from 

coarser resolution elevation data (30m) are no longer sufficient to extract continuous surface flow from lidar-derived 

DEMs because of the increased vertical accuracy of the finely 

detailed elevation surface. Therefore, the U.S. Geological 

Survey (USGS) has developed selective drainage methods 

to extract continuous surface flow from high-resolution 

lidar-derived DEMs. Because of the quality of the lidar data 

acquired in Minnehaha County in southeastern South Dakota, 

the USGS Sioux Falls West Quadrangle was selected to 

research and test the selective draining methods.

DEMs can be processed to extract a wealth of hydrologic 

information. Numerous methods have been developed to 

extract hydrologic data from coarser resolution elevation data, 

but the identification and filling of spurious depressions to 

derive continuous surface flow still remains a significant problem for most models. This problem intensifies when deriving 

continuous surface flow from lidar-derived DEMs because of 

the finely detailed complex elevation surface. Although the 

detail is valuable for incorporating the best available elevation 

data into the National Elevation Dataset (NED), extracting 

continuous surface flow from high-resolution DEMs presents 

challenges because obstructions, such as elevation data representing bridges or roads, are introduced. 

In high-resolution elevation data, such as lidar, geographic features are more prominent; consequently, they cause 

impediments to extracting continuous surface flow. The USGS 

developed semi-automated methods to drain flow through 

96°50' 96°49'30"

43°33'

43°32'30"

0 445 890 1,780 FEET

0 135 270 540 METERS

EXPLANATION

Water bodies extracted from analysis

 of lidar voids over water surfaces

Base from U.S. Geological Survey, City of Sioux Falls, Minnehaha County, and 

Sanborn Map Company Inc., 2008

Universal Transverse Mercator projection

Zone 14N

Horizonal coordinate information referenced to the 

North American Datum of 1983 (NAD 83)

Figure 7. Water bodies extracted from the absence of lidar returns over water surfaces in 1-meter lidar point cloud data 

overlain on aerial imagery of Minnehaha County, South Dakota.
References Cited 11

obstructions by identifying and classifying depressions based 

on parameters. By classifying surface flow depressions, lidarderived feature extraction is enhanced by providing continuous surface flow that is consistently integrated with elevation 

and hydrographic and geographic features. This information 

is valuable for water resource managers who are dependent 

upon topographic and hydrologic features that traditionally 

have been derived from topographic maps, field surveys, or 

photographic interpretations. 

As an enhancement to existing geospatial data, continuous surface flow features derived from lidar offer a remote 

sensing framework for improving the quality and currency of 

hydrologic data that are important for hydrologic modeling 

and water-resource management. As the demand for lidarderived continuous surface flow increases, lidar’s integrative 

qualities are useful for hydrologic applications involving flood 

hazard mapping, flood inundation, and coastal erosion.

Acknowledgments

The contributions of the following individuals are 

gratefully acknowledged: Dean Gesch (USGS) for advice 

and encouragement; Gayla Evans (USGS), Karl Heidemann 

(USGS), Sandra Prince (SGT, Inc.), Jordan Menig (SGT, Inc.), 

Zheng Zhang (SGT, Inc.), and Mike Oimoen (SGT, Inc.) for 

lidar data collection, lidar processing, and National Elevation 

Dataset (NED) production. Also, thank you to the following 

for access to the Minnehaha County lidar data: the city of 

Sioux Falls, Minnehaha County, and Sanborn Map Company, 

Inc. 

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Poppenga and others—Using Selective Drainage Methods to Extract Continuous Surface Flow from Digital Elevation Data—Scientific Investigations Report 2010–5059

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