U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2016–5141

Prepared in cooperation with the Southwest Tennessee Development District

Automated Identification of Stream-Channel Geomorphic 

Features From High-Resolution Digital Elevation Models in 

West Tennessee Watersheds
Cover: A head cut 3 meters tall that was not detectable from high-resolution aerial 

imagery but associated with a flow-network zone of steep slope and high slope-area 

index, Upper Sandy study area.
Automated Identification of Stream-Channel 

Geomorphic Features From High-Resolution 

Digital Elevation Models in West Tennessee 

Watersheds

By Jennifer M. Cartwright and Timothy H. Diehl

Prepared in cooperation with the Southwest Tennessee Development District

Scientific Investigations Report 2016–5141

U.S. Department of the Interior

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

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Director

U.S. Geological Survey, Reston, Virginia: 2017

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Suggested citation:

Cartwright, J.M., and Diehl, T.H., 2017, Automated identification of stream-channel geomorphic features from 

high-resolution digital elevation models in West Tennessee watersheds: U.S. Geological Survey Scientific 

Investigations Report 2016–5141, 44 p., https://doi.org/10.3133/sir20165141.

http://catalog.loc.gov/

ISSN 2328-031X (print)

ISSN 2328-0328 (online)

ISBN 978-1-4113-4100-5
iii

Acknowledgments

The authors wish to thank Timothy Poole, Park Manager at Pinson Mounds State Archaeological 

Park, Tennessee Department of Environment and Conservation, Division of State Parks, for 

facilitating access and providing information for one of the study sites. This report was improved 

through reviews by Alfred Kalyanapu of Tennessee Technological University and Jeffrey Fore of 

The Nature Conservancy. The authors are also grateful to Gregg Hileman and David Ladd of the 

U.S. Geological Survey for assistance with field data collection and postprocessing, technical 

assistance with field equipment, and valuable technical discussions during the course of 

the study.

v

Contents

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

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

Purpose and Scope ..............................................................................................................................3

Description of Study Areas .................................................................................................................3

Pinson Mounds Study Area .......................................................................................................3

Upper Sandy Study Area ............................................................................................................6

Methods of Study...........................................................................................................................................6

Acquisition and Preprocessing of Elevation Data...........................................................................6

Hydroconditioning.................................................................................................................................7

Flow Network Delineation and Field Verification ............................................................................7

Tool Development for Flow Network Delineation ...........................................................................8

Tool Development for Automated Geomorphic Feature Identification......................................10

Bank Slope Tool..........................................................................................................................10

Channel Depth Tool....................................................................................................................10

Channel Slope Tool ....................................................................................................................10

Slope-Area Index Tool...............................................................................................................11

Rough Terrain Tool.....................................................................................................................11

Topographic Position Index...............................................................................................................12

Tool Output Field Verification............................................................................................................12

Evaluation of Automated Tools for Geomorphic Feature Identification..............................................12

Culvert Modification and Hydroconditioning of Digital Elevation Models ................................12

Flow Networks.....................................................................................................................................15

Bank Slope...........................................................................................................................................17

Channel Depth.....................................................................................................................................20

Channel Slope and Slope-Area Index .............................................................................................23

Rough Terrain ......................................................................................................................................28

Topographic Position Index...............................................................................................................30

Spatial Analysis of Multiple Geomorphic Indicators ....................................................................30

Summary and Conclusions.........................................................................................................................35

References Cited..........................................................................................................................................36

Glossary.........................................................................................................................................................41

Appendix 1. Prospectus for Tool Application to Larger Geographic Areas....................................43

Field-Verification Resources.............................................................................................................43

Work Plan.............................................................................................................................................44
vi

Figures

1. Map showing location of the Pinson Mounds study area, containing the Hudson

Branch watershed and Pinson Mounds State Archaeological Area, Madison and

Chester Counties, Tennessee .....................................................................................................4

2. Map showing location of the Upper Sandy study area containing the headwater

basin of Sandy Creek, Madison County, Tennessee...............................................................5

3. Diagram showing suggested workflow for tools in the digital elevation model

Geomorphology Toolbox .............................................................................................................9

4. Maps showing locations in the Upper Sandy and Pinson Mounds study areas

where tool output verification was performed using Global Positioning

System-enabled photography and field-note dictation........................................................13

5. Maps showing examples of cut and fill records produced by the Optimized Pit

Removal tool and flow-network modification based on incorporation of culvert

locations into digital elevation models, Pinson Mounds study area..................................14

6. Maps showing Flow Network tool outputs compared to the high-resolution flowline

from the National Hydrography Dataset for the Upper Sandy and Pinson Mound

study areas ..................................................................................................................................16

7. Maps showing outputs from the Bank Slope tool within the Upper Sandy study area

and along gully networks within the Pinson Mounds study area.......................................18

8. Composite showing an example of an actively eroding and overhanging bank in a

stream-channel section identified by the Bank Slope tool as having a slope

exceeding 50 percent but not exceeding 100 percent in the Pinson Mounds study

area ...............................................................................................................................................19

9. Composite showing an area mapped with slope exceeding 100 percent that is

vegetated and is probably stable, an area mapped with a slope exceeding 

50 percent that shows signs of active erosion including exposed soil and 

overhanging roots, and output from the Bank Slope tool in the Pinson Mounds 

study area ....................................................................................................................................21

10. Composite showing upstream view of a gully head upstream view of a narrow,

steep-walled gully system upstream view of a channel connected to a floodplain

in the base of a trench downstream view of the outlet of a gully system where flow

enters the river floodplain, and calculated channel depth within gully networks

using a 10-foot search radius, Pinson Mounds study area.................................................22

11. Composite showing a head cut 3 meters tall that was not detectable from

high-resolution aerial imagery but associated with a flow-network zone of steep

slope and high slope-area index, Upper Sandy study area.................................................25

12. Composite showing a, 0.5-meter-tall active head cut a root-stabilized head cut, 

and output from the Channel Slope and Slope-Area Index tools, Upper Sandy study 

area ...............................................................................................................................................26

13. Composite showing examples of errors produced by the Channel Slope tool and

the Slope-Area Index tool, including a head cut not identified by the tools, a

location in the flow-network with high slope-area index but no head cut, and tool

outputs, Pinson Mounds study area........................................................................................27

14. Composite showing an example of erosion at a culvert outlet that was not

identified in Channel Slope tool or Slope-Area Index tool output, Upper Sandy 

study area ....................................................................................................................................29
vii

15. Composite showing an example of a gully head eroding urban infrastructure,

including an upstream view of the gully head a downstream view of the gully a

close-up view of the eroded area undercutting asphalt, and boundaries of

polygons generated by the Rough Terrain tool, Upper Sandy study area ........................31

16. Composite showing output polygons from the Rough Terrain tool superimposed 

on profile curvature derived from the digital elevation model, and on aerial

orthoimagery of identical extent, Upper Sandy study area ................................................32

17. Composite showing actively eroding head cuts in the main channel of a gully

system, and a small tributary to the gully system, associated with feature clusters

of outputs from multiple tools, Pinson Mounds study area.................................................33

18. Composite showing A tributary gully, and an overhanging bank, associated with

feature clusters of outputs from multiple tools, Upper Sandy study area ........................34

Tables

1. Numbers and lengths of digitized stormwater-infrastructure features ............................15

2. Summary of channel slope values produced by the Channel Slope tool..........................23

3. Summary of point values produced by the Slope-Area Index (SAI) tool ..........................24

Conversion Factors

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

hectare (ha) 2.471 acre

square meter (m2

) 10.76 square foot (ft2) 

square kilometer (km2

) 0.3861 square mile (mi2)

Flow-network density

meter per hectare (m/ha) 8.107 foot per acre (ft/acre)

Datums

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

(NAVD 88).

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

World Geodetic System of 1984 (WGS 84).

Elevation, as used in this report, refers to distance above the vertical datum.
viii

Abbreviations

3DEP 3D Elevation Program

AGPNS Agricultural Non-Point Source Pollution Model

CREAMS Chemicals, Runoff, and Erosion from Agricultural Management Systems

DEM digital elevation model

FIPS Federal Information Processing Standard

GIS Geographic Information System

GLONASS Global Navigation Satellite System

GNSS Global Navigation Satellite System

GPS Global Positioning System

lidar light detection and ranging

MS4 municipal separate storm sewer system

NAIP National Agriculture Imagery Program

NHD National Hydrography Dataset

NRCS Natural Resources Conservation Service

OPR Optimized Pit Removal

QL2 quality level 2

RPS Recovery Potential Screening

SAI slope-area index

SWTDD Southwest Tennessee Development District

TPI topographic position index

USDA U.S. Department of Agriculture

USGS U.S. Geological Survey

UTM Universal Transverse Mercator

WAAS Wide Area Augmentation System

WARSSS Watershed Assessment of River Stability and Sediment Supply 
Automated Identification of Stream-Channel Geomorphic 

Features From High-Resolution Digital Elevation Models in 

West Tennessee Watersheds

By Jennifer M. Cartwright and Timothy H. Diehl

Abstract

High-resolution digital elevation models (DEMs) derived 

from light detection and ranging (lidar) enable investigations 

of stream-channel geomorphology with much greater 

precision than previously possible. The U.S. Geological 

Survey has developed the DEM Geomorphology Toolbox, 

containing seven tools to automate the identification of 

sites of geomorphic instability that may represent sediment 

sources and sinks in stream-channel networks. These tools 

can be used to modify input DEMs on the basis of known 

locations of stormwater infrastructure, derive flow networks 

at user-specified resolutions, and identify possible sites of 

geomorphic instability including steep banks, abrupt changes 

in channel slope, or areas of rough terrain. Field verification 

of tool outputs identified several tool limitations but also 

demonstrated their overall usefulness in highlighting likely 

sediment sources and sinks within channel networks. In 

particular, spatial clusters of outputs from multiple tools can 

be used to prioritize field efforts to assess and restore eroding 

stream reaches.

Introduction

Identifying sites of geomorphic instability1 within 

stream-channel networks is an important component of 

several watershed-management practices. For example, 

locating sites of erosion is vital for enhancing water 

quality and preserving aquatic habitat, because eroded 

sediment transported by streams is a leading cause of 

habitat impairment in lotic ecosystems (Ribaudo, 1986; 

Wood and Armitage, 1997; Henley and others, 2000) and in 

downstream estuaries (Thrush and others, 2004). Erosion 

has been linked to reduced biomass and diversity of benthic 

invertebrates in downstream reaches (Wantzen, 2006) and 

has negatively affected amphibian diversity and abundance 

(Smiley and others, 2009). Identifying erosional features 

within stream-channel networks is also important in efforts to 

mitigate potential threats to economically valuable resources, 

such as agricultural land and urban infrastructure. Erosional 

features near roads and other transportation infrastructure can 

undermine their structural stability (Cocker, 2007; Saksa and 

Minár, 2012). Gas pipelines, power lines, and other energy 

infrastructure features can be damaged over time by erosion 

(Cocker, 2007). In addition, erosional processes contribute 

to the degradation of agricultural land because of the loss of 

fertile topsoil (Vandekerckhove and others, 2001; Svoray and 

others, 2012).

Much of the eroded sediment in degraded landscapes 

comes from gully erosion, including ephemeral gullies in 

agricultural fields that are removable by cultivation, and 

permanent gullies that remain in place for years (Poesen and 

others, 2003). Gully erosion is a major cause of sedimentation 

in reservoirs (Valentin and others, 2005; Shellberg and 

Brooks, 2013) and on floodplains (Happ and others, 

1975) downstream. Gully erosion generally enhances the 

connectivity or coupling of the sediment transport system from 

upstream slopes to downstream channels (Poesen and others, 

2003; Aksoy and Kavvas, 2005), but in some places sediment 

from gullies overwhelms the transport capacity of streams 

and creates partial or complete barriers to sediment transport, 

including braided streams, alluvial fans, and valley plugs

(Happ and others, 1940; Happ and others, 1975; Trimble, 

2008). Depending on the location within the stream-channel 

network, the effects of gully erosion may be dominated either 

by channel deepening and widening or by channel filling 

(Booth and Henshaw, 2001).

The frequency, distribution, and severity of gully erosion 

are difficult to predict (Poesen, 2011; Poesen and others, 

2011; Svoray and others, 2012). Existing models of erosion 

and sediment transport do not predict gully formation, but 

some can model gully erosion if data on gully characteristics 

are available, such as from the Agricultural Non-Point 

Source Pollution (AGNPS) model; Chemicals, Runoff, and 

Erosion from Agricultural Management Systems (CREAMS) 

model; and the SedNet model (Bull and Kirkby, 1997; 

Vandekerckhove and others, 1998; Merritt and others, 2003; 

Dougall and others, 2007; Eustace and others, 2009; Dabney 

and others, 2010; Taguas and others, 2012; Daggupati and 1

Terms in bold are defined in the glossary.
2 Stream-Channel Geomorphic Features From High-Resolution Digital Elevation Models, West Tennessee

others, 2013). Some watershed management tools can 

incorporate spatial information on gully characteristics 

relevant to watershed health and recovery (U.S. Environmental 

Protection Agency, 2012). In particular, the Watershed 

Assessment of River Stability and Sediment Supply 

(WARSSS) procedure can incorporate local observations 

of gully channel types and the recovery potential screening 

(RPS) method includes several metrics directly affected 

by gullies, particularly in the flow and channel dynamics 

subcategory (Norton and others, 2009; U.S. Environmental 

Protection Agency, 2012). Although documentation for 

each of these approaches acknowledges the importance of 

watershed-specific information about sediment sources and 

sinks, none of them include methods for the automated spatial 

identification of geomorphic features associated with erosion 

and sediment deposition.

Spatially precise information linking erosional features to 

downstream targets is important for achieving cost-effective 

reductions in suspended-sediment loads (Lu and others, 

2004); however, acquiring this information is commonly 

time-consuming and thus expensive (Booth and Henshaw, 

2001; Biron and others, 2013). Investigations of streamchannel geomorphology have historically relied on field 

survey techniques (Harrelson and others, 1994; Fitzpatrick 

and others, 1998). Traditional methods to quantify gully 

erosion and gully wall retreat have relied on time-intensive 

field methods such as erosion pin monitoring, measurement 

by theodolite, and repeated cross sections (Lawler, 1993; 

Vandekerckhove and others, 1998; Nyssen and others, 2006; 

Rustomji, 2006). Acquisition of field data is typically limited 

by resource constraints and land-access restrictions, requiring 

interpretation and inference for entire stream reaches based 

on limited numbers of surveyed cross sections and stream 

profiles. This is a major limitation of field-survey approaches, 

because individual cross sections provide little information 

about conditions upstream and downstream (Booth and 

Henshaw, 2001). In nonforested landscapes, mapping of 

erosional features such as gullies may be achievable using 

high-resolution aerial orthoimagery (Daggupati and others, 

2013; Conoscenti and others, 2014). This approach is typically 

labor-intensive, because it relies on visual assessment either to 

analyze imagery or to verify results from automated methods. 

Moreover, the usefulness of aerial imagery in detecting 

erosional features is limited where canopy cover or thick 

ground vegetation obscures stream-channel geomorphology 

(Heine and others, 2004; James and others, 2007).

An alternative or complementary approach is to 

characterize stream-channel geomorphology in a geographic 

information systems (GIS) framework using hydrologic and 

elevation data. Some studies using this approach have relied 

on hydrologic networks and elevation data derived from 

topographic contour maps at scales of 1:10,000 or 1:24,000; 

for example, Desmet and others (1999) and Conoscenti and 

others (2014). The resolution of such datasets may not be 

sufficient to resolve small-scale erosional features, such as 

head cuts, especially if they are located in headwater streams 

(James and others, 2007). Moreover, the hydrography depicted 

in 1:24,000 U.S. Geological Survey (USGS) topographic maps 

has been shown to underestimate total stream length by more 

than 50 percent (Heine and others, 2004; James and others, 

2007) and by as much as 80 percent if perennial, intermittent, 

and ephemeral streams are considered (Hansen, 2001). As a 

result, these data sources are of limited use in locating sites of 

geomorphic instability with small drainage areas. In addition, 

because such datasets are more than 30 years old, on average 

(Snyder, 2012), they are commonly outdated in urbanizing 

watersheds.

A promising, newly available data source derives from 

airborne light detection and ranging (lidar), an active 

remote-sensing technology that uses laser scanning to generate 

high-resolution digital elevation models (DEMs) over 

large areas. A variety of quality levels have been assigned to 

digital elevation data, with quality level 2 (QL2) representing 

0.7-meter (m) horizontal point spacing and 9.25-centimeter 

(cm) vertical accuracy (Snyder, 2012). On the basis of 

a cost-benefit analysis conducted as part of the National 

Enhanced Elevation Assessment (Dewberry, 2011), the USGS 

3D Elevation Program (3DEP) set a target to acquire QL2 

elevation data for the conterminous United States by the end 

of 2022, with an 8-year acquisition cycle thereafter (Carswell, 

2013). In Tennessee, where this study was conducted, QL2 

elevation data were available for less than one-third of the 

State’s land area as of 2013 (Carswell, 2013). The State 

of Tennessee was awarded a grant by the USGS 3DEP in 

2015 for planned acquisition of QL2 data for an additional 

27 counties covering much of the eastern part of the state 

(Pederson, 2015). Thus, high-resolution DEM analysis will 

become increasingly feasible over the coming decades, even 

for remote or rural locations. In addition, time-series analysis 

of high-resolution DEMs becomes possible as repeated 

flights are conducted over time (Hessel and Van Asch, 2003; 

Martínez-Casasnovas, 2003; Shellberg and Brooks, 2013; 

Hogan and others, 2014). 

Analysis of high-resolution DEMs can complement 

traditional approaches to the study of geomorphology by 

allowing spatially comprehensive assessment of topographic 

indicators, such as slope and curvature (Ferencevic and 

Ashmore, 2012; Biron and others, 2013; Daggupati and 

others, 2013), and rapid assessments of gully morphology 

(Perroy and others, 2010; Höfle and others, 2013). DEM-based 

analysis can be used to estimate indicators associated with 

fluvial erosion risk, such as bank slope and channel slope. 

Spatial identification of certain stream-channel geomorphic 

features may be useful to land managers and water-quality 

planners seeking to identify locations within stream-channel 

networks that are sources and sinks of sediment (Ferencevic 

and Ashmore, 2012). Because DEM-based analysis typically 

requires many steps and nested, iterative processes, efficiency 

can be improved by developing automated tools to process 

DEMs and produce spatial locations for geomorphic indicators 
Introduction 3

of concern. Flexibility can be built into automated tools 

by allowing the user to adjust parameters of interest. An 

automated approach makes inherently subjective choices of 

geomorphic criteria consistent across the area of analysis, 

documents these choices, and enables comparison of results 

across multiple choices (user-adjusted parameter settings). 

The USGS, in cooperation with the Southwest Tennessee 

Development District (SWTDD), conducted a 12-month pilot 

study to develop automated tools to identify possible locations 

of sediment contribution within stream-channel networks on 

the basis of QL2 DEMs. This pilot study was conducted in 

two contrasting study areas (one urban watershed, one rural) 

in Madison County, Tennessee. This pilot study was intended 

to provide (1) a preliminary set of tools for geomorphic 

feature identification to be used in local and regional planning 

efforts related to water quality, stream-channel restoration, 

infrastructure protection, and stormwater management, and 

(2) a prospectus describing the requirements for applying such 

tools to larger geographic areas in future work.

Purpose and Scope

The purpose of this report is to document the 

development of the DEM Geomorphology Toolbox2, which is 

a set of automated tools for the identification of geomorphic 

features in stream-channel networks that may be associated 

with erosion and sediment deposition. These geomorphic 

features are identified herein using high-resolution digital 

elevation models of watersheds in western Tennessee. 

Field verification of tool outputs is documented for 

selected areas within the watersheds used for the pilot study 

based on accessibility, land ownership, and the spatial 

distribution of tool outputs. As such, the results presented 

herein represent preliminary findings from the pilot study 

and demonstrate a proof of concept that automated GIS tools 

for processing high-resolution DEMs can produce results 

indicative of geomorphic instability in stream-channel 

networks. The automated GIS tools produced in this study 

have not been verified in other watersheds or physiographic 

settings. To apply these tools beyond the two test watersheds, 

additional field verification and tool calibration would be 

required. A prospectus for tool application to larger geographic 

areas is included in appendix 1.

Description of Study Areas

Two test watersheds were selected as study areas and 

have similar slopes, soils, and subsoils. Both study areas 

are located within the Coastal Plain Physiographic Province 

2

The DEM Geomorphology Toolbox and associated metadata, including 

the tools described in this report, are available at http://dx.doi.org/10.5066/

F78C9TBQ.

of western Tennessee and cover a total of 15.3 square 

kilometers (km2) of drainage area (figs. 1 and 2; Fenneman 

and Johnson, 1946; Hardeman and others, 1966; Brown and 

others, 1978). The two study areas have contrasting land-use 

histories and differing concentrations of urban infrastructure. 

Thus, these areas allowed tool development and calibration to 

be informed by differences in historical and current land use.

Pinson Mounds Study Area 

The Pinson Mounds study area encompasses the Hudson 

Branch watershed and Pinson Mounds State Archaeological 

Area (fig. 1). Hudson Branch is a tributary of the South 

Fork Forked Deer River and has a total drainage area of 

about 945 hectares (ha), located southwest of the city 

of Jackson in Madison and Chester Counties, Tenn. The 

Hudson Branch watershed partially overlaps Pinson Mounds 

State Archaeological Area, a National Historic Landmark 

containing a complex of over a dozen prehistoric earthen 

mounds constructed during the Middle Woodland Period 

(circa 1–500 A.D.), making it one of the largest Middle 

Woodland ceremonial centers in eastern North America 

(Mainfort, 1988). Pinson Mounds State Archaeological 

Area contains approximately 500 ha of plateau-like upland, 

steep slopes, and floodplain immediately north of the South 

Fork Forked Deer River. This upland area ranges from 

approximately 130 to 150 m in elevation. The floodplain of 

the South Fork Forked Deer River is approximately 110 m in 

elevation and has minimal relief. The sloping bluffs separating 

the upland from the river floodplain are dissected by drainage 

networks characterized by permanent gully erosion. Although 

stormwater infrastructure features were mapped over the 

entire study area, these gullied areas were the focus of tool 

development and validation in this study area. 

Soils within the Pinson Mounds study area are primarily 

silt loams. Depositional areas along the floodplain of the South 

Fork Forked Deer River are mapped as Waverly silt loam 

(Brown and others, 1978). Nearby, alluvial fans are mapped as 

Iuka and Enfield soils, but the alluvial fans in this study were 

too small to be mapped as independent units in the Madison 

County soil survey (Brown and others, 1978). Permanently 

gullied areas and steep slopes generally are Smithdale silt 

loam. The broad, gently sloping, formerly agricultural areas of 

Pinson Mounds State Archaeological Area are Lexington silt 

loam, with severe erosion in swales (Brown and others, 1978). 

The watershed is underlain by bedrock of the Midway Group 

(Hardeman and others, 1966). 

The land-use history of the Pinson Mounds study area 

since the mid-19th century is characterized by agricultural 

use followed by succession to mixed hardwood forest. 

Euro-American settlement in the area surrounding what is now 

the Pinson Mounds State Archaeological Area began in the 

1820s and involved decades of intensive timber harvesting and 

the clearing of land for agriculture, predominantly corn and 

cotton under ridge cultivation (Mainfort and others, 2011). 
4 Stream-Channel Geomorphic Features From High-Resolution Digital Elevation Models, West Tennessee

fig16-0746_fig 01

South Fork Forked Deer River

MCNAIRY HARDIN

MADISON

HARDEMAN

HENDERSON

CHESTER

Location of Pinson Mounds study area in 

Chester and Madison Counties, Tennessee

EXPLANATION

Boundary of the Pinson Mounds study area

Boundary of Pinson Mounds State Archaeological Area

Boundary of the Hudson Branch watershed

Coastal Plain Physiographic Province

88°44' 88°43' 88°42' 88°41' 88°40' 88°39' 88°38'

35°

31'

30'

35°

29'

0 1 2 MILES

Base from USGS The National Map: National Boundaries Dataset, National Elevation Dataset, Geographic 0 1 2 KILOMETERS

Names Information System, National Hydrography Dataset, National Land Cover Database, National 

Structures Dataset, and National Transportation Dataset; U.S. Census Bureau - TIGER/Line; HERE Road 

Data. Mercator Auxiliary Sphere Projection, Datum: World Geodetic System of 1984

Figure 1. Location of the Pinson Mounds study area, containing the Hudson Branch watershed and Pinson Mounds 

State Archaeological Area, Madison and Chester Counties, Tennessee.
Introduction 5

fig16-0746_fig 02

MCNAIRY HARDIN

MADISON

HARDEMAN

HENDERSON

CHESTER

Location of Upper Sandy study area in 

Madison County, Tennessee

EXPLANATION

Boundary of the Upper Sandy study area

Coastal Plain Physiographic Province

88°52' 88°51' 88°50' 88°49' 88°48' 88°47' 88°46'

35°

40'

35°

39'

35°

38'

0 1 2 MILES

Base from USGS The National Map: National Boundaries Dataset, National Elevation Dataset, Geographic 0 1 2 KILOMETERS

Names Information System, National Hydrography Dataset, National Land Cover Database, National 

Structures Dataset, and National Transportation Dataset; U.S. Census Bureau - TIGER/Line; HERE Road 

Data. Mercator Auxiliary Sphere Projection, Datum: World Geodetic System of 1984

Figure 2. Location of the Upper Sandy study area containing the headwater basin of Sandy Creek, Madison County, 

Tennessee.
6 Stream-Channel Geomorphic Features From High-Resolution Digital Elevation Models, West Tennessee

Row-cropping on the gently sloping uplands near the Pinson 

Mounds ceremonial sites continued until the mid-20th century, 

when the State of Tennessee purchased the land and, in 

1974, designated the Pinson Mounds State Archaeological 

Area as a Tennessee State Park. After several decades of 

succession, the Pinson Mounds State Archaeological Area 

is now predominantly forested and has a network of roads 

and trails connecting visitor attractions such as campsites, 

picnic shelters, and archaeological areas. Upland forests are 

predominantly oak-hickory, forests on sloping bluffs between 

the upland and the river floodplain are mixed beech-oak, and 

the river floodplain is primarily cypress swamp (Broster and 

Schneider, 1977). Outside the boundary of the Pinson Mounds 

State Archaeological Area, the Hudson Branch watershed 

contains a mosaic of forest and agricultural land uses and a 

wider variety of soils (Brown and others, 1978). 

Upper Sandy Study Area

The Upper Sandy study area is composed of the 

watershed for the headwaters of Sandy Creek, which is a 

tributary to the South Fork Forked Deer River. This study area 

encompasses approximately 180 ha in Madison County, Tenn., 

in the northern part of the city of Jackson (fig. 2). Elevation 

within the Upper Sandy study area ranges from approximately 

130 to 165 m. 

Soils within the Upper Sandy study area are primarily silt 

loams and are subject to varying degrees of erosion. Soil along 

the downstream 300 m of Sandy Creek is mapped as Collins 

silt loam, formed in silty alluvium (Brown and others, 1978). 

Most of the stream, its tributaries, and nearby slopes are 

mapped as Lexington silt loam and have slopes ranging from 

12 to 20 percent. Margins of the watershed and internal ridges 

are mapped as Memphis silt loam and are severely eroded in 

steeper areas. The Lexington and Memphis loams are similar, 

both developed in loess overlying sandy subsoil, but in the 

Memphis loam, this subsoil lies deeper than 150 cm where 

erosion is absent. Crops grown on the Memphis silt loam were 

the same as those grown on the Lexington silt loam, but with 

larger yields (Brown and others, 1978). Both soils belong to 

the Memphis-Lexington-Smithdale soil association, which is 

common in Madison County. The Upper Sandy study area is 

underlain by the Claiborne and Wilcox Formations (Hardeman 

and others, 1966), composed of nonmarine sands interspersed 

with clay lenses, which are exposed in the bases of some 

eroding streambanks (Roberts and Collins, 1926). 

Land use within the Upper Sandy study area is a mixture 

of residential and commercial development and mixed 

hardwood forest. Development in this area began in the 1940s 

and 1950s along major roads along the eastern and western 

boundaries of the study area, then spread through the interior 

of the study area in the 1960s through 1990s (City of Jackson, 

2015). Because of the greater urbanization in this watershed 

compared to the Pinson Mounds study area, the Upper 

Sandy study area contains a greater concentration of urban 

infrastructure features such as culverts, road ditches, and 

storm sewers. 

Urbanization increases the importance of gully erosion, 

particularly relative to other sources of sediment, in two ways. 

First, a decrease in the area of bare and cultivated ground 

reduces surface erosion as a major sediment source (Poesen 

and others, 2003). Second, an increase in impervious area 

and drainage infrastructure generates greater runoff volume 

and faster delivery of water to stream-channel networks in 

frequent rainfall events, thereby promoting erosion in reaches 

downstream from culverts and armored ditches (Poesen 

and others, 2003; Cocker, 2007; Katz and others, 2014). 

Sandy Creek has characteristics that indicate geomorphic 

instability, including gully erosion in close proximity to 

urban infrastructure and degradation of water quality because 

of sediment loads. These issues have prompted the West 

Tennessee River Basin Authority to focus on restoring the 

stream (Blackwood, 2012).

Methods of Study

This study involved the development and refinement 

of automated tools to process DEMs derived from lidar. For 

the two study areas, DEMs based on lidar data produced in 

2011–12 were acquired, preprocessed, and hydroconditioned. 

The hydroconditioned DEMs were used to derive 

preliminary flow networks that were then modified using data 

indicating the locations of stormwater infrastructure. The 

hydroconditioned DEMs and modified flow networks were 

used as data inputs to develop automated tools to identify 

geomorphic features that may be associated with streamchannel erosion, sediment transport, and sediment deposition. 

All geospatial preprocessing, modification, and analysis 

were performed using ArcGIS Desktop version 10.2 (Esri, 

Redlands, Calif.). Automated tools were developed using 

ArcGIS ModelBuilder (Esri, Redlands, Calif.).

Acquisition and Preprocessing of Elevation Data

Lidar-derived DEMs were obtained from the U.S. 

Department of Agriculture (USDA), Natural Resources 

Conservation Service (NRCS) (Dewberry, 2012). These DEMs 

represent “bare earth” elevation, meaning the elevation of 

the land surface (or water surface in the case of inundated 

areas) regardless of vegetation or canopy cover (Dewberry, 

2012; Heideman, 2014). Lidar data were acquired between 

December 2010 and April 2011 for parts of 13 counties in 

western Tennessee at a nominal point spacing of 0.7 m. The 

dataset is horizontally referenced to the North American 

Datum of 1983 (NAD 83), and has a horizontal accuracy 

of 1.04 m at the 95-percent confidence level. The dataset is 

vertically referenced to the North American Vertical Datum 

of 1988 (NAVD 88), and has a vertical accuracy of 0.18 m 
Methods of Study 7

at the 95-percent confidence level. The coordinate system is 

Universal Transverse Mercator (UTM) Zone 16. Horizontal 

and vertical units are in meters. Cell size is 1 square meter 

(m2) and individual DEM tiles measure 1 by 1 kilometer 

(km). Further details about this dataset are available in 

Dewberry (2012).

The following preprocessing steps were conducted to 

prepare the DEM tiles obtained from NRCS:

1. Individual DEM tiles for each study area were combined 

into a single DEM.

2. Study area polygons were produced by adding a 100-m 

buffer around watershed polygons obtained from the 

USGS StreamStats Web application (Ladd and Law, 

2007). For the Pinson Mounds study area, the buffered 

watershed polygon for Hudson Branch was merged 

with a polygon representing the extent of Pinson 

Mounds State Archaeological Area to produce the study 

area polygon. 

3. DEMs were clipped to study area polygons.

4. Clipped DEMs were projected into the State Plane 

Coordinate System (NAD 83 State Plane Tennessee 

FIPS 4100 feet [ft]), with horizontal and vertical units in 

feet, while maintaining a 1-m (0.304801-ft) cell size.

Hydroconditioning

The DEMs typically must be hydroconditioned prior 

to deriving hydrologic features such as flow networks 

(Jenson, 1991; Poppenga and others, 2010; Jackson, 2013). 

Hydroconditioning is a process by which elevation values 

are modified to produce coherent flow directions and flow 

accumulations. In particular, hydroconditioning requires the 

removal of internal topographic depressions (raster cells 

of low elevation completely surrounded by cells of higher 

elevations). Although some DEM depressions represent true 

features of the terrain, such as karst sinkholes, many others 

are artifacts of various processing operations that are needed 

to produce bare-earth DEMs, such as the removal of elevation 

values representing vegetation (Jenson, 1991; Poppenga and 

others, 2010; Dewberry, 2012; Heideman, 2014). In areas 

drained by stormwater infrastructure, areas upstream from 

infrastructure features, such as culverts and storm sewers, 

commonly appear in DEMs as depressions. Because the 

routing of water through culverts and under small bridges 

is not represented in lidar-derived DEMs3, algorithms for 

deriving hydrologic features effectively treat roads and other 

infrastructure features underlain by culverts as “digital dams” 

and treat the areas upstream from culverts as depressions 

(Poppenga and others, 2010; Li and others, 2013; Chan and 

Crabtree, 2014).

A variety of techniques exist for modifying DEMs to 

remove topographic depressions in preparation for deriving 

hydrologic features—reviewed in Kenny and others (2008, 

table 2)—including DEM smoothing (O’Callaghan and 

Mark, 1984), outlet breaching (Martz and Garbrecht, 1999), 

and stream burning using existing vector representations 

of hydrologic networks (Callow and others, 2007). The 

filling of depressions (Jenson and Domingue, 1988) can be 

accomplished quickly in ArcGIS (Esri, Redlands, Calif.) 

using the Fill tool, which simply increases the elevation 

values within depression cells and thus allows water to be 

routed across the filled depression. With high-resolution 

lidar data, especially in areas of relatively flat terrain, this 

approach commonly results in large alterations that obscure 

meaningful topographic features upstream of any dam-like 

feature (Jackson, 2013). For this study, hydroconditioning 

was performed using the Optimized Pit Removal (OPR) 

tool (Jackson, 2013). This tool uses a combination of cuts 

(reductions in cell elevation) and fills (increases in cell 

elevations) to accomplish hydroconditioning while minimizing 

overall changes to the DEM. The OPR tool was set to 

minimize absolute elevation change. 

Flow Network Delineation and Field Verification

Following hydroconditioning of the DEMs for each 

study area, preliminary flow networks were derived using 

standard flow-accumulation threshold methods as described by 

Jenson and Domingue (1988), Jenson (1991), and Poppenga 

and others (2010), using the ArcGIS Flow Direction and 

Flow Accumulation tools. Flow-accumulation thresholds 

of 1,000 and 5,000 cells (1,000 and 5,000 m2

, or 0.1 and 

0.5 ha, respectively) were applied to the output raster datasets 

from the Flow Accumulation tool. Thus, the preliminary 

flow networks included only those cells that drained at 

least 1,000 or 5,000 m2, respectively, with “no data” values 

assigned to all cells having flow accumulation values below 

the thresholds. 

Because the functions of stormwater infrastructure, such 

as culverts and storm sewers, are not captured by lidar-derived 

DEMs (Poppenga and others, 2010; Li and others, 2013; 

Chan and Crabtree, 2014), the preliminary flow networks 

delineated in this study required field verification and, in 

many cases, flow-network correction. To prepare for field 

verification of flow networks, digital maps were prepared 

including the following base map layers: color orthoimagery 

from the National Agriculture Imagery Program (NAIP), 

hydroconditioned DEMs, DEM-derived profile curvature, 

3

A common practice among lidar vendors, including the producer of the 

lidar-derived digital elevation models (DEMs) used in this study, is to remove 

elevation values corresponding to large bridges by interpolating groundsurface elevation values immediately surrounding bridges. Culverts and small 

bridges, especially bridges on secondary or tertiary roads, are not removed 

from DEMs (Dewberry, 2012).
8 Stream-Channel Geomorphic Features From High-Resolution Digital Elevation Models, West Tennessee

cut and fill records from the OPR tool (which in many cases 

were useful for identifying likely culvert locations), and the 

preliminary flow networks at flow-accumulation thresholds 

of 1,000 and 5,000 cells. In addition, vector line files were 

included representing suspected culvert locations that were 

digitized in the office on the basis of visual examination of 

DEMs and derivative data layers. These data layers were 

loaded onto a Surface Pro 3 tablet (Microsoft, Redmond, 

Wash.) and accessed in the field using ArcGIS version 10.2 

(Esri, Redlands, Calif.). Navigation was performed using a 

SkyPro XGPS160 Global Navigation Satellite System (GNSS) 

receiver (Dual Electronics, Heathrow, Fla.) with dual access to 

the U.S. Global Positioning System (GPS) and Russian Global 

Navigation Satellite System (GLONASS).

Flow-network verification was performed in the Pinson 

Mounds study area on December 9, 2014, and in the Upper 

Sandy study area on March 18 and April 10, 2015. Culvert 

locations were digitized in the field as vector line files viewed 

on top of the reference data layers. Culvert vector lines that 

had been digitized in the office were field-verified and edited 

as needed. Each culvert was automatically assigned a unique 

identification number, time stamp, length (in feet), and user 

identification based on the person who digitized each line. 

Culverts were also assigned a code to record whether they 

were digitized in the field or digitized in the office and then 

verified in the field. The southwestern part of the Upper Sandy 

study area contains relatively dense commercial development. 

In this area, the complexity of subterranean stormwater 

infrastructure rendered comprehensive digitization impossible. 

Surface stormwater infrastructure features were inspected, 

including culvert inlets, culvert outlets, levees along the edges 

of parking lots, and storm drains, to determine the most likely 

patterns of connectivity. 

Following field inspection of preliminary flow networks, 

the culvert location files were used to modify the DEMs to 

improve flow-network accuracy. Culverts were incorporated 

into the DEMs as linear strips of elevation sufficiently low 

as to allow their inclusion on flow networks. The culvert 

locations were incorporated using the following steps:

1. Vector line files representing culvert locations were 

converted to raster.

2. Elevation values for all cells in each rasterized culvert 

were assigned the elevation of the lowest cell along that 

culvert. For this purpose, elevation values were taken 

from the raw (not hydroconditioned) DEMs.

3. The raw (not hydroconditioned) DEMs were modified 

such that elevation values for culvert cells were equal to 

the output of step 2 above, whereas all nonculvert cells 

retained their original elevation values.

4. The culvert-modified DEMs (outputs from step 3 above) 

were hydroconditioned using the OPR tool.

By following the processing steps just described, raster 

datasets were produced representing hydroconditioned 

DEMs that incorporated stormwater-infrastructure locations. 

Using these culvert-modified4, hydroconditioned DEMs 

as inputs, modified flow networks were delineated using 

flow‐accumulation thresholds of 1,000 and 5,000 cells, as 

described previously. These modified flow networks were 

checked against field notes (Cartwright and Diehl, 2016) and 

were compared to the high-resolution (1:24,000) flowlines 

from the National Hydrography Dataset (NHD; Simley and 

Carswell, 2009). In some areas of commercial development 

in the Upper Sandy watershed, deeper culverts crossed 

under roadways drained by shallower culverts, or under 

surface waterways carrying flow. These situations were only 

approximately represented by the culvert-modified DEM 

in which flow lines cannot cross each other; however, this 

complication did not appear to affect computed drainage area 

or flow routing to downstream sections of the flow network.

Tool Development for Flow Network Delineation 

To make the processes of flow-network delineation 

quicker and more user-friendly, two ArcGIS tools were 

created as part of the DEM Geomorphology Toolbox: the 

Add Culverts tool and the Flow Network tool. The suggested 

workflow for all tools in the DEM Geomorphology Toolbox 

shown in figure 3. All tools include detailed user instructions 

available in the tool dialogue windows.

The Add Culverts tool requires an input DEM and 

an input culvert file, which is an input vector line file 

representing the locations of stormwater infrastructure. The 

input DEM should be preprocessed to combine individual 

DEM tiles as needed and should be clipped to the watershed 

that includes the study area. From these inputs, the Add 

Culverts tool generates a culvert-modified DEM that 

includes modified elevation values along the locations of 

stormwater‐infrastructure inputs. Notably, this modified DEM 

requires hydroconditioning (such as with the OPR tool) prior 

to use as an input for flow-network delineation.

The Flow Network tool requires, as input, a 

culvert‐modified DEM (output of the Add Culverts tool) that 

has been subsequently hydroconditioned. The Flow Network 

tool allows the user to set a flow-accumulation threshold, 

which is the minimum number of cells that must drain into 

a raster cell for that cell to be included in the output flow 

network. Using the user-specified flow-accumulation threshold 

and the input DEM, the Flow Network tool produces three 

output files: 

4

Stormwater-infrastructure features digitized in the two study areas 

were primarily culverts but also included a few longer and more complex 

subterranean features, such as conduits draining storm sewers. In this report, 

the term “culvert-modified” refers to digital elevation models that have 

been modified to incorporate culverts and other related types of stormwater 

infrastructure.
Methods of Study 9

fig16-0746_fig 03

Raster flow network Flow network

accumulation 

Bank Slope tool

Channel Slope tool

Rough Terrain tool

Flow Network tool

Add Culverts tool

Input culvert file 

Input DEM

Vector flow network

Steep bank polygons

Bank slope raster

Channel depth raster

High channel slope points

Channel slope raster

Rough terrain polygons

High slope-area index points

Culvert-modified DEM

Hydro-conditioned 

DEM

Hydro-conditioning*

*for example, using the Optimized Pit 

Removal Tool (Jackson, 2013)

Channel Depth tool

Slope-Area Index tool

Figure 3. Suggested workflow for tools in the digital elevation model (DEM) Geomorphology Toolbox. Red boxes 

with pink shading represent the individual tools in the DEM Geomorphology Toolbox, green boxes represent data 

inputs, orange boxes represent intermediate data products, and blue boxes represent tool outputs. The purple 

box represents the hydroconditioning process, which is performed with tools available separately from the DEM 

Geomorphology Toolbox.
10 Stream-Channel Geomorphic Features From High-Resolution Digital Elevation Models, West Tennessee

1. A raster flow network file, with cells in the flow network 

having a value of 1 and cells not in the flow network 

having “no data” values.

2. A vector flow network file, which has a polyline for 

each flow-network segment, with segments separated 

by confluences.

3. A raster flow-network accumulation file, equivalent to 

the raster flow network except that values along the flow 

network contain flow accumulation values.

These output files are intended to serve as inputs for several 

automated tools within the DEM Geomorphology Toolbox that 

are used to identify geomorphic features, as described next.

Tool Development for Automated Geomorphic 

Feature Identification

Five tools were developed for the DEM Geormorphology 

Toolbox to automate the identification of certain geomorphic 

features from DEMs: the Bank Slope tool, the Channel Depth 

tool, the Channel Slope tool, the Slope-Area Index tool, and 

the Rough Terrain tool. These tools require, as input, the 

flow-network files and the culvert-modified, hydroconditioned 

DEMs produced using the Add Culverts and Flow Network 

tools. All tools include itemized user instructions available in 

the tool dialogue windows.

Bank Slope Tool

The Bank Slope tool requires, as input, a culvertmodified, hydroconditioned DEM, a raster flow network 

(output of the Flow Network tool), and an input vector line 

file representing stormwater-infrastructure locations. The 

Bank Slope tool allows the user to set a flow corridor width 

across which bank slope is calculated. User instructions for 

the Bank Slope tool explain that the units for this flow corridor 

width must be the same as the linear unit for the coordinate 

system for the input DEM. The user is also prompted to input 

a bank slope threshold, in percent rise, which is used to create 

vector polygons representing zones with bank slope exceeding 

the threshold. 

The Bank Slope tool uses the input files and the 

user‐specified flow-corridor width and bank-slope threshold to 

produce two output files: 

1. A bank slope raster, representing the slope of cells within 

the user-specified flow corridor.

2. A steep banks polygon file, which is a vector polygon 

file representing areas of bank slope exceeding the 

user‐specified bank-slope threshold. 

The process of incorporating stormwater-infrastructure 

locations to modify DEMs using the Add Culverts tool 

produces artifacts resembling steep bank slopes. These 

artifacts are included in the output bank slope raster produced 

by the Bank Slope tool; however, this tool uses the input 

culvert location file to remove polygons within 30 ft (9.14 m) 

of culverts from the output steep banks polygon file.

Channel Depth Tool

The Channel Depth tool requires, as input, a 

culvert‐modified, hydroconditioned DEM, a raster flow 

network (output of the Flow Network tool), and an input 

vector line file representing stormwater-infrastructure 

locations. The user can set a search radius within the Channel 

Depth tool dialogue window to identify the maximum 

elevation value in the input DEM. Using the input files 

and a user-specified search radius, the Channel Depth tool 

produces a channel depth raster corresponding to the input 

flow network. For each cell in the channel depth raster, the cell 

value represents the difference between that cell’s elevation 

and the maximum elevation within the user-specified search 

radius from that cell. Units of the channel depth raster are the 

same as the units of the input DEM.

The process of incorporating stormwater infrastructure 

locations to modify DEMs using the Add Culverts tool 

produces artifacts resembling deep channels. The Channel 

Depth tool uses the input culvert location file to remove 

from the output raster the cells along the flow network 

corresponding to culvert locations. Instead of depth values, 

these cells have “no data” values. 

Channel Slope Tool

The Channel Slope tool requires, as input, a 

culvert‐modified, hydroconditioned DEM, a raster flow 

network (output from the Flow Network tool), and an input 

vector line file representing stormwater-infrastructure 

locations. Using these inputs, the tool calculates the 

slope along the flow network and produces the following 

output files:

1. A channel-slope raster containing slope values along the 

flow network, in percent rise. 

2. A vector point file representing locations of channel 

slope greater than 2 percent. 

The process of incorporating stormwater-infrastructure 

locations to modify DEMs using the Add Culverts tool 

produces artifacts resembling steep channel slopes at culvert 

entrances. These artifacts are included in the output channel 

slope raster produced by the Channel Slope tool; however, 

the Channel Slope tool uses the input culvert location file 

to remove points within 10 ft (3.05 m) of culverts from the 

output vector point file.
Methods of Study 11

Slope-Area Index Tool

The Slope-Area Index tool requires as input a 

culvert‐modified, hydroconditioned DEM, a raster flow 

network and a raster flow-network accumulation file (both are 

outputs of the Flow Network tool). Using these inputs, the tool 

calculates the slope along the flow network, equivalent to the 

output from the Channel Slope tool. The Slope-Area Index 

tool then calculates the slope-area index (SAI) (Vandaele and 

others, 1996; Daggupati and others, 2013) for each cell on the 

flow network as follows:

SAI = S × Ab (1)

where 

SAI is the slope-area index, 

S is the channel slope, 

A is the drainage area (the number of cells 

draining into the target cell), and

b is a user-specified exponent. 

Previous investigations have demonstrated that ephemeral 

gully erosion is typically best predicted by SAI using an 

exponent value of b = 0.4, but area exponents found in a 

variety of gully-erosion settings range from approximately 

0.1 to 0.6 (Montgomery and Foufoula-Georgiou, 1993; 

Vandaele and others, 1996; Vandekerckhove and others, 

1998; Desmet and others, 1999; Daggupati and others, 2013). 

Therefore, the default value for the exponent parameter in the 

Slope-Area Index tool dialogue window is set to 0.4, but this 

parameter can be adjusted by the user. The tool also includes 

as a user-specified parameter an SAI threshold above which to 

identify high-SAI point locations. 

The tool uses the inputs and user-specified parameter 

settings to produce as output a point feature class representing 

locations along the input flow network where SAI equals 

or exceeds the SAI threshold. The process of incorporating 

stormwater-infrastructure locations to modify DEMs using the 

Add Culverts tool produces artifacts resembling steep channel 

slopes at culvert entrances. Because channel slope is a variable 

used to calculate SAI, culvert locations can produce artifacts 

resembling high SAI values. Therefore, the Slope-Area Index 

tool uses the input culvert location file to remove points within 

10 ft (3.05 m) of culverts from the output vector point file.

Rough Terrain Tool

The Rough Terrain tool identifies areas within the input 

DEM that represent concentrations of high profile curvature 

(high convexity and high concavity) connected by steep 

slopes. Profile curvature is defined as curvature in the direction 

of maximum slope and thus represents the first derivative of 

slope. This tool was designed and calibrated so as to identify 

areas of known gully erosion in the two study areas. These 

areas have been produced by channel incision and can be 

identified from DEMs, because their bank tops produce high 

levels of upward convexity, their thalwegs produce high 

levels of upward concavity, and these two zones are typically 

connected spatially by steep banks.

The Rough Terrain tool can be run on any high-resolution 

DEM. Unlike the other tools in the DEM Geomorphology 

Toolbox described herein, for which analysis is confined to the 

immediate vicinity of flow networks based on user‐specified 

distances, the Rough Terrain tool processes data across 

the entire landscape. As a result, the input DEM does not 

necessarily have to be hydroconditioned (meaning that the 

tool will run successfully on an input DEM with internal 

depressions). Users may wish to use raw (not culvert‐modified 

or hydroconditioned) DEMs as inputs to avoid generating 

artifacts of rough terrain produced by the processes of 

incorporating stormwater infrastructure and removing internal 

depressions. Because the Rough Terrain tool performs 

landscape-level analysis rather than channel-based analysis, an 

input flow network is not required.

The Rough Terrain tool uses an input DEM to produce 

an output vector polygon representing zones of rough terrain 

(high profile curvature and steep slope) according to three 

user-specified threshold parameters: the slope‐threshold 

parameter, the curvature-threshold parameter, and the 

polygon-area-threshold parameter. The slope-threshold 

parameter is the slope value, in percent rise, above which 

zones within the DEM will be eligible for inclusion in the 

output polygon file. The default value is 50, indicating that 

zones with a slope that exceeds a 50-percent rise will be 

eligible for inclusion. The curvature-threshold parameter is 

the absolute value of profile curvature above which zones 

within the DEM will be eligible for inclusion in the output 

polygon file. Profile curvature values are positive for DEM 

zones having upward concavity (such as stream-channel 

thalwegs) and negative for zones having upward convexity 

(such as ridges and channel bank tops). The default value 

for the curvature threshold parameter is set at 5.0, indicating 

that zones having upward concavity values greater than or 

equal to 5.0 will be eligible for inclusion, as will zones having 

upward convexity less than or equal to -5.0. Using these two 

parameter settings, the tool creates areas of steep slope and 

areas of high profile curvature and then spatially merges these 

zones. The merged zones, composed of steep slope and (or) 

high profile curvature, are then compared to the user-defined 

polygon-area-threshold parameter to produce the final output 

polygon file. The polygon area threshold is the minimum area, 

in number of raster cells of input DEM, of the contiguous area 

of merged zones required for those zones to be included as 

an output polygon. The default value is set at 300, indicating 

that merged zones will be included as output rough terrain 

polygons if and only if their contiguous area is equal to or 

greater than 300 raster cells.
12 Stream-Channel Geomorphic Features From High-Resolution Digital Elevation Models, West Tennessee

Topographic Position Index

Raster files of topographic position index (TPI) were 

created at multiple neighborhood sizes (Weiss, 2001) for both 

study areas. Positive TPI values represent cells, such as along 

ridgetops, with higher elevation values than the average of 

the specified neighborhood. Negative TPI values represent 

cells, such as along stream-channel thalwegs, with lower 

elevation values than the neighborhood average. TPI values 

near zero represent cells in areas of relatively constant slope, 

including flat sloped areas and flat level areas. The upstream 

ends of depositional areas within stream-channel networks, 

such as the heads of alluvial fans, generally represent 

topographic contexts where confined stream channels 

rapidly transition downstream into areas of generally open 

topography. Therefore, rapid increases in TPI values along 

flow networks were hypothesized to be useful indicators of 

depositional features.

Using TPI calculated with a three-cell-radius circular 

neighborhood, point locations were derived as follows 

to represent relatively abrupt changes in TPI along flow 

networks:

1. TPI values were averaged along flow networks 

by applying a focal mean with a 20-cell-radius 

circular neighborhood.

2. Averaged TPI values from step 1 were used to assign 

binary values along the flow network equal to 0 if 

averaged TPI exceeded -0.5 or equal to 1, if not.

3. Slope was calculated on binary values from step 2.

4. Binary values from step 2 were multiplied by slope 

values from step 3. 

5. Points were created representing cells with product 

values from step 4 not equal to zero.

Tool Output Field Verification

Tool outputs from the Bank Slope, Channel Depth, 

Channel Slope, Slope-Area Index, and Rough Terrain tools 

were field-verified in selected parts of both study areas (fig. 4). 

In particular, field locations where tool outputs suggested 

possible geomorphic instability—such as steep bank polygons, 

high-channel-depth locations, high-SAI points, and rough 

terrain polygons—were photographed and described in field 

notes (Cartwright and Diehl, 2016). Commission errors were 

indicated where geomorphic instability was suggested by tool 

outputs but not observed in the field. Conversely, omission 

errors were indicated where geomorphic instability was 

observed in the field but not represented by tool outputs. Field 

locations representing both types of errors were photographed 

and described. 

Tool output verification was performed in the Pinson 

Mounds study area on August 13 and 14, 2015, and in the 

Upper Sandy study area on September 12 and 14, 2015. In 

both study areas, field navigation was performed using printed 

maps of tool outputs and an eTrex H handheld GPS (Garmin, 

Olathe, Kan.). Field photography was performed using a 

Pentax Optio WG–2 GPS camera (Ricoh Ltd., Tokyo, Japan) 

with enabled Wide Area Augmentation System (WAAS) 

GPS to produce GPS-embedded photographs. At each field 

location where photographs were taken, field notes were 

dictated as audio (.WAV) files using the camera’s microphone 

and were later transcribed to text (.RTF) files using Dragon 

NaturallySpeaking premium edition, version 12.50.000.142 

(Nuance, Burlington, Mass.). Field notes were linked to 

photographs and to GPS locations using a common system of 

unique identification numbers.

Evaluation of Automated Tools for 

Geomorphic Feature Identification

Application of the tools in the DEM Geomorphology 

Toolbox to the DEMs from the Pinson Mounds and Upper 

Sandy study areas successfully produced flow networks 

and other tool outputs. Field-verification of tool outputs 

indicated that, although not all features identified by the tools 

corresponded to field indicators of geomorphic instability, 

the tools generally produced spatial data that were useful in 

locating and identifying areas of erosion and deposition within 

stream-channel networks. 

Culvert Modification and Hydroconditioning of 

Digital Elevation Models

Application of the OPR tool (Jackson, 2013) to the 

DEMs for both study areas yielded hydroconditioning 

sufficient for delineating flow networks. By differencing the 

input DEM from the output DEM obtained from the OPR 

tool, a record of cuts and fills was produced. Examination of 

spatial patterns in cuts and fills provided information about 

the nature of alterations performed by the OPR tool and, in 

some cases, provided clues about the geomorphic context of 

those alterations (fig. 5A). For example, isolated single cells 

that were filled by the OPR tool may represent DEM artifacts 

(fig. 5A, blue arrow). In some cases, culverts produced 

“depressions” in the input DEM that were removed by the 

OPR tool. These features are identifiable as linear zones 

of cut cells downstream from multicell filled areas that are 

immediately upstream of culverts (fig. 5A, pink arrows). 

Field inspections of preliminary flow networks identified 

multiple sites where preliminary flow networks were 

inaccurate owing to the presence of stormwater infrastructure, 

primarily culverts, but also gutters, ditches, and storm sewers. 
Evaluation of Automated Tools for Geomorphic Feature Identification 13

fig16-0746_fig 04

MADISON

HARDEMAN

TENNESSEE

HENDERSON

CHESTER

Tool output verification location

Elevation data from U.S. Department of Agriculture, Natural 

Resources Conservation Service, 1-meter-cell-size digital 

elevation model, 2011. Lambert Conformal Conic Projection, 

Horizontal datum: North American Datum of 1983 (NAD 

83), Vertical datum: North American Vertical Datum of 1988 

(NAVD 88)

EXPLANATION

Elevation, in meters above NAVD88

Upper Sandy study area

High : 167.5

Low : 127.9

Pinson Mounds study area

High : 153.2

Low : 110.7

88°49'45" 88°49'30" 88°49'15" 88°49' 88°48'45"

35°

40'

35°

39'

45"

35°

39'

30"

35°

39'

15"

35°

39'

88°42' 88°41' 88°40'

35°

31'

35°

30'

0 0.5 1 MILE

0 0.5 1 KILOMETER

0 0.25 0.5 MILE

0 0.25 0.5 KILOMETER

0 400 800 1,200 1,600 FEET

0 200 400 METERS

A

C

B

Figure 4. Locations in the, A, Upper Sandy and, B, C, Pinson Mounds study areas where tool output verification was 

performed using Global Positioning System (GPS)-enabled photography and field-note dictation.
14 Stream-Channel Geomorphic Features From High-Resolution Digital Elevation Models, West Tennessee

fig16-0746_fig 05

Map

area

High : 129.7

Low : 118.2

EXPLANATION

Cells cut by the OPR tool

Cells filled by the OPR tool

Elevation, in meters above NAVD88

Isolated single-fill cell

OPR-artifact channel

Culverts

Flow network before culvert modification

Flow network after culvert modification

88°40'18" 88°40'16" 88°40'14" 88°40'12" 88°40'10" 88°40'8" 88°40'6"

35°

30'

32"

35°

30'

30"

35°

30'

28"

88°40'18" 88°40'16" 88°40'14" 88°40'12" 88°40'10" 88°40'8" 88°40'6"

35°

30'

32"

35°

30'

30"

35°

30'

28"

B

A

Base from U.S. Department of Agriculture, Natural Resources 

Conservation Service, 1-meter-cell-size digital elevation model, 

2011. Lambert Conformal Conic Projection, Horizontal datum: North 

American Datum of 1983 (NAD 83), Vertical datum: North American 

Vertical Datum of 1988 (NAVD 88).

0 100 200 300 FEET

0 40 80 METERS

Figure 5. Examples of A, cut and fill records produced by the Optimized Pit Removal (OPR) tool and, B, flownetwork modification based on incorporation of culvert locations into digital elevation models, Pinson Mounds study 

area. Both flow networks in B were delineated using a flow-accumulation threshold of 5,000 cells.
Evaluation of Automated Tools for Geomorphic Feature Identification 15

These findings are similar to those of previous studies that 

found substantial errors in mapped stream networks resulting 

from the presence of roads and other drainage obstructions 

acting as “digital dams” in DEMs (Poppenga and others, 

2009; Li and others, 2013; Chan and Crabtree, 2014). A total 

of 128 stormwater-infrastructure features were digitized in 

the two study areas (table 1). In the Upper Sandy study area, 

the longest lines digitized (representing features longer than 

60 m) were clustered primarily in the southwestern part of 

the study area, which is characterized by dense commercial 

development. The process of mapping these long underground 

conveyances was disproportionately slower and more difficult 

than mapping the other stormwater-infrastructure features 

documented in this study. 

By comparing flow networks delineated before and 

after use of the Add Culverts tool (example in fig. 5B), it 

was possible to identify areas where the incorporation of 

stormwater infrastructure (culvert modification of DEMs) 

caused substantial changes to the flow network and areas 

where it did not. In some cases, the OPR tool alone was 

able to “cut” flow paths into the unmodified DEM, allowing 

preliminary flow networks (before culvert modification) to 

approximate the pattern of the culvert-modified flow networks. 

In other cases, the culvert-modified flow network differed 

greatly from the preliminary flow network. These results 

suggest that DEM modifications based on data representing 

stormwater-infrastructure locations can improve the accuracy 

of DEM-derived flow networks (Poppenga and others, 2009; 

Li and others, 2013; Chan and Crabtree, 2014).

The primary limitation of the Add Culverts tool is the 

requirement of spatially precise data concerning the locations 

of stormwater infrastructure. For small geographic areas 

where the density of stormwater infrastructure is low and 

infrastructure features are readily apparent by field inspection, 

field data collection may be practical. For example, rural 

areas where stormwater infrastructure is generally limited to 

short culverts under roads and driveways may be suitable for 

field-data collection. With an increase in geographic scale or 

infrastructure density and complexity, the time and resources 

required to digitize stormwater infrastructure locations in 

the field also increase and may become prohibitive. For 

large geographic areas or areas having extensive stormwater 

infrastructure, culvert modifications to DEMs could be 

achieved using culvert location data obtained from partnering 

institutions, such as municipalities administering a municipal 

separate storm sewer system (MS4; Cubanski, 2013; Li and 

others, 2013; Venner, 2014). 

Flow Networks

Culvert-modified flow networks delineated using the 

Flow Network tool, with flow-accumulation thresholds of 

5,000 and 1,000 cells, showed general alignment to the highresolution (1:24,000) flowlines from the NHD (Simley and 

Carswell, 2009). The tool outputs, however, continued farther 

upstream to include more headwater reaches and included 

many small tributaries not present in the NHD flowline 

(fig. 6). The flow networks produced by the Flow Network 

tool were generally more sinuous and appeared less smoothed 

than the NHD flowline (fig. 6B). The NHD flowline was 

not present within the gullied areas in Pinson Mounds State 

Archaeological Area (fig. 6C), indicating the value of the Flow 

Network tool in this study area for delineating flow networks 

having relatively small drainage areas. 

In both study areas, flow-network delineation using 

a flow-accumulation threshold of 1,000 cells produced 

considerably denser drainage networks than delineation using 

a threshold of 5,000 cells. In the Pinson Mounds study area, 

the 1,000-cell threshold produced a flow network having a 

density of 296 meters per hectare (m/ha), which was more 

than twice as dense as the flow network produced using a 

5,000-cell threshold (136 m/ha). Similarly, in the Upper Sandy 

study area, flow-network density was 239 and 87 m/ha for 

the flow networks produced with 1,000-cell and 5,000-cell 

thresholds, respectively. 

Table 1. Numbers and lengths of digitized stormwater-infrastructure features.

[NA, not applicable]

Study

area

Number of 

stormwater features

Length of digitized lines 

representing stormwater infrastructure features

Digitized

in field

Digitized in office 

and verified by 

field inspection

Total Minimum

(meters)

Maximum

(meters)

Upper Sandy 42 16 58 1.5 530

Pinson Mounds 32 38 70 7.0 105

Total 74 54 128 NA NA
16 Stream-Channel Geomorphic Features From High-Resolution Digital Elevation Models, West Tennessee

fig16-0746_fig 06

EXPLANATION

High-resolution (1:24,000) NHD flowlines

Flow Network tool output, 5,000-cell 

flow-accumulation threshold

Flow Network tool output, 1,000-cell 

flow-accumulation threshold

Gullied areas in Pinson Mounds State

Archaeological Area

88°42' 88°41' 88°40'

35°

31'

30'

88°49'45" 88°49'30" 88°49'15" 88°49'

35°

40'

35°

39'

45"

35°

39'

30"

35°

39'

15"

35°

39'

Base modified from U.S. Department of Agriculture, Natural 

Resources Conservation Service, 1-meter-cell-size digital 

elevation model, 2011. Lambert Conformal Conic Projection, 

Horizontal datum: North American Datum of 1983), Vertical 

datum: North American Vertical Datum of 1988

0 0.5 1 MILE

0 0.5 1 KILOMETER

0 0.25 0.5 MILE

0 0.25 0.5 KILOMETER

0 200 400 FEET

0 200 100 METERS

A

C

B

MADISON

HARDEMAN

TENNESSEE

HENDERSON

CHESTER

Figure 6. Flow Network tool outputs compared to the high-resolution flowline from the National Hydrography Dataset (NHD) for the, 

A, Upper Sandy and, B, C, Pinson Mound study areas.
Evaluation of Automated Tools for Geomorphic Feature Identification 17

Because the Flow Network tool delineates flow 

networks along cells having the lowest local elevation values, 

the mapped positions of resulting flow networks may be 

slightly different from those of actual stream centerlines. 

In some locations, the flow network appears to migrate in a 

sinuous fashion across the streambed. The accuracy of this 

geomorphology was assessed by field inspection. In most 

cases, such configurations represented the true geomorphology 

of streambeds, such as locations where highly sinuous stream 

channels are down-cutting into deposits of sediment. In a few 

cases, these apparent configurations may be byproducts of 

the analytical tools used to delineate flow networks, such as 

apparently sinuous “cuts” produced by the OPR tool. Where 

stream channels are very shallow or where flow passes over 

a vegetated surface without forming a distinct channel, small 

variations in elevation can divert the apparent flow direction 

derived from the DEM and create an artifact flow path; some 

such artifacts were located in the field. Local accumulations 

of woody debris also can obscure actual flow paths, causing 

erroneous delineation of flow paths over some short reaches.

An important limitation concerning the delineation 

of flow networks using lidar-derived DEMs is that where 

surface water is present at the time of lidar data collection, 

the lidar data represent water-surface elevations rather than 

bathymetric elevations (Charlton and others, 2003; Jones and 

others, 2008). As DEMs are prepared from raw QL2 lidar 

data, the hydroflattening process produces representations 

of water surfaces with constant elevation at all locations on 

the water surface for inland ponds and lakes of 8,000 m2

 or 

larger (Heideman, 2014). For inland streams of at least 30-m 

nominal width, hydroflattening produces elevations that are 

level from bank to bank and that follow downhill gradients 

mirroring the surrounding terrain (Heideman, 2014). For 

stream channels that were inundated at the time of lidar data 

collection, the presence of water surfaces wider than the cell 

size of the resulting DEM obscures actual stream-channel 

geomorphology (Charlton and others, 2003) and may result in 

inaccurate flow-path locations within stream channels. Within 

the two study areas used to develop the DEM Geomorphology 

Toolbox, most stream channels contained ephemeral streams 

that were dry at the time of lidar data collection. Limitations 

related to inundation should be considered before applying 

the Flow Network tool to new study areas, as this and other 

tools in the DEM Geomorphology Toolbox were designed and 

calibrated for stream channels with sufficiently small drainage 

areas so that stream-channel geomorphology was not obscured 

by inundation. 

In addition, it should be noted that the Flow Network 

tool and other tools in the DEM Geomorphology Toolbox 

were designed and calibrated for study areas having moderate 

topographic relief. Low-relief landscapes such as floodplains 

and river deltas, especially those having substantial areas 

of inundation at the time of lidar data collection and those 

where surface-water flow is bidirectional or divergent, may 

not be suitable for application of these tools and may be 

better analyzed by methodology developed by Jones and 

others (2008).

Bank Slope

In both study areas, the Bank Slope tool identified 

variations in channel bank slope along the flow networks 

(fig. 7). Slopes within a flow-corridor width of 60 ft (18.3 m) 

commonly exceeded 50 percent and in some locations 

exceeded 100 percent along the main stem of Sandy Creek 

and several of the larger tributaries in the Upper Sandy study 

area (fig. 7A). Similar results were obtained within the gully 

networks dissecting the bluffs on the southern boundary of the 

Pinson Mounds study area (fig. 7B).

Interpreting outputs from the Bank Slope tool requires 

awareness of several limitations. First, slope values produced 

by the Bank Slope tool may or may not be equivalent to 

those calculated from channel cross sections, because 

slope is calculated across the entire land surface within the 

user‐specified flow corridor with the slope value for each cell 

calculated using the maximum elevation difference over the 

set of eight neighboring cells. In many—but not all—cases, 

this maximum elevation difference will occur in a direction 

roughly perpendicular to the direction of the channel thalweg. 

Second, the spatial resolution of the input DEM 

constrains the Bank Slope tool’s ability to accurately identify 

small, highly localized areas of steep slope. James and others 

(2007) demonstrated that cross sections produced from 

DEMs derived from lidar data at a nominal 3-m spacing 

underestimated gully depth and side slopes in permanent gully 

systems that are narrow and steep-sided. Using 1-m resolution 

DEMs as inputs, the Bank Slope tool also underestimated 

slope for certain small, steep, actively eroding streambanks 

within gully systems at Pinson Mounds. For example, an 

actively eroding and overhanging bank is present in a section 

of streambank that was identified by the Bank Slope tool 

as having slope exceeding 50 percent but not exceeding 

100 percent (fig. 8). 

Third, the flow-corridor width specified by the user 

represents the width over which the user believes steep slopes 

can deliver sediment directly to the stream channel, not the 

width of the channel itself. The Bank Slope tool requires 

the user to set a constant flow corridor width value for the 

entire study area. This is a limitation because study areas may 

contain stream channels of varying widths, some narrowly 

confined, others in broad flat-bottomed swales bounded by 

steep banks that are only intermittently near concentrated flow. 

Where flow-corridor width specified by the user is set equal 

to the bankfull width, the Bank Slope tool may not include 

all zones of steep bank slope that deliver sediment to the 

stream-channel network. Conversely, where the flow-corridor 

width specified by the user is much wider than the streamchannel width, the Bank Slope tool will probably highlight 

areas of steep slope that are not able to contribute sediment to 

the channel.
18 Stream-Channel Geomorphic Features From High-Resolution Digital Elevation Models, West Tennessee

fig16-0746_fig 07

Elevation data from U.S. Department of Agriculture, Natural 

Resources Conservation Service, 1-meter-cell-size digital elevation 

model, 2011. Lambert Conformal Conic Projection, Horizontal 

datum: North American Datum of 1983 (NAD 83), Vertical datum: 

North American Vertical Datum of 1988 (NAVD 88).

EXPLANATION

Elevation, in meters above NAVD88

Pinson Mounds study area (B, C)

High : 153.2

Low : 110.7

Upper Sandy study area (A)

High : 167.5

Low : 127.9

Slope exceeds 100 percent

Slope exceeds 50 percent

Output from the Bank Slope tool

MADISON

HARDEMAN

TENNESSEE

HENDERSON

CHESTER

35°

39'

30"

35°

39'

15"

35°

31'

30'

88°49'30" 88°49'15"

88°42' 88°41' 88°40'

0 0.5 1 MILE

0 0.5 1 KILOMETER

0 0.1 0.2 MILE

0 0.1 0.2 KILOMETER

A

B

C

0 1,000 2,000 FEET

0 300 600 METERS

Figure 7. Outputs from the Bank Slope tool, A, within the Upper Sandy study area and, B, C, along gully networks within 

the Pinson Mounds study area.
Evaluation of Automated Tools for Geomorphic Feature Identification 19

fig16-0746_fig 08

Slope exceeds 100 percent

Slope exceeds 50 percent

Elevation data from U.S. Department of Agriculture, Natural 

Resources Conservation Service, 1-meter-cell-size digital 

elevation model, 2011. Lambert Conformal Conic Projection, 

Horizontal datum: North American Datum of 1983 (NAD 83), 

Vertical datum: North American Vertical Datum of 1988 (NAVD 88)

EXPLANATION

Elevation, in meters above NAVD 88

High : 153.2

Low : 110.7

Output from the Bank Slope tool

Flow network (5,000-cell 

flow-accumulation)

88°40'30" 88°40'28" 88°40'26" 88°40'24"

35°

29'

48"

35°

29'

46"

0 100 200 FEET

0 25 50 METERS

A

B

Figure 8. A, An example of an actively eroding and overhanging bank in, B, a stream-channel section identified by the Bank 

Slope tool as having a slope exceeding 50 percent but not exceeding 100 percent in the Pinson Mounds study area.
20 Stream-Channel Geomorphic Features From High-Resolution Digital Elevation Models, West Tennessee

Fourth, the Bank Slope tool calculates slope only 

along the input flow network. Depending upon the 

flow‐accumulation threshold used to produce the input flow 

network, the Bank Slope tool may fail to identify zones of 

steep bank slope upstream of the flow network. In cases 

where the user suspects this to be true, such as when polygons 

representing steep banks are truncated abruptly at the upstream 

ends of flow-network tributaries, the Bank Slope tool can be 

reapplied using a denser flow network produced by a lower 

flow-accumulation threshold.

Fifth, the Bank Slope tool does not differentiate actively 

eroding streambanks from stable, vegetated streambanks 

(fig. 9). At some locations in the Pinson Mounds study area, 

field inspection of steep bank polygons produced by the Bank 

Slope tool were found to represent steep but vegetated and 

probably stable sections of channel bank (fig. 9A). At other 

locations, the steep bank polygons were found to represent 

steep banks that were actively eroding and were probably 

contributing sediment into the stream-channel network, as 

indicated by exposed soil and overhanging roots (fig. 9B). 

Channel-bank stability is a function of several properties 

and processes in addition to slope, including physical soil 

properties, stream power, and types of vegetation present 

(Vandaele and others, 1996; Booth and Henshaw, 2001; 

Kirkby and Bracken, 2009). As a result, the Bank Slope tool 

alone cannot differentiate between actively eroding and stable 

streambanks. Instead, the tool can be used to identify zones of 

steep bank slope and these zones can be inspected in the field 

to determine their geomorphic stability and erosion risk. 

Channel Depth

In both study areas, the Channel Depth tool indicated 

variations in stream-channel depth along the flow networks. 

Of particular interest were channel sections within the gully 

networks in the Pinson Mounds study area (fig. 10). Channel 

depth values were low upstream of gully heads, then increased 

rapidly as the flow network descended into a gully system 

(fig. 10A). The tool produced high channel-depth values in 

gully systems that were narrow and steep-walled (fig. 10B). 

Local decreases in channel depth values were produced along 

the flow network where the trench containing the active 

stream channel widened and the trench bottom contained 

an active floodplain, such as on the right side of the area 

shown in figure 10C. Tool output values also decreased at the 

downstream end of the gully system, where the flow network 

entered the floodplain of the South Fork Forked Deer River 

(fig. 10D). 

Interpreting outputs from the Channel Depth tool requires 

awareness of several limitations. A primary limitation is that 

the tool applies a fixed, user-specified search radius across 

the entire study area. Results from the Channel Depth tool 

will vary with the search radius set by the user. Depending on 

stream-channel size, geomorphology, and the search radius 

applied, the tool may produce deeper or shallower values than 

the channel depth calculated from surveyed cross sections. 

Applying variable search radii was beyond the scope of this 

study; however, options exist for tool users to perform this 

type of analysis using a few additional steps. For example, the 

user could subdivide the input flow network into multiple flow 

networks, categorized by drainage area, and run the Channel 

Depth tool on each subdivided flow network. With each tool 

run, the user would set the search radius for the flow-network 

subset as deemed appropriate on the basis of drainage area or 

field measurements of channel width. 

Because of the algorithm used by the Channel Depth tool, 

output values from this tool do not correspond to bankfull 

depth at most locations. Where the points within the userspecified search radius are all within the stream channel, 

such as in a wide braided reach, the tool output reflects the 

thalweg depth below in-channel bars. Where the highest 

points within the search radius are on the active floodplain, 

the tool output reflects the depth of the thalweg below the 

active floodplain. Where the stream channel is too narrow to 

be fully resolved by the DEM, however, such that elevations 

along the DEM-derived flow network are higher than actual 

thalweg elevations, the tool output underestimates channel 

depth. Where no distinct stream channel exists—as in the 

case of (1) alluvial fans, (2) valley bottoms and swamps with 

dispersed flow, and (3) small drainage areas—bankfull depth 

is undefined and the Channel Depth tool produces values 

reflecting local land slope. Lastly, in contexts of entrenched 

channels where the thalweg is near a steep slope of the 

adjacent terrace, the tool output reflects the height of the 

terrace slope within the search radius. Although this result 

greatly exceeds bankfull depth, it is valuable in indicating 

locations where flow is adjacent to steep banks. Along many 

stream channels, one bank is considerably steeper and (or) 

taller than the other. In such cases, the Channel Depth tool 

will produce depth estimates that are based on the height of 

the taller, steeper bank so long as both banks are within the 

user-specified search radius. As a result, the tool does not 

differentiate between stream channels having two tall, steep 

banks and stream channels having only one such bank.

Results from the Channel Depth tool should be 

interpreted cautiously with the aforementioned issues in 

mind and verified by field inspection. In the gully systems 

of the Pinson Mounds study area, field inspection of 

channel depth results indicated that the tool generally does 

not produce measurements of bankfull channel depth nor 

does it approximate measurements that would be obtained 

from surveyed cross sections. Nevertheless, the tool was 

effective in highlighting sections of channel adjacent to tall, 

actively eroding banks as well as incised channels in gully 

systems, both indicated by linear zones of high depth values. 

In addition, examining sections of the flow network with 

changing channel-depth values was useful in identifying gully 

heads (erosional features indicated by an abrupt increase in 

channel depth) and alluvial fan heads (depositional features 

indicated by an abrupt decrease in channel depth).
Evaluation of Automated Tools for Geomorphic Feature Identification 21

fig16-0746_fig 09

B

A

Slope exceeds 100 percent

Slope exceeds 50 percent

EXPLANATION

Elevation, in meters above NAVD88

High : 153.2

Low : 110.7

Output from the Bank Slope tool

Flow network (5,000-cell 

flow-accumulation)

88°40'35" 88°40'30"

35°

29'

40"

35°

29'

45"

0 200 400 FEET

0 50 100 METERS

A B

C

Elevation data from U.S. Department of Agriculture, 

Natural Resources Conservation Service, 1-metercell-size digital elevation model, 2011. Lambert 

Conformal Conic Projection, Horizontal datum: North 

American Datum of 1983 (NAD 83), Vertical datum: 

North American Vertical Datum of 1988 (NAVD 88)

Figure 9. A, An area mapped with slope exceeding 100 percent that is vegetated and is probably stable, B, an area mapped 

with a slope exceeding 50 percent that shows signs of active erosion including exposed soil and overhanging roots, and C,

output from the Bank Slope tool in the Pinson Mounds study area.
22 Stream-Channel Geomorphic Features From High-Resolution Digital Elevation Models, West Tennessee

fig16-0746_fig 10

EXPLANATION

Channel Depth tool output (meters)

High : 3.65

Low : 0.03

Profile curvature

High : 271.6

Low : -159.2

Elevation, in meters above NAVD88

High : 153.2

Low : 110.7

88°40'55" 88°40'50" 35°

29'

35"

35°

29'

30"

35°

29'

25"

0 100 200 FEET

0 25 50 METERS

A

A

B

C D

E

B

Elevation data and derivative profile curvature from U.S .Department of 

Agriculture, Natural Resources Conservation Service, 1-meter-cell-size 

digital elevation model, 2011. Lambert Conformal Conic Projection, 

Horizontal datum: North American Datum of 1983 (NAD 83), Vertical 

datum: North American Vertical Datum of 1988 (NAVD 88)

C

D

Figure 10. A, Upstream view of a gully head, B, upstream view of a narrow, steep-walled gully system, C, upstream view of 

a channel connected to a floodplain in the base of a trench, D, downstream view of the outlet of a gully system where flow 

enters the river floodplain, and E, calculated channel depth within gully networks using a 10-foot (3.05-meter) search radius, 

Pinson Mounds study area.
Evaluation of Automated Tools for Geomorphic Feature Identification 23

Channel depth is one potential indicator of an erosional 

process known as channel incision. Factors contributing to the 

likelihood and rate of channel incision include erodible bed 

material and high stream power; stream power may increase 

in response to local changes in the channel or changes to 

the watershed (Schumm, 1979; Vandaele and others, 1996; 

Booth and Henshaw, 2001; Alonso and others, 2002; Poesen 

and others, 2003; Ferencevic and Ashmore, 2012; Daggupati 

and others, 2013). Channel incision can have multiple 

consequences for water quality and riparian habitat quality. 

Within incising channel reaches, consequences can include 

loss of aquatic habitat, increased erosion of channel banks, 

and the conversion of the former floodplain to an infrequently 

inundated terrace (Bradford and Piest, 1980; Booth and 

Henshaw, 2001; Kirkby and Bracken, 2009). Even after 

incision slows to apparent equilibrium, the resulting channel 

may lack desirable habitat features (Booth and Henshaw, 

2001). Because channel incision contributes to sediment loads, 

the resulting downstream sedimentation can also affect aquatic 

habitat and exacerbate flooding in depositional areas. Barriers 

to sediment transport created by such deposition disconnect 

(or decouple) the downstream channel network from the 

upstream erosion (Happ and others, 1940; Pierce and King, 

2007; Harvey, 2012; Katz and others, 2014). 

Channel Slope and Slope-Area Index

The channel slope raster produced by the Channel Slope 

tool indicated slope values along flow networks as high as 

377 percent in the Upper Sandy study area and as high as 

114 percent in the Pinson Mounds study area (table 2). These 

raster output files did not differentiate slope values in the 

vicinity of stormwater-infrastructure features—which could 

be artificially high because of the process used to incorporate 

those features into the DEM—from those in the remainder of 

the flow network. By contrast, the point file produced by the 

Channel Slope tool excluded points within a 3.05-m buffer 

around culverts. Comparison of the raster results to the point 

results produced by the Channel Slope tool indicated that the 

highest slope values were all collocated with stormwaterinfrastructure features. Thus, these values were probably 

artifacts created by incorporation of those features into the 

DEM, because the maximum slopes in the point files were 

52 percent and 61 percent for the Upper Sandy and Pinson 

Mounds study areas, respectively (table 2). Of the points with 

a slope greater than 2 percent (the output points of the Channel 

Slope tool), the majority were between 2 and 5 percent and 

only a small number exceeded 10 percent (table 2). In the 

Upper Sandy study area, points having slope values greater 

than 10 percent were typically in headwater tributaries to 

Sandy Creek rather than in the main stem of Sandy Creek. 

Channel slope values exceeded 10 percent immediately 

upstream of several tributary confluences with Sandy Creek. 

In the Pinson Mounds study area, several of the gullies 

draining into Hudson Branch and several gullies dissecting 

the bluff above the floodplain of the South Fork Forked Deer 

River included locations along the flow networks where the 

channel slope exceeded 10 percent. 

When the Slope-Area Index tool was run using an 

exponent setting of 0.4, an SAI threshold setting of 500, 

and an input flow network that had been produced using a 

flow‐accumulation threshold of 5,000 cells, the tool produced 

300 points in the Upper Sandy study area and 1,287 in the 

Pinson Mounds study area (table 3). Of these high-SAI points, 

each corresponding to the center of a 1-m2 cell on the flow 

network, majorities in each study area were in cells having a 

channel slope greater than 5 percent, based on the output of 

the Channel Slope tool. 

Table 2. Summary of channel slope values produced by the Channel Slope tool.

Study 

area

Range of channel slope values 

produced by the Channel Slope tool, 

minimum to maximum 

(percent rise) 

Percentage of points with channel 

slope greater than 2 percent for 

which channel slope was

Output raster file Output point file

Between 

2 and 

5 percent

Between 

5 and 

10 percent

Greater 

than 

10 percent

Upper Sandy 0–377 0–52 68 23 9

Pinson Mounds 0–114 0–61 76 19 5
24 Stream-Channel Geomorphic Features From High-Resolution Digital Elevation Models, West Tennessee

Both the Channel Slope tool and the Slope-Area Index 

tool were useful in identifying erosional features in both 

study areas, including features that were not apparent in 

aerial imagery. In the Upper Sandy study area, for example, a 

head cut 3 m tall is associated with four cells along the flow 

network with slope exceeding 10 percent and SAI exceeding 

500 (fig. 11). This large erosional feature is not visible in the 

highest-resolution aerial imagery available for this study area, 

which is a 1-ft (0.3-m) resolution, leaf-off orthophotograph 

obtained from the Tennessee Department of Transportation. 

Smaller erosional features detected by these tools included 

active and root-stabilized head cuts (examples in figs. 12A and 

12B, respectively). These findings indicate that the resolution 

of 1-m DEMs and the analytic approaches of the Channel 

Slope tool and the Slope-Area Index tool can be useful in 

locating head cuts of varying sizes and morphologies within 

stream-channel networks. 

Although the Channel Slope tool and the Slope-Area 

Index tool successfully identified head cuts in some cases, 

field inspection revealed a number of locations where these 

tools misidentified or failed to identify head cuts. Some of 

these locations represented omission errors, where head 

cuts were discovered by field inspections in stream-channel 

locations where no high-slope or high-SAI cells were 

identified by the tools, such as the head cut in figure 13A. 

Although the vertical drop of this feature is comparable to that 

of others successfully identified by the Channel Slope tool and 

the Slope-Area Index tool, field inspection of this head cut 

indicated that it did not represent a step in the thalweg profile. 

Immediately downstream of the plunge pool at the base of 

this head cut is a deposit of sand, shown in figure 13A,B with 

a clipboard resting at the upstream end of the deposit. Visual 

inspection in the field indicated the elevation of the deposit 

was only a few centimeters lower than the top of the root at 

the top of the plunge pool. The horizontal distance along the 

flow path from the top of the head cut to the upstream end 

of the deposition was estimated to be slightly less than 1 m. 

Based on these observations, it is likely that either this site of 

geomorphic instability in the stream-channel network was too 

small to be resolved by the DEM, resulting in its failure to be 

detected by the Channel Slope tool or the Slope-Area Index 

tool, or that the DEM contained a single-cell depression that 

was filled by the OPR tool.

Commission errors also occurred in which high-slope 

or high-SAI cells along the flow network did not contain 

head cuts. For example, the section of stream channel shown 

in figure 13B contains no head cuts but corresponds to a 

flow‐network cell with SAI exceeding 500, which was the 

user-specified SAI threshold used to identify high-SAI points 

in this study. Notably, the SAI value for the cell associated 

with this reach was 555, which does not greatly exceed 

the threshold. The Channel Slope tool did not identify this 

location as having steep slope; the maximum slope in a 5-cell 

neighborhood from this cell is 4.1 percent, with most cells 

having a slope of less than 2 percent. Users can adjust input 

parameters and interpretation of tool outputs to balance errors 

of omission and commission depending on management 

applications and local geomorphic and hydrologic factors. In 

landscapes prone to gully erosion, for example, management 

needs may prioritize the minimization of omission errors 

to ensure that few gullies are left undetected, resulting in a 

relatively high number of commission errors (Daggupati and 

others, 2013).

As the aforementioned examples illustrate, the Channel 

Slope tool and the Slope-Area Index tool are subject to a 

variety of limitations, such that tool users are advised to 

interpret tool results with caution and verify tool outputs in 

the field. Both tools are sensitive to the resolution of the input 

flow network. For example, both tools will fail to identify 

head cuts that are upstream of the input flow network; that is, 

at flow accumulations less than the threshold that was used 

to derive the input flow network. Additionally, both tools will 

probably fail to identify head cuts present at culvert outlets 

because of the way the Add Culverts tool incorporates culverts 

and other stormwater-infrastructure features into the DEM. 

The Add Culverts tool adjusts the elevation values of the input 

DEM along the length of each feature in the input vector file 

to equal the minimum elevation value crossed by that feature. 

These minimum elevation values typically occur at or near 

the downstream end of the feature, such as at a culvert outlet. 

Table 3. Summary of point values produced by the Slope-Area Index (SAI) tool.

[Tool parameter settings were as follows: exponent, 0.4; SAI threshold, 5,000. The input flow network was produced using a flow-accumulation threshold of 5,000 cells]

Study area Number of high-SAI points 

produced by the SAI tool

Number of high-SAI points 

with channel slope

Between 

5 and 10 percent

Greater than 

10 percent

Total 

(greater than 

5 percent)

Upper Sandy 300 72 196 268

Pinson Mounds 1,287 366 548 914
Evaluation of Automated Tools for Geomorphic Feature Identification 25

fig16-0746_fig 11

EXPLANATION

Elevation, in meters above NAVD88

High : 167.5

Low : 127.9

Flow network (5,000-cell flow-accumulation

threshold)

Channel Slope tool output exceeds 10 percent

Slope-Area Index tool output exceeds 500

88°49'27" 88°49'26" 88°49'25" 88°49'24" 88°49'23"

35°

39'

10"

35°

39'

9"

0 40 80 FEET

0 10 20 METERS

A

B

Base orthoimagery from Tennessee Department of 

Transportation, 1-foot resolution, 2012. Tennessee State 

Plane coordinate system (FIPS Zone 4100). Horizontal 

datum: North American Datum of 1983 (NAD 83)

Figure 11. A, A head cut 3 meters tall that was not detectable from high-resolution aerial imagery but associated with, B, a flownetwork zone of steep slope and high slope-area index (SAI), Upper Sandy study area.
26 Stream-Channel Geomorphic Features From High-Resolution Digital Elevation Models, West Tennessee

fig16-0746_fig 12

A

B

EXPLANATION

Elevation, in meters above NAVD88

High : 167.5

Low : 127.9

Flow network (5,000-cell flowaccumulation threshold)

Channel Slope tool output exceeds

10 percent

Slope-Area Index tool output exceeds 500

Profile curvature

High : 18.77

Low : -20.68

88°49'28" 88°49'26" 88°49'24"

35°

39'

36"

35°

39'

34"

0 60 120 FEET

0 15 30 METERS

A B

C

Elevation and profile curvature derivative from U.S. Department of Agriculture, 

Natural Resources Conservation Service, 1-meter-cell-size digital elevation 

model, 2011. Lambert Conformal Conic Projection, Horizontal datum: North 

American Datum of 1983 (NAD 83), Vertical datum: North American Vertical 

Datum of 1988 (NAVD 88)

Figure 12. A, 0.5-meter-tall active head cut, B, a root-stabilized head cut, and C, output from the Channel Slope and SlopeArea Index tools, Upper Sandy study area.
Evaluation of Automated Tools for Geomorphic Feature Identification 27

fig16-0746_fig 13

A

B

EXPLANATION

Elevation, in meters above NAVD88

High : 153.2

Low : 110.7

Flow network (5,000-cell flowaccumulation threshold)

Channel Slope tool output between

5 and 10 percent

Slope-Area Index tool output exceeds 500

Profile curvature

High : 27.01

Low : -37.46

88°40'55"

35°

29'

30"

0 80 160 FEET

0 20 40 METERS

A

Elevation and profile curvature derivative from U.S. Department of 

Agriculture, Natural Resources Conservation Service, 1-meter-cell-size 

digital elevation model, 2011. Lambert Conformal Conic Projection, 

Horizontal datum: North American Datum of 1983 (NAD 83), Vertical 

datum: North American Vertical Datum

B

C

Figure 13. Examples of errors produced by the Channel Slope tool and the Slope-Area Index tool, including, A, a head cut not 

identified by the tools, B, a location in the flow-network with high slope-area index (SAI) but no head cut, and C, tool outputs, 

Pinson Mounds study area.
28 Stream-Channel Geomorphic Features From High-Resolution Digital Elevation Models, West Tennessee

As a result, calculated channel slope values in the vicinity 

of culvert outlets are generally small, whether or not a steep 

slope associated with erosion was observed in the field. 

For example, a culvert that crosses diagonally under a road in 

the Upper Sandy study area shows signs of erosion and woody 

debris accumulation at its outlet, including an approximately 

0.25-m vertical drop between the culvert apron and the stream 

channel below (fig. 14). Channel slope values produced by 

the Channel Slope tool are less than 2 percent in the vicinity 

of this culvert outlet and no high-SAI cells are present at the 

culvert outlet, reflecting the similarity between the modified 

(artificial) elevation values of the culvert and the elevation 

values along the flow network immediately downstream of the 

culvert. As is illustrated in figure 14, the culvert-modification 

process also results in confluences erroneously located inside 

culverts. Although these confluences produce artificially steep 

slopes in the output channel slope raster, the Channel Slope 

tool excludes output points in the immediate vicinity (10 ft, 

3.05 m) of the input stormwater-infrastructure features such 

that high-slope or high-SAI artifacts are not included in the 

vector output files at these locations.

Because the Channel Slope tool calculates slopes from 

one cell to another along flow networks, variability in channel 

slope may be present along any given reach. Although 

reach-scale averaging of channel slope was beyond the scope 

of this study, several options are available if the user wants to 

average channel slope across multiple cells. One option is to 

calculate a focal mean using the channel slope raster as the 

input and applying a desired radius within which to calculate 

mean values. A second option is to calculate the mean channel 

slope for each of the vector flow-line segments (line segments 

between confluences).

Because head cuts are defined by their abruptness 

(Montgomery and Dietrich, 1992; Montgomery and 

Foufoula-Georgiou, 1993), points along the thalweg with 

relatively high slope identify head cuts either at the upstream 

ends of gullies or elsewhere. Channel slope is also high at 

artificial or natural drops stabilized by rock or other resistant 

materials. Steep local drops stabilized by woody debris or 

roots may not currently exhibit the erosion and upstream 

migration typical of head cuts, but they have the potential to 

become head cuts through the decomposition or undermining 

of the woody material. The rates of head-cut erosion and 

upstream migration have been related to the height, steepness, 

and drainage area of head cuts along with other variables 

related to flow (Vandekerckhove and others, 2001; Alonso and 

others, 2002; Vandekerckhove and others, 2003; Rieke-Zapp 

and Nichols, 2011; DeLong and others, 2014). In general, 

changes in channel geomorphology that occur after lidar 

data collection will not be represented by tool outputs and 

may contribute to omission and commission errors. In this 

study, field investigation in 2015 suggested that several head 

cuts were located a few meters upstream from the locations 

indicated by the tool outputs produced using DEMs based on 

lidar data produced in 2011–12.

In general, the Channel Slope tool is likely to overpredict 

head cuts in small channels and underpredict it in larger 

channels, because it does not account for stream power. 

Stream power is a physical variable that represents the rate 

at which potential energy is expended in a stream channel as 

water flows downstream (Ferencevic and Ashmore, 2012). 

Stream power is a function of energy slope (approximated by 

water slope) and discharge; specific stream power is stream 

power per unit of stream-channel width (Ferencevic and 

Ashmore, 2012). Estimations of stream power or specific 

stream power were beyond the scope of tool development 

described in this report. However, the slope-area index 

can be a useful surrogate for stream power if discharge is 

assumed to be proportional to contributing drainage area 

(Ogren, 2012). Thus, the Slope-Area Index tool may be 

useful in some situations for identifying sites of geomorphic 

instability driven by stream power. In particular, slope-area 

index models have proven useful in predicting ephemeral 

gully development (Vandaele and others, 1996; Desmet and 

others, 1999; Daggupati and others, 2013) and in prioritizing 

implementation of best-management practices to mitigate 

erosion in agricultural landscapes (Ogren, 2012).

Because the Slope-Area Index tool allows the user to 

specify the exponent setting as well as the SAI threshold 

setting, spatial distributions of output points can be compared 

using different tool parameter settings. In general, when using 

the tool to identify possible locations of geomorphic instability 

in stream-channel networks, higher SAI threshold settings 

are expected to produce greater numbers of omission errors 

by failing to identify true sites of instability. Conversely, 

lower threshold settings would produce more commission 

errors by producing high-SAI points as tool outputs that do 

not correspond to sites of instability as indicated by field 

inspection. The tool parameter setting representing the 

exponent on drainage area in the calculation of slope-area 

index also influences the spatial configuration of results. In 

general, higher exponent settings are expected to shift the 

locations identified by the tool as having high SAI toward 

locations having larger drainage areas and less steep slopes, 

whereas lower exponent settings will preferentially identify 

steep locations with smaller drainage areas. Because both the 

SAI threshold and the exponent tool parameter settings are 

likely to affect tool outputs, users are encouraged to perform 

a thorough field verification of tool outputs, preferably with 

multiple tool parameter settings, to identify the optimal 

parameter settings for the study area and for the particular 

objectives of the investigation.

Rough Terrain

Application of the Rough Terrain tool to the 

culvert‐modified, hydroconditioned DEM for the Upper Sandy 

study area using default tool settings produced an output 

file containing 58 polygons, ranging in size from 303 m2 to 
Evaluation of Automated Tools for Geomorphic Feature Identification 29

fig16-0746_fig 14

EXPLANATION

Profile curvature

High : 18.77

Low : -20.68

Flow Network tool output, 5,000-cell 

flow-accumulation threshold

Elevation, in meters above NAVD88

High : 167.5

Low : 127.9

Channel Slope tool output exceeds 10 percent

Slope-Area Index tool output exceeds 500

Culvert

ROAD

88°49'34" 88°49'32" 88°49'30" 88°49'28" 88°49'26"

35°

39'

32"

35°

39'

30"

0 120 240 FEET

0 30 60 METERS

A

B

Elevation and profile curvature derivative from U.S. Department of 

Agriculture, Natural Resources Conservation Service, 1-meter-cell-size 

digital elevation model, 2011. Lambert Conformal Conic Projection, 

Horizontal datum: North American Datum of 1983 (NAD 83), Vertical 

datum: North American Vertical Datum of 1988 (NAVD 88)

Figure 14. A, An example of erosion at a culvert outlet that was, B, not identified in Channel Slope tool or Slope-Area Index 

tool output, Upper Sandy study area.
30 Stream-Channel Geomorphic Features From High-Resolution Digital Elevation Models, West Tennessee

approximately 8 ha with a median of 441 m2. In the Pinson 

Mounds study area, the output file contained 307 polygons, 

ranging in size from 300 m2 to approximately 15.3 ha with a 

median of 581 m2.

Because the Rough Terrain tool processes data across the 

entire landscape and does not require a flow network as a data 

input, this tool is not sensitive to flow-network resolution (the 

flow-accumulation threshold that was used to create the input 

flow network), unlike the other tools described in this report. 

This quality of the Rough Terrain tool has advantages in some 

contexts. For example, the Rough Terrain tool highlighted 

a gully head in the Upper Sandy study area that is actively 

eroding urban infrastructure (fig. 15). Because this gully head 

has a relatively small drainage area, it was included on the 

flow network derived using a 1,000-cell flow-accumulation 

threshold (fine-resolution flow network) but not on the flow 

network derived using a flow-accumulation threshold of 

5,000 cells (coarse-resolution flow network). As a result, 

this active and striking erosional feature was not identified 

by any of the other tools described in this report when the 

coarse-resolution flow network was used as the input. Other 

gullies having small drainage areas and eroded, kudzu-covered 

badlands were also successfully detected with this tool.

Results from the Upper Sandy study area indicate that 

the Rough Terrain tool identifies certain urban infrastructure 

features, such as stabilized steep embankments, retaining 

walls, and steep-sided ditches, in addition to gullied areas. 

For example, the Rough Terrain tool identified steep 

embankments in southwestern part of both maps in figure 16 

as well as gullied areas in the north-central part of both maps. 

Rough-terrain polygons representing urban infrastructure 

features typically have distinctly linear boundaries and are 

commonly aligned with roads, building footprints, or property 

lines, enabling them to be visually distinguished as cultural 

features. These features may be at high risk for erosion when 

unmaintained and hidden from view in brush along rear 

property lines and hence may warrant inspection for signs of 

gully erosion. 

Topographic Position Index

Visual inspection of the point locations resulting from 

TPI calculations indicated that calculated TPI values were 

of limited value in identifying depositional features such as 

alluvial fan heads. Many of the alluvial fan heads that are 

visually apparent in the three-cell-radius TPI raster for the 

Pinson Mounds study area were successfully identified by 

the aforementioned approach of analyzing TPI along flow 

networks, indicating that it produced relatively few omission 

errors. The approach produced a large number of commission 

errors; however, because many point locations were produced 

that did not correspond to any depositional feature. This 

preliminary assessment indicates the approach needs further 

refinement and calibration before it can be adapted into 

an informative tool for alluvial fan-head identification. 

Therefore, no TPI-based tool was included in the DEM 

Geomorphology Toolbox.

Spatial Analysis of Multiple Geomorphic 

Indicators

When the tools in the DEM Geomorphology Toolbox 

described in this report were run on the input DEMs for the 

two study areas, areas of spatial overlap in tool outputs were 

apparent. Locations of densely concentrated features generated 

by two or more tools represent feature clusters. Figure 17 

illustrates a feature cluster representing the head of a gully 

system in the Pinson Mounds study area. Field inspection 

indicated that this gully system contains multiple sites of 

active erosion including several head cuts, some exceeding 

1 m in vertical drop (fig. 17A, B). These erosional features 

are located in feature clusters represented by outputs from 

multiple tools (fig. 17C). The clusters are contained within 

output polygons of the Rough Terrain tool and are located 

in flow-network sections more than 2.5 m deep, as indicated 

by output from the Channel Depth tool. Additionally, these 

active erosional features are in flow-network locations having 

a channel slope exceeding 10 percent based on Channel 

Slope tool output, SAI exceeding 500 based on Slope-Area 

Index tool output, and are adjacent to bank slopes exceeding 

50 percent based on the Bank Slope tool output. Similarly, 

figure 18 illustrates a feature cluster in an entrenched reach 

of the main stem of Sandy Creek in the Upper Sandy study 

area. In this area, field inspection indicated a tributary gully 

(fig 18A) and a 3-m-tall overhanging bank (fig. 18B) were sites 

of active erosion. The tributary gully is within a flow-network 

section having a depth exceeding 2.0 m, channel slope 

exceeding 10 percent, SAI exceeding 500, and adjacent bank 

slopes exceeding 50 percent (fig. 18C). The overhanging bank 

is on a flow-network section having a channel depth exceeding 

4.5 m and adjacent bank slopes exceeding 100 percent. Both 

are located within polygons produced by the Rough Terrain 

tool that effectively outline the entrenched reach.

Feature clusters produced by running multiple tools 

on the same input DEM may be useful for prioritizing 

resource deployment for field investigation, because they 

represent possible erosional features as indicated by multiple 

complementary analytical methods. For example, although 

the Bank Slope tool and the Channel Slope tool both use slope 

calculations, the former focuses on the larger flow corridor 

whereas the latter focuses on slope along the flow network. 

The Rough Terrain tool is unique from the other tools in this 

report in two respects: it includes analysis of profile curvature 

and it does not limit analysis to the immediate vicinity of 

flow networks. The Channel Depth tool identifies maximum 

vertical distances present within a user-defined search radius 

and thus provides information that is complementary, not 

redundant, to slope-based indicators. As a result, feature 

clusters suggest that multiple approaches to deriving 

geomorphic indicators from the DEM converge spatially on 

locations of geomorphic instability. 
Evaluation of Automated Tools for Geomorphic Feature Identification 31

fig16-0746_fig 15

Profile curvature

High : 18.77

Low : -20.68

Boundaries of polygons generated by the Rough Terrain tool

EXPLANATION

Flow Network tool output, 5,000-cell flow-accumulation threshold

Flow Network tool output, 1,000-cell flow-accumulation threshold

Elevation, in meters above NAVD88

High : 167.5

Low : 127.9

88°49'29" 88°49'28" 88°49'27"

35°

39'

15"

35°

39'

14"

0 40 80 FEET

0 10 20 METERS

A

C D

B

Elevation data and derivative profile curvature from U.S. 

Department of Agriculture, Natural Resources Conservation 

Service, 1-meter-cell-size digital elevation model, 2011. 

Lambert Conformal Conic Projection, Horizontal datum: North 

American Datum of 1983 (NAD 83), Vertical datum: North 

American Vertical Datum of 1988 (NAVD 88)

Figure 15. An example of a gully head eroding urban infrastructure, including, A, an upstream view of the gully head, B, a 

downstream view of the gully, C, a close-up view of the eroded area undercutting asphalt, and D, boundaries of polygons generated by 

the Rough Terrain tool, Upper Sandy study area.
32 Stream-Channel Geomorphic Features From High-Resolution Digital Elevation Models, West Tennessee

fig16-0746_fig 16

EXPLANATION

Boundaries of polygons generated by the Rough Terrain tool

Flow Network tool output, 5,000-cell flow-accumulation threshold

Flow Network tool output, 1,000-cell flow-accumulation threshold

Elevation, in meters above NAVD88

High : 167.5

Low : 127.9

Profile curvature

High : 18.77

Low : -20.68

88°49'38" 88°49'36" 88°49'34" 88°49'32"

35°

39'

20"

35°

39'

18"

88°49'38" 88°49'36" 88°49'34" 88°49'32"

35°

39'

20"

35°

39'

18"

A

0 100 200 FEET

0 25 50 METERS

Elevation data and derivative profile curvature from U.S. 

Department of Agriculture, Natural Resources Conservation 

Service, 1-meter-cell-size digital elevation model, 2011.

Base orthoimagery from Tennessee Department of 

Transportation, 1-foot resolution, 2012. Tennessee State 

Plane coordinate system (FIPS Zone 4100). Horizontal 

datum: North American Datum o f1983 (NAD 83)

Figure 16. Output polygons from the Rough Terrain tool superimposed on, A, profile curvature derived from the digital 

elevation model (DEM), and on, B, aerial orthoimagery of identical extent, Upper Sandy study area.
Evaluation of Automated Tools for Geomorphic Feature Identification 33

fig16-0746_fig 12

Channel Slope tool output exceeds 10 percent

Slope-Area Index tool output exceeds 500

Bank Slope tool output exceeds 50 percent

Boundaries of polygons generated by the 

Rough Terrain tool

EXPLANATION

Channel Depth tool output (meters)

High : 3.65

Low : 0.03

Profile curvature

High : 27.01

Low : -37.46

Elevation, in meters above NAVD88

High : 153.2

Low : 110.7

88°40'55" 88°40'50" 88°40'45"

35°

29'

40"

35°

29'

35"

0 100 200 300 FEET

0 40 80 METERS

A

A

B

B

C

Elevation and profile curvature derivative from U.S. Department of 

Agriculture, Natural Resources Conservation Service, 1-meter-cell-size 

digital elevation model, 2011. Lambert Conformal Conic Projection, 

Horizontal datum: North American Datum of 1983 (NAD 83), Vertical 

datum: North American Vertical Datum

Figure 17. Actively eroding head cuts in, A, the main channel of a gully system, and B, a small tributary to the gully system, associated 

with, C, feature clusters of outputs from multiple tools, Pinson Mounds study area.
34 Stream-Channel Geomorphic Features From High-Resolution Digital Elevation Models, West Tennessee

fig16-0746_fig 18

Channel Slope tool output exceeds 10 percent

Slope-Area Index tool output exceeds 500

Bank Slope tool output exceeds 100 percent

Bank Slope tool output exceeds 50 percent

Boundaries of polygons generated by the 

Rough Terrain tool

EXPLANATION

Channel Depth tool output (meters)

High : 4.88

Low : 0.02

Profile curvature

High : 18.77

Low : -20.68

Elevation, in meters above NAVD88

High : 167.5

Low : 127.9

88°49'30"

35°

39'

0 500 1,000 1,500 FEET

0 2,000 4,000 METERS

A

A B

B

C

Elevation and profile curvature derivative from U.S. Department of Agriculture, 

Natural Resources Conservation Service, 1-meter-cell-size digital elevation 

model, 2011. Lambert Conformal Conic Projection, Horizontal datum: North 

American Datum of 1983 (NAD 83), Vertical datum: North American Vertical 

Datum of 1988 (NAVD 88)

Figure 18. A, A tributary gully, and B, an overhanging bank, associated with, C, feature clusters of outputs from multiple 

tools, Upper Sandy study area.
Summary and Conclusions 35

Summary and Conclusions

Identifying sites of geomorphic instability within 

stream-channel networks is a key component of watershed 

management and is particularly important for reducing 

suspended-sediment loads. Historically, investigations of 

stream-channel geomorphology—and of gully erosion in 

particular—have required time-intensive methods such as 

field surveys or visual analysis of aerial orthoimagery. More 

recently, high-resolution digital elevation models (DEMs) 

derived from airborne light detection and ranging (lidar) 

have enabled the identification of erosional and depositional 

features within stream-channel networks. 

To improve the efficiency of DEM-based analysis, 

the U.S. Geological Survey conducted a pilot study in 

two contrasting study areas in West Tennessee to develop 

automated tools for identifying possible locations of 

geomorphic instability within stream-channel networks. The 

DEM Geomorphology Toolbox was developed for the study 

and contains seven tools to aid in the automated identification 

of stream-channel geomorphic features. Following acquisition 

and preprocessing of DEMs, stormwater-infrastructure 

locations were digitized in the field and used to modify 

the DEMs. Hydroconditioning was performed using the 

Optimized Pit Removal (OPR) tool (Jackson, 2013) to 

remove internal topographic depressions. Flow networks were 

derived using flow-accumulation thresholds of 1,000 and 

5,000 cells from the culvert-modified, hydroconditioned 

DEMs. Field verification of tool outputs was performed 

in selected parts of both study areas and documented by 

photographs and field notes, with particular emphasis on 

identifying commission and omission errors in tool outputs. 

The Add Culverts tool and the Flow Network tool allow users 

to derive flow networks at user-specified flow-accumulation 

thresholds using preprocessed DEMs and vector files of 

stormwater infrastructure locations as inputs. Results from 

field verification efforts suggest that modifying DEMs on 

the basis of stormwater-infrastructure data can improve the 

accuracy of the derived flow networks. Those derived using 

flow-accumulation thresholds of 1,000 and 5,000 cells were 

both considerably more detailed than the high-resolution 

(1:24,000) flowlines included in the National Hydrography 

Dataset (NHD) (Simley and Carswell, 2009), including 

additional headwater reaches and many small tributaries not 

present in the NHD flowline.

The Bank Slope tool produces a raster of slope within a 

user-specified flow corridor surrounding an input flow network 

and a vector polygon file representing areas of bank slope 

exceeding the bank-slope threshold specified by the user. In 

eroded sections of stream-channel networks in both study 

areas, bank slopes commonly exceeded 50 percent and in 

some locations exceeded 100 percent. This tool successfully 

highlighted some areas of eroded and unstable streambank 

but failed to detect highly localized areas of steep slope that 

were probably too small to be resolved by 1-meter DEMs. In 

addition, the Bank Slope tool does not differentiate actively 

eroding streambanks from stable, vegetated streambanks of 

similar slope.

The Channel Depth tool employs a user-specified search 

radius to produce a channel depth raster representing the 

maximum elevation difference in the vicinity of each cell 

of an input flow network. High channel-depth values were 

associated with narrow, steep-walled gully systems. Rapid 

increases in channel depth values along the flow network were 

observed at gully heads. Locally decreasing channel-depth 

values were observed at the downstream ends of gully systems 

and at locations where the trench containing an active stream 

channel widened. Results from the Channel Depth tool are 

sensitive to the application of a fixed, user-specified search 

radius. Results do not correspond to bankfull depth at most 

locations, but nevertheless are useful for locating gully heads, 

alluvial fan heads, sections of channel adjacent to tall, actively 

eroding banks, and incised channels in gully systems.

The Channel Slope tool produces a channel slope raster 

containing slope values along an input flow network along 

with a vector point file representing the locations of channel 

slope greater than 2 percent. The Slope-Area Index (SAI) 

tool highlights points along an input flow network where the 

slope-area index (Vandaele and others, 1996; Daggupati and 

others, 2013) exceeds a user-specified threshold. The Channel 

Slope and Slope-Area Index tools were useful in identifying 

erosional features in both study areas, such as head cuts of 

varying sizes and morphologies. Field inspection indicated 

that some head cuts were neither detected by the Channel 

Slope tool nor the SAI tool. In other cases, high channel slope 

or SAI values were obtained in areas of the flow network 

having no observable signs of geomorphic instability. Users 

are advised to adjust input parameters and interpretation of 

tool outputs to balance errors of omission and commission 

depending on management applications and local geomorphic 

and hydrologic factors.

The Rough Terrain tool identifies areas within the input 

DEM that represent concentrations of high upward profile 

curvature (high convexity and high concavity) connected 

by steep slopes. In this study, such areas were commonly 

associated with gully systems that likely developed in 

agricultural fields and persisted under second-growth forest. 

The Rough Terrain tool processes data across the entire 

landscape, does not require an input flow network, and 

therefore is not sensitive to flow-network resolution unlike 

the other tools described in this report. Field verification of 

tool outputs suggests that the Rough Terrain tool identifies 

gullied areas as well as urban infrastructure features, such 

as stabilized steep embankments, retaining walls, and 

steep-sided ditches.

In both study areas, spatial overlaps in tool outputs 

(feature clusters) were apparent when multiple tools in 

the DEM Geomorphology Toolbox were run on the same 

input DEM. Feature clusters may be useful in prioritizing 

field investigations, because the clusters represent possible 
36 Stream-Channel Geomorphic Features From High-Resolution Digital Elevation Models, West Tennessee

erosional features indicated by multiple complementary 

analytical methods. For example, large active gully heads were 

present in the study areas where multiple cells having high 

SAI values coincided with areas of high channel depth and 

high bank slope. 

The tools in the DEM Geomorphology Toolbox were 

developed in two relatively small study areas having similar 

soils, geology, and physiography but contrasting land-use 

histories (one urban, one rural). Application of these 

tools to larger geographic areas or to different geologic or 

physiographic settings would require an iterative process 

involving field identification of geomorphic features of interest 

and adjustment of tool parameter settings to discriminate those 

features most effectively. For example, parameter settings such 

as the flow corridor width in the Bank Slope tool and search 

radius in the Channel Depth tool would need to be increased 

to effectively analyze larger channels. Parameter settings 

might also need to be adjusted on the basis of landscape 

characteristics such as land-use and disturbance history. For 

example, SAI thresholds for detecting ephemeral gullies 

may be lower in tilled agricultural fields than in undisturbed 

forested watersheds. 

References Cited

Aksoy, H., and Kavvas, M., 2005, A review of hillslope and 

watershed scale erosion and sediment transport models: 

Catena, v. 64, p. 247–271.

Alonso, C.V., Bennett, S.J., and Stein, O.R., 2002, Predicting 

head cut erosion and migration in concentrated flows typical 

of upland areas: Water Resources Research, v. 38, no. 12, 

p. 39–1–39–15.

Biron, P.M., Choné, G., Buffin-Bélanger, T., Demers, S., 

and Olsen, T., 2013, Improvement of streams hydrogeomorphological assessment using LiDAR DEMs: Earth 

Surface Processes and Landforms, v. 38, p. 1808–1821.

Blackwood, D.C., 2012, Watershed management in West 

Tennessee, in 2012 Watershed symposium: Tennessee 

Water Resources Research Center, University of Tennessee, 

Knoxville, Tenn.

Booth, D.B., and Henshaw, P.C., 2001, Rates of channel 

erosion in small urban streams, in Wigmosta, M., and 

Burges, S. eds., Land use and watersheds—Human 

influence on hydrology and geomorphology in urban and 

forest areas: AGU Monograph Series, Water Science and 

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Glossary 41

Glossary

Glossary definitions marked with an asterisk 

(*) are direct quotations from the references 

cited; definitions without an asterisk were 

adapted from the references cited.

alluvial fan A wedge-shaped deposit of 

recent stream alluvium (transported sediment) 

that radiates outward and downslope as, in 

plan view, an open fan from a site draining 

an area of high relief or topography, such as 

the mouth of a valley, onto a gentler slope, 

typically an alluvial plain; the deposit is 

thickest at the fan apex, near the valley mouth, 

and thins to a feather edge at the terminus 

of the fan (W. Osterkamp, U.S. Geological 

Survey, written commun., 2015). 

alluvium Sediment deposited by flowing 

water in a streambed, delta, or on a 

floodplain or other bottomland feature 

during comparatively recent geologic time 

(W. Osterkamp, U.S. Geological Survey, 

written commun., 2015).

artifact An inaccurate result or artificial 

anomaly introduced into a surface model, such 

as a DEM, resulting from a data manipulation 

process (Heideman, 2014).

braided stream* [A stream] with a wide, 

relatively horizontal channel bed over which 

water during low flows forms an interlacing 

pattern of splitting into numerous small 

conveyances that again coalesce a short 

distance downstream (W. Osterkamp, U.S. 

Geological Survey, written commun., 2015).

cell* A single element of a raster dataset. 

Each cell contains a single numeric value of 

information representative of the area covered 

by the cell (Heideman, 2014).

channel* A natural, or constructed, 

passageway or depression of perceptible 

linear extent containing continuously or 

periodically flowing water and sediment, or a 

connecting link between two bodies of water 

(W. Osterkamp, U.S. Geological Survey, 

written commun., 2015). Used in this report as 

a synonym for stream channel.

channel incision Lowering of a channel bed; 

also known as downcutting.

circular neighborhood The twodimensional space around a point that includes 

all locations at a distance from the point less 

than or equal to r, where r is the radius of 

the circle defining the outer boundary of the 

circular neighborhood.

commission error A false positive in the 

context of remote sensing (Franklin, 2001); 

used in this report to refer to tool outputs 

indicating possible locations of geomorphic 

instability that do not show signs of instability 

indicated by field inspection.

culvert* A tunnel carrying a stream or open 

drainage under a road or railroad, or through 

another type of obstruction to natural drainage. 

Typically, constructed of formed concrete or 

corrugated metal and surrounded on all sides, 

top, and bottom by earth or soil (Heideman, 

2014).

deposition The constructive process of 

sediment accumulation into beds or irregular 

masses, especially the mechanical settling 

of sediment from suspension or tractive 

movement in water (W. Osterkamp, U.S. 

Geological Survey, written commun., 2015).

digital elevation model (DEM)* The digital 

cartographic representation of the elevation 

of the land at regularly spaced intervals in x 

and y directions, using z values referenced to a 

common vertical datum (Heideman, 2014).

entrenched channel A confined channel that 

is not connected to a floodplain.

ephemeral gully* A gully, typically in an 

agricultural field, that develops due to water 

erosion during a growing season but which 

is subject to removal by any primary tillage 

operation (W. Osterkamp, U.S. Geological 

Survey, written commun., 2015).

ephemeral stream A stream characterized 

by inconsistent or infrequent streamflow 

within a normally dry channel; except during 

periods of streamflow, the channel bed is 

directly underlain by unsaturated alluvium (W. 

Osterkamp, U.S. Geological Survey, written 

commun., 2015).
42 Stream-Channel Geomorphic Features From High-Resolution Digital Elevation Models, West Tennessee

erosion* The detachment and movement 

of soil or rock by water, wind, ice, or gravity 

(Soil Science Society of America, 2008).

floodplain* A strip of relatively smooth 

land bordering a stream incision, built of 

sediment carried by the stream and dropped in 

slackwater beyond the influence of the swift 

current of the channel (W. Osterkamp, U.S. 

Geological Survey, written commun., 2015).

flow corridor In the context of this report, 

an area of user-specified width within or 

surrounding the active channel, applied 

consistently across the study area within 

which the user may apply a tool.

focal mean The mean (average) value of 

raster cell values within a specified distance or 

neighborhood surrounding an input raster cell.

geomorphic instability A landform property 

marked by erosion or deposition at rates that 

are rapid relative to the surrounding landscape.

geomorphology* The study of landforms 

including, in recent times especially, 

investigations into the processes that cause 

and alter the landforms (W. Osterkamp, U.S. 

Geological Survey, written commun., 2015).

gully In the context of this report, a small, 

steep-sided channel cut in unconsolidated 

materials by an ephemeral stream. The 

gullies in this study are permanent gullies

rather than ephemeral gullies; that is, they are 

not obliterated by field cultivation, but rather, 

remain as long-term landscape features. The 

sides of gullies are defined by steep slopes that 

represent either the edges of stream terraces 

(terrace slopes) or the sides of the valley 

(valley walls). Some of the gullies mentioned 

in this report have narrow bottoms occupied 

entirely by the stream channel, whereas 

others have wider gully bottoms that include 

discontinuous floodplains.

head cut An abrupt, steep step in the 

thalweg profile of a channel at which the 

stream has sufficient power to erode the bed, 

occurring both at the upstream end of gullies 

and along incising channels.

hydroconditioning (hydrologic 

conditioning) A process by which elevation 

values are modified to produce coherent 

flow directions and flow accumulations. In 

particular, hydroconditioning requires the 

removal of internal topographic depressions 

(raster cells of low elevation completely 

surrounded by cells of higher elevations).

light detection and ranging (lidar) An active 

remote-sensing technology that uses laser 

scanning to generate high-resolution elevation 

data over large areas.

omission error A false negative in the 

context of remote sensing (Franklin, 2001); 

used in this report to refer to locations of 

geomorphic instability identified by field 

inspection that were not identified by tool 

outputs.

parameter In the context of this report, a 

user-specified tool setting that helps define a 

tool’s behavior as it is run (Esri, 2015).

permanent gully* A channel resulting from 

erosion and caused by the concentrated but 

intermittent flow of water, usually during and 

immediately following heavy rains. Permanent 

gullies are deep enough (usually > 0.5 meters) 

to interfere with, and not to be obliterated by, 

normal tillage operations (Soil Science Society 

of America, 2008).

profile curvature Curvature in the direction 

of steepest slope; curvature is the second 

derivative of elevation (the slope of slope).

raster A type of geospatial data structure 

composed of a spatially continuous array of 

equally sized cells, with each cell containing 

a single piece of numeric information 

representative of the area covered by the cell 

(Heideman, 2014; Esri, 2015).

sediment Transported and deposited 

particles or aggregates derived from rocks, 

soil, or biological material (Soil Science 

Society of America, 2008).

stormwater infrastructure In the context 

of this report, man-made structures installed 

for the purpose of routing runoff from 

precipitation; especially culverts but also 

including ditches and storm sewers.

stream power A physical variable that 

represents the rate at which potential energy is 

expended in a stream channel as water flows 

downstream (Ferencevic and Ashmore, 2012).

valley plug A location within a channel 

where flow is blocked by sediment deposition, 

forcing floodwater and suspended sediment 

out of the channel and onto the floodplain (see 

Pierce and King, 2007).

vector A type of geospatial data structure, 

with subtypes including points, lines, and 

polygons, that is geometrically described 

by coordinate pairs (Heideman, 2014; Esri, 

2015).
Appendix 1 43

Appendix 1. Prospectus for Tool Application to Larger Geographic Areas

The tools in the digital elevation model (DEM) 

Geomorphology Toolbox that are described in this report 

were developed and calibrated in two relatively small study 

areas. As a result, additional field verification and tool 

calibration would be required for these tools to be applied to 

larger geographic areas. This appendix contains a prospectus 

intended to support future planning and implementation efforts 

for tool application to geographic areas beyond the scope of 

this pilot study. Field verification resources were estimated for 

the study areas for the scope of field work described in this 

report and were compared across study areas. A generalized 

work plan was developed, itemizing the steps that would be 

required for tool application to other geographic areas.

Field-Verification Resources

Field-verification resources were estimated for both 

study areas in terms of hours spent on field verification of 

flow networks and tool outputs (table 1-1). Field verification 

of flow networks was considerably more resource-intensive 

in the Upper Sandy study area (8.5 person-hours spent to 

cover a 1.8-square-kilometer [km2] study area containing 

11 kilometers [km] of roads) compared to the Pinson Mounds 

study area (7.0 person-hours for a 13.6-km2 study area 

containing 25 km of roads). Field verification of tool outputs 

was also more resource-intensive in the Upper Sandy study 

area (9.5 person-hours spent to cover an 8,600-square-meter 

[m2] study area containing 2,500 meters [m] of flow network) 

compared to the Pinson Mounds study area (12.5 person-hours 

for a 20,600 m2 study area containing 5,500 m of flow 

network). In fact, field verification of tool outputs was 

conducted almost twice as quickly in the Pinson Mounds area 

(approximately 440 m of flow network inspected per hour) as 

in the Upper Sandy area (approximately 265 m per hour).

The differences in field-verification resources between 

the two study areas reflect differences in a number of practical 

and logistical constraints. Accessibility is an important 

determinant of field-verification resource requirements and 

includes considerations such as the distance between parking 

areas and the field features to be inspected, the topography 

of the terrain that must be navigated, the density and type of 

vegetation cover, and the access permission granted by private 

landowners. In several respects, the Pinson Mounds study 

area was more accessible than the Upper Sandy study area. 

For example, the Pinson Mounds study area has a relatively 

sparse understory, whereas the Upper Sandy study area has a 

greater density of brush and kudzu that impedes access and 

visibility. Season is an important consideration in planning 

field-verification efforts, because dense vegetation obstructs 

observation and movement. 

Another set of determinants of resource requirements, 

especially for field verification of flow networks, is the 

number, spatial arrangement, and accessibility of stormwater 

infrastructure features. In general, urban or suburban 

watersheds are likely to require more field effort than 

agricultural or forested watersheds to digitize the locations 

of stormwater infrastructure features, because of the number 

and complexity of culverts and other infrastructure features. 

Table 1-1. Field verification resources used in tool development and calibration for two 

study areas.

[Field verification of tool outputs was performed using flow networks derived with a 5,000-cell flow accumulation threshold. km, kilometer; km2

, square kilometer; m, meter; m2, square meter]

Data category Upper Sandy

study area

Pinson Mounds

study area

Flow network field verification

Distance (km) of roads in study area 11 25

Size of study area (km2

) 1.8 13.6

Number of culverts digitized 58 70

Person-hours spent on field verification 8.5 7

Tool output field verification

Area (m2

) used for field verification 8,600 20,600

Distance (m) of flow network inspected 2,500 5,500

Person-hours spent on field verification 9.5 12.5
44 Stream-Channel Geomorphic Features From High-Resolution Digital Elevation Models, West Tennessee 

In urban areas, for example, storm sewers commonly drain 

into complex subterranean networks of conveyances, the 

configuration and connectivity of which may not be readily 

apparent at the ground surface. In such cases, field inspections 

may require the use of flashlights and compasses to determine 

the orientation of subsurface pipes. By contrast, culverts under 

driveways and small roads in rural areas commonly have inlets 

and outlets that are easily visible from roadways. Digitization 

of stormwater infrastructure features is likely to be a 

rate limiting step in the process of flow-network delineation 

and verification. Indeed, digitizing these features on the basis 

of field inspection would probably create substantial barriers 

to tool application across large geographic areas, such as 

across multiple counties. As a result, if existing, high-precision 

digital datasets of stormwater infrastructure locations can 

be obtained from municipalities or other agencies, such data 

could greatly expedite the tool application process and could 

potentially enable tool application to large geographic areas.

Notably, the field resource estimates in table 1-1 do 

not account for all required activities associated with field 

verification. In particular, these estimates do not include time 

that was spent preparing for field work (compiling base layers 

for navigation), setting up or putting away field equipment, 

travelling to and between field sites, and processing field data. 

For this study, field notes were dictated as audio files and later 

transcribed. Transcription required approximately 10 hours 

for the Pinson Mounds study area and approximately 5 hours 

for the Upper Sandy study area. Additional post-field-data 

processing included inspection and, where needed, manual 

correction of Global Positioning System (GPS) locations 

for field photographs. It should also be noted that the study 

areas described in this report are not representative of West 

Tennessee as a whole, much less other physiographic areas. 

Study areas were selected partly on the basis of ease of access, 

especially a lack of barriers to entry based on land ownership. 

Additionally, the field areas used to verify tool outputs were 

relatively free of obstructions to navigation, such as dense 

vegetation, inundation, fences, or hazardous terrain.

Work Plan

Applying the tools in the DEM Geomorphology Toolbox 

that are described herein to geographic areas beyond the two 

study areas will generally involve the following steps: 

1. Reconnaissance—Preliminary field visits are conducted 

at the proposed study site and available data layers 

are consulted to determine accessibility, road network 

density and configuration, land ownership, presence of 

dense vegetation and other possible obstacles. 

2. DEM preparation—The DEM tiles for the study site are 

obtained, compiled, and preprocessed according to the 

steps itemized in the methods section of this report.

3. Derivation of preliminary flow networks—Using the 

tools described in this report, preliminary flow networks 

are derived from the DEM. 

4. Field verification of preliminary flow networks—

Preliminary flow networks are inspected in the field, and 

vector line files are digitized representing culverts and 

other stormwater infrastructure.

5. Culvert modification—Using the files created in step 4, 

the DEM is modified and hydroconditioned, enabling 

derivation of modified flow networks using the tools 

described in this report.

6. Tool application—The tools applied in this report are 

applied to generate geomorphic indicators.

7. Spatial analysis—Outputs of tools applied in step 6 are 

mapped and spatially analyzed to determine locations 

having concentrations of features identified by multiple 

tools; such locations may be used to prioritize field 

inspection efforts.

8. Field inspection of tool outputs—Tool outputs from 

step 6 are inspected in the field and documented using 

photography and field notes, with special attention paid 

to commission errors (features identified by tools that 

do not correspond to signs of geomorphic instability 

as indicated by field inspection) and omission errors 

(field indicators of geomorphic instability that were not 

identified by tool outputs). 

9. Tool calibration—Field data collected in step 8 are used 

to refine and calibrate tools to minimize occurrences of 

omission and commission errors.
Publishing support provided by the U.S. Geological Survey

Science Publishing Network, Lafayette and Tacoma Publishing Service 

Centers

For more information concerning the research in this report, contact the

 Director, Lower Mississippi-Gulf Water Science Center

U.S. Geological Survey

540 Grassmere Park, Suite 100

Nashville, Tennessee 37211

http://tn.water.usgs.gov/
Cartwright and Diehl—Stream-Channel Geomorphic Features From High-Resolution Digital Elevation Models in West Tennessee—Scientific Investigations Report 2016–5141

ISSN 2328-031X (print)

ISSN 2328-0328 (online)

https://doi.org/10.3133/sir20165141