U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2015–5009

Prepared in cooperation with the U.S. Army Corps of Engineers

An Assessment of Two Methods for Identifying 

Undocumented Levees Using Remotely Sensed Data

An Assessment of Two Methods for 

Identifying Undocumented Levees Using 

Remotely Sensed Data

By Christiana Czuba, Byron Williams, Jack Westman, and Keith LeClaire

Prepared in cooperation with the U.S. Army Corps of Engineers

Scientific Investigations Report 2015–5009

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, Acting Director

U.S. Geological Survey, Reston, Virginia: 2015

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Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials 

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Suggested citation:

Czuba, Christiana, Williams, Byron, Westman, Jack, and LeClaire, Keith, 2015, An assessment of two methods for

identifying undocumented levees using remotely sensed data: U.S. Geological Survey Scientific Investigations Report 

2015–5009, 19 p., http://dx.doi.org/10.3133/sir20155009.

ISSN 2328-0328 (online)
iii

Acknowledgments

The authors wish to thank the U.S. Army Corps of Engineers for their cooperation in this study 

and for the project coordination provided by Terry Zien. Jennifer Nelson, the State Hazard 

Mitigation Officer at Minnesota Homeland Security and Emergency Management, along with 

other members of the Minnesota Silver Jackets Hazard Mitigation Team, are acknowledged for 

supporting this study. Local knowledge of the river and levee management was provided for 

Delano, Minnesota, by Robert Van Lith (Fire Chief, city of Delano) and by Paul Swearengin (city 

of Delano Public Works Department); and for Springfield, Minnesota, by Scott Johnson (Emergency Manager and Utilities Superintendent, city of Springfield) and by Shaun Luker (Bolton and 

Menk, Inc.). Jonathan Czuba of the University of Minnesota is thanked for sharing his MATLAB®

expertise.

v

Contents

Acknowledgments........................................................................................................................................iii

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

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

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

Study Area Descriptions......................................................................................................................3

South Fork Crow River at Delano, Minnesota.........................................................................3

Cottonwood River at Springfield, Minnesota ..........................................................................4

Two Methods for Identifying Undocumented Levees Using Remotely Sensed Data.........................4

Hillshade Method and Site Visit Verification....................................................................................5

Wavelet-Transform Method................................................................................................................5

Comparison of Methods ......................................................................................................................9

Assessment of the Methods Used to Identify Undocumented Levees...............................................13

Potential Method for Future Applications ......................................................................................13

Considerations............................................................................................................................18

Limitations of Methods .............................................................................................................18

Future Advancements of Methods ..................................................................................................18

Summary........................................................................................................................................................18

References Cited..........................................................................................................................................19

Figures

1. Maps showing location of study areas for the cities of Delano on the South Fork

Crow River and Springfield on the Cottonwood River, Minnesota .......................................2

2. Schematic showing measured dimensions of levees............................................................5

3. Map showing potential levees identified using the hillshade method and field 

visits, Delano, Minnesota, 2013 ..................................................................................................6

4. Photographs showing levees assessed in the field by the U.S. Army Corps of

Engineers, July 18, 2013, Delano, Minnesota...........................................................................7

5. Graph showing Mexican hat wavelet as a function of distance, scale parameter,

and location parameter ..............................................................................................................8

6. Maps showing wavelet-transform coefficients for selected combinations of

wavelet-scale parameter and output-filter threshold, with thresholds at selected

percentiles of the distribution of coefficients, Springfield, Minnesota.............................10

7. Maps showing wavelet-transform coefficients for selected combinations of

wavelet-scale parameter and output-filter threshold, with thresholds at selected

percentiles of the distribution of coefficients, Delano, Minnesota....................................14

8. Maps showing comparison of potential levees identified by two methods, Delano,

Minnesota ....................................................................................................................................17
vi

Conversion Factors

International System of Units to Inch/Pound

Multiply By To obtain

Length

meter (m) 3.281 foot (ft) 

Multiply By To obtain

Length

foot (ft) 0.3048 meter (m) 

Inch/Pound to International System of Units

Datum

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

(NAVD 88).

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

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

Tables

1. Ten highest historical flood wave crests through 2014 for the South Fork Crow River

at Delano, Bridge Avenue streamgage, Minnesota................................................................3

2. Ten highest historical flood wave crests through 2014 for the Cottonwood River near 

Springfield, CR2 streamgage, Minnesota .................................................................................4

3. Summary of dimensions of assessed potential levees, Delano, Minnesota, 2013 ............6

4. Summary of dimensions of assessed potential levees, Springfield, Minnesota, 2013 ...13
vii

Abbreviations

AHPS Advanced Hydrologic Prediction Service

DEM digital elevation model

GIS geographic information system

GPS global positioning system

lidar light detection and ranging

MNDNR Minnesota Department of Natural Resources

NAD 83 North American Datum of 1983

NAVD 88 North American Vertical Datum of 1988

NLD National Levee Database

NWS National Weather Service

USACE U.S. Army Corps of Engineers

USGS U.S. Geological Survey

An Assessment of Two Methods for Identifying 

Undocumented Levees Using Remotely Sensed Data

By Christiana Czuba,1 Byron Williams,2 Jack Westman,2 and Keith LeClaire2

1

 U.S. Geological Survey.

2

 U.S. Army Corps of Engineers, Saint Paul District.

Abstract

Many undocumented and commonly unmaintained levees 

exist in the landscape complicating flood forecasting, risk 

management, and emergency response. This report describes a 

pilot study completed by the U.S. Geological Survey in cooperation with the U.S. Army Corps of Engineers to assess two 

methods to identify undocumented levees by using remotely 

sensed, high-resolution topographic data. For the first method, 

the U.S. Army Corps of Engineers examined hillshades computed from a digital elevation model that was derived from 

light detection and ranging (lidar) to visually identify potential 

levees and then used detailed site visits to assess the validity 

of the identifications. For the second method, the U.S. Geological Survey applied a wavelet transform to a lidar-derived 

digital elevation model to identify potential levees. The 

hillshade method was applied to Delano, Minnesota, and the 

wavelet-transform method was applied to Delano and Springfield, Minnesota. Both methods were successful in identifying 

levees but also identified other features that required interpretation to differentiate from levees such as constructed barriers, 

high banks, and bluffs. Both methods are complementary to 

each other, and a potential conjunctive method for testing in 

the future includes (1) use of the wavelet-transform method to 

rapidly identify slope-break features in high-resolution topographic data, (2) further examination of topographic data using 

hillshades and aerial photographs to classify features and map 

potential levees, and (3) a verification check of each identified 

potential levee with local officials and field visits.

Introduction

Local, State, and Federal managers that are tasked with 

forecasting flood peaks, predicting the extent of flood inundation, mitigating the risk associated with flooding or levee 

failure, and responding during flood emergencies require 

detailed knowledge about locations and characteristics of 

land features that affect the flow of water along a river. An 

example of a land feature is an artificial levee. An artificial 

levee is a man-made flood-control structure, generally an 

earthen embankment or concrete floodwall, that is designed to 

contain or divert the flow of water along a river to reduce the 

risk of flooding in the area protected by the levee (U.S. Army 

Corps of Engineers, 2014). Other man-made features, such 

as roadways, may also function, intentionally or unintentionally, as levees. The U.S. Army Corps of Engineers (USACE) 

maintains the National Levee Database (NLD) of known levee 

locations, primarily those within the USACE Levee Program 

(U.S. Army Corps of Engineers, 2014). Many undocumented 

and commonly unmaintained levees exist in the landscape, 

which complicates flood forecasting, risk management, 

and emergency response. For the purpose of this report, an 

undocumented levee refers to man-made features intended to 

contain or divert the flow of floodwaters that are not included 

in the NLD. Undocumented levees may be maintained locally 

to unknown standards.

The cities of Delano, Minnesota, located on the South 

Fork Crow River, and Springfield, Minnesota, located on the 

Cottonwood River, are communities in Minnesota that do not 

have levees documented in the NLD (fig. 1). Both of the communities constructed temporary levees during flooding in 1969 

and have since modified or raised the levees to continue to 

reduce flood risk for residents and infrastructure (Bolton and 

Menk, Inc., 2011; Robert Van Lith, city of Delano, Minn., oral 

commun., 2013). The two communities were selected for a 

pilot study completed by the U.S. Geological Survey (USGS) 

in cooperation with the USACE to assess two methods to 

identify undocumented levees using remotely sensed, highresolution topographic data.

The availability of high-resolution topography derived 

from light detection and ranging (lidar) data provides a dataset 

from which undocumented levees can be identified. Lidar is 

a remote sensing technology using laser light to measure the 

elevation of features of the Earth’s surface (Minnesota Geospatial Information Office, 2014). Two methods were developed to identify undocumented levees from available gridded 

bare-earth digital elevation models (DEMs) derived from lidar 

data (Minnesota Geospatial Information Office, 2012). The 

first method involves the visual inspection of shaded relief 

layers (hillshades) computed from the DEMs (Esri, 2014) and 
2 An Assessment of Two Methods for Identifying Undocumented Levees Using Remotely Sensed Data

Delano

93°50' 93°48' 93°46'

45°03'

45°01'

Springfield

Cottonwood River

95° 94°59' 94°58'

44°15'

44°14'

44°13'

0 1 MILE

0 1 KILOMETER

0 1 MILE

0 1 KILOMETER

City and study area boundary 

EXPLANATION

!

!

Springfield

Delano

Base from Minnesota Geological Survey, 1:500,000

Minnesota Deptartment of Natural Resources 1:24,000

U.S. Geological Survey 1:24,000

Universal Transverse Mercator, zone 15

North American Datum of 1983

Mississippi River

Minnesota River

Cottonwood

 River

South Fork Crow River

North Fork

Crow River

South Fork

Crow River

Index map

Figure 1. Location of study areas for the cities of Delano on the South Fork Crow River and Springfield on the Cottonwood 

River, Minnesota.
Introduction 3

manual delineation of potential levees. The second method is 

a semi-automated approach that involves applying a wavelet transform to the DEM, by which topographic signatures 

of potential levees are highlighted. A wavelet is a localized 

waveform (a curve) that is used in a wavelet transform (mathematical operation of comparing the wavelet to the landscape) 

to determine the dominant spatial scales of local landscape 

features (Addison, 2002). The wavelet-transform method 

highlights any land feature that has an abrupt vertical change 

in elevation, which includes levees as well as other features 

in the landscape. Verification of the methods was done by 

checking the identified features with local knowledge or site 

visits to confirm if the features were levees. By documenting 

the methods and selected characteristics of the potential levees 

examined, future efforts to identify undocumented levees in 

other and larger areas can be streamlined.

Purpose and Scope

The purpose of this report is to describe a pilot study 

to assess two methods for identifying undocumented levees 

using remotely sensed, high-resolution topographic data. The 

general approach was to use the locations of known undocumented levees to develop search criteria and to test methods 

for identifying potential levees near rivers using lidar-derived 

DEMs. The scope of this study is limited to the communities 

of Delano and Springfield, Minn., where undocumented levees 

were constructed during historical flood events.

The methods tested to identify undocumented levees 

were the hillshade and wavelet-transform methods. For the 

hillshade method, the USACE examined hillshades computed 

from the DEM to visually identify potential levees and then 

made a detailed site visit to assess the validity of the potential 

features. For the wavelet-transform method, the USGS applied 

a wavelet transform to the DEM to identify potential levees. 

Reporting the USGS method might lead to more automated 

identification of levees. Characteristics of the potential levees 

are presented to demonstrate the typical size of levees analyzed in this pilot study.

Study Area Descriptions

The communities of Delano and Springfield, Minn., 

were chosen as study areas for this pilot study because they 

are locations with undocumented levees. City officials of 

Delano, located on the South Fork Crow River, and Springfield, located on the Cottonwood River (fig. 1), constructed or 

subsequently raised levees during past flood threats to reduce 

flood risk for residents and infrastructure. The study areas do 

not have any levees listed in the NLD; therefore, any levees 

that exist in Delano or Springfield are undocumented.

South Fork Crow River at Delano, Minnesota

The South Fork Crow River is located in south-central 

Minnesota. The South Fork Crow River flows generally 

eastward to its confluence with the North Fork Crow River 

to become the Crow River, which flows into the Mississippi River (fig. 1). The Minnesota Department of Natural 

Resources (MNDNR) has streamflow records beginning in 

August 1998 for the South Fork Crow River at Delano, Bridge 

Avenue streamgage (MNDNR site identifier 19001001) (http://

www.dnr.state.mn.us/waters/csg/site_report.html?mode=get_

site_report&site=19001001). The National Weather Service 

(NWS) Advanced Hydrologic Prediction Service (AHPS) 

issues flood forecasts for the South Fork Crow River at Delano, Bridge Avenue streamgage location during high flow and 

maintains a historical record of peak-flood stages dating back 

to 1965 before the installation of the MNDNR streamgage. 

The 10 highest historical crests from flood waves are listed in 

table 1, and all crests exceeded the major flood stage defined 

as 18.5 feet (National Weather Service, Advanced Hydrologic 

Prediction Service, 2014a).

The peak flood of record was in spring of 1965. Levees 

along the South Fork Crow River, which were initially built 

for temporary flood protection in preparation of the spring 

1969 flood, have been maintained and enhanced sporadically 

through time according to annual flood forecasts (Robert Van 

Lith, city of Delano, Minn., oral commun., 2013).

Table 1. Ten highest historical flood wave crests through 

2014 for the South Fork Crow River at Delano, Bridge Avenue 

streamgage, Minnesota (National Weather Service, Advanced 

Hydrologic Prediction Service, 2014a).

[Stage is referenced to the local gage datum]

Rank Stage, in feet Date 

(month/day/year)

1 23.25 04/14/1965

2 21.02 06/24/2014

3 20.45 04/12/1969

4 20.30 03/21/2010

5 20.00 03/28/2011

6 19.95 04/15/2001

7 19.25 04/08/1997

8 18.85 04/30/2001

9 18.75 09/16/1991

10 18.75 06/25/1993
4 An Assessment of Two Methods for Identifying Undocumented Levees Using Remotely Sensed Data

Cottonwood River at Springfield, Minnesota

The Cottonwood River is located in southwestern Minnesota and generally flows eastward to its confluence with 

the Minnesota River (fig. 1). The MNDNR has streamflow 

records beginning in October 1999 for the Cottonwood River 

near Springfield, CR2 streamgage (MNDNR site identifier 

29015001) (http://www.dnr.state.mn.us/waters/csg/site_report.

html?mode=get_site_report&site=29015001). The NWS 

AHPS issues flood forecasts for the Cottonwood River near 

Springfield, CR2 streamgage location during high flow. The 10 

highest historical crests from flood waves are listed in table 2 

(National Weather Service, Advanced Hydrologic Prediction 

Service, 2014b).

Springfield, Minn., is situated along the banks of the 

Cottonwood River (fig. 1), and within the city limits, the river 

flows generally northeastward. Flood protection for the city 

includes an earthen levee along the north side of the Cottonwood River that was first constructed in spring of 1969 in 

preparation for a flood threat (Bolton and Menk, Inc., 2011). 

The levee was constructed in an emergency situation to protect 

against rising floodwaters, using any materials that could be 

found (Bolton and Menk, Inc., 2011). Although constructed 

as a temporary levee, this feature has remained in place. In 

the spring of 2011, additional material was used to raise the 

earthen levee in response to a large forecasted flood (Bolton 

and Menk, Inc., 2011). Because of the lack of quality control 

when the levee was first constructed, and seepage observed 

in fall 2010, the city of Springfield has reconstructed and 

enhanced the levee (Bolton and Menk, Inc., 2011).

Two Methods for Identifying 

Undocumented Levees Using Remotely 

Sensed Data

Two methods were used to identify undocumented levees 

from remotely sensed, high-resolution topographic raster 

(gridded) data. For the first method, the USACE examined 

hillshades computed from a DEM derived from lidar for 

Delano, Minn., to visually identify potential levees. The 

USACE then made a detailed site visit to assess the validity of 

the potential levees. For the second method, the USGS applied 

a wavelet transform to the topography to identify potential 

levees in Delano and Springfield, Minn. Both methods were 

verified by comparing the results to known undocumented 

levee locations, and results from the methods were compared 

for the city of Delano.

The lidar data used in this study were available from 

the Minnesota Geospatial Information Office (http://www.

mngeo.state.mn.us/chouse/elevation/LiDAR.html). The data 

were downloaded as bare-earth DEMs that are free of vegetation, buildings, and other man-made structures and have an 

elevation point at each 1-meter (m) grid spacing (Minnesota 

Geospatial Information Office, 2012). The data for Delano 

were part of the spring 2008 Wright County lidar flight, and 

the data for Springfield were part of the spring and fall 2010 

Brown County lidar flight.

Four characteristics of the identified potential levees were 

described as part of the analysis. The characteristics are the 

average top width of the levee, average width of the base of 

the levee (levee width), average landward height, and average streamward height (shown schematically in figure 2). The 

characteristics were measured from elevation cross sections 

that were sampled at 3– 10 locations of approximately equal 

spacing along each potential levee and averaged. The sampled 

locations were determined based on professional judgment 

to estimate how the levee sizes compare to each other, to the 

DEM cell size, and to the wavelet scale. The locations were 

not intended as a rigorous quantification of the characteristics. 

The levee top width is the width of the approximately horizontal highest part of the levee. The levee width is the width 

of the levee where it meets the land surface. The streamward 

height is the difference in elevation from the top of the levee 

to the approximate water surface represented in the DEM. The 

landward height is the difference in elevation from the top of 

the levee to the land surface behind the levee. The characteristics were measured to demonstrate the typical size of potential 

levees identified by the methods used in this pilot study.

Table 2. Ten highest historical flood wave crests through 2014 

for the Cottonwood River near Springfield, CR2 streamgage, 

Minnesota (National Weather Service, Advanced Hydrologic 

Prediction Service, 2014b).

[Stage is referenced to the local gage datum]

Rank Stage, in feet Date

(month/day/year)

1 32.89 09/24/2010

2 32.70 04/24/2001

3 31.55 04/08/1969

4 31.40 06/18/1993

5 30.06 03/23/2011

6 29.12 06/25/1984

7 28.79 05/08/1983

8 28.77 03/29/1997

9 26.82 09/22/1986

10 26.62 03/31/1979
Two Methods for Identifying Undocumented Levees Using Remotely Sensed Data 5

Hillshade Method and Site Visit Verification

The USACE tested a visual analysis (manual) approach 

to identifying potential levees from hillshade data and then 

verified the identified features through site visits and discussions with local managers. The method was tested for the 

South Fork Crow River at Delano, Bridge Avenue, Minn.

Hillshade layers were computed from the DEM using 

the default settings in the Hillshade tool in the Spatial Analyst 

toolbox of ArcMap software (Esri, Redlands, California). A 

hillshade is a shaded relief image or raster computed from a 

DEM using an illumination source at a specific elevation and 

direction from the DEM (Esri, 2014). The Hillshade tool can 

be used to highlight areas that face the illumination source 

and shade areas that face away from the source. Topographic 

relief, including levees, is more prominently visible in a 

hillshade layer computed from the DEM than in displays of 

the DEM itself. The hillshades computed from the DEM for 

Delano were visually analyzed to identify potential levees. 

Raised linear features near the river that appeared to be levees 

were delineated in a geospatial line file of potential levees. 

The delineation was validated by another individual of the 

USACE.

Eight features were initially identified as potential levees 

(red-line features labelled d-1, d-2, d-3a, d-4, d-5a, d-7, d-8, 

and d-9 [fig. 3]) from the hillshade layers. The eight potential 

levees were assessed in the field on June 27, 2013, to document the condition and characteristics of the features and 

determine if they were, in fact, levees. The locations of the 

levees also were confirmed by representatives of the city of 

Delano (Robert Van Lith, Fire Chief, city of Delano, Minn., 

oral commun., 2013; and Paul Swearengin, city of Delano, 

Minn., Public Works Department, oral commun., 2013). 

Following the initial site visit in June 2013, five of the 

eight initially identified potential levees were confirmed as 

levees (table 3). Of the remaining three features initially identified as potential levees, feature d-1 was a natural beach ridge 

only slightly higher than the floodplain and would not substantially affect the flow of floodwaters. Feature d-7 was a low 

embankment built to keep mining spoils out of the river and is 

not expected to function as a levee or to provide flood protection. Feature d-9 was an earthen embankment that was created 

to provide a visual barrier between a housing development and 

the sewage treatment plant and does not tie into higher ground. 

Therefore, feature d-9 would not function as a levee because 

water is able to freely flow around the feature.

After completion of the initial site visit, all collected data 

(geotagged photos, field notes, and geospatial data) were submitted to a review team comprised of USACE personnel. The 

review team determined that three additional potential levees 

(yellow-line features labeled d-3b, d-5b, and d-6 [fig. 3]) warranted further investigation. A second field visit to Delano on 

July 18, 2013, and discussions with a local official, confirmed 

that the three features were levees that also were constructed 

as part of the effort to contain the 1969 flood (table 3; Robert 

Van Lith, city of Delano, Minn., Public Works Department, 

oral commun., 2013).

By using the hillshade method and verification field 

visits, the team confirmed the location and extent of eight 

undocumented levees in Delano (table 3). The hillshade layer 

was analyzed in the office before the site visit. Therefore, the 

site visit was more efficient because the team could focus on 

the assessment and on the discussions with city personnel. The 

field visits determined that several of the actual levee features 

were not obvious from ground-level inspection without prior 

knowledge of the levee locations. For example, figure 4A

shows an easily identifiable, maintained levee (feature d-8) 

and figure 4B shows a heavily vegetated, abandoned, and 

unmaintained levee (feature d-6) that was not identified until 

the review team analyzed the topography and photographs following the first field visit.

In some areas, the DEM did not completely match conditions on the ground in summer 2013. For example, a new 

bridge had been constructed over the river since the lidar data 

collection in spring of 2008. The bridge was not apparent in 

the DEM, but was apparent in newer aerial photography. Even 

with some differences between the 2008 lidar data collection and the 2013 ground conditions, the DEM was superior 

for the purposes of levee detection to other readily available 

sources of information, such as USGS topographic maps and 

aerial photography. The 1-m horizontal resolution of the DEM 

was adequate to identify most potential levees in the city of 

Delano. 

Wavelet-Transform Method

The USGS developed a method to identify potential levees by applying a wavelet transform to the DEM of 

Springfield, Minn. A wavelet is a localized waveform (a curve) 

that is used in a wavelet transform (Addison, 2002). For the 

purposes of this study, the mathematical operation compared 

the wavelet to the landscape to determine the dominant spatial 

scales of local landscape features. As a data analysis tool, 

wavelets have been used in many disciplines such as engineering, fluid mechanics, geophysics, medicine, and finance 

Streamward

height

Not to scale

Landward 

height

Top 

width

Levee width

Channel

Approximate 

water surface

Figure 2. Schematic showing measured dimensions of levees.
6 An Assessment of Two Methods for Identifying Undocumented Levees Using Remotely Sensed Data

d-6

d-7

d-2

d-9

d-3a

d-5b

d-1

d-5a

d-4

d-3b

d-8

93°48' 93°47'

45°03'

45°02'

EXPLANATION

Base from the Minnesota Department of Natural Resources

Universal Transverse Mercator projection, zone 15 

North American Datum of 1983

0 0.25 0.5 MILE

0 0.25 0.5 KILOMETER

Initially identified potential levee before 

first field visit and identifier (table 3) 

Potential levee identified after first field 

visit and identifier (table 3) 

d-9

d-5b

South Fork Crow River

South Fork

Cro

w

River Delano

Figure 3. Potential levees identified using 

the hillshade method and field visits, Delano, 

Minnesota, 2013.

Table 3. Summary of dimensions of assessed potential levees, Delano, Minnesota, 2013.

Feature 

(fig. 3) Final classification Condition

Average top 

width, 

in meters

Average 

levee width, 

in meters

Average 

landward 

height, 

in meters

Average 

streamward 

height, 

in meters

d-1 Natural ridge Moderate vegetation 1.2 5.2 0.8 3.0

d-2 Levee Light vegetation/moderate vegetation 4.3 18.0 0.9 2.1

d-3a Levee Walking path 3.7 21.9 3.0 0.9

d-3b Levee Heavy vegetation 3.4 20.7 2.1 4.3

d-4 Levee Heavy vegetation 2.7 18.0 2.1 4.6

d-5a Levee Heavy vegetation 2.1 14.6 1.5 2.1

d-5b Levee Heavy vegetation/light vegetation 1.5 18.0 0.3 4.9

d-6 Levee (abandoned) Heavy vegetation 2.4 12.2 0.6 1.8

d-7 Pollution control embankment Heavy vegetation 3.3 12.3 0.8 3.0

d-8 Levee Heavy vegetation/no vegetation 4.6 25.3 2.1 2.4

d-9 Visual barrier Light vegetation 3.7 30.8 5.8 3.9
Two Methods for Identifying Undocumented Levees Using Remotely Sensed Data 7

A

B

Figure 4. Levees assessed in the field by the U.S. Army Corps of Engineers, July 18, 2013, 

Delano, Minnesota (locations are shown in figure 3: A, is feature d-8 and B, is feature d-6).
8 An Assessment of Two Methods for Identifying Undocumented Levees Using Remotely Sensed Data

(Addison, 2002). In geophysics, wavelets have been used to 

study several processes including precipitation, hydrologic 

fluxes, turbulence, tree canopy cover, landscape topography, 

seafloor bathymetry, and ocean waves (Kumar and FoufoulaGeorgiou, 1994). Specifically related to topographic analysis, 

Lashermes and others (2007) used wavelets to identify river 

channels from high-resolution topographic data. As wavelets 

are well suited to identify localized abrupt changes in signal or 

image data (edge detection), wavelets were used in this study 

to identify potential levees in the landscape as represented 

by a DEM. A brief introduction to wavelets and the wavelet 

transform in one dimension, x, follows. Additional information on wavelets, the wavelet transform, and their application 

is presented by Addison (2002).

This study used the Mexican hat wavelet,  ( ) x (fig. 5). 

The Mexican hat wavelet is the negative of the second derivative of the Gaussian distribution function for a mean of zero 

and standard deviation of one and is given for a single dimension by

ψ

π

x x e x ( ) = − ( ) 2 −

3

1 1 4

2 2 2

/

/

, (1)

where 

x is the support distance of the wavelet (unitless 

distance from the origin).

The wavelet described by equation 1 is known as a 

mother wavelet because it is the basic form of the wavelet 

from which the scaled and translated versions are obtained 

from that are used in the wavelet transform. This wavelet 

(eq. 1) can be stretched and squeezed (through a scale parameter, a , which is the distance between the center of the 

Mexican hat wavelet and its crossing of the x-axis [fig. 5]) and 

translated (through a location parameter, b ) along the x-axis. 

Specifically, scaled and translated versions of the mother 

wavelet, a b x , ( ) , are given by

  a b x a

x b

a , ( ) =  −



 



 1 . (2)

The mother wavelet (eq. 1) is recovered from equation 2 

by setting a =1 and b =0.

The wavelet transform, T a( ) ,b , of a series, z x( ) , with 

scaled and translated versions of the mother Mexican hat 

wavelet (eq. 2) is defined as 

T a b z x x dx a b , ( ) = () () ,

−∞

∞

∫  . (3)

In this study, the wavelet transform is a measure of the 

local agreement in shape between the scaled wavelet and the 

elevation profile series. Equations 1–3 describe the Mexican 

hat wavelet and the wavelet transform in one dimension, but 

the ideas can be extended to two-dimensional elevation data 

for considering the wavelet transform of a three-dimensional 

surface like a DEM. In the two-dimensional case, the Mexican 

hat wavelet is rotated around its central axis (where x=b) and 

its graph looks like a bell, with x representing the distance 

−5 −4 −3 −2 −1 0 1 2 3 4 5

−0.4

−0.2

0

0.2

0.4

0.6

0.8

1

Support distance of wavelet, x

Wavelet value, 

ψ(x)

a

Figure 5. Mexican hat wavelet 

as a function of distance (x), scale 

parameter (a), and location parameter 

(b) (a=1 and b=0 in the case shown).
Two Methods for Identifying Undocumented Levees Using Remotely Sensed Data 9

from the local origin in terms of easting and northing. Simplistically, a two-dimensional wavelet transform moves a scaled 

version of the bell around on the landscape, and the wavelet 

transform returns a coefficient describing the agreement in 

shape between the bell and the elevations of local landscape 

features, scaled by the height of the landscape feature. If the 

wavelet, at a specific scale and location, matches the shape 

of the landscape well, then a large wavelet-transform coefficient is obtained. For example, a round hill matching the 

scale of the wavelet would result in a large positive coefficient. However, if the wavelet and the shape of the landscape 

are not similar, for example uneven level or sloped ground, 

then a value near zero for the wavelet-transform coefficient is 

obtained. Thus, landscape features that look like the Mexican 

hat wavelet can be located in space by looking for large values 

of the wavelet-transform coefficient. Because the profile of 

levees approximates the shape of the Mexican hat wavelet 

well, the wavelet and the wavelet transform are ideally suited 

to identifying levees in the landscape and result in large coefficients along the length of the levee.

Potential levees were identified in Springfield by applying the two-dimensional Mexican hat wavelet transform to the 

DEM. The DEM data were formatted as an American Standard 

Code for Information Interchange (ASCII) file and read into 

MATLAB® version 2013b (The MathWorks® Inc., Natick, 

Massachusetts, http://www.mathworks.com/) for processing. 

The MATLAB® function cwtft2, available in the Wavelet Toolbox, was used to compute the two-dimensional continuous 

wavelet transform of the elevation data at a particular scale. 

The two-dimensional wavelet transform was done at 

scales of 3, 5, 10, and 15 (parameter a). For the DEM resolution of 1 m, the scales gave the most weight for identifying 

potential levees that have widths of about 6, 10, 20, and 30 m, 

respectively. Levees with widths different than 6, 10, 20, and 

30 m can still be identified in the results from the wavelet 

transform at the scales of 3, 5, 10, and 15, but the levees may 

not be the most prominent features identified.

For any scale, the result of the two-dimensional continuous wavelet transform is a grid of wavelet-transform coefficients (dimensionless) that covers the extent of the DEM. 

Near the edges of the DEM, the wavelet-transform coefficients 

contain edge effects where the analyzed wavelet shape extends 

beyond the extent of the data. Wavelet-transform coefficients 

exhibiting edge effects are coefficients within 5× a (for the 

1-m DEM and Mexican hat wavelet) of the data boundary, and 

the coefficients in this part of the analyzed domain were set to 

zero. 

The convention used in the MATLAB® function for the 

two-dimensional continuous wavelet transform computed the 

Mexican hat wavelet as the two-dimensional version of the 

second derivative of the Gaussian function (not the negative of the Gaussian function as previously described for the 

one-dimensional case). Therefore, large negative coefficients 

returned by the two-dimensional wavelet transform corresponded to ridges of potential levees. To isolate the coefficients in the grid, all positive coefficients were set to zero, and 

then, for display purposes, all coefficients were multiplied by 

negative one to make positive values. Next, to further isolate 

potential levees, a threshold value was applied to the remaining nonzero coefficients and the coefficients below the applied 

threshold value were set to zero. Threshold values were set at 

the 0, 50, 75, 90, and 95th percentiles of the coefficients.

Wavelet-transform coefficients at scales, a, of 3, 5, and 

10 and with threshold values at the 50th and 90th percentiles 

for Springfield are shown in figure 6. Also shown on figure 6 

are potential levees that include a levee reconstructed by the 

city (s-1), a roadway embankment (s-2), a quarry road (s-3), 

high steep river banks (s-4 [approximately 3-m high]), and 

bluffs set back from the river (s-5 [approximately 8-m high]) 

(table 4). Features s-4 and s-5 were chosen as representative 

examples of sections of bank and bluffs among many such 

features that appear as highlighted areas in the wavelet-transform coefficients. As an edge-detection technique, the wavelet 

transform indiscriminately identifies land features with an 

abrupt change in elevation regardless of whether or not the 

land features are in fact levees. Currently (2014), some interpretation of the wavelet-transform coefficients is required to 

separate levees from other highlighted features such as steep 

banks and bluffs.

The features (s-1 to s-5) identified in Springfield were 

checked and confirmed by Scott Johnson, Emergency Manager 

and Utilities Superintendent for the city of Springfield. Without obtaining more information on specific features from field 

visits or from local managers, the effects of the features on 

flood flows may be misinterpreted from topographic analysis 

alone. For example, the quarry road (feature s-3) might function as a levee during a flood; however, a culvert is underneath 

the road that would hydraulically connect any floodwaters 

on each side of the feature. Therefore, even though feature 

s-3 might have some effect on flood flows, the feature is not 

expected to function as a levee (Scott Johnson, city of Springfield, Minn., oral commun., 2014). 

Comparison of Methods

The two methods were compared for Delano, Minn., to 

determine how well each method was able to independently 

identify levees. Wavelet-transform coefficients at scales of 

3, 5, and 10 and with threshold values at the 50th and 90th 

percentiles for Delano are shown in figure 7. Several potential levees were identified as linear features along the river. 

At high thresholds, only the most prominent features remain 

because other values of the wavelet-transform coefficients are 

filtered out. However, at even larger scales, the highlighted 

features change as some features appear or disappear depending on the characteristic size of the feature. 

The major levees at Delano were identified by both methods (fig. 8). Certain levees were more difficult to identify from 

looking at the filtered display of wavelet-transform coefficients such as the southern extent of feature d-8, which is narrower than the rest of the levee. The southern part of feature 
10 An Assessment of Two Methods for Identifying Undocumented Levees Using Remotely Sensed Data

Cot

ot

nwood River

Springfield

s-5

s-4

s-3

s-2

s-1 s-5

s-4

s-3

s-2

s-1

Cot

ot

nwood River

Springfield

Cot

ot

nwood River

Cot

ot

nwood River

0 0.5 0.25 MILE

0 0.5 0.25 KILOMETER

94°58'30" 94°57'30" 94°58' 94°58'30" 94°57'30" 94°58'

41°13'30"

41°14'

A

Base from Minnesota Geological Survey, 1:500,000Minnesota Deptartment of Natural Resources 1:24,000U.S. Geological Survey 1:24,000Universal Transverse Mercator projection, zone 15North American Datum of 1983

B

Springfield Springfield

s-5

s-4

s-3

s-2

s-1 s-5

s-4

s-3

s-2

s-1

Scale: 3, threshold 90th percentilecoefficientsFeatures of interest and identifier (table 4)

EXPLANATION

s-1

Scale: 3, threshold 50th percentilecoefficients

EXPLANATION

0.3−0.50.6−0.70.8−0.91.0−1.71.8−7.77.8−44.1

Features of interest and identifier (table 4)

s-1

2.1−2.3

2.4−2.82.9−4.24.3−8.08.1−17.918−44.1

Figure 6.

Wavelet-transform coefficients for selected combinations of wavelet-scale parameter and output-filter threshold, with thresholds at selected percentiles of the 

distribution of coefficients, Springfield, Minnesota. A, scale of 3 and threshold at 50th percentile; 

B, scale of 3 and threshold at 90th percentile; 

C, scale of 5 and threshold 

at 50th percentile; 

D, scale of 5 and threshold at 90th percentile; 

E, scale of 10 and threshold at 50th percentile; and 

F, scale of 10 and threshold at 90th percentile. 

Summary of dimensions of assessed potential levees, Springfield, Minnesota, 2013.
Two Methods for Identifying Undocumented Levees Using Remotely Sensed Data 11 Springfield Springfield Cot ot nwood River Cot ot nwood Rivers-5

s-4

s-3

s-2

s-1 s-5

s-4

s-3

s-2

s-1

94°58'30" 94°57'30" 94°58' 94°58'30" 94°57'30" 94°58'

41°14'

41°14'30"

C D

Springfield Springfield

Base from Minnesota Geological Survey, 1:500,000Minnesota Deptartment of Natural Resources 1:24,000U.S. Geological Survey 1:24,000Universal Transverse Mercator projection, zone 15North American Datum of 1983

Cot ot nwood River Cot ot nwood River

0 0.5 0.25 MILE

0 0.5 0.25 KILOMETER

s-5

s-4

s-3

s-2

s-1 s-5

s-4

s-3

s-2

s-1

Scale: 5, threshold 50th percentilecoefficientsFeatures of interest and identifier (table 4)

EXPLANATION

1.3−1.92.0−2.12.2−2.72.8−5.65.7−19.519.6−84.3

Scale: 5, threshold 90th percentilecoefficients

EXPLANATION

6.3−8.38.4−8.99.0−10.911−16.716.8−33.934−84.3 s-1 Features of interest

 and identifier (table 4)

s-1

Figure 6. Wavelet-transform coefficients for selected combinations of wavelet-scale parameter and output-filter threshold, with thresholds at selected percentiles of the distribution of coefficients, Springfield, Minnesota. A, scale of 3 and threshold at 50th percentile; B, scale of 3 and threshold at 90th percentile; C, scale of 5 and threshold at 50th percentile; D, scale of 5 and threshold at 90th percentile; E, scale of 10 and threshold at 50th percentile; and F, scale of 10 and threshold at 90th percentile. Summary of dimensions of assessed potential levees, Springfield, Minnesota, 2013.—Continued
12 An Assessment of Two Methods for Identifying Undocumented Levees Using Remotely Sensed Data s-5 s-4 s-3 s-2 s-1 s-5 s-4 s-3 s-2 s-1 Springfield

Cot

ot

nwood River

Springfield

Cot

ot

nwood River

94°58'30" 94°57'30" 94°58' 94°58'30" 94°57'30" 94°58'

41°14'

E

F

Springfield Springfield

Base from Minnesota Geological Survey, 1:500,000Minnesota Deptartment of Natural Resources 1:24,000U.S. Geological Survey 1:24,000Universal Transverse Mercator projection, zone 15North American Datum of 1983

Cot

ot

nwood River

Cot

ot

nwood River

0 0.5 0.25 MILE

0 0.5 0.25 KILOMETER

41°13'30"

Features of interest and identifier (table 4)

s-1

Scale: 10, threshold 50th percentilecoefficients

EXPLANATION

5.9−8.1

8.2−8.68.7−10.810.9−21.3

21.4−70.570.6−301.36

Scale: 10, threshold 90th percentilecoefficients

EXPLANATION

24.8−26.2

26.3−29.729.8−3939.1−63.763.8−129129.1−301.3

Features of interest and identifier (table 4)

s-1

Figure 6.

Wavelet-transform coefficients for selected combinations of wavelet-scale parameter and output-filter threshold, with thresholds at selected percentiles of the 

distribution of coefficients, Springfield, Minnesota. 

A, scale of 3 and threshold at 50th percentile; 

B, scale of 3 and threshold at 90th percentile; 

C, scale of 5 and threshold 

at 50th percentile; 

D, scale of 5 and threshold at 90th percentile; 

E, scale of 10 and threshold at 50th percentile; and 

F, scale of 10 and threshold at 90th percentile. 

Summary of dimensions of assessed potential levees, Springfield, Minnesota, 2013.—Continued
Assessment of the Methods Used to Identify Undocumented Levees 13

d-8 was more apparent in results for the wavelet scale of 3 

(figs. 7B and 8B) and was harder to identify when wavelets 

of larger scales were used (fig. 7F), generally because of the 

small top width (approximately 2 m). The wavelet-transform 

coefficients highlight a variety of breaks in slope that are not 

levees, requiring examination of the results to identify potential levees. However, some features that were not initially 

identified by the hillshade method were identified consistently 

by the wavelet-transform method. 

Assessment of the Methods Used to 

Identify Undocumented Levees 

The hillshade method, which was used for the Delano, 

Minn., area, used a combination of the basic spatial analysis 

procedures for a geographic information system (GIS), the 

knowledge of local officials, and thorough site visits. The 

method allowed for a robust, though relatively straightforward, way of examining the existence and positioning of 

undocumented levees that relied on manual examination of the 

data. Therefore, the approach was time and resource intensive 

as well as possibly inconsistent because of the reliance on 

individual professional judgment. The field visit provided a 

detailed review of the identified potential levees in the vicinity 

of the river.

The wavelet-transform method was used to apply a 

variety of wavelet transforms to the DEM to identify potential 

levees and, because it was semi-automated, was qualitatively 

less time and resource intensive than the hillshade method. 

Using different wavelet-scale parameters and applying different thresholds to filter the transform results helped to extract 

the most prominent features as a wide array of potential levees 

are identified using the wavelet-transform method. By visually 

comparing identified potential levees with aerial imagery and 

other available GIS datasets, computer-identified features that 

would not affect the flow of flood waters could be disregarded.

The hillshade and wavelet-transform methods were 

successful in identifying levees. However, the methods also 

identified other features that required interpretation or site 

visits to differentiate from levees such as constructed barriers, high banks, and bluffs. Additionally, testing the wavelettransform method in Delano allowed for direct comparison 

of the two methods. Each method identified actual levees that 

were missed when the other method was used; thus, when 

used in isolation, each method had limitations. If the methods 

had been used together, all of the features possibly would 

have been identified remotely before the second site visit. In 

summary, the two methods are complementary to each other. 

Using the hillshade method in conjunction with the wavelettransform method would provide more reliable results more 

efficiently by possibly eliminating the need for a second site 

visit or, at least, limiting the scope of the second visit.

Potential Method for Future Applications

The two methods assessed in this study are complementary for future applications, creating a potential three-step 

procedure for identifying potential levees from lidar-derived 

DEMs. The semi-automated wavelet-transform method can be 

used as a first step for a large area to rapidly highlight slopebreak features in the landscape that are potential levees. For 

the second step, a smaller area then can be more closely examined using the interpreted results of the wavelet-transform 

method along with the hillshades computed from the DEM. 

The interpretive second step, which may involve ancillary data 

as well, aids in the development of a map of potential levees. 

The third step is verifying the potential levees by field inspection and by talking to local officials. 

Table 4. Summary of dimensions of assessed potential levees, Springfield, Minnesota, 2013.

[--, not applicable]

Feature 

(fig. 6) Final classification Condition

Average 

top width, 

in meters

Average 

levee width, 

in meters

Average 

landward 

height, 

in meters

Average 

streamward 

height, 

in meters

s-1 Levee Lightly vegetated 3.1 14.7 1.9 4.7

s-2 Roadway, likely functioning as 

levee

Road 13.8 40.0 2.6 1

3.2

s-3 Roadway, not likely functioning 

as levee

Road, culverts underneath 8.3 25.6 2.3 1

3.3

s-4 Bank2 Light vegetation/moderate vegetation -- -- -- 3.2

s-5 Bluff 2 Heavy vegetation -- -- -- 8.0

1

These features are not adjacent to the river and the average streamward height in this case refers to the height of the feature above the adjacent land on the 

side nearest the river.

2

These features do not have a levee-like shape, so the only applicable measurement is the height of the bank or bluff above the water.
14 An Assessment of Two Methods for Identifying Undocumented Levees Using Remotely Sensed Data

A

B 94°48' 94°47' 94°48' 94°47'

45°03'45°02'

South Fork Crow River

Base from National Agricultural Imagery Program 2010Universal Transverse Mercator projection, zone 15North American Datum of 1983

0 1 2 MILES0 1 2 KILOMETERS

Delano

South Fork Crow River

South Fork Crow

Delano

River

South Fork Crow River

Scale: 3, threshold 50th percentilecoefficients

EXPLANATION

0.9−1.2

1.3−1.41.5−1.61.7−33.1−9.59.6−40.2

Scale: 3, threshold 90th percentilecoefficients

EXPLANATION

3.9−4.1

4.2−4.64.7−5.85.9−9.19.2−17.617.7−40.2

Figure 7.

Wavelet-transform coefficients for selected combinations of wavelet-scale parameter and output-filter threshold, with thresholds at selected percentiles 

of the distribution of coefficients, Delano, Minnesota. A, scale of 3 and threshold at 50th percentile; 

B, scale of 3 and threshold at 90th percentile; 

C, scale of 5 and 

threshold at 50th percentile; 

D, scale of 5 and threshold at 90th percentile; 

E, scale of 10 and threshold at 50th percentile; 

F, scale of 10 and threshold at 90th percentile.
Assessment of the Methods Used to Identify Undocumented Levees 15

C D 94°48' 94°47' 94°48' 94°47'

45°03'45°02'

So

u

th

Fork

Crow River

Base from National Agricultural Imagery Program 2010Universal Transverse Mercator projection, zone 15North American Datum of 1983

0 1 2 MILES0 1 2 KILOMETERS

Delano

So

u

th

Fork

Crow River

So

u

th

Fork

Crow

Delano

River

So

u

th

Fork

Crow River

Scale: 5, threshold 50th percentilecoefficients

EXPLANATION

2.9−4.3

4.4−4.74.8−6.16.2 −11.1

11.2−28.8

28.9−91

Scale: 5, threshold 90th percentilecoefficients

EXPLANATION

11.1−12.5

12.6−12.913−14.3

14.4−19.1

19.2−35.535.6−91

Figure 7. Wavelet-transform coefficients for selected combinations of wavelet-scale parameter and output-filter threshold, with thresholds at selected percentiles of the distribution of coefficients, Delano, Minnesota. A, scale of 3 and threshold at 50th percentile; B, scale of 3 and threshold at 90th percentile; C, scale of 

5 and threshold at 50th percentile; D, scale of 5 and threshold at 90th percentile; E, scale of 10 and threshold at 50th percentile; F, scale of 10 and threshold at 90th 

percentile.—Continued
16 An Assessment of Two Methods for Identifying Undocumented Levees Using Remotely Sensed Data

E

F 94°48' 94°47' 94°48' 94°47'

45°03'45°02'

South Fork Crow River

Base from National Agricultural Imagery Program 2010Universal Transverse Mercator projection, zone 15North American Datum of 1983

0 1 2 MILES0 1 2 KILOMETERS

Delano

South Fork Crow River

South Fork Crow

Delano

River

South Fork Crow River

Scale: 10, threshold 50th percentilecoefficients

EXPLANATION

11.1−15.5

15.6−16.816.9−21.221.3−36.736.8−91.491.5−284

Scale: 10, threshold 90th percentilecoefficients

EXPLANATION

39−42.943−44.144.2−4848.1−6262.1−111111.1−284

Figure 7.

Wavelet-transform coefficients for selected combinations of wavelet-scale parameter and output-filter threshold, with thresholds at selected percentiles 

of the distribution of coefficients, Delano, Minnesota. 

A, scale of 3 and threshold at 50th percentile; 

B, scale of 3 and threshold at 90th percentile; 

C, scale of 

5 and threshold at 50th percentile; 

D, scale of 5 and threshold at 90th percentile; 

E, scale of 10 and threshold at 50th percentile; 

F, scale of 10 and threshold at 90th 

percentile.—Continued
Assessment of the Methods Used to Identify Undocumented Levees 17

d-6

d-7

d-2

d-9

d-3a

d-5b

d-1

d-5a

d-4

d-3b

d-8

South Fork Crow RiverSouth Fork Crow River

Delano Delano

A B

Scale: 3, threshold 90th percentile

Wavelet transform coefficients

EXPLANATION EXPLANATION

3.9−4.1

4.2−4.6

4.7−5.8

5.9−9.1

9.2−17.6

17.7−40.2

South Fork Crow RiverSouth Fork Crow River

Delano Delano

93°48' 93°47' 93°48' 93°47'

45°03'

45°02'

0.5 MILE

0 0.25 0.5

0 0.25

KILOMETER

Base from National Agricultural Imagery Program 2010 

Universal Transverse Mercator projection, zone 15

North American Datum of 1983

Initially identified potential levee 

 before first field visit and 

 identifier (table 3)

Potential levee identified after 

 first field visit and identifier 

 (table 3)

d-3b

d-4

d-6

d-7

d-2

d-9

d-3a

d-5b

d-1

d-5a

d-4

d-3b

d-8

Figure 8. Comparison of potential levees identified by two methods, Delano, Minnesota. A, the hillshade method and B, 

the wavelet-transform method.
18 An Assessment of Two Methods for Identifying Undocumented Levees Using Remotely Sensed Data

Considerations

Several considerations for future efforts to identify 

levees from remotely sensed, high-resolution topographic data 

include the following:

1. The typical size of a levee differs, even for the same 

feature (for example, the top width of feature d-8 [fig. 3] 

ranges from approximately 1– 7 m along its length).

2. The hillshade method will preferentially emphasize the 

linear features in the landscape that are perpendicular 

to the illumination source and can fail to identify those 

features aligned with the illumination source. Thus, 

the results from the wavelet analysis could help select 

the location of the illumination source for the hillshade 

analysis.

3. The top width of many levees was 3–5 m, and the 

features were most prominently identified in the wavelet 

transform using a scale of 3.

4. Construction practices may differ regionally, such as the 

use of ring levees or roadways serving as levees. 

5. In urban areas, the topographic and imagery data sources 

can rapidly become outdated, which stresses the need 

for site visits, latest aerial imagery surveys, and updated 

topography.

6. Levee construction or enhancement has commonly taken 

place during or following large historical flood-fight 

activities. Historical research for future studies may 

be warranted to identify periods and locations of such 

construction episodes.

7. A global positioning system (GPS)-enabled camera and 

field notes of onsite observations help provide important 

documentation of identified features in the field.

Limitations of Methods

A limitation of both methods is that floodwalls are not 

apparent in bare-earth topography because the base- and topwidths frequently are less than the 1-m resolution of DEM 

data. Although the features can substantially affect flood flows, 

floodwalls would only be identified by field visits or from 

local knowledge of city managers. In some cases, floodwalls 

may be visible in aerial photography but would still require 

verification.

Although the wavelet-transform method provides more 

automation to highlight potential levee locations in comparison to the hillshade method, the wavelet-transform method 

still requires a knowledgeable person to review, interpret, and 

optimize the wavelet-transform coefficient results, topography, 

hillshades, transportation data, and aerial photographs in order 

to distinguish negligible features from potential levees or other 

structures that may affect flood flows.

For the methods to work, the cell size of the DEM must 

be smaller than the size of the features to be detected, and 

the vertical accuracy of the DEM needs to be finer than the 

height of features to be detected, which is generally the case 

for lidar-derived DEMs. Applying the methods to large areas 

might be difficult because lidar datasets commonly are large; 

thus, making the datasets difficult to download and process. 

The limitation is conditioned by present (2014) internet and 

computing speeds and data storage typically available to the 

image analyst, which differ greatly and could become negligible issues as technology advances. However, the limitation 

also may be overcome by using coarser topographic data or 

resampling the lidar data before processing.

Future Advancements of Methods

Identifying potential levees from remotely sensed, 

high-resolution topographic data in the office can streamline 

the assessment of undocumented levees across large areas. 

This pilot study tested two methods that, as complementary 

approaches, indicate the possibility of providing more rapid 

detection of undocumented levees in communities along 

rivers.

To further develop and more fully automate a geospatial 

tool for identifying potential levees, a few future advancements could be considered. The analysis for this pilot study 

used proprietary software for performing the wavelet transform; however, with knowledge of applications coding in a 

GIS, a programmer could enable the wavelet transform to be 

applied within the GIS environment. Resulting benefits may 

include relief from some required proprietary licenses and less 

transfer of information from outside the GIS. Also, if a general 

size for levees being searched for is presumed, then scaleparameter thresholds can be set at the initiation of the analysis 

that bound parameter optimization within a specific range of 

values and only the features that appear prominently across 

several different scales within the optimization could be kept. 

Finally, the wavelet-transform results could be analyzed by an 

expert system that would compare the identified features with 

other geographic information, such as roads, topographic feature layers (river banks, valley bluffs, canals, and ditches), or 

other infrastructure to more rapidly classify features identified 

by the wavelet transform.

Summary

Many undocumented and commonly unmaintained levees 

exist in the landscape limiting flood forecasting, risk management, and emergency response. This report describes a pilot 

study completed by the U.S. Geological Survey in cooperation 

with the U.S. Army Corps of Engineers to assess two methods 

to identify undocumented levees using remotely sensed, highresolution topographic data. For the first method, the U.S. 

Army Corps of Engineers examined hillshades computed from 
References Cited 19

a digital elevation model to visually identify potential levees 

and then made a detailed site visit to assess the validity of the 

potential features. For the second method, The U.S. Geological Survey applied a wavelet transform to a digital elevation model to identify potential levees that may lead to more 

automated identification of levees. The hillshade method was 

applied to Delano, Minnesota, and the wavelet-transform 

method was applied to Delano and Springfield, Minnesota. 

Both methods were successful in identifying levees but 

also identified other features that required interpretation to differentiate from levees such as constructed barriers, high banks, 

and bluffs. Some potential levees that were difficult to identify 

at first using the hillshade method were readily identified 

by the wavelet-transform method. However, some potential 

levees identified by the hillshade method were only identified 

by the wavelet-transform method using certain scales and were 

more difficult to identify at larger scales because of the small 

spatial extent of the levees. Characteristics of the potential 

levees were presented to demonstrate the typical size of levees 

analyzed in this pilot study. 

The two methods assessed in this study are complementary to each other. To identify potential levees for field verifications, using the hillshade method in conjunction with the 

wavelet-transform method would provide more reliable results 

more efficiently. A potential approach for future applications 

may be to (1) use the wavelet-transform method to rapidly 

identify potential levees, (2) further examine topographic data 

using hillshades and aerial photos to classify each feature, and 

(3) check each identified potential levee with local managers 

and field visits.

References Cited

Addison, P.S., 2002, The illustrated wavelet transform 

handbook—Introductory theory and applications in science, engineering, medicine and finance: Philadelphia, Pa., 

Institute of Physics Publishing, 353 p.

Bolton and Menk, Inc., 2011, City of Springfield feasibility 

report—Reconstruction of the levee between Cass Avenue 

and C.S.A.H. No. 5 in Springfield, Minnesota: Mankato, 

Minn., Bolton and Menk, Inc.

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For more information concerning this publication, contact:

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Czuba and others—An Assessment of Two Methods for Identifying Undocumented Levees Using Remotely Sensed Data—Scientific Investigations Report 2015–5009

ISSN 2328-0328 (online)

http://dx.doi.org/10.3133/sir20155009