U.S. Department of the Interior

U.S. Geological Survey

Open–File Report 2008–1276

Prepared in cooperation with Northern Arizona University and Utah State University

Monitoring Fine-Grained Sediment in the Colorado River 

Ecosystem, Arizona—Control Network and Conventional 

Survey Techniques
This page intentionally left blank
Monitoring Fine-Grained Sediment in the 

Colorado River Ecosystem, Arizona—

Control Network and Conventional Survey 

Techniques

By Joseph E. Hazel, Jr., Matt Kaplinski, Roderic A. Parnell, Keith Kohl, and John 

C. Schmidt

Prepared in cooperation with Northern Arizona University and Utah State University

Open–File Report 2008–1276

U.S. Department of the Interior

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

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Mark D. Myers, Director

U.S. Geological Survey, Reston, Virginia: 2008

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

reproduce any copyrighted materials contained within this report.

Suggested citation:

Hazel, J.E., Jr., Kaplinski, Matt, Parnell, R.A., Kohl, K., and Schmidt, J.C., 2008, Monitoring fine-grained sediment in 

the Colorado River ecosystem, Arizona—control network and conventional survey techniques: U.S. Geological Survey 

Open–File Report 2008–1276, 15 p.

Cover: View to the southeast of Comanche Point in eastern Grand Canyon National Park, Arizona (Photograph courtesy 

of Matt Kaplinski, Northern Arizona University).
iii

Contents

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

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

Study Area.......................................................................................................................................................2

Geodetic Control Network............................................................................................................................3

Overview.................................................................................................................................................3

Methods for Acquiring Multivector Global Positioning System-Based Control ........................4

Accuracy Assessment of the Control Network ...............................................................................4

Conventional Surveying................................................................................................................................5

Overview.................................................................................................................................................5

Instrument Precision and Control Point Verification ......................................................................5

Field Data Collection.............................................................................................................................7

Results and Discussion.................................................................................................................................8

Spatial Point Distribution.....................................................................................................................8

Accuracy Assessment of Topographic Surveys............................................................................11

Summary...............................................................................................................................................13

Acknowledgments.......................................................................................................................................13

References Cited..........................................................................................................................................13

Figures

1. Map showing major tributaries and locations of the long-term monitoring reaches in 

the Colorado River in Grand Canyon National Park, Ariz.......................................................2

2. Global positioning system receiver on control point SO123209R in reach 9 ......................4

3. Total station on control point SC0299854R in reach 4.............................................................5

4. Metal prism pole, equipped with bipod legs for accurate leveling, used to locate each 

photogrammetry panel.................................................................................................................8

5. Pinflags used to mark the location of each grain-size measurement .................................9

6. Seven-meter motorized raft equipped with the grain-size microscope system..............10

7. Global positioning system control-network points and survey-point distribution for the 

December 2004 survey of reach 5.............................................................................................10

8. Detail of the topographic survey and point distribution of the December 2004 survey at 

river mile 44.6 in reach 5............................................................................................................11

9. Topographic surveying in offshore areas in December 2004..............................................12

Tables

1. Characteristics of the long-term monitoring reaches within the study area .....................3

2. Summary statistics for each level of the Grand Canyon Monitoring and Research Center control network.......................................................................................................................5

3. Average conventional survey positional errors and summary statistics at control points 

utilized as backsites .....................................................................................................................6
iv

4. Types of survey data collected during reach-based monitoring river trips. NC, data not 

collected.........................................................................................................................................7

5. Leveling-rod horizontal and vertical accuracies...................................................................13

Conversion Factors and Datums

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

square meter (m2

) 0.0002471 acre 

Volume

cubic meter (m3

) 35.31 cubic foot (ft3)

Flow rate

cubic meter per second (m3

/s) 35.31 cubic foot per second (ft3/s)

Mass

megagram (Mg) 1.102 ton, short (2,000 lb)

Horizontal and vertical coordinate information is referenced to the North American Datum of 

1983 (NAD 83).
Monitoring Fine-Grained Sediment in the Colorado River 

Ecosystem, Arizona—Control Network and Conventional 

Survey Techniques

By Joseph E. Hazel, Jr.1, Matt Kaplinski1, Roderic A. Parnell1, Keith Kohl2, and John C. Schmidt3

1

Northern Arizona University, Department of Geology, Flagstaff, Ariz.

2

U.S. Geological Survey, Southwest Biological Science Center, Grand 

Canyon Monitoring and Research Center, Flagstaff, Ariz.

3

 Utah State University, Department of Watershed Sciences, Logan, Utah 

Abstract 

In 2002, fine-grained sediment (sand, silt, and clay) 

monitoring in the Colorado River downstream from Glen Canyon Dam was initiated to survey channel topography at scales 

previously unobtainable in this canyon setting. This report 

presents the methods used to establish the high-resolution 

global positioning system (GPS) control network required for 

this effort as well as the conventional surveying techniques 

used in the study. Using simultaneous, dual-frequency GPS 

vector-based methods, the network points were determined to 

have positioning accuracies of less than 0.03 meters (m) and 

ellipsoidal height accuracies of between 0.01 and 0.10 m at a 

95-percent degree of confidence. We also assessed network 

point quality with repeated, electronic (optical) total-station 

observations at 39 points for a total of 362 measurements; the 

mean range was 0.022 m in horizontal and 0.13 in vertical at a 

95-percent confidence interval. These results indicate that the 

control network is of sufficient spatial and vertical accuracy 

for collection of airborne and subaerial remote-sensing technologies and integration of these data in a geographic information system on a repeatable basis without anomalies. The monitoring methods were employed in up to 11 discrete reaches 

over various time intervals. The reaches varied from 1.3 to 6.4 

kilometers in length. Field results from surveys in 2000, 2002, 

and 2004 are described, during which conventional surveying 

was used to collect more than 3000 points per day. Ground 

points were used as checkpoints and to supplement areas just 

below or above the water surface, where remote-sensing data 

is not collected or is subject to greater error. An accuracy of 

±0.05 m was identified as the minimum precision of individual 

ground points. These results are important for assessing digital 

elevation model (DEM) quality and identifying detection 

limits of significant change among surfaces generated from 

remote-sensing technologies.

Introduction 

Recent developments in surveying, mapping, geodesy, 

remote sensing, and digital terrain modeling have made it feasible to study continuous lengths of the river bed and banks of 

the Colorado River, in the Colorado River ecosystem (CRE), 

downstream from Glen Canyon Dam. Previous geomorphic 

studies in this remote canyon setting have focused primarily 

on planimetric changes (for example, Cluer, 1995; Schmidt 

and others, 1999), coarsely spaced cross sections (for example, 

Graf and others, 1995; Flynn and Hornewer, 2003; Grams and 

others, 2007), and three-dimensional evaluation of change at a 

limited number of study sites (for example, Schmidt and Graf, 

1990; Beus and others, 1992; Kaplinski and others, 1995; 

Hazel and others, 1999). These studies were limited in their 

ability to fully examine the three-dimensional relationship 

between river form and process and resulted in conflicting 

conclusions regarding impacts of dam operations and associated sediment storage change. The different methods were 

hampered by low-frequency sampling, the limited observable 

areas above the subaqueous zone in aerial photographs, the 

limited size of detailed topographic surveys, and the inherent variability among limited numbers of detailed study sites 

(Schmidt and others, 2004). 

In an attempt to better study channel change and finesediment (sand, silt, and clay) transport in the CRE, the 

Grand Canyon Monitoring and Research Center (GCMRC), 

in cooperation with Northern Arizona University and Utah 

State University, initiated a channel mapping project in 2002. 

This project required a remote-sensing approach of sufficient 

accuracy to detect potentially small changes in sediment 

volume at the reach scale (102

−103 meters [m]). The approach 

needed to be applied biannually and also rapidly repeated 

before and after experimental floods from Glen Canyon Dam. 

Remote-sensing data, including airborne laser scanning or 

light detection and ranging (LIDAR), acoustic multibeam 

bathymetry, aerial photography, and underwater imagery, were 

collected at various intervals in 11 study reaches (fig. 1). To 

enable and ensure the collection of accurate topography, the 

remotely sensed technologies required a high-accuracy global 
2 Monitoring Fine-Grained Sediment in the Colorado River Ecosystem, Arizona

positioning system (GPS) control network and also conventional surveys for groundtruthing checkpoints and filling in 

data gaps. Bathymetry of shallow nearshore environments that 

could not be surveyed with multibeam bathymetry and areas 

of dense vegetation were measured by ground-survey crews. 

These spatial datasets were then combined into high-resolution 

digital elevation models (DEMs) in a geographic information 

system (GIS) and used to compare maps of topography, grain 

size, and other information in order to study the spatial and 

temporal distribution of sand-sized sediment in this system 

(for example, Topping and others, 2006; Kaplinski and others, 2007). The techniques and errors associated with remote 

sensing (Davis, 2004), acoustic multibeam bathymetry (U.S. 

Geological Survey, unpub. data), and subaerial and subaqueous instrumentation (Rubin and others, 2006; Rubin and 

others, 2007) are not described herein. This report presents an 

overview of the conventional surveying procedures employed 

during this study, including establishment of the GPS control 

network and conventional surveying using electronic (optical) 

total stations. The methodological background is reviewed and 

the potential source and nature of errors are outlined.

Study Area

The study area is the CRE in Glen, Marble, and Grand 

Canyons, Ariz. (fig. 1). Locations in the study area are traditionally defined by river-mile (RM) distance downstream or 

upstream from Lees Ferry, Ariz. (RM 0). Although we use 

metric units for describing our methods and results, we adhere 

to the use of river miles as well as informal names to specify 

study-site locations. The river miles used in this report are 

defined by the location along the river centerline developed 

by the GCMRC (U.S. Geological Survey, 2006). This rivermile centerline was developed in a GIS utilizing spatial data 

referenced to the GPS network and is considered more accurate than previous river-mile estimates (for example, Stevens, 

1983; Belknap, 2001).

The 11 reaches selected for repeat surveys are shown 

in figure 1 and listed in table 1. This subset of the channel 

comprises approximately 10 percent of the CRE between Glen 

Canyon Dam and Diamond Creek. Protocol development was 

accomplished in four of the reaches during June, August, and 

September 2000, as part of a separate project during the low 

Little Colorado River

KANAB

CREEK

Havasu Creek

DIAMOND CREEK

PARIA RIVER

LAKE POWELL

LAKE MEAD

RESERVOIR

PROJECTION IS 1983 ARIZONA STATE PLANE, CENTRAL ZONE

111°W

111°W

112°W

112°W

113°W

113°W

114°W

114°W

37°N 37°N

36°N 36°N

ARIZONA

MAP LOCATION

UTAH

N E V A D A N E V A D A

ARIZON A

0 60 MILES

0 60 KILOMETERS

EXPLANATION

LONG TERM MONITORING REACH

GRAND CANYON

NATIONAL PARK

'

#1 LEES FERRY

#2 PARIA

#3 ROARING TWENTIES

#4 REDWALL GORGE

#5 POINT

HANSBOROUGH

#6 GRANARY VIEW

#6 TAPEATS GORGE

#8 CREMATION

#9 AISLES

#10 GRANITE PARK

#11 DIAMOND CREEK

Figure 1. Map showing major tributaries and locations of the long-term monitoring reaches in the Colorado River in Grand Canyon 

National Park, Ariz. 
Table 1. Characteristics of the long-term monitoring reaches within the study area.

Long-term Local name of Starting river mile1 Ending river mile Average channel Channel slope3

monitoring long-term width2 (meters)

reach number monitoring reach

1 Lees Ferry –2.4 0.0 123 0.0001

2 Paria 1.1 2.7 113 0.0002

3 Roaring Twenties 21.9 23.7 56 0.0016

4 Redwall Gorge 29.4 32.1 64 0.0009

5 Pt. Hansborough 42.5 45.5 82 0.0009

6 Granaries 54.5 56.3 90 0.0003

7 Tapeats Gorge 63.4 66.4 95 0.0012

8 Cremation 86.6 88.1 64 0.0020

9 Aisles 119.3 123.3 65 0.0010

10 Granite Park 207.7 209.2 72 0.0013

11 Diamond Creek 224.8 225.6 66 0.0002

1

Based on the river-mile centerline (U.S. Geological Survey, 2006) downstream from Lees Ferry (river mile 0) in Grand Canyon National Park, except for the 

Lees Ferry reach, which is in Glen Canyon National Recreation Area.

2

At 227 m3/s, an average based on cross-section data from Magirl and Breedlove (2005).

3

Based on measured water-surface elevations at a steady discharge of 227 m3/s.

summer steady flow experiment (Schmidt and others, 2007). 

The reaches vary from 1.3 to 6.4 kilometers (km) in length, 

and the average length is 3.5 km. One reach is located in Glen 

Canyon between Glen Canyon Dam and Lees Ferry (RM −15 

to 0). Five reaches are located in Marble Canyon between Lees

Ferry and the confluence with the Little Colorado River (RM 

0 to 61.7). Two reaches are located in eastern Grand Canyon between the Little Colorado River and Phantom Ranch 

(RM 61.7 to 88). Three reaches are located in western Grand 

Canyon between Phantom Ranch and Diamond Creek (RM 88

to 226). In 2004, emphasis was placed on reaches in Marble 

Canyon and eastern Grand Canyon. These reaches compose 

approximately 18 percent of this portion of the CRE. The 

reaches located in Glen and western Grand Canyons were only

surveyed once in 2002.

Geodetic Control Network

Overview

In the 1990s, 0.5-m topographic contours of kilometerscale lengths of the channel in the CRE were developed from 

aerial photographs using stereo photogrammetry (Werth and 

others, 1993). Control was monumented and surveys performed to set photo panels, which were then used to position 

the aircraft at the time of film exposure. These methods only 

required that the field measurements of the distances between 

photo panels be accurate, and did not necessitate a high- accuracy geodetic control network to position reach-scale channel 

morphology to a datum. Analysis of vertical change in the 

CRE, such as topographic surface aggradation and degradation 

and positioning of important features, was limited by the poor 

resolution of the aerial photography. As a result, quantitative 

geomorphic studies of three-dimensional channel form in the 

CRE were restricted to individual sites.

By 1999, technological developments in the application 

of GPS had made it possible to solve kinematic positioning 

of spatial data collected from an aircraft. Photo panels were 

not required to position the aircraft in the CRE, but were still 

necessary to check the accuracy of the spatial data (Davis, 

2004). This was a fundamental change in mapping scope and 

presented new opportunities for geomorphic investigation 

at scales previously unobtainable. As part of this effort, 16 

geodetic control-network points were established along the 

canyon rim and connected to the National Spatial Reference 

System (NSRS). Doyle (1994) describes the NSRS as a combination of discrete geodetic components: horizontal positions 

(latitude and longitude, State Plane Coordinates) referenced 

to a two-dimensional datum, the North American Datum of 

1983 (NAD83), and elevations (Helmert orthometric heights) 

referenced to a one-dimensional datum, the North American 

Vertical Datum of 1988 (NAVD88). Primary monumented 

river-corridor control points used for spatial referencing in the 

Geodetic Control Network 3
4 Monitoring Fine-Grained Sediment in the Colorado River Ecosystem, Arizona

1980s and 1990s were then referenced to the NSRS rim point 

network with GPS, and other river-level control points were 

referenced to the primary river network with conventional 

survey measurements. The control points are stable survey 

marks monumented by a chiseled or scribed x, a ParkerKalon−hardened masonry nail, a carriage bolt, or aluminum 

and brass caps.

Methods for Acquiring Multivector Global 

Positioning System-Based Control

Coordinates for the NSRS rim stations and for primary river-corridor control points were obtained by using 

simultaneous dual-frequency GPS techniques. GPS observations yield ellipsoidal heights, which reference the Geodetic 

Reference System of 1980 (GRS80) ellipse fixed to the 

geocentric NAD83. GPS receivers provide position information by repeated measurements on the travel times of digitally 

tagged radio signals generated by a constellation of satellites. 

Comparison of data from four or more satellites provides vector information (Δx, Δy, Δz distances) for the trilateration of 

latitude, longitude, and altitude. The coordinates of primary 

river-corridor control points were determined by multiple 

8−10-hour occupations using dual-frequency full-wavelength 

receivers stationed on each control point and on three nearby 

NSRS rim stations (fig. 2), using the procedures described by 

Zilkowski and others (1997). Points determined by single- or 

shorter-period occupations are less accurate and were considered a secondary level of the control network. Conventional 

vector measurements (using total stations) were added in areas 

where GPS was not functional due to canyon wall obstructions; these tertiary control points were referenced to the GPS 

network adjustment using least-squares adjustment software 

for survey networks. The control network coordinates were 

converted to the Arizona State Plane central zone 0202 grid in 

meters. Ellipsoidal heights were not converted to the NAVD88 

orthometric heights because the current national geoid model 

(GEOID03) does not incorporate sufficient gravity measurements in the region to account for the effects of topography 

(mass/void) on height measurements. As a result, spatial data 

collected for resource monitoring by the GCMRC are currently 

referenced to the NAD83 ellipsoid (Saleh and others, 2003).

Accuracy Assessment of the Control Network

Project requirements for the rim-level control network 

points were to ensure 0.02-m local accuracy in the horizontal 

component, as well as 0.02-m local accuracy for ellipsoid 

heights. General statistics and accuracies of the different levels 

of the control network are shown in table 2. These values were 

computed in a manner consistent with the Federal Geographic 

Data Committee. At the time of this study, the control network 

points have positioning accuracies of less than 0.03 m and 

ellipsoidal height accuracies of between 0.01 and 0.10 m at 

a 95-percent degree of confidence. The positional accuracy 

of the 0.01-m−0.03-m height of the rim points ensures the 

desired standard of 0.05-m horizontal and 0.08-m vertical 

at secondary and tertiary levels of the control point network 

(table 2). These results suggest that the spatial and vertical 

accuracy of the control network is sufficient for integration of 

Figure 2. Global positioning 

system receiver on control 

point SO123209R in reach 9 

(location shown in fig. 1) (U.S. 

Geological Survey photograph).
Table 2. Summary statistics for each level of the Grand Canyon Monitoring and Research Center control network.

Control network levels Number Vector measurements Horizontal accuracy Vertical accuracy 

(meters) at 95-percent (meters) at 95-percent 

confidence confidence

National Spatial Reference System 16 153 0.019 0.029

rim points

Primary river points (PC) 25 224 0.021 0.053

Secondary river points (SC) 170 1633 0.031 0.061

Tertiary river points (TC) 130 >500 0.062 0.108

multiple datasets on a repeatable basis without anomalies. In the 

section titled Accuracy Assessment of Topographic Surveys, 

we independently evaluate network point quality with repeated 

conventional measurements at a subset of network points. 

Conventional Surveying

Overview

Ground-based capture of terrain data and spatial referencing of sampling technologies was undertaken with traditional survey methods. The canyon setting and associated 

environmental conditions precluded the use of kinematic-GPS 

techniques for rapid acquisition of field survey data. The 

reaches are characterized by steep slopes and, in places, dense 

vegetation, which leads to loss of satellite lock and position fix 

during GPS surveying. As a result, conventional total-station 

surveying provided the best compromise of speed, accuracy, 

and coverage. Nonetheless, the irregular channel planform and 

topographic characteristics of each reach required development of efficient procedures for ensuring quality control of 

collected data, and also to minimize offset between datasets 

collected from different control points.

The following were collected using conventional surveying techniques: (1) topographic data for gaps in remotesensing coverage, (2) checkpoints for groundtruthing airborne 

laser scanning or LIDAR and acoustic multibeam technologies, (2) photo panels for image rectification and photogrammetry, and (3) spatial locations of subaerial and subaqueous 

sampling instrumentation (for example, scour chains, digital 

microscopes for determining sediment grain size [Rubin and 

others, 2006; Rubin and others, 2007] and underwater video 

cameras for determining bed-sediment texture). The data 

acquisition process involved two stages. First, the points in the 

GCMRC control network utilized as benchmark and backsites 

were verified on all total-station setups. This was required 

to establish the precision of the total-station setup, and for 

periodic rechecking of benchmark-backsite angle and distance 

to ensure validity of measurements. The second stage involved 

field data collection and processing.

Instrument Precision and Control Point 

Verification

Surveying protocol was developed and documented 

according to standard practices for ground surveying. Line-ofsight requirements dictated which control points were utilized 

as a benchmark for a total station (fig. 3). Control points that 

could be viewed by more than one benchmark were preferred 

as backsites. A backsite consisted of one or more Sokkia 

reflective prisms mounted on an optical plummet-equipped 

tribrach (Seco or Sokkia) attached to a Crain Tri-Max slipleg adjustable tripod. The optical plummet has a push-pull 

slide focus to ensure that when leveled, the prism is centered 

directly over the control point. To maintain tripod stability in 

windy conditions, rocks were placed on the tripod feet. Upon 

Figure 3. Total station on control point SC0299854R in reach 4 

(location shown in fig. 1); view looking upstream (Photograph 

courtesy of Joseph E. Hazel, Jr., Northern Arizona University). 

Conventional Surveying 5
6 Monitoring Fine-Grained Sediment in the Colorado River Ecosystem, Arizona

Table 3. Average conventional survey positional errors and summary statistics at control points utilized as backsites. 

[Control-network point identification (ID) names are given by location along the river-mile centerline (U.S. Geological Survey, 2006) downstream from Lees 

Ferry in Grand Canyon National Park. PC, SC, and TC refer to primary, secondary, and tertiary levels, respectively, of the control network. C, O, and S indicate 

points determined by multiple GPS or conventional observations, single GPS observation, and single conventional observation, respectively. L and R refer to the 

left and right banks, respectively, as viewed in a downstream direction.]

Point ID N Horizontal distance 

(meters)

Horizontal distance 

standard deviation 

(meters)

Vertical distance 

(meters)

Vertical distance 

standard deviation 

(meters)

TS001285R 15 0.024 0.019 –0.004 0.022

SC001473L 7 0.010 0.008 0.020 0.013

SC002045R 9 0.022 0.011 0.018 0.035

SC002473L 25 0.014 0.009 –0.005 0.020

PC021601R 5 0.011 0.010 0.007 0.010

SC022082L 11 0.008 0.011 –0.003 0.008

SC022744L 25 0.009 0.008 0.013 0.020

TC023460L 11 0.016 0.016 0.004 0.013

SO029428L 7 0.011 0.005 0.028 0.018

TC030051R 14 0.011 0.007 –0.003 0.018

PC030653R 22 0.011 0.007 0.006 0.015

SC030696L 4 0.013 0.004 0.031 0.012

SC031518R 6 0.007 0.007 0.000 0.017

SC031851R 5 0.004 0.006 0.003 0.021

TC032089R 5 0.012 0.011 –0.009 0.014

SO042766L 11 0.019 0.028 0.017 0.019

TC043281L 11 0.014 0.004 0.016 0.030

TC043289R 6 0.006 0.003 0.006 0.009

SC043508L 12 0.006 0.004 0.006 0.004

TC043589L 3 0.018 0.012 –0.001 0.013

TC043971L 12 0.014 0.008 0.016 0.011

SO044411L 16 0.007 0.006 0.012 0.010

PC044480L 6 0.007 0.005 0.017 0.014

SC044914L 8 0.009 0.008 0.010 0.009

TC054325L 7 0.015 0.013 –0.029 0.032

SC054895R 8 0.008 0.005 0.008 0.019

SC055320L 4 0.006 0.004 0.002 0.008

SC055630R 11 0.007 0.005 0.006 0.015

TC055751R 6 0.008 0.006 0.008 0.024

SO063760R 3 0.006 0.001 0.012 0.011

SC064301L 14 0.009 0.009 0.008 0.016

SC065131R 15 0.013 0.012 0.002 0.007

SO065738L 5 0.005 0.006 0.003 0.016

SO065978L 10 0.009 0.006 0.004 0.009

TC065956R 4 0.007 0.003 0.002 0.008
Table 3. Average conventional survey positional errors and summary statistics at control points utilized as backsites.—Continued 

Point ID N Horizontal distance Horizontal distance Vertical distance Vertical distance 

(meters) standard deviation (meters) standard deviation 

(meters) (meters)

SC086644R 4 0.016 0.017 0.012 0.044

SC087128L 6 0.012 0.007 0.006 0.018

SC087628R 4 0.017 0.018 0.008 0.010

SC087734L 5 0.002 0.001 –0.005 0.013

MEAN 9.3 0.011 0.008 0.006 0.016

careful measurement, the height of the target was radioed back 

to the surveyor and recorded.

The coordinate values for each benchmark and backsite 

were verified by the surveyor using multiple angles in both 

direct and reverse scope and by multiple distance measurements using Topcon GTS−313 and GPT−2003 total stations 

(table 3). Vertical angles were adjusted for Earth curvature 

and refraction, and horizontal distances were adjusted by 

appropriate Arizona State Plane scale factors, atmospheric 

pressure, and temperature. Prism constant was set to –30 

millimeter (mm) on each total station and 0 mm in the data 

collector to allow measurement distance correction for 

prism offset and to negate the possibility of double correction. Tripod Data Systems (TDS) handheld Rangers with 

TDS Survey Pro surveying software were used for data 

collection and storage in the field. Unlike older digital data 

collectors, collected data are immediately written to internal storage. Even with complete loss of power or software 

lockup, the data are retained.

Field Data Collection

Ground surveys utilized 7.6-m collapsible rods mounted 

with tilting Sokkia reflective prisms. To minimize target height 

error, all rods were Crain LR STD-series fiberglass leveling 

rods of the same height. The rods telescope smoothly through 

four extensions, have minimal sway when extended, and are 

waterproof. Round rods are better than oval rods in windy conditions. The rods have internal locking and stop mechanisms 

to ensure that under- or overextension of the collapsible sections does not occur. Ground surveys included breaks of slope 

such as sandbar and bank tops and bottoms, but generally 

slope points were collected with the intention of supplementing and groundtruthing photogrammetric- and LIDAR-derived 

topography (table 4). More intensive surveys were conducted 

in areas such as wet sand where LIDAR is subject to spurious 

returns (Davis, 2004) and shallow subaqueous shoreline areas 

not covered by concurrent bathymetry surveys (U.S. Geological Survey, unpub. data). To enhance rapid point collection a 

sideshot (a single bearing and distance measurement) was used 

for ground surveying.

Dry, bare, and relatively flat surfaces were chosen for 

photogrammetry panel location and placement. Typically, the 

panels were spaced about 250 m apart, alternating on either 

side of the river, and two were placed on both sides of the river 

at the upstream and downstream terminus of surveyed monitoring reaches. In addition, care was taken to place panels so 

they were not observable from the river by recreational users 

(for example, hikers, anglers, and boaters). A collapsible metal 

prism pole with a level bubble and thumb-release bipod legs 

was placed over the center of each panel (fig. 4). All panels 

were surveyed to the same accuracy as the control points using 

Table 4. Types of survey data collected during reach-based monitoring river trips. NC, data not collected.

Survey trip Reaches1 Ground points Subaqueous Subaerial Subaqueous Photogrammetric Scour 

(pts/km) camera camera video transects panels chains

location location

August 2000 2,4,5,7 487 80 NC NC NC 65

September 2000 2,4,5,7 511 316 NC NC 169 NC

May 2002 1,2,3,4,5,6,7, 224 1623 NC NC NC NC

8, 9,10,11

June 2004 2,3,4,5,6,7,8 211 1220 NC 30 NC NC

November 2004 2,3,4,5,6,7 364 1129 581 NC NC 115

December 2004 2,3,4,5,6,7 672 947 569 17 NC NC

1

Numbers describe reach designations shown in table 1.

Conventional Surveying 7
8 Monitoring Fine-Grained Sediment in the Colorado River Ecosystem, Arizona

Figure 4. Metal prism pole, 

equipped with bipod legs for 

accurate leveling, used to 

locate each photogrammetry 

panel. Photograph by T. Gushue, 

U.S. Geological Survey.

multiple angle and distance measurement. The panel edges 

were anchored with rocks for stability. Where suitable, control 

points were also used as panel locations. The panels were 

recovered on a subsequent river trip after the aerial overflight.

Onshore spatial positioning included the locations of 

scour chains and sediment-grain-size measurement stations. 

Scour chains were installed in November 2004. The 1-mlength chains were emplaced vertically and the locations 

marked with pinflags. A sideshot using a leveling rod was 

used to record each pinflag location. Pinflag number and 

rod height were recorded in a notebook and also radioed 

to and recorded by the surveyor. Sedimentologic analyses 

of the excavations at recovered chain locations aid in the 

ability to identify flood deposits and to measure scour and 

fill (Schmidt and others, 1999). Likewise, locations where 

sediment grain size was determined with a handheld digital 

microscope camera were marked with pinflags and surveyed 

with a single sideshot (fig. 5). The digital images acquired by 

the microscope system were used to analyze grain size and 

negate the need to manually collect samples (Rubin and others, 2006; Rubin and others, 2007).

The underwater version of the grain-size microscope 

system and a video sled were both tracked and spatially 

referenced with a total station (fig. 6). Both instruments were 

winched and lowered to the riverbed from a 7-m motorized raft. The position was targeted with a round cluster of 

eight reflective prisms mounted on a mast adjacent to the 

winch. Spatial-location acquisition required close coordination between the camera console operator, the radio operator/

notetaker, the boat operator, and the surveyor. Each digital 

video image of the riverbed was tagged with a number that 

was then relayed to the surveyor via radio. When a sideshot 

number was recorded, it was repeated back to the notetaker by 

the surveyor to minimize error. The raft was held stationary 

until getting a signal from the notetaker that the sideshot had 

been acquired.

Upon completion of each survey trip, the field data 

were transferred to computers and edited. Preliminary maps 

were made to detect anomalous survey points using Sokkia 

MapVista mapping software. Survey data were modeled using 

triangular irregular networks (TINs), by Delaunay triangulation (McCullagh, 1988; McCullagh, 1998). Interpolation by 

Delaunay triangulation is exact, directly incorporating the survey points as vertices, thus simplifying erroneous rod height 

detection and subsequent correction.

Results and Discussion

Spatial Point Distribution

An example of the distribution of survey-point data in a 

reach is shown in figure 7 and summarized for all surveys in 

table 4. The average reach ground-point distribution for each 

survey ranged from 211 to 672 points (pts) per kilometer. The 

variability reflects changes in sampling strategy as spatial 

location requirements increased and as refinement of the accuracy and utility of remotely sensed data evolved. Even so, the 

conventional survey methods permitted the acquisition of up to 

3000 total points within a typical 10-hour field day, including 

periods of downtime due to changes in control-point occupation and inclement weather. 
In August and September 2000, ground-point data 

collection focused on the water surface margins for more 

accurate TIN interpolation of gaps in point coverage between 

multibeam and LIDAR data. However, there were substantial systematic errors present in the 2000 LIDAR dataset that 

required the development of procedures to reduce the errors 

to tolerable levels (Davis, 2004). In May 2002, the GCMRC 

implemented aerial photogrammetry rather than LIDAR for 

mapping exposed topography in the reaches, but dense vegetation remained problematic in both the photogrammetric and 

LIDAR approaches (Davis, 2004). In areas where the ground 

was obscured by vegetation, we increased point density to 

fill in perceived gaps in terrain coverage and also to produce 

checkpoints for photogrammetric point accuracy. Considerable time was also spent placing and surveying a total of 169 

photogrammetric panels. The number and quality of checkpoints utilized for photogrammetry and LIDAR are reported 

elsewhere and are not repeated here (Davis and others, 2007). 

As channel bed data from the multibeam hydrographic 

surveys became available, it became apparent that the multibeam transducer did not function well at shallow depths 

(U.S. Geological Survey, unpub. data). As a result, the June 

2004 dataset reflects an increase in ground-point sampling 

frequency of shallow, offshore areas and a decrease in point 

density of vegetated areas. Approximately 75 percent of the 

ground points were collected in offshore areas. The low point 

density of 211 ground pts/km reflects the greater time and 

effort involved in acquiring topographic data in water depths 

up to 2 m. In addition, 30 underwater video transects were 

collected in potentially sensitive areas (for example, eddies 

and pool exit slopes with rapid bed-sediment spatial changes). 

A total of 503 sideshots, with an average spacing of about 3 

m, were collected along the transects to spatially position the 

video images. 

The highest ground point density (672 pts/km) was collected in December 2004 (table 4), immediately following the 

release of the November 2004 high experimental flow (HEF) 

(Topping and others, 2006). The sampling frequency was 

increased in areas of substantial topographic change because 

it was believed that the LIDAR data collection overflight had 

failed. As a result, new deposition from the 2004 HEF was 

surveyed with the intent of providing enough three-dimensional coordinates for terrain modeling (fig. 8). Although the 

spatial distribution and density of points was highly variable, 

points concentrated in areas of substantial change varied from 

0.5 to 1 pt/m2

. In contrast to the June 2004 data, approximately 

23 percent of the ground points were collected in offshore 

areas (fig. 7). In addition, 17 video transects were collected 

with a point spacing similar to that collected in June 2004. 

Spatial referencing of the underwater microscope 

occurred on all monitoring trips. Application of the system 

was limited in August and September 2000 by cable breakage. 

Point distributions were roughly similar in May 2002 and June 

2004, with densities ranging from 40 to 50 pts/km (table 4). In 

addition, in November and December 2004, a total of 581 and 

561 subaerial microscope grain-size locations were surveyed, 

Figure 5. Pinflags used to 

mark the location of each 

grain-size measurement. Inset: 

digital microscope camera. 

Photographs by D. Rubin, U.S. 

Geological Survey.

Results and Discussion 9
10 Monitoring Fine-Grained Sediment in the Colorado River Ecosystem, Arizona

Figure 6. Seven-meter 

motorized raft equipped with 

the grain-size microscope 

system. The approximate 

position of each measurement 

was spatially referenced 

with a total station by 

targeting the cluster of eight 

reflective prisms mounted on 

a mast adjacent to the winch. 

Photograph by D. Rubin, U.S. 

Geological Survey.

Figure 7. Global positioning system control-network points and survey-point distribution for the December 2004 survey of reach 5. The 

total number of points surveyed during 2 field days was 4013. Of this total, 3544 were ground points (22 percent of which were collected in 

offshore areas), 195 were locations of underwater video positioned along 5 transects, 149 were subaerial microscope camera locations, 

and 125 were subaqueous microscope-camera measurement locations. Location is shown in figure 1. Locations of control network 

points are also shown.
Figure 8. Detail of the topographic survey and point distribution of the December 2004 survey at river mile 44.6 in reach 5 (location is 

shown in fig. 5).

respectively. In contrast to the underwater microscope positions

which are a record of boat position and do not include measuree

y

, 

ments of bed elevation, the subaerial spatial positions also serv

as ground points and were used for terrain modeling. 

Accuracy Assessment of Topographic Surveys

We assessed control network quality and survey point 

accuracy as a first step in quantifying uncertainty in the generation of DEMs from multiple data sources and determining 

an appropriate threshold for detection of significant change 

between surfaces. Reoccupation of benchmarks and backsites

in seven of the original eleven reaches, totaling 26 surveys, 

can be used to identify the presence of setup errors, or identif

spurious control points due to errors arising from incorrect 

antenna heights or offsets during GPS measurements. Summary statistics of the distance range and standard deviation 

of the measurements to control points used as backsites are 

shown in table 3.

Individual observations at 39 backsites ranged from 3 to 

25 in a total of 362 measurements (table 3). The distribution 

of observations is approximately normal. No systematic errors 

were detected. Because total-station drift is also reflected in 

this analysis, individual observations may deviate markedly 

from the mean. Leveling drift occurs as the tripod and instrument are subjected to changing environmental conditions; 

as a rule, the backsites were checked and the error recorded 

about every 50 sideshots, at which point the total station was 

releveled and rezeroed on the backsite. Vertical precision was 

slightly superior to horizontal precision. The mean horizontal 

distance error was 0.011±0.008 m; the mean vertical distance 

error was 0.006±0.016. The mean range of 0.022 m in horizontal and 0.13 in vertical at a 95-percent confidence interval, 

with some points having a distance vector greater than 1000 

m, indicates that positional accuracies of the river-level points 

in the control network are sufficient for collecting spatially 

referenced field data 

The appropriate level of accuracy to assign to individual 

ground-survey points is difficult to determine. The most easily 

Results and Discussion 11
12 Monitoring Fine-Grained Sediment in the Colorado River Ecosystem, Arizona

B

A

Figure 9. Topographic surveying in offshore areas in December 

2004. A, Two rodmen positioning for sideshots at three rods (two 

extensions) in a water depth of more than 1 m. B, When in position 

and plumb, the prism is turned toward the total-station location 

and readiness radioed to the surveyor, ensuring an accurate 

measurement. Location is the return-current channel shoreward 

of the sandbar platform in the last pool of reach 5 (Photographs 

courtesy of Matt Kaplinski, Northern Arizona University).

quantified source of error in conventional survey-point collection is spatial integrity of the control-point network. The 

analysis of control points revealed a standard deviation in the 

planform of 0.008 m, implying a measurement limit ±0.016 

m, 95 percent of the time (table 3). Likewise, the standard 

deviation in the vertical of 0.016 m implies a measurement 

limit ±0.022 m, 95 percent of the time. Because these results 

are within the stated control network accuracy in table 2, these 

values were assigned as the horizontal and vertical error for 

the control points utilized in this study. We consider setup 

error by assuming a 0.003-m horizontal centering error of the 

tribrach over the control point and 0.003-m tape measurement error of the total-station height above the control point, 

respectively. Horizontal rod error must account for plumbness 

and the different heights to which the rod can be extended. 

This error is difficult to quantify and depends on the rodman’s 

ability to plumb the rod, environmental conditions, and rod 

condition (for example, fig 9). Thus, we assign a conservative 

estimate of 0.05-m horizontal error for the standard rod height 

(1.837-m height), 0.10 m for 1 extension (3.285-m height), 

0.15 m for 2 extensions (4.735-m height), 0.20 m for 3 extensions (6.185-m height), and 0.25 m for 4 extensions (7.579-m 

height), respectively. These estimates were determined by 

measuring the precision of multiple measurements on the same 

control points using the standard rod height and each of the 4 

extensions. Precise positioning of the leveling rod also constitutes a source of random sampling error, which will result in 

vertical inconsistencies depending on the substrate upon which 

the rod is placed (for example, mud, wet sand, gravel, etc.). 

This source of error is impossible to quantify and we assign an 

arbitrary estimate of 0.05 m for vertical rod error and assume 

this increases by 0.01 m for each extension of the leveling rod. 

The propagated error associated with these various sources of 

error can be determined by the following expression:

2

3

2

2

2

1 ++= eeeE ,

where E is the combined error, e1 is the error associated with 

the spatial and vertical integrity of the control network, e2

is the vertical and horizontal setup error at the benchmark 

and backsite, and e3 is the error associated with placement 

and plumbing of the leveling rod. The results of the equation 

for each rod extension are shown in table 5. Horizontal and 

vertical rod accuracy ranged from 0.05 to 0.25 m and 0.05 to 

0.09 m in the vertical, respectively. About 90 percent of the 

sideshots collected during the surveys were collected at the 

standard rod height, and the accuracy is closer to the minimum 

level of 0.05 m; we conclude that the point data collected 

with conventional surveying in our study is within an acceptable margin of error compared to the errors associated with 

airborne and bathymetric data collection. These results also 

indicate that sampling errors associated with individual survey 

points should be considered when ground points are utilized 

as checkpoints. The checkpoints should not be considered free 
Table 5. Leveling-rod horizontal and vertical accuracies.

Error (meters) 1 rod height One extension Two extensions Three extensions Four extensions

Horizontal 0.053 0.101 0.151 0.201 0.251

Vertical 0.055 0.064 0.073 0.083 0.093

of error, especially if collected from areas of dense vegetation where the rod was presumably telescoped to two or more 

extensions. Other technologies spatially referenced with total 

stations during the study (for example, the video sled and 

subaqueous digital microscope) have far greater error associated with the spatial measurements, primarily because of 

streamflow, boat positioning, and cable slant. These factors are 

highly variable, and we assume a conservative horizontal error 

of ±3 m. 

The sampling strategy employed in this study was designed 

to augment digital terrain modeling (DTM) constructed from 

remote-sensing data and to provide accurate spatial location 

for other sampling technologies. With the exception of one 

survey trip (December 2004), the ground points alone were 

not intended to provide full and accurate three-dimensional 

coordinates for DTM development. In addition, the sampling strategy and point density changed with each successive 

survey as more was learned about the utility of each remotesensing technology. As a result, problems associated with 

point-sampling frequency in DTM quality were not examined 

in this report.

Summary

A high-resolution GPS-based control network and conventional survey techniques were used to collect topographic 

data in large areas of the channel at scales previously unobtainable in the Colorado River ecosystem. The accuracy of 

the control network and survey points is sufficient to generate 

repeatable, combined topographic surfaces when combined 

with remotely sensed data. A detailed assessment of survey 

errors is presented and an accuracy of ±0.05 m was identified 

as the minimum precision of individual survey points. These 

results are important for assessing DEM quality and identifying detection limits of significant change between surfaces 

generated from remote-sensing technologies. The data collection techniques in this study would be useful for establishing 

sampling strategies in other rivers where topographic characteristics preclude GPS methods in direct, geomorphological 

investigations. Real-time kinematic (RTK) data acquisition 

requires longer occupation times at individual points in this 

canyon setting because satellite lock is often blocked by steep 

slopes or vegetation canopy. Conventional surveying provided 

the best compromise of speed, accuracy, and coverage but 

increased the need for large-scale field surveys. The methods employed in this study permitted the acquisition of up to 

3000 points during a typical field day. Data was collected in 

a variety of environmental conditions, ranging from subfreezing to temperatures greater than 110°F. The major disadvantages of the surveying procedures employed during this study, 

compared to high-resolution GPS surveying, are requirements 

for increased manpower, an extensive and accurate control 

network, and direct line-of-sight operations. A dedicated 

robotic total station (geodimeter) could possibly increase data 

collection speed of boat-collected data, but the required setup 

time would likely negate the increase in survey speed. Medium-range GPS equipment can track boat position at 2−3-m 

horizontal accuracies but will include periods of downtime due 

to loss of satellite lock.

Acknowledgments

This study was sponsored by the U.S. Geological Survey 

and Bureau of Reclamation through the Grand Canyon Monitoring and Research Center. Numerous people provided field 

assistance, but we thank Mark Gonzalez, Eric Kellerup, Mark 

Manone, Meagan Polino, and Greg Sponenburgh in particular. The manuscript benefited from technical reviews by Paul 

Grams, David Topping, and an anonymous reviewer. Thorough editorial reviews and revisions were provided by Patty 

McCredie.

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Hazel and others—Monitoring Fine-Grained Sediment in the Colorado River Ecosystem, Arizona—Control Network and Conventional Survey Techniques—

Open–File Report 2008–1276