Evaluation of Terrestrial LIDAR for Monitoring Geomorphic 

Change at Archeological Sites in Grand Canyon National 

Park, Arizona

Open–File Report 2008–1384

U.S. Department of the Interior

U.S. Geological Survey
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Evaluation of Terrestrial LIDAR for Monitoring 

Geomorphic Change at Archeological Sites in Grand 

Canyon National Park, Arizona 

By Brian D. Collins, Kristin M. Brown, and Helen C. Fairley 

Open–File Report 2008–1384 

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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its natural and living resources, natural hazards, and the environment: 

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Suggested citation: 

Collins, B.D., Brown, K.M., and Fairley, H.C., 2008, Evaluation of terrestrial LIDAR for 

monitoring geomorphic change at archeological sites in Grand Canyon 

National Park, Arizona: U.S. Geological Survey, Open–File Report 2008–1384, 60 p. 

World Wide Web: http://pubs.usgs.gov/of/2008/1384/ 

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

endorsement by the U.S. Government. 

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

copyright owners to reproduce any copyrighted material contained within this report. 

 ii
Contents 

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

Background Information ...................................................................................................................................................2

Methodology .......................................................................................................................................................................6

Research Design.............................................................................................................................................................6

Study Areas .....................................................................................................................................................................8

Terrestrial LIDAR Laser Scanning...............................................................................................................................9

Data Collection............................................................................................................................................................9

Data Processing........................................................................................................................................................13

Error Analysis ............................................................................................................................................................15

Conventional Total Station Surveying.......................................................................................................................17

Data Collection and Processing.............................................................................................................................17

Error Analysis ............................................................................................................................................................19

Impact Evaluation .........................................................................................................................................................21

Results ................................................................................................................................................................................23

Topographic Data Collection......................................................................................................................................23

Impact Evaluation .........................................................................................................................................................36

Variations within Survey Methods (Consistency Check—Parameters A1, A2, A3) .....................................40

Variations between Survey Methods (Impact Difference Check⎯Parameters B1, B2, B3).....................41

Postprocessing Time Comparison.........................................................................................................................42

Discussion .........................................................................................................................................................................42

Topographic Data Evaluation .....................................................................................................................................43

Thalweg Extraction Comparison............................................................................................................................43

Feature Identification...............................................................................................................................................44

Surface Modeling and Whole-Site Monitoring...................................................................................................45

Vegetation ..................................................................................................................................................................47

Impact Evaluation Comparison ..................................................................................................................................47

Factors Affecting the Direct Impact Evaluation⎯Footstep Count ..................................................................47

Factors Affecting the Indirect Impact Evaluation⎯Time in Sensitive Area..................................................48

Other Factors⎯Postprocessing Methods ...........................................................................................................48

Future Directions ..............................................................................................................................................................49

Conclusions .......................................................................................................................................................................50

 iii
Acknowledgments............................................................................................................................................................52

References ........................................................................................................................................................................52

Appendix A. Evaluation Metrics ....................................................................................................................................55

Appendix B. Impact Evaluation Results .......................................................................................................................57

Appendix C1. Variation Within Each Mapping Method for Total Time of Survey, Time in Sensitive Area, 

and Footstep Impact...........................................................................................................................................59

Appendix C2. Variation Between Total Station and LIDAR Methods for Total Time of Survey, Time in 

Sensitive Area, and Footstep Impact ..............................................................................................................60

Figures 

1. Regional map showing Grand Canyon National Park, Ariz.................................................................................2

2. Archeological artifacts and site excavation in Grand Canyon National Park, Ariz .......................................3

3. Topography of gullied archeological sites ............................................................................................................4

4. Map showing general location of eight sites included in this investigation ...................................................7

5. Terrestrial light detection and ranging data collection in Grand Canyon, Ariz., and transporting the 

system.............................................................................................................................................................................10

6. Typical terrestrial light detection and ranging setup locations and data coverage from a single scan .11

7. Views of terrestrial light detection and ranging points and surface model data........................................12

8. Terrestrial light detection and ranging control points.......................................................................................14

9. Extraction of a gully thalweg using 1-cm contours constructed from the surface model triangulated 

irregular network..........................................................................................................................................................15

10. Conventional total station data collection showing back site location and total station instrument.....18

11. Conventional total station data collection showing use of survey rod and prism in a gully system ......19

12. Footstep imprints impact archeological sites during surveying....................................................................21

13. Typical gully plan view showing thalweg and relative difference in point density between terrestrial 

light detection and ranging and total station survey methods.............................................................................24

14. Site C:13:006⎯Locations of Gullies 1a, 1b, and 2 .............................................................................................25

15. Site C:13:006⎯Gullies 1a and 1b: Plan view of light detection and ranging-derived and total stationderived thalwegs .........................................................................................................................................................26

16. Site C:13:006⎯Long-profile comparison of light detection and ranging and total station data for Gully 

1a and Gully 1b..............................................................................................................................................................27

17. Site C:13:006⎯Gully 2: Plan view of light detection and ranging-derived and total station-derived 

thalwegs.........................................................................................................................................................................28

18. Site C:13:006⎯Gully 2: Long-profile comparison of light detection and ranging and total station data 29 

19. Site G:03:041⎯Locations of gullies.....................................................................................................................30

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20. Site G:03:041⎯Gullies 1 and 2: Plan view of light detection and ranging-derived and total stationderived thalwegs ..........................................................................................................................................................31

21. Site G:03:041⎯Gully 1: Long-profile comparison of light detection and ranging and total station data32

22. Site G:03:041⎯Gully 2: Long-profile comparison of light detection and ranging and total station data32

23. Site G:03:072⎯Locations of Gullies 1 and 2 ......................................................................................................33

24. Site G:03:072⎯Gullies 1 and 2: Plan view of light detection and ranging-derived and total stationderived thalwegs ..........................................................................................................................................................34

25. Site G:03:072⎯Gully 1: Long-profile comparison of light detection and ranging and total station data34

26. Site G:03:072⎯Gully 2: Long-profile comparison of light detection and ranging and total station data35

27. Comparison of total survey time and time in sensitive area for terrestrial light detection and ranging 

and total station methods ...........................................................................................................................................37

28. Comparison of footstep count for terrestrail light detection and ranging and total station methods ....38

29. Comparison of cumulative total survey time for terrestrial light detection and ranging and total station 

methods plotted for each site and for each data collection effort and averaged for both surveys. ............39

30. Section of long-profile comparison of terrestrial light detection and ranging and total station data 

showing knickpoint locations determined by total station surveying ................................................................45

31. Detail of terrestrail light detection and ranging point data from a single scan showing key features ..46

Tables 

1. Site name and dates of terrestrial light detection and ranging and total station data collection at 

archeological sites in Grand Canyon National Park, Ariz.......................................................................................6

2. Errors associated with terrestrial light detection and ranging measurements............................................16

3. Survey data summary ..............................................................................................................................................24

4. Statistical comparison of gully thalweg profiles between light detection and ranging and total station 

data .................................................................................................................................................................................36

5. Summary of variation of impact evaluation results between terrestrial light detection and ranging and 

total station surveying .................................................................................................................................................41

6. Processing time for terrestrial light detection and ranging-derived and total station surveying-derived 

topographic data ..........................................................................................................................................................42

Datum Information 

Coordinate information is not provided in order to protect the sensitive archeological sites discussed in 

this report. 

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Evaluation of Terrestrial LIDAR for Monitoring 

Geomorphic Change at Archeological Sites in Grand 

Canyon National Park, Arizona 

By Brian D. Collins,1 Kristin M. Brown,2,3 and Helen C. Fairley2

Introduction 

This report presents the results of an evaluation of terrestrial light detection and ranging 

(LIDAR) for monitoring geomorphic change at archeological sites located within Grand Canyon 

National Park, Ariz. Traditionally, topographic change-detection studies have used total station methods 

for the collection of data related to key measurable features of site erosion such as the location of 

thalwegs and knickpoints of gullies that traverse archeological sites (for example, Pederson and others, 

2003). Total station methods require survey teams to walk within and on the features of interest within 

the archeological sites to take accurate measurements. As a result, site impacts may develop such as 

trailing, damage to cryptogamic crusts, and surface compaction that can exacerbate future erosion of the 

sites. National Park Service (NPS) resource managers have become increasingly concerned that 

repeated surveys for research and monitoring purposes may have a detrimental impact on the resources 

that researchers are trying to study and protect. 

Beginning in 2006, the Sociocultural Program of the U.S. Geological Survey’s (USGS) Grand 

Canyon Monitoring and Research Center (GCMRC) initiated an evaluation of terrestrial LIDAR as a 

new monitoring tool that might enhance data quality and reduce site impacts. This evaluation was 

conducted as one part of an ongoing study to develop objective, replicable, quantifiable monitoring 

protocols for tracking the status and trend of variables affecting archeological site condition along the 

Colorado River corridor. The overall study consists of two elements: (1) an evaluation of the 

methodology through direct comparison to geomorphologic metrics already being collected by total 

station methods (this report) and (2) an evaluation of terrestrial LIDAR’s ability to detect topographic 

change through the collection of temporally different datasets (a report on this portion of the study is 

anticipated early in 2009). The main goals of the first element of study were to 

1. test the methodology and survey protocols of terrestrial LIDAR surveying under actual 

archeological site field conditions, 

2. examine the ability to collect topographic data of entire archeological sites given such 

constraints as vegetation and rough topography, and 

3. evaluate the ability of terrestrial LIDAR to accurately map the locations of key 

geomorphic features already being collected by total station methods such as gully 

thalweg and knickpoint locations. 

 

1

 U.S. Geological Survey, Western Earth Surface Processes Team, Menlo Park, Calif. 2

 U.S. Geological Survey, Southwest Biological Science Center, Grand Canyon Monitoring and Research Center, Flagstaff, 

Ariz. 

3

 Present Affiliation: University of Northern Arizona, Flagstaff, Ariz. 

 1
This report focuses on the ability of terrestrial LIDAR to duplicate total station methods, 

including typical erosion-related change features such as the plan view gully thalweg location and the 

gully thalweg long profile. The report also presents information concerning the use of terrestrial LIDAR 

for archeological site monitoring in a general sense. In addition, a detailed comparison of the site 

impacts caused by both total station and terrestrial LIDAR survey methods is presented using a suite of 

indicators, including total field survey time, field footstep count, and data-processing time. A thorough 

discussion of the relative benefits and limitations of using terrestrial LIDAR for monitoring erosioninduced changes at archeological sites in Grand Canyon National Park concludes this report. 

Background Information 

The Colorado River bisects Grand Canyon National Park, which is located just downstream 

from Glen Canyon Dam near the Arizona-Utah border (fig. 1). The Grand Canyon Protection Act of 

1992 required the Secretary of the Interior to establish and implement a program of research and longterm monitoring to document and mitigate downstream effects of Glen Canyon Dam operations on 

cultural resources, natural resources, and visitor use in Glen Canyon National Recreation Area and 

Grand Canyon National Park. The USGS GCMRC is responsible for research and monitoring, including 

projects to evaluate potential impacts to archeological sites along the entire length of the Colorado River 

through Grand Canyon National Park. 

Figure 1. Regional map showing Grand Canyon National Park, Ariz. 

 2
Archeological sites in Grand Canyon National Park are an irreplaceable cultural and scientific 

resource. Studies of these sites have shown that human activity in Grand Canyon dates back to 11,000 

years before the present (Fairley, 2005). The sites contain materials that reflect not only the range and 

scope of past human activities in Grand Canyon but also information about past environmental 

conditions. Consequently, archeological sites provide important source evidence for understanding the 

cultures that inhabited this area in the past as well as the environmental conditions that influenced 

human use before modern times (fig. 2).

A 

B C 

Figure 2. A and B, Archeological artifacts, and C, site excavation, in Grand Canyon National Park, Ariz. 

The area in C was previously buried by aeolian deposits and became exposed as gullying and sedimentdepletion revealed features of the habitation site. 

 3
A thorough inventory of archeological resources along the 255-mile Colorado River corridor 

downstream of Glen Canyon Dam was performed between 1990 and 1991; the inventory located 475 

individual sites, including 336 sites that were determined to be potentially impacted by dam operations 

(Fairley and others, 1994). Many of the archeological sites along the river corridor were found to be 

located in sediment deposits forming alluvial terraces (for example, Hereford, 1993; Hereford and others, 

1993, 1996). Fairley and others (1994) identified a variety of erosion-related impacts occurring at these 

sites, including surface deflation, gullying (fig. 3), bank slumpage, and animal- and human-caused erosion 

such as trailing. Subsequent research has indicated a linkage between dam-controlled river hydrology, 

sediment-depleted sandbars at the river level, and erosion of archeological sites located in close proximity 

to the river owing to a lack of sediment replenishment at higher elevations within the river corridor and a 

reduction in aeolian sand transport to the sites. This theory was first suggested by Hereford and others 

(1991, 1993), and more recent studies by Draut and Rubin (2008) have begun to quantify these linkages. 

Since 1992, NPS archaeologists have conducted repetitive site monitoring along the river corridor 

to document erosion and other impacts (Leap and others, 1996). In addition, NPS archaeologists have 

undertaken a variety of remedial activities to mitigate the effects of erosion, including mapping, 

maintenance and construction of erosion control features (fig. 3), and data recovery (excavation) efforts 

(for example, Leap and Kunde, 2000). As a part of the monitoring effort, detailed mapping using 

conventional survey techniques was conducted over portions of 70 sites to document potential erosioninduced changes to site topography over time. For a variety of reasons, this effort was halted before a 

change-detection study could be completed (Leap and others, 2000). Subsequently, researchers from Utah 

State University carried out a project from 2001 to 2003 to evaluate the effectiveness of check dams at 

slowing gully erosion at archeological sites (Pedersen and others, 2003). All of these efforts included the 

use of total station survey techniques, which required direct researcher access to the archeological sites 

being studied. Visible trampling of cryptogamic soil crusts and the development of incipient trails 

resulting from this work increased NPS resource managers’ concerns about detrimental site impacts 

caused by researchers’ footsteps on archeological sites (J. Dierker, personal comm., 2006). 

A B C

Figure 3. Topography of gullied archeological sites. Precipitation runoff causes terrace erosion and 

may lead to archeological resource degradation. A, Gully in early stage of formation. B, Fully incised 

gully. C, Gully with check dam to mitigate erosion. 

 4
To eliminate human-caused erosion produced by conventional survey methods, Pederson and 

others (2003) explored airborne mapping techniques to monitor archeological site erosion and check 

dam effectiveness. Low-altitude photogrammetry (1:1,600 scale) with mean vertical accuracy of 6 to 10 

cm was conducted in 2002 and compared to simultaneously collected total station survey data. 

Unfortunately, even this high-scale airborne mapping failed to consistently detect change at the required 

10- to 20-cm scale—the minimum scale deemed necessary to detect significant erosion related changes 

to gully thalweg and knickpoint evolution. Although Pederson and others (2003) suggested that 

technological improvements could potentially reduce errors, the researchers also acknowledged that 

flights performed any lower (to further increase photo scale) were not likely to be performed because of 

the noise and visual impacts of low-flying aircraft in Grand Canyon National Park. 

In a separate study, airborne LIDAR was also explored as an alternative means of collecting 

topographic data remotely from archeological sites (Davis, 2004). Like photogrammetry, the accuracy 

and resolution of airborne LIDAR is dependent on flight altitude and vegetation cover. Error analysis of 

airborne LIDAR data collected during this study along the Colorado River corridor in spring and fall 

2000 and fall 2002 showed that a single pass of low-altitude LIDAR produced a point density of 10 

points/m2

 and vertical accuracy of 8 cm on bare ground but produced relatively low accuracy in dense 

vegetation (1−2 m). Since change detection monitoring is dependent on roughly twice the vertical error 

(one each from each dataset collected over time), this method also produced change detection 

capabilities near to the limit of the features being investigated (centimeter to decimeter scale). The study 

also determined that the point density, and hence resolution, of airborne LIDAR maps could be 

increased by conducting multiple passes over the same area, which has concomitant increases in costs 

and impacts to visitors. As a result of these findings, this method has not been pursued any further as a 

viable means for archeological site monitoring in the river corridor. 

In response to these studies, USGS researchers, in collaboration with the NPS, initiated a project 

to explore other remote-sensing technologies that would be more efficient, more accurate, and less 

intrusive for monitoring site geomorphic change. One of these, terrestrial LIDAR, was selected for 

testing because of its proven track record during the past 5 years for documenting site conditions with 

high resolution (hundreds of points per m2) and accuracy (centimeter level) in a variety of applications 

(for example, Collins and Sitar, 2004; Nagihara and others, 2004; Doneus and Neubauer, 2005; Kayen 

and Collins, 2005; and Collins and Sitar, 2008). Terrestrial LIDAR has also been used in previous 

studies in Grand Canyon National Park since 2001 as part of the Vanishing Treasures Program to 

document the existing conditions of archeological structures and resources (for example, Hough and 

Brennan, 2008); however, data collection has been limited to single temporal datasets of archeological 

structures rather than focused on fine-scale change detection of geomorphic features. Thus, terrestrial 

LIDAR’s use as a change-detection tool had not been tested in Grand Canyon National Park or within 

the logistically challenging confines found at the bottom of Grand Canyon. 

In 2004, ground-based LIDAR was tested along the Colorado River corridor in Grand Canyon 

National Park during an experimental high-flow release from Glen Canyon Dam; this initial application 

focused on a variety of scientific uses, including mapping sandbar geomorphology, archeological sites, 

and biological habitat (Collins and Kayen, 2006). This pilot study suggested that although the use of 

terrestrial LIDAR for archeological site monitoring in Grand Canyon was promising, its full use 

required additional evaluation. Following on these findings, the GCMRC Sociocultural Resources 

Program proposed an evaluation study comparing the effectiveness, portability, impacts, and accuracy 

of terrestrial LIDAR to the existing monitoring technology (total station surveying) currently used by 

GCMRC supported projects. The results of this study are presented in this report. 

 5
Methodology 

Research Design 

LIDAR and total station data were collected at eight archeological sites (table 1) from May 4 to 

May 19, 2006, and from April 25 to May 11, 2007. Topographic results from the May 2006 surveys at 

three of the sites (C:13:006, G:03:041, and G:03:072) served as the basis for comparing the 

effectiveness and accuracy of the two methods for quantifying topographic measurements. To protect 

the cultural sites studied, the archeological site identification numbers are used for reference when 

describing site locations (fig. 4). Note that an “AZ:” precedes site identification numbers for officially 

cross-referenced archeological sites (for example, AZ:C:13:006). Topographic data comparisons of the 

other sites and collection date will be evaluated in a separate report to be completed in 2009. Impact 

evaluation data, consisting of the time and number of personnel footsteps taken within each site area 

during data collection were collected from all eight sites in both 2006 and 2007. These data are used as 

an indication of possible degradation caused by each survey method to sensitive archeological sites. 

Table 1. Site name and dates of terrestrial light detection and ranging (LIDAR) and total station data 

collection at archeological sites in Grand Canyon National Park, Ariz. (bold indicates sites where 

topographic data is presented in this report). 

Site name Date 1 Date 2 

C:13:006 May 6, 2006 April 28, 2007 

C:13:336 May 7, 2006 April 29, 2007 

C:13:099 May 8, 2006 April 29, 2007 

C:13:348 May 8, 2006 April 30, 2007 

G:03:041 May 14, 2006 May 7, 2007 

G:03:002 May 15, 2006 May 8, 2007 

G:03:072 DS (downstream gullies) May 16, 2006 May 10, 2007 

G:03:072 US (upstream gullies) May 17, 2006 May 9, 2007 

 6
G:03:002-US 

-DS 

C:13:099 

G:03:002 

C:13:348 

C:13:336 

B 

Figure 4. Map showing general location of eight sites included in this investigation. Terrestrial light 

detection and ranging (LIDAR) and total station data were collected at these eight archeological sites. 

Topographic results from three of the sites (red) served as the basis for comparing the effectiveness and 

accuracy of the two methods for quantifying topographic measurements. Five sites (blue) were 

evaluated only for site impacts in this report. 

Data collection efforts were focused on one or more gully systems, referred to in this report as 

Gully 1, Gully 2, etc., and numbered sequentially at each site, moving from upstream to downstream 

along the river corridor or contributing side drainage. The total station (conventional) survey collected 

data along the gully systems, whereas the LIDAR survey focused on both the gully systems and the 

surrounding topography. Although this report concentrates on evaluation of data collected from the 

gully systems, it should be recognized that the LIDAR data collection and processing methodologies 

were selected to achieve an efficient collection of data over the entire site and to compare extracted 

gully features from this data. Thus, extended data coverage and survey efficiency were important in the 

selection of surveying protocols. 

Support for survey activities was provided by two GCMRC motor rafts, which carried all 

personnel and equipment for each 16-day trip. Equipment included the terrestrial LIDAR unit (courtesy 

of the USGS Coastal and Marine Geology Program), GCMRC’s total station unit, battery charging units 

for all electronics, and camping gear and food supplies. The terrestrial LIDAR team consisted of 

personnel from the USGS Earth Surface Processes Team and Coastal and Marine Geology Team in 

Menlo Park, Calif. The total station team was from the USGS GCMRC in Flagstaff, Ariz., assisted by a 

team from Utah State University performing geomorphologic assessments. Additional trip personnel 

included archeologists from NPS, USGS GCMRC scientists, Zuni Tribe members, and raft pilots 

(boatmen). 

 7
Study Areas 

Archeological sites that were previously monitored by the GCMRC and studied for NPS projects 

(Pederson and others, 2003) were selected as study areas. Thus, baseline datasets on gully thalweg 

topography already existed for the study areas before this study, and these datasets were already being 

evaluated independently as part of a larger research effort (Fairley and others, 2007). All sites evaluated 

in this study are within the elevation limits of areas potentially subjected to influence by dam operations 

and, with one exception, are located 100 to 200 m from the Colorado River. In general, site 

geomorphology consists of Holocene and Pleistocene alluvial, slope wash, or debris deposits; aeolian 

dune sands also cover these deposits in many places. In some locations, the dune sands have been 

stabilized by cryptogamic crust or small cacti vegetation. Sites are typically low gradient in the upper 

catchment but increase dramatically as they descend to river level. Thus, knickpoint formation has been 

most often monitored in these locations. The gullies studied are on the order of 10 to 30 m in length, up 

to 1 m in width, and generally several centimeters to tens of centimeters in depth. Additional details of 

the three sites evaluated for topographic comparison are provided here for reference. Pederson and 

others (2003) also provide additional site and geomorphology related information. 

Site C:13:006 (fig. 4) is a prehistoric habitation area in close proximity to the Colorado River. 

The site is composed of overwash cliff deposits from Bright Angel Shale bedrock covered by aeolian 

dune sands and is of increasingly convex gradient moving down in elevation towards the Colorado 

River. Total catchment site for the area is 0.12 ha (Pederson and others, 2003). Four small drainages 

(G1a, G1b, G2, and G3) traverse the site, three of which appear to be nearing a state of incipient gully 

incision. The other drainage (G3) is a deeply incised gully that formed as a result of monsoonal summer 

rain in 2002 (Pederson and others, 2003). Vegetation cover is a mix of small cacti, chiefly prickly pear, 

generally averaging less than 20 cm in height with a few larger mesquite trees near the boundaries of the 

site. This report evaluates the topography of the first three gullies. 

Site G:03:041 (fig. 4) is a prehistoric habitation area upstream of a large arroyo that forms the 

main geomorphic feature of the area. In general, the site is flatter at its upper end and increases in 

gradient toward the Colorado River. Four small gullies traverse the site, parallel in flow direction to the 

larger arroyo, and drain directly to the Colorado River. Individual catchment size of the gullies ranges 

from 0.02 to 0.18 ha, with gullies generally steep (12–28 percent) in gradient (Pederson and others, 

2003). This report evaluated only two of the gullies, both of which are located almost entirely within 

aeolian sediments. Vegetation cover consists of a mix of both (1) small grasses, prickly pear cacti, and 

shrubs and (2) larger ocotillo and cholla cactus and creosote and mesquite trees. Overall, very few open, 

unvegetated areas are present, and, in some cases, the gullies traverse the site below dense shrubs and 

trees. 

Archeological site G:03:072 (fig. 4) consists of multiple roasting pits spread over a debris fan. 

To facilitate mapping, the site was divided into upstream and downstream areas, designated 

G:03:072US and G:03:072DS. Site G:03:072DS (the only site evaluated for topographic comparison) 

forms a triangular-shaped area bounded by large (several meters in width) boulder-chocked drainages 

on two sides and a significant change in slope on the third side. The area is generally of increasingly 

convex gradient; three gullies (G1, G2, G3) of various development traverse the site. Because the third 

gully is already incised to bedrock, only the first two gullies are evaluated in this report. The gullies 

begin in an area of generally flat topography but dramatically increase in gradient as they reach the edge 

of the site. Catchment size is small (about 0.002 to 0.06 ha), but, again, the gullies have steep (24–27 

percent) gradients (Pederson and others, 2003). The gullies lead into the two larger bordering drainages 

that then converge and drain to the Colorado River some 40 m downstream. Site vegetation includes 

 8
several larger mesquite trees and a broad mix of cacti, including prickly pear, ocotillo, and yucca. In the 

gullies, vegetation is generally absent. Vegetation and cryptogamic crust appear to have generally 

stabilized the soils at this site. 

Terrestrial LIDAR Laser Scanning 

Data Collection 

Terrestrial LIDAR, or three-dimensional (3-D) laser scanning, involves sending and receiving 

laser pulses to build a point file of 3-D coordinates of virtually any surface. A laser-scanning device is 

set up at a fixed location with an open field of view and aimed at the ground surface. The time of travel 

for a single laser pulse reflection is measured along a known trajectory such that the distance from the 

laser scanner, and consequently the exact location of a point of interest, is computed. Precise stepper 

motors within the scanner are typically used to aim the laser at defined areas or in a 360° path around 

the scanner location. These devices, which were in many cases specifically developed for rapid 

topographic surveys, allow data to be collected at rates up to many thousands of points per second, 

generating a “point cloud” of 3-D coordinates. Acquisition of sufficiently dense point clouds can fully 

describe site topography. The point files generated from data collection are typically transformed into 3-

D surfaces for cross-section and volumetric analyses. 

In the present study, a Riegl Z210 laser scanner (Riegl, 2008) was used as a tripod-mounted 

survey instrument (fig. 5A) and transported from the raft to each site by backpack (fig. 5B). An elevated 

tripod was used to position the laser above each site to capture a wide range and more direct line of 

sight to the area topography. Multiple scans were collected during each survey to fill in “shadow zones” 

of locations not directly in the line of sight of the laser and to expand the range and density of the point 

data (fig. 6). In some cases, additional scan locations are required to provide sufficient overlap of point 

clouds from one gully area to the next. This is often necessary for accurate registration of the point 

clouds (discussed in more detail in the following section). In general, to minimize impacts, scan 

locations were positioned outside archeological site boundaries; locating scans outside of site 

boundaries was not possible in all cases because of the complex geometry of some of the gullies. Data 

were collected at a rate of 8,000 points/second, scanning a range of 336° in the horizontal direction and 

plus and minus 40° from the horizontal in the vertical direction. This provided approximately 2.3 

million points for each scan, although, only a portion of those points actually defined the gully 

topography of interest. With LIDAR, the area immediately adjacent to the gully and throughout the 

range of the scanner is captured in the same high-resolution detail (fig. 7A). On nearly flat ground, an 

area within a radius of about 20 m from the scanner location is captured in this detail; although, the data 

range is dependent entirely on the incident angle of the laser, as determined by the height of the scanner 

above the ground and the obliqueness of the scanner to the site topography. Combining many scans 

results in a topographic surface image of an entire site, such that entire site monitoring is possible (fig. 

7B). Many other features can therefore be monitored in addition to the gully topography. 

 9
A 

B 

Figure 5. A, Terrestrial light detection and ranging (LIDAR) data collection in Grand Canyon, Ariz. 

Extendable tripod allows a larger area of data collection and less oblique laser returns from flat ground. 

Inset shows laser pulse and return windows and survey prism used to georeference the instrument 

location. B, Transporting the system requires two people in most cases, the first to carry the laser and 

tripod and the second to carry the data acquisition unit (laptop computer) and laser batteries. 

 10
fine1 

~20 m 

Figure 6. Typical terrestrial light detection and ranging (LIDAR) setup locations (fine2, fine3, etc.) and 

data coverage from a single scan (fine1) for site G:03:041. “Fine” refers to the point density setting of the 

laser (approximately 2.3 million points per scan). Each scan provides a different view of the gully system, 

sometimes overlapping with neighboring scans but expanding the data coverage and point density. 

Black areas indicate shadow zones or areas where no data were collected beyond the range of the 

laser. The circle has approximately a 20-m radius and delineates the area of highest point density. 

 11
A Elev. = 432.4m 

Elev. = 426.8m 

B Elev. = 432.4m 

Elev. = 426.8m 

Figure 7. Views of A, terrestrial light detection and ranging (LIDAR) points and B, surface model data. 

(Site G:03:041⎯Gully 1. Blue line is 22 m in length, arrow shows direction of flow.) Point data in A are 

from a single scan (fine1) and show extremely high point density from terrestrial LIDAR data. Other scan 

locations (fine3, fine4) and control points (tmp1, ball1, etc.) are labeled. The surface model in B is 

constructed of 5-cm triangular facets developed from multiple scans and shows the extensive definition 

of the site topography, including gully bottom, side walls, and neighboring ground. Vegetation has been 

removed through filtering algorithms; black areas define zones with no ground points due to filtering or 

limited data collection. 

 12
 13

Georeferenced coordinates of the scan locations were determined by either locating the 

instrument over known survey benchmarks or by collecting total station positioning via a survey prism 

mounted directly to the laser (fig. 5A). Additional registration fit was provided by scanned survey 

control points (LIDAR balls) located in and adjacent to each site (fig. 8). Metadata for the data 

collection effort are available at http://walrus.wr.usgs.gov/infobank/l/lg106gc/html/l-g1-06-

gc.meta.html. 

Data Processing 

The processing methodology was selected to result in high-resolution topographic models of the 

entire site area, such that advanced hydrologic modeling could be performed on the final surface model 

to extract gully features. This methodology best met the duality of the project goals by both 

investigating the applicability of terrestrial LIDAR to model site morphology and extracting meaningful 

geomorphic features at the same level of detail as total station data. 

The data were processed using I-SiTE software (I-SiTE, 2008), which was specifically designed 

to handle laser scan data. Scans for each location were first registered to each other and georeferenced 

through a surface registration algorithm that finds best-fit solutions to overlapping areas of the point 

clouds. Increased overlapping point density typically results in more accurate data registration. The 

origin of each scan was held fixed according to its known survey position, and the scans were allowed 

to rotate in three dimensions. The final 3-D georeferencing of the dataset was achieved by registering at 

least three scans to form an approximately equilateral triangle, thus forming a plane with a single 

solution. Additional scans were then registered to this georeferenced plane. 

Data filtering to define the bare ground surface (without vegetation) was performed through a lowest 

point filter on a 0.5-m grid, followed by a 0.10-m surface proximity filter. The surface proximity filter 

reintroduced all points within 0.10 m of the lowest point surface. Then a 0.05-m minimum separation 

filter provided a homogenously dense final dataset for surface processing. Surfaces were created from 

the bare-ground point data in triangulated irregular network (TIN) format and analyzed using an 

automated water-drop pathway algorithm to define the lowest points (that is, the thalweg) for each 

gully. The algorithm is based on an evaluation of the centroid elevation of each TIN triangle; choosing 

smaller triangles results in a more exact solution but possibly more sinuous thalweg. In some cases, the 

TIN was prohibitively complex for determining the thalweg using automated techniques. For these 

gullies, the thalweg was extracted manually by linking the lowest drainage path of 1-cm contour 

intervals constructed from the site TIN (fig. 9). Long profiles and cross sections were then developed 

from the surface and thalweg data. 

Note that since the selected methodology did not use the entire set of data points collected, the 

methodology design could be considered biased towards the goal of whole site modeling. Although 

other processing and modeling techniques are available and could have been used to investigate 

increased accuracy of the results, they come at a commensurate and greatly increased price in terms of 

data-processing efficiency and were therefore not selected for this investigation. 
A B 

3.1 cm 

C 

Figure 8. Terrestrial light detection and ranging (LIDAR) control points (LIDAR balls). Each ball is 

covered with strips of reflective tape, highly visible in the point-cloud data. A, The ball is either switched 

out with a survey prism and its position determined by total station survey or B, a survey prism is located 

beneath the LIDAR ball and surveyed directly. C, Error analysis is performed by matching a spherical 

model of the balls (green) to the points reflected from the balls (purple points). The distance (3.1 cm) 

between the surveyed coordinate (tmp1) and the center of the sphere (TMP1−BALLCENTER) is a 

measure of the accuracy of the data. 

 14
826.45 m 

826.40 m 

826.35 m 

LIDARderived 

thalweg 

826.30 m 

826.25 m 

~1.0 m

Figure 9. Extraction of a gully thalweg (thin dark-blue line) using 1-cm contours constructed from the 

surface model triangulated irregular network (TIN). Blue arrows indicate downward direction of surface 

water flow in the gully. 

Error Analysis 

Several sources of error are present in the LIDAR data that should be considered when using 

either the points or their associated surfaces for sources of linear and volumetric measurements. These 

errors include those from the laser instrument, those from total station survey control, and those from 

the registration process. 

The total error varies depending on the type of measurement or specific use of the data (table 2). 

For example, measurements made within a LIDAR point cloud from a single scan are accurate to within 

the instrument error (Elaser = 1.5 cm). Thus, a measurement made between two points may contain twice 

this error (2 ×1.5 cm = 3.0 cm), since these errors are considered dependent (the error is from one 

source⎯the laser). The accuracy of a single point in a registered (but not georeferenced) scan depends 

on the laser error and the registration error. The registration error is not easily calculated since the 

alignment of two scans depends on the proximity of overlap of the specified point clouds. It is estimated 

here by calculating the best fit of the point clouds from a set of three scans registered to one another in a 

triangle layout with the three degrees of translational freedom removed (that is, the laser origins are 

 15
Table 2. Errors associated with terrestrial light detection and ranging (LIDAR) measurements.

Type of measurement 

Total threedimensional 

error 

Single-point accuracy (local coordinates) 1.5 cm 

Distance between two points in a single scan 3.0 cm 

Single-point accuracy (registered coordinates) 5.1 cm 

Distance between two points in two registered scans 5.8 cm 

Single-point accuracy (relative georeferenced coordinates) 7.3 cm 

fixed in space). Using this technique, the registration error obtained in this study was approximately 5 

cm. The laser and registration errors are considered to be independent and the total error is calculated as 

the root of the sums of the squares of each ( 22 = laser + EEE reg = 5.1 cm). Measurements made between 

two points in two different scans in a single dataset are accurate to the (doubled) instrument error (one 

each for each point in the measurement) and the registration process error for the two scans (Ereg), which 

is not doubled ( 2 2 )2( = laser + EEE reg = 5.8 cm).

Measurements made between any LIDAR point and geographic coordinates include these 

previous errors plus the total station error (Econtrol = 1.0 cm) for the scanner locations (used to 

georeference the data). This error was calculated through dual-repeat measurements of a single LIDAR 

scanner position and the total station back sight. They are therefore more accurate than any single total 

station measurement used in that part of the survey. Because identical benchmarks were used for 

surveying both the total station data and the laser setup locations, any additional georeferenced error 

introduced through the original survey of the control points is zero and can be ignored, assuming 

minimal tectonic drift and maximum control point stability. However, if these datasets are used in 

comparisons to studies that do not use these benchmarks, the error associated with the initial benchmark 

survey and stability must be included. For the control points used in this evaluation, mean 3-D error is 

approximately 5 to 8 cm (U.S. Geological Survey, 2008) but, as noted, is not included in the error 

estimate. Here, we refer to the total error as the 3-D relative georeferenced error (table 2). 

Each of these errors is independent and calculated by the root of the sum of the squares of each 

error, but here Elaser is not doubled because only one laser measurement is being quantified. Further, 

because the registration technique is calculated based on the point-cloud matching of three triangularly 

positioned scans in two independent computations, the registration error is included twice (for one scan 

to the next, Ereg1 [5 cm], followed by this pair of scans to the third scan, Ereg2 [5 cm], to define a plane 

that is “locked” in the three rotational degrees of freedom of the point clouds). The total error for a 

single georeferenced point is therefore = 7.3 cm. 

An independent check of these errors was performed by comparing the locations of the LIDAR 

survey control balls in the point clouds to the surveyed locations from the total station data (fig. 8C). 

The average 3-D positional error was 7.8 cm for the three datasets presented in this report, verifying the 

expected order of magnitude of the calculated errors. 

 16

2

2

2

1

2 2

sum laser control reg +++= EEEEE reg
For evaluation of the vertical accuracy of the data (⊗z), the 3-D error (Esum) calculated previously 

(about 7.5 cm) can be decomposed into only an error for a single dimension by way of the following 

formulation: sum 3Δ= zE , which assumes that the errors are distributed evenly between the three 

coordinate dimensions. Thus, the expected vertical accuracy of the data is 4.3 cm. This value is 

therefore less than that obtained by previous efforts using both airborne LIDAR (8 cm; Davis, 2004) and 

photogrammetry (6–10 cm; Pederson and others, 2003), verifying that terrestrial LIDAR should be 

expected to detect topographic change at the subdecimeter scale. 

All of these estimates should be noted as being conservative (maximum) errors, representative of 

worst-case scenarios, because they depend on the registration of data from the entire site. In areas close 

in to the scanner locations, and with better registration fit (that is, near the gully thalwegs), maximum 

errors should be expected to be less, since the laser and registration errors are systematic errors that 

increase in magnitude with increasing distance from the scanner origin. It is therefore likely that 

change-detection measurements using temporally different datasets will be somewhat less than these 

maximum errors. 

Conventional Total Station Surveying 

Data Collection and Processing 

Conventional survey techniques using an electronic total station are the most common means of 

surveying in Grand Canyon and continue to be used to meet the GCMRC’s mapping objectives, 

including topography, site location, and control. In total station surveying, the surveyor sights on one or 

more survey rod personnel (that is, rodmen) holding a survey rod mounted with a reflective prism on a 

point of interest. The total station instrument accurately measures horizontal angles, vertical angles, and 

slope distances in order to position or locate survey points of interest or define a feature. The GCMRC 

uses four Topcon total stations for all mapping: two GPT 2003 models and two GTS 313 models. 

To establish positional reference to the State Plane Coordinate System and provide verification of 

positional accuracy, conventional survey methods require the total station to be centered over a known 

control point and a prism centered over another known control point (that is, back site; fig. 10). In the 

case of a topographic survey, this two-point occupation establishes the State Plane coordinate grid, and 

all other points needing location are systematically measured as side shots by sighting on the prism that 

the rodman accurately centers over a required survey point. A data collector is used as an electronic 

field book to record the total station measurements, which are subsequently electronically downloaded 

to a computer for reduction and processing. A total station survey requires a minimum of a surveyor and 

a rodman (fig. 11) but usually involves several rodmen for increased efficiency. For the gully surveys, a 

lone rodman is most efficient where the gullies are narrow or the point spacing is less than 1.5 m. When 

two or more rodmen are involved in the surveying process, measurements are collected from one 

rodman, while the other rodman moves to the next data point and prepares for measurement collection. 

Because this surveying method requires personnel to locate themselves directly in or near the surveyed 

points to define the gully, impact to the archeological site in the form of footsteps is typical and 

generally concentrated in the gully itself. 

 17
A B 

Figure 10. Conventional total station data collection showing A, back site location with prism assembly 

(arrow) for angular and distance control. In this figure, the prism is located directly beneath a global 

positioning system (GPS) receiver being used for a separate survey. In B, the total station instrument is 

set up at a fixed, known location (that is, benchmark or control point) for positional control. 

Specific geomorphic features in the gully (the thalweg, the gully bottom, and the knickpoints) 

are coded into the data collector before taking a measurement. For monitoring check dams, the process 

requires surveying the area covered by the check dam as well as noting check dam type in the data 

collector. The angle and distance data are reviewed and corrections are edited and then converted to 

State Plane coordinates using specialized software (for example, Survey Link 7.5.5 software by Tripod 

Data Systems). After this process, coordinates for points are available as a text file for modeling in a 

variety of geographic information system (GIS) or surveying-based software packages. 

 18
Figure 11. Conventional total station data collection showing use of survey rod and prism in a gully 

system. Communication between the surveyor at the total station and the personnel in the gullies is 

achieved through a combination of voice, arm signals, or handheld two-way radios. 

Error Analysis 

Total station measurements are subject to error from three sources: the instrument error, prism 

location error, and georeferencing error. The instrument error is calculated by a root mean sum of the 

error induced by both range (Erange) and angular measurements (Ehoriz, and Evert) for 3-D positioning. Thus, 

the total instrument error is 2

2

2

1

2 Einstrument range ang ++= EEE ang . The instruments used in this study have 

a documented angular accuracy of 3 s or 1 milligon (mgon) and a distance accuracy of ±(3 mm + 2 ppm 

 19
× D) mean squared error (MSE), where D = measuring distance (mm). Based on a mean average range 

from instrument to target of 150 m, the range error is 3.3 mm and the angular errors are each 2.2 mm. 

The total instrument error is therefore 4.5 mm or approximately 0.5 cm. 

The prism location error is based on both environmental conditions and errors introduced by the 

rodmen. Environmental factors affecting measurements include heat shimmers and wind. Heat 

shimmers interfere with precise sightings on the survey prism and affect laser distance measurements; 

strong wind may cause the tripod and total station to quiver while taking a measurement. The heat 

shimmer and wind error are dependent on the intensity of the environmental factor and were not 

quantified in this study. Wind may also cause the survey rod to arc when it is extended beyond 3.2 m, 

which is necessary when the line of sight is obstructed by vegetation, boulders, or landforms. Humaninduced errors are mainly associated with miscoding data points, selecting the wrong point to survey, 

and holding the survey rod off plumb. To remedy the last problem, a bubble level is attached to the 

survey rod that allows the rodman to verify that the survey rod is plumb when taking a measurement. 

The bubble level can only verify that the two lower portions of the extendable survey rod are plumb, so 

when the survey rod is extended by three or more extensions (up to 4.7 m), the total station operator 

must verify that the rod is plumb by using the eyepiece crosshair to sight the center of the rod as close to 

the ground as possible to capture the horizontal position of the point of interest. The point measurement 

is then taken by moving the eyepiece to the elevation of the survey prism while maintaining the 

previously established horizontal angle. While this approach generally provides usable data with a 

horizontal error estimate of 10 cm when the survey rod is visible within a meter or two of the ground, 

the error value increases when the lower portion of the rod is hidden and only the survey prism or upper 

portion of the survey rod is visible to sight on for a measurement. In this case, the horizontal error can 

increase to tens of centimeters. The survey data included in this evaluation were primarily collected 

using less than three survey rod extensions; three or more extensions were used only occasionally when 

data collection occurred in high vegetation, deep gullies, or behind protruding areas of the ground 

surface. In this study, the total prism location error is estimated to be between 3 and 10 cm. 

Georeferencing error is based on possible error in the known coordinate positions of the total 

station and back sight control points. However, since the control points are used throughout each survey 

and are occupied identically during both previous and subsequent surveys of the area, they form only a 

relative measure of error, do not directly effect the total error estimate, and are therefore not quantified 

in this study. Similarly to the LIDAR error analysis, the error estimates calculated here should be 

interpreted as relative georeferenced errors because the error associated with the control point 

benchmarks is not included. Again, since identical benchmark control points were used in both the total 

station and LIDAR surveys, this error can be ignored when the two datasets are compared directly. 

Total station error is computed in this study by two methods: (1) summing the three components 

of error and (2) comparing measurements taken directly on points with known coordinates. The sum of 

the three components of error (instrument = 0.5 cm, prism location = 2 to 10 cm, and georeferencing = 0 

cm) results in a total error estimate of 2.5 to 10.5 cm in 3-D positioning. Measurements made on known 

points (that is, back sight control points) provide a secondary error check. Here, the measured average 

difference coordinate value for the back sight is compared to the published coordinate value known for 

each point. On average in this survey, the positional difference between the published coordinate value 

and the measured coordinate value was 2.4 cm. Thus, the total error in any single point is likely on the 

lower end of the initial total error estimate range. 

Because of the nature of the differences between data processing in the terrestrial LIDAR and 

total station survey methods, direct comparisons of the various errors summarized in table 2 for 

terrestrial LIDAR cannot be made. In total station surveying, the single point accuracies for local, 

 20
registered, and georeferenced coordinates are all identical and on the order of 2 to 3 cm. Measurements 

between two points based on these values can be calculated similarly to the LIDAR data, and are 

therefore accurate to twice the single point error (that is, 4–6 cm). 

In this evaluation, the maximum 3-D errors associated with each method are used to calculate a 

maximum inaccuracy threshold for the data comparison. Whereas the vertical accuracy estimate 

technically should be used, it is assumed that some errors may not be independent between the 

horizontal and vertical components; therefore, this evaluation uses the more conservative 3-D errors 

instead. Thus, the terrestrial LIDAR 3-D error of approximately 7.8 cm combined with the total station 

3-D error of 2.4 cm results in an expected maximum accuracy threshold of 10.2 cm between the 

datasets. A threshold of 10 cm is used in the topographic evaluation, with vertical or horizontal 

differences more than 10 cm being unacceptable. 

Impact Evaluation 

Site impacts were evaluated using two chief metrics: the number of footsteps taken and the time 

spent within each archeological site boundary (sensitive area). These metrics were selected based on the 

premise that the more footsteps taken and the longer time spent within the boundary, the higher the 

likelihood for site degradation resulting from the researchers’ presence (that is, surface erosion and soil 

compaction; fig. 12). More footsteps are likely to occur when researchers are moving around within the 

site boundary, but even when a researcher is not moving around, there is still a potential for soil 

compaction and site erosion simply from the shuffling of feet. Site boundaries investigated in this study 

had been previously established by NPS archaeologists; these archaeologists were also present in the 

field during each survey effort. 

A B 

Figure 12. Footstep imprints impact archeological sites during surveying by compacting soils or 

damaging cryptogamic soil crusts. In A, fresh footprints are in a well-developed crust, whereas in B, the 

fresh footprints are set amid other footprints from previous site visits, which results in a mottled soil 

texture. 

 21
To measure footsteps (footstep count), mid-quality-brand hip-unit pedometers were used on all 

surveyors to sense footstep-related leg movement. At the start of each survey and before entering the 

sensitive area, the pedometer was clipped onto the hip and the step count reset to zero. When exiting the 

sensitive area, the accumulated footsteps were recorded, or, alternatively, the pedometers were removed 

and left near the site until the person returned and reclipped the unit to the hip. If one of the pedometers 

malfunctioned or the procedures were not followed correctly, the erroneous data were discounted and 

footsteps recorded by another surveyor were doubled for that time interval. This method was considered 

an appropriate approximation for footstep count because the surveyors for both mapping techniques 

worked closely with each other. In this study, we found that the pedometer footstep count was biased 

toward a heavier gait or greater hip movement; a tiptoeing wearer rendered no count, while stomping 

counted each step as two. For example, since rodmen for the total station surveying moved more slowly 

and over a smaller distance with each footstep, some footsteps may not have been recorded. Given these 

limitations, the footstep counts must be considered approximations useful only for making gross 

comparisons. 

Time in the sensitive area was measured by wristwatch and recorded similarly to footstep count 

as the surveyors entered and exited the site boundary. This provided an independent proxy measurement 

of site impact. 

To further investigate trends in the impact data, additional analysis was performed by calculating 

the variation within each type of mapping method (2006 versus 2007) as well as the variation between 

mapping methods (total station versus LIDAR). While the difference between the survey efforts within 

each method provided a measure of consistency from year to year, the difference between the survey 

method values provided a direct indication of varying impacts caused by each method. 

The variation within each type of mapping technique was determined by subtracting each 

method’s 2007 values from their 2006 values for each site (TS = total station): 

A1 = [(total survey time)2006 − (total survey time)2007]TS or LIDAR (1a) 

A2 = [(time in sensitive area)2006 − (time in sensitive area)2007]TS or LIDAR (1b) 

A3 = [(footstep count)2006 − (footstep count)2007]TS or LIDAR (1c) 

Each parameter represents variations in either person-hours or footstep count: a negative number 

indicates fewer person-hours or footsteps in 2006, a positive number indicates fewer person-hours or 

footsteps in 2007, and an overall average near zero reflects consistency within the survey method. The 

variation between mapping methods was calculated similarly: 

B1 = [(TS total survey time) − (LIDAR total survey time)]2006 or 2007 (2a) 

B2 = [(TS time in sensitive area) − (LIDAR time in sensitive area)]2006 or 2007 (2b) 

B3 = [(TS footstep count) − (LIDAR footstep count)]2006 or 2007 (2c) 

Similar to the A-parameters, a negative number indicates fewer person-hours or footsteps in the total 

station method, a positive number indicates fewer person-hours or footsteps in the LIDAR method, and 

an overall average near zero reflects consistency within the survey methods. 

As an additional purpose of this study was to evaluate all facets of the use of terrestrial LIDAR 

for gully thalweg and whole site monitoring, several other indirect impact components were also 

measured, including quantity and quality of data and relative cost. Protocols for these criteria were 

selected a priori and are detailed in appendix A. Data density and accuracy were evaluated through 

direct comparison of the number of points collected and the postprocessed data point accuracy from 

each survey method. Data accuracies from each method are reported in the previous error analysis 

 22
section of this report. The cost component was divided into two categories: (1) equipment purchase and 

maintenance and (2) personnel cost. Personnel cost was evaluated by comparison of both total survey 

time and the time required to postprocess the data to State Plane (X, Y, Z) coordinates, both measured in 

person-hours. During fieldwork, the time interval was documented by recording the start and end times 

of each survey, and subtracting break time and down time. The total survey time includes such items as 

transportation of equipment to and from each site to the raft, setup time, and surveying time outside the 

site boundary. This is in contrast to the time in the sensitive area metric, which only measures those 

times when personnel were within the site boundaries. Thus, while the total time provides a measure of 

survey efficiency, the time in the sensitive area provides a measure of site impact. 

Results 

Topographic Data Collection 

For the three sites investigated in this study for topographic change (C:13:006, G:03:041, and 

G:03:072), data from seven individual gully systems were collected (table 3). Using the terrestrial 

LIDAR method, a total of 21 scan locations were required to develop the appropriate surface models for 

these sites, generating a total of 3,343,600 ground (nonvegetation) points. Using the total station 

method, a total of 2,408 points were collected of the gully thalweg, gully bottom edge, gully top edge, 

and locations of knickpoints and check dams. Note that in some cases, a greater number of LIDAR scan 

locations were required to capture a smaller overall surface area of terrain. This is because of the 

complicated topography (that is, nonlinear or steep thalwegs) of some of the sites, which required 

multiple vantage points to image a comparatively small area. Overall, the point density collected is three 

orders of magnitude greater for LIDAR than for total station survey (table 3, fig. 13). 

The LIDAR-derived thalweg for each gully is presented in both long-profile section and plan 

view, along with an image of each gully, in figures 14 through 26. The long-profile section and plan 

view of the thalweg data from the total station data are also shown in the cross sections and serve as the 

baseline to which the LIDAR data are compared. Whereas the entire LIDAR-derived thalwegs are 

shown in plan view, they have been normalized with the total station channel head location in the 

profile views for direct comparison. Mean (μ) and one standard deviation (σ) statistics are included in 

the cross sections to delineate the vertical comparison between the total station and LIDAR data. 

In general, the plan view data (figs. 15, 17, 20, and 24) indicate that the gully locations identified 

by the LIDAR technique were generally in close proximity (<10 cm) to that obtained by total station 

methods. The overall length of extracted gully topography is greater in all cases using the LIDAR 

dataset because of the inability to conclusively select the channel head or channel bottom from the 

LIDAR dataset, a point discussed in more detail later in this report. 

 23
Table 3. Survey data summary.

Site number 

Number 

of gullies 

surveyed 

Terrestrial light detection and 

ranging (LIDAR) Total station 

Number 

of scans 

Number of 

ground points 

surveyed 

Modeled 

surface 

area (m2

) 

Number of 

ground points 

surveyed 

C:13:006 3 6 1,323,471 7,713 953 

G:03:041 2 9 1,564,445 3,063 656 

G:03:072 2 6 455,684 1,160 799 

~1 m 

Figure 13. Typical gully plan view showing thalweg (blue line) and relative difference in point density 

between light detection and ranging (LIDAR) (green points) and total station (red points) survey methods. 

LIDAR points are spaced approximately 5 cm from one another and have been down filtered according 

to LIDAR processing methods. 

 24
1a 1b 2 

A 

2 

1a 1b 

B 

Figure 14. Site C:13:006⎯Locations of Gullies 1a, 1b, and 2 in A, oblique view; and B, plan view. 

 25
TS data

5m 10m 15m

5m 

10m 

15m 

20m 

25m 

0 

Gully 1a Gully 1b

LIDAR Data 

Figure 15. Site C:13:006⎯Gullies 1a and 1b: Plan view of light detection and ranging-derived (LIDAR; 

blue) and total station-derived (TS; purple) thalwegs. 

 26
814

816

818

820

822

824

826

828

830

832

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Distance Along Section (m)

Elevation above NAD83 Ellipsoid (m)

-0.50

0.00

0.50

1.00

1.50

Vertical difference (m)

Lidar Data

Total Station Data

Vertical Difference

Total Station

LIDAR

μ = -5cm, σ = 18cm

A

814

816

818

820

822

824

826

828

830

0 2 4 6 8 10 12 14 16 18 20 22 24 26

Distance Along Section (m)

Elevation above NAD83 Ellipsoid (m)

-0.50

0.00

0.50

1.00

1.50

Vertical difference (m)

Lidar Data

Total Station Data

Vertical Difference

Total Station

LIDAR

μ = -2cm, σ = 19cm

B

Figure 16. Site C:13:006⎯Long-profile comparison of light detection and ranging (LIDAR) and total 

station data for A, Gully 1a; and B, Gully 1b.

 27
25m 

10m LIDAR Data 

TS Data

5m 10m 15m 20m

20m 

15m 

5m 

0 

25m 

Figure 17. Site C:13:006⎯Gully 2: Plan view of light detection and ranging-derived (LIDAR; blue) and 

total station-derived (TS; purple) thalwegs. 

 28
812

814

816

818

820

822

824

826

828

830

832

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32

Distance Along Section (m)

Elevation above NAD83 Ellipsoid (m)

-0.50

0.00

0.50

1.00

1.50

2.00

Vertical difference (m)

Lidar Data

Total Station Data

Vertical Difference

Total Station

LIDAR

μ = 3cm, σ = 8cm

Figure 18. Site C:13:006⎯Gully 2: Long-profile comparison of light detection and ranging (LIDAR) and 

total station data.

 29
1 

A 

2 

B 

1 

2 

C 

Figure 19. Site G:03:041⎯Locations of gullies in A, oblique view, Gully 1; B, oblique view, Gully 2; and C,

plan view. 

 30
LIDAR data

TS data 

Gully 2 

No LIDAR 

data due to 

dense 

vegetation. 

Gully 1

5m 10m 15m 20m 25m 

25m 

20m 

15m 

10m 

5m 

0 

Figure 20. Site G:03:041⎯Gullies 1 and 2: Plan view of light detection and ranging-derived (LIDAR; 

blue) and total station-derived (TS; purple) thalwegs. 

 31
422

424

426

428

430

432

0 2 4 6 8 10 12 14 16 18

Distance Along Section (m)

Elevation above NAD83 Ellipsoid (m)

-0.50

0.00

0.50

1.00

1.50

2.00

Vertical difference (m)

Lidar Data

Total Station Data

Vertical Difference

Total Station

LIDAR

μ = 3cm, σ = 5cm

Figure 21. Site G:03:041⎯Gully 1: Long-profile comparison of light detection and ranging (LIDAR) and 

total station data. 

418

420

422

424

426

428

430

432

434

0 2 4 6 8 10 12 14 16 18 20 22 24 26

Distance Along Section (m)

Elevation above NAD83 Ellipsoid (m)

-0.50

0.00

0.50

1.00

1.50

Vertical difference (m)

Lidar Data

Total Station Data

Vertical Difference

Total Station

LIDAR

μ = 1cm, σ = 6cm

Figure 22. Site G:03:041⎯Gully 2: Long-profile comparison of light detection and ranging (LIDAR) and 

total station data. 

 32
2 

1 

A 

2 

1 

B 

Figure 23. Site G:03:072⎯Locations of Gullies 1 and 2 in A, oblique view; and B, plan view. 

 33
25m 

20m 

LIDAR 

Data 

Gully 1

TS 

Data 

0 

15m 

5m 

10m Gully 2

5m 10m 15m 20m 25m 

Figure 24. Site G:03:072⎯Gullies 1 and 2: Plan view of light detection and ranging-derived (LIDAR; 

blue) and total station-derived (TS; purple) thalwegs. 

380

382

384

386

388

390

392

394

396

398

400

402

404

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38

Distance Along Section (m)

Elevation above NAD83 Ellipsoid (m)

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

Vertical difference (m)

Lidar Data

Total Station Data

Vertical Difference

Total Station

LIDAR

μ = -15cm, σ = 15cm (all data)

μ = 0cm, σ = 5cm (Section 21m to 35m)

Figure 25. Site G:03:072⎯Gully 1: Long-profile comparison of light detection and ranging (LIDAR) and 

total station data. 

 34
390

392

394

396

398

0 2 4 6 8 10 12 14 16

Distance Along Section (m)

Elevation above NAD83 Ellipsoid (m)

-0.50

0.00

0.50

1.00

1.50

Vertical difference (m)

Lidar Data

Total Station Data

Vertical difference Total Station

LIDAR

μ = -5cm, σ = 6cm

Figure 26. Site G:03:072⎯Gully 2: Long-profile comparison of light detection and ranging (LIDAR) and 

total station data. 

Whereas errors were often within the methodology limits, error increased to unacceptable ranges 

(>10 cm) when the topography was either flat or the gully did not form a distinct pathway through the 

topography. For example, at Site C:13:006, the plan view comparison shows horizontal errors up to 1 m 

in the flatter plateau areas (figs. 15 and 17). Here, the gullies are not very deep and were difficult to 

distinguish in the LIDAR data. In these cases, the automated water-drop pathway algorithm was not 

able to distinguish a clear thalweg pathway, and manual contour linking likely introduced greater errors. 

The profile view data (figs. 16, 18, 21, 22, 25, and 26) show similar and improved results but 

with indications of systematic errors as determined by the calculated maximum and minimum 

differences (table 4). The data show that with the exception of gully 1 at Site G:03:041, all sites had 

either isolated or integrated errors between 14 and 46 cm for some portion of each long-profile section. 

However, taken as a whole, each gully cross section extracted from the LIDAR data was within 10 cm 

of the total station data throughout most of the length of the profiles. Mean (⎧) and standard deviation 

(⌠) values calculated along 1-m sections for all extracted thalweg cross sections are summarized in table 

4. The total percent length of the thalweg surveyed accurately within a ±10 cm error bound ranged from 

23 percent in the worst case (C:13:006—Gully 1b) to 95 percent in the best case (G:03:041—Gully 2). 

However, overall, only 56 percent of the total thalweg lengths were within these error bounds. 

Confidence intervals cannot be calculated from the standard deviation data because of the unlikelihood 

that the errors are normally distributed; errors appear systematic with regard to their location within a 

particular gully. Errors propagated beyond acceptable ranges (>10 cm) in areas of flat topography (for 

example, Site G:03:072; compare the upstream (flat) and downstream (steep) sections of figs. 25 and 

26) and where no clearly defined gully bottom could be distinguished in the LIDAR data (for example, 

Site C:13:006; fig. 16). Meanwhile, at Site G:03:072—Gully 2 (fig. 25), the mean error drops from -15 

cm to zero when only the steep data (Station 21 to 36 m) is compared. 

 35
Table 4. Statistical comparison of gully thalweg profiles between light detection and ranging (LIDAR) 

and total station data. 

Site name Mean, μ

(cm) 

Standard 

deviation, σ

(cm) 

Maximum 

(cm) 

Minimum 

(cm) 

Percent gully 

length within 

±10 cm error 

bound 

C:13:006—Gully 1a -5 18 31 -40 35 

C:13:006—Gully 1b -2 19 31 -42 23 

C:13:006—Gully 2 3 8 20 -14 72 

G:03:041—Gully 1 3 5 11 -10 82 

G:03:041—Gully 2 1 6 20 -18 95 

G:03:072 DS—Gully 1* -15 15 8 -46 42 

G:03:072 DS—Gully 2 -5 6 4 -16 71 

All gully data -4 15 31 -46 56 

*

 Mean and standard deviation for Station 21 to 36 m (steeper section) are 0 and 5 cm, respectively. 

A limitation of the LIDAR methodology is shown clearly in figure 20, which identifies an area 

of the gully where the thalweg location could not be obtained from the LIDAR data because of dense 

vegetation (fig. 19B). Here, the total station method has an advantage, since it is possible for survey 

personnel to set the survey prism rod through the vegetation to identify the gully thalweg ground point. 

The LIDAR technique is also limited in its ability to discern the location of knickpoints and check dam 

features (shown, for example, in fig. 22 between stations 4 and 8 in the total station data), which are not 

always obvious in the LIDAR cross sections. Because the LIDAR method does not identify or name 

particular features during the data collection process (“feature coding”), these must be extracted during 

the postprocessing stage by comparison with site photographs or a previously collected total station 

dataset. This limitation is also discussed in more detail later in this report. 

Impact Evaluation 

The data from the impact evaluation on all eight sites are shown in appendix B and plotted in 

figures 27 and 28, where values for total survey time, time in sensitive area, and footstep count are 

detailed by site name and survey method for the two field excursions in May 2006 and May 2007. The 

cumulative mapping times for each survey method and field effort and the average cumulative mapping 

time for each method were also calculated (fig. 29). Note that one site was not monitored by total station 

methods in 2007 (site C:13:099); the average cumulative mapping time should therefore be used for 

direct comparison between the two survey methods. The data are used to make gross quantitative 

evaluations of the direct (footstep count) and indirect (hours in sensitive area) impacts to each 

archeological site. In addition, the total person-hours value provides an indication of the efficiency of 

each method (that is, less total survey time means greater efficiency). 

 36
0

2

4

6

8

10

12

14

16

18

20

SITE

PERSON HOURS

C:13:006 C:13:336 C:13:099 C:13:348 G:03:041 G:03:002 G:03:072U G:03:072D

- TOTAL DATA COLLECTION PERSON HOURS

 TIME IN SENSITIVE AREA

 - 2006 LIDAR

 - 2007 LIDAR

 - 2006 TS

 - 2007 TS

TOTAL SURVEY TIME 

TIME IN SENSITIVE AREA 

2006 TS 2007 TS 

2006 LIDAR 2007 LIDAR 

0

2

4

6

8

10

12

14

16

18

20

SITE

PERSON HOURS

C:13:006 C:13:336 C:13:099 C:13:348 G:03:041 G:03:002 G:03:072U G:03:072D

- TOTAL DATA COLLECTION PERSON HOURS

 TIME IN SENSITIVE AREA

 - LIDAR

 - TS LIDAR Ave. TS 

TIME IN S

TOTAL SURVEY TIME 

ENSITIVE AREA 

Ave. 

Figure 27. Comparison of total survey time and time in sensitive area for terrestrial light detection and 

ranging (LIDAR) and total station (TS) methods plotted for each site and data collection effort (upper 

graph) and averaged for both surveys (2006 and 2007) (lower graph). The shaded portion of the bar 

graphs represents time in the sensitive area of the site, while the open portion is the additional time 

required to complete the mapping effort outside the sensitive area of the site. Note: site C:13:099 was not 

surveyed by total station surveying in 2007. 

 37
FOOTSTEP IMPACT

0

1000

2000

3000

4000

5000

6000

7000

8000

C:13:006

C:13:336

C:13:099

C:13:348

G:03:041

G:03:002

G:03:072

G:03:072

Site Name

Cumulative Footstep Count

LIDAR May 2006

LIDAR May 2007

TS May 2006

TS May 2007 TS May 2006 TS May 2007 LIDAR May 2006 LIDAR May 2007 

FOOTSTEP IMPACT

0

1000

2000

3000

4000

5000

6000

C:13:006

C:13:336

C:13:099

C:13:348

G:03:041

G:03:002

G:03:072

G:03:072

Site Name

Cumulative Footstep Count

LIDAR Average

TS AverageTS 

Average 

LIDAR 

Average 

Figure 28. Comparison of footstep count for terrestrial light detection and ranging (LIDAR) and total 

station (TS) methods plotted for each site and data collection effort (upper graph) and averaged for both 

surveys (2006 and 2007) (lower graph). Note: site C:13:099 was not surveyed by total station surveying in 

2007 and the LIDAR footstep count was not measured. 

 38
CUMULATIVE MAPPING TIMES

0

10

20

30

40

50

60

70

80

90

100

LIDAR May 2006 LIDAR May 2007 TS May 2006 TS May 2007

Method and Date

Person Hours

G:03:072D

G:03:072U

G:03:002

G:03:041

C:13:348

C:13:099

C:13:336

C:13:006

CUMULATIVE AVERAGE MAPPING TIMES

0

10

20

30

40

50

60

70

80

90

100

Lidar Average TS Average

Mapping Method

Person Hours

G:03:072D

G:03:072U

G:03:002

G:03:041

C:13:348

C:13:099

C:13:336

C:13:006

* C:13:099 not 

monitored by TS 

in May 2007. 

Figure 29. Comparison of cumulative total survey time for terrestrial light detection and ranging 

(LIDAR) and total station (TS) methods plotted for each site and for each data collection effort (upper 

graph) and averaged for both surveys (2006 and 2007) (lower graph). Sites are ordered from top to bottom 

as indicated in the legend. 

 39
Figures 27 and 28 show notable variation between mapping methods (LIDAR versus total 

station) as well as variation within each type of mapping method from 2006 to 2007. The magnitude of 

the time and footstep values between sites also varies; this variation is a result of the number and 

complexity of the gully systems that were mapped. In general, total field survey time was higher by 

approximately 15 percent for total station surveys (fig. 29, lower), while time in sensitive area and 

footstep counts were similar (±8 percent) for both methods. Because this cursory explanation of the 

results masks several attributes not immediately visible in the graphs, the A and B variation parameters 

set forth in equations 1 and 2 are used for additional analysis of the data. Results from these calculations 

are presented in appendix C1 for the variation within each mapping method (A-parameters) and in 

appendix C2 for variation between mapping methods (B-parameters). Range and overall averages for 

both sets of parameters are summarized in table 5 and discussed separately in the following sections. 

Variations within Survey Methods (Consistency Check—Parameters A1, A2, A3) 

In general, total survey time for both methods was consistent between survey efforts. Although 

the difference in the total survey time for LIDAR and total station data collection (parameter A1, table 

5) at each site varied by 8.7 h and 11 h, respectively, the average difference in data collection times per 

site from 2006 to 2007 were near zero (-0.5 h for LIDAR and 2.9 h for total station). The decrease in 

survey time for total station cannot be attributed to any one factor, but increased familiarity with the 

sites probably accounts for at least some of this difference. 

The difference in the time in the sensitive area from 2006 to 2007 at each site (parameter A2, 

table 5) varied by 6.3 h for LIDAR data collection and by 9 h for total station surveying. The average 

difference for time in the sensitive area for both mapping methods shows a decrease in hours spent in 

the sensitive area in 2007 and likely reflects increased familiarity with each study site. 

The difference in footstep impact from 2006 to 2007 (parameter A3, table 5) varied by about 

4,500 footsteps for LIDAR data collection and by about 6,300 footsteps for total station surveying. The 

average difference in footstep impact shows that LIDAR surveying has more consistent footstep impact 

values and that the efficiency of total station surveying increased in 2007. Results indicate consistency 

within each survey method with slight increases in efficiency between survey efforts. 

 40
Table 5. Summary of variation of impact evaluation results between terrestrial light detection and 

ranging (LIDAR) and total station surveying. 

Total survey time within each mapping method 

Parameter Method Minimum Maximum Average Range 

Difference 

A1 LIDAR –4.7 4 –0.5 8.7 

A1 Total station –1.5 9.5 2.9 11.0 

Total survey time between LIDAR and total station for each date 

Parameter Date Minimum Maximum Average 

B1 2006 –3.5 8 3.0 

B1 2007 –2.5 2 –0.4 

Time in sensitive area within each mapping method 

Parameter Method Minimum Maximum Average Range 

Difference 

Method Min Max Ave Range 

Diff. 

A2 LIDAR –0.5 5.8 1.4 6.3 

A2 Total station –1 8 2.3 9.0 

Time in sensitive area between LIDAR and total station for each date 

Parameter Date Minimum Maximum Average 

B2 2006 –6.5 3.8 0.4 

B2 2007 –4 5 –1.0 

Footstep count within each mapping method 

Parameter Method Minimum Maximum Average Range 

Difference 

Method Min Max Ave Range 

Diff. 

A3 LIDAR –2,469 2,071 –25.3 4,540 

A3 Total station –2,015 4,279 800 6,294 

Footstep count between LIDAR and total station for each date 

Parameter Date Minimum Maximum Average 

B3 2006 –1,886 5,153 654.4 

B3 2007 –1,595 2,083 –218.4 

Variations between Survey Methods (Impact Difference Check⎯Parameters B1, B2, B3) 

The difference in person-hours between LIDAR and total station for total survey time (parameter 

B1, table 5) shows that the positive numbers are higher in relative magnitude and of higher frequency 

for all sites considered, showing LIDAR to be more efficient than total station surveying in 2006 

 41
(appendix C2, top). In 2007, the difference in total survey time is more balanced between the two 

survey methods, as expressed by the difference average near zero. 

Both survey methods show a comparable time in the sensitive area of each site (parameter B2, 

table 5), expressed in the average difference near zero in 2006, with LIDAR surveying being slightly 

higher in 2007. Although the overall average values (table 5) are very similar, the data must be studied 

carefully to extract meaningful conclusions. For example, a positive value for parameter B2 occurs only 

once in 2007 (appendix C2), indicating total station surveying as being more efficient that year; 

however, the overall average for the dataset was near zero (table 5), indicating that there was no 

appreciable difference when the project was considered as a whole. 

For footstep count, the data (appendix C2) indicates a nearly equivalent number of occurrences 

for which each method scored more efficient values. The average values (table 5, parameter B3) support 

this conclusion, with each method scoring as more efficient depending on the year. The data also show 

that the footstep impact is consistent both years for each method at five of the seven sites surveyed. In 

summary, in terms of statistics, the two methods are not significantly different. 

Postprocessing Time Comparison 

The person-hours required to postprocess the LIDAR data and total station data to State Plane 

coordinates for the three topographic data comparison sites are detailed in table 6. As expected, the data 

show that the postprocessing person-hours for LIDAR data are an order of magnitude higher than for 

total station data, since the total station data are essentially collected in a near-processed format, 

whereas the LIDAR data must be postprocessed according to the series of steps highlighted in the 

methods section of this report. Although the goal of this study was to compare the time required to 

obtain georeferenced coordinates of the gully thalweg, it should be noted for future consideration that 

the LIDAR postprocessing produced georeferenced models of the entire site topography, as well. 

Table 6. Processing time for terrestrial light detection and ranging (LIDAR)-derived and total station 

surveying-derived topographic data (May 2006 sites). 

Site name Survey method 

Total 

postprocessing 

time (h) 

C:13:006 LIDAR 11.5 

Total station 1.5 

G:03:041 LIDAR 16 

Total station 1.5 

G:03:072 DS LIDAR 11 

Total station 0.75 

Discussion 

This section explores in more detail issues related to the results that highlight the strengths, 

limitations, and variability of the two survey methods. The discussion is focused primarily on 

interpretation of the impact and gully thalweg comparisons of LIDAR to total station survey methods; 

however, the following discussion also focuses on additional issues particular to terrestrial LIDAR 

surveying. 

 42
Topographic Data Evaluation 

Thalweg Extraction Comparison 

The primary technical comparison of the data was performed by assessing the ability of the 

LIDAR technique to extract gully thalweg topography that is at least as accurate as that obtained by the 

total station method. The results showed that although most of the data were within the error limits of 

the selected methodologies, errors outside these limits were common within the thalweg datasets. These 

errors highlight a limitation of the LIDAR method: LIDAR’s limited ability to track key field-visible 

features such as the gully thalweg. Whereas LIDAR was able to accurately model the topography of 

each site, it was not capable of identifying exactly the entire length of the selected geomorphic feature 

chosen for evaluation in this study (that is, the thalweg) within that same topography. One explanation 

for this conclusion is simply that digital, automated methods are not able to resolve such subtle features. 

Another explanation is that the subtle nature of some thalwegs result in a miscalculation of the exact 

position of the thalweg by the total station rodman (In some cases, there may be in fact more than one 

thalweg and determining which thalweg assumes the majority of overland flow may be hard to detect 

visually.) However, an additional explanation exists and concerns the methodology with which the 

LIDAR data were processed. As outlined previously, the goal of this study was to develop topographic 

models of the gully topography and extract key geomorphic features. In the construction of the 

topographic model, filtering of the full-density point cloud was necessary for processing efficiency, 

which removed many points from the dataset used for thalweg extraction. It is therefore possible that the 

measured errors could be reduced by using a higher resolution point cloud (<5 cm TIN edge length), but 

this would have prevented the efficient construction of the whole site topographic model. Generally 

speaking, it appears that the human eye may be a better judge of the exact thalweg location than the 

position calculated by automated methods. In fact, one of the reasons that thalwegs are surveyed with 

total station methods is because these features are often most readily identified in the field by the eyes 

of trained observers. 

An additional limitation concerns the use of automated thalweg extractor algorithms in areas of 

subtle topography. In this study, this algorithm generally broke down because of point cloud noise and 

the subtle features littering the ground surface (small rocks, etc.). In these cases, the thalweg was either 

not extracted, formed a closed loop, or took a longer path within a flat section of the gully, and, as a 

result, the thalweg became less steep. This outcome is clearly shown in the data for Site C:13:006 (fig. 

16), which show flatter long profiles along the entire gully lengths and results in underestimation of the 

thalweg elevation beginning in the upper reaches, causing overestimation in the lower reaches. 

The manually linked contour methodology provided a reasonable solution to this problem, but 

this method also had limitations. When linked 1-cm contours are used to define the thalweg, the total 

plan view thalweg length is likely to be greater than that obtained by the total station method. At site 

G:03:072⎯Gully 1 (fig. 24), the total plan view thalweg length using the LIDAR data was 41.4 m, 

obtained by connecting 1,374 individual contour lines. Using the total station data for identical start and 

end elevation points as the LIDAR data, a total plan view thalweg length of 37.7 m was obtained from 

130 connected points. Likewise, this computation shows that the linked contour interval method from 

the LIDAR data will also result in a longer, more sinuous, and subdued long-profile section compared to 

the total station survey data. Thus, whether the automated or manual algorithm was used, the results 

show that plan view and long profiles generated from the LIDAR data are subject to explainable but 

inherent systematic limitations for fine-scale thalweg mapping. That said, it could be argued, at least as 

 43
an academic exercise, that digitally extracting a higher resolution, more sinuous thalweg could be more 

accurate in terms of modeling the exact pathway of low-flow water in a channel. Either way, this points 

out the importance of consistency in gully thalweg extraction analysis. However, for the purposes of the 

present study, this point is not investigated any further. 

Feature Identification 

As discussed, one of the most important differences between the LIDAR and total station 

methods is each method’s ability to identify particular features from the data. In total station surveying, 

the prism rod is placed on the feature of interest and identified by a point identification label (for 

example, ckdm for check dam, k for knickpoint, etc.). In postprocessing, these features can then be 

readily identified for analysis and change detection. Thus, it is known for certain that the surveyed point 

actually represents the feature of interest. With LIDAR surveying, individual points are not identified in 

this way, although some features can be recognized based on their shape and color. In general, features 

with sharp edges and obvious shapes are easiest to identify (tripods, prisms, people, etc.), whereas 

natural features are harder to distinguish. 

The LIDAR gully datasets were examined carefully to identify such items as knickpoints and 

check dams constructed of both rocks and woody debris. With the exception of large knickpoints, 

identifying the exact locations of specific features was not possible. Large-size knickpoint (tens of 

centimeters) locations from the LIDAR data could be successfully extracted based directly on the 

processed topography but with less confidence than by field identification. Knickpoints of subtle 

topography (for example, fig. 30) could not always be identified in the LIDAR data. At this location, 

only the lower two knickpoints are obvious in the LIDAR long profile; the upper knickpoint is not 

readily identifiable. Check dam locations could only be identified through the use of either 

accompanying photographs of the site or through comparison with the total station dataset collected in 

tandem. Part of this problem is related to the LIDAR data processing methods, which emphasized 

creating continuous topographic models of each gully. It is not, therefore, the accuracy of the LIDAR 

data or processing methodology that is called into question but rather the resolution of the data to pick 

out exact features. Additional detail of the gully thalweg could be extracted, if the entire point cloud 

dataset were to be used for the gully bottom (with typical point spacing on the order of only 2 to 3 cm). 

Even then, however, it must be acknowledged that the exact location of a feature can only be identified 

as an approximation with the LIDAR method (that is, a knickpoint could still be located between two 

LIDAR data points and not be perfectly identified). 

This limitation of the presented LIDAR data collection and processing methods could be 

rectified in the future by scanning with increased density or by inclusion of site-specific reflectors 

(control points) located on the features of interest. Here, site-specific “tags” consisting of small but 

highly reflective targets could be placed on the feature locations. The tags would then be more easily 

identified in the point cloud data. Both of these solutions has a cost⎯processing time increases with 

increased point density in the former case, and field layout time and site impact increases in the latter 

case. The cost-benefit ratio of tracking these types of features, therefore, needs to be weighed on a caseby-case basis. A third alternative to overcoming the feature recognition limitation of LIDAR is to use a 

laser scanning system that incorporates photo-draping capabilities; this option is discussed in more 

detail in a following section of this report dealing with future directions. 

 44
826

828

830

6 810 12

Distance Along Section (m)

Elevation above NAD83 Ellipsoid (m)

Lidar Data

Total Station Data

Knick Points

Figure 30. Section of long-profile comparison of terrestrial light detection and ranging (LIDAR) and 

total station data showing knickpoint locations determined by total station surveying (site 

C:12:006⎯Gully 2). 

Surface Modeling and Whole-Site Monitoring 

In contrast to the limitations presented concerning feature identification, the ability to efficiently 

collect the entire site topography is an obvious advantage of the terrestrial LIDAR data. A brief 

description of how these data can be used for whole-site monitoring is provided here to highlight this 

advantage. Table 3 shows that the LIDAR method collects roughly 1,000 times the density of point data 

than does the total station method. This finding was previously highlighted for a small section of one of 

the gullies (fig. 13). Reaching a LIDAR-equivalent density of data using the total station method to 

survey an archeological site would be time and cost prohibitive and result in extremely high footstep 

impacts. 

Figure 31 provides an overview of the types of features that can be accurately identified. Here, 

vegetation, boulders, and the entire site topography are visible in the point data. Thus, surface models 

created from temporally different datasets can be compared to one another. Should vegetation features 

change or massive erosion events such as debris flows affect a site, the point data collected before the 

change can provide an accurate baseline for change-detection measurements. But as outlined previously, 

if the resolution of the point cloud data is not sufficiently high or the feature not particularly well defined 

in the topography, change detection of smaller, visibly distinguished features is not always possible. 

 45
Area of 

detail in B Ocotillo 

Boulders 

LIDAR ball

Gully thalweg 

A from LIDAR 1 m 

Gully thalweg 

from LIDAR Boulder 

B 0.3 m 

Figure 31. Detail of terrestrial light detection and ranging (LIDAR) point data from a single scan 

showing key features (site G:03:072⎯Gully 1). Distance between points in A varies greatly depending on 

distance from scanner origin. Typical distance between adjacent points in B is 5 cm. 

 46
Vegetation 

The presence of vegetation in gullies creates inaccuracies in the resulting topographic data using 

both survey methods. In total station surveying, vegetation can prevent the survey rodman from locating 

the precise gully bottom. In LIDAR surveying, vegetation can block the transmission of laser pulses, 

and the gully itself may not be visible. This is one of the reasons why multiple scan setup locations are 

often used, although sufficiently dense vegetation may not be penetrable from any vantage point. 

The problems of dealing with vegetation (and likewise, the magnitudes of error from vegetation 

in each method) are treated in different ways according to each survey method. In total station 

surveying, several centimeters of error can be expected from an improperly placed survey rod. 

Likewise, several centimeters of error can also be expected if the survey rod must be extended above the 

vegetation by two rod lengths or higher because of the side-to-side flexibility of the survey rod and 

potential shifting from wind, if present. Total station surveyors are able to minimize this error by 

waiting for the prism to “center out” in the course of any side-to-side movement; overall, a few 

centimeters of additional error, however, may be expected when working in these areas. 

In LIDAR surveying, errors may be generated by either poor surface registration of vegetation 

points collected from two different angles or through misinterpretation of the vegetation as bare ground 

in the final surface model. Both of these error sources can be minimized through use of postprocessing 

filters that remove most aboveground points. However, if filters are not used correctly, useful data can 

be lost by the improper removal of points, and points remaining in error can lead to a final surface that 

does not accurately model the bare-earth ground surface. In the worst case, dense vegetation may 

prevent data collection from the ground surface entirely (that is, Gully 2 at site G:03:041; see figs. 19, 

20, and 22). Sites should be carefully examined with this issue in mind before surveying. 

Impact Evaluation Comparison 

Factors Affecting the Direct Impact Evaluation⎯Footstep Count 

The footstep count is directly related to the number of people supporting each surveying 

technique in the sensitive area. Since LIDAR surveying essentially requires two people to carry the 

equipment from one setup location to another, and total station surveying requires one person only to 

move around the site, the footstep count from the LIDAR team could be expected to be larger. 

However, before undertaking this study, it appeared that the LIDAR survey crew might be able to 

eliminate entry into the sensitive area altogether by using a survey device capable of remote sensing. 

The results show that this was not possible at many of the sites because of the sinuous nature of the 

gully systems, which often required multiple, and closely spaced, scan setups to capture the required 

data. 

The weight of the LIDAR equipment also plays a role in site impacts, which was not quantified 

for this study. Personnel carrying heavier equipment can produce deeper footprints than a single person 

carrying a total station prism pole. However, the impacts resulting from the additional weight of LIDAR 

equipment may be a limited factor here because the LIDAR survey locations were typically never in the 

geomorphic feature being surveyed, as opposed to total station survey, which may have required less 

heavy equipment but had to be located directly in the feature being measured (the gully). Both survey 

methods may lead to the introduction of new trail systems to the archeological sites. There is possibly 

more potential for new trails from the LIDAR survey method because the required setup locations are 

 47
generally in new, undisturbed areas of the site, while the total station survey locations are already within 

gullies. The footstep impact of total station surveying is concentrated in the critical gully area, where 

standing or placing the survey rod on the top edge of the gully or on top of a knickpoint may cause 

collapse of the steep face, exacerbating erosion. The physical impact from total station surveying 

includes both footprints and the outline of the 3-inch-diameter prism pole, although a conscientious, 

light-footed rodman should be able to cause minimal overall impact. Whether additional erosion of an 

existing gully feature is less desirable than the possibility of introducing new erosion in other areas of a 

site is a difficult tradeoff to quantify, but it is highlighted here as a factor for consideration for future 

surveys. 

Factors Affecting the Indirect Impact Evaluation⎯Time in Sensitive Area 

The time in sensitive area element of the study—based on the assumption that the more time 

spent within the sensitive area, the more time for additional impacts to occur—provided an alternative 

estimate for assessing the impact associated with each survey technique. Impacts were also assumed to 

be exacerbated with the increased number of support personnel working within the sensitive area. While 

both survey techniques were accomplished at times with only one person in the sensitive area, the effect 

of single versus multiple personnel was not examined in this study. 

An additional factor affecting the time element for both the total time of survey and time in the 

sensitive area impact parameters is the experience level, or quality factor, of the personnel collecting the 

data. Quality factors include familiarity with the project objectives and the study sites, as well as 

familiarity with the equipment, software or mechanical problems, and troubleshooting hardware. The 

quality of personnel during this assessment was similar for both survey techniques. The lead surveyor 

for both techniques was present for both impact data collection events, while the support personnel for 

both techniques varied. For LIDAR, the support technician was in training for the sites beginning with 

C in 2006 and was considered experienced in 2007. (No support personnel were used at the sites 

beginning with G in 2006.) The support personnel for total station surveying were knowledgeable for 

the beginning of 2006 (sites beginning with C) and the end of 2007 (sites beginning with G), but in 

training for the end of 2006 and beginning of 2007 (sites beginning with G). An increase in personnel 

experience was reflected in the footstep count and the person-hour data: with a few exceptions caused 

by equipment problems, both methods posted decreases in total survey time, time in sensitive area, and 

footstep count (figs. 27, 28) when experienced personnel accompanied the lead surveyor. An evaluation 

of this comparison is not possible for the LIDAR survey between the end of 2006 and end of 2007 

because of the use of different numbers of personnel during these times. 

Other Factors⎯Postprocessing Methods 

The main parameters for assessing the overall efficiency of a mapping technique are the total 

survey time and the postprocessing time. The postprocessing time for LIDAR data obviously reduces 

the overall efficiency, but the field time necessary to acquire the LIDAR data was also slightly (15 

percent) lower. A factor not accounted for in these results is the use of total station survey equipment 

and personnel to measure georeferenced coordinate values for the LIDAR survey team in the field. This 

is reflected in the 2006 cumulative mapping time (fig. 29) for total station surveying, which exceeded 

the LIDAR survey by more than 20 h in 2006 (although the exact contribution to that time of additional 

surveying for the LIDAR team cannot be quantified). When the LIDAR survey used an independent 

real-time kinematic global positioning system (RTK GPS) for georeferencing laser and LIDAR ball 

control in 2007, the total station and LIDAR surveys were comparable in cumulative time (fig. 29), 

 48
although this also indicates that the LIDAR survey was much more efficient in 2007, since collection of 

both LIDAR and GPS data was performed by the LIDAR survey team. 

The additional postprocessing time required by LIDAR data is a direct result of two factors: (1) 

the data collection and processing methodology and (2) the data selected for comparison. The LIDAR 

data in this study were collected as a series of independent point clouds georeferenced to the scan origin 

and aligned to other scans through a best fit of overlapping point data. This procedure is time 

consuming because of the number of degrees of freedom that exist in registering overlapping point 

clouds—each cloud may rotate about three axes. Thus, a large number of possible combinations exist, 

with only one exact solution. Finding this solution is often time consuming and computationally 

demanding. Processing methodologies are constantly evolving, and newer methods rely on the presence 

of overlapping control points in adjacent scans to find this best fit solution. This makes for a 

computationally simpler postprocessing methodology but requires additional field time necessary for 

setup, surveying, and take down of the control points. Additional studies using these newer methods are 

ongoing with respect to evaluating the pros and cons of field versus postprocessing time. 

In this study, the long profile of the gully thalweg was selected for use in topographic 

comparisons. Whereas this is essentially directly surveyed by total station methods, it is a metric that 

must be extracted from postprocessed LIDAR data. Development of a surface model followed by 

subsequent hydrologic analysis must therefore be performed. Investigations of small-scale gully thalweg 

metrics will likely always be more time consuming using LIDAR methods. Other possible metrics, such 

as the total amount of surface change between temporally different datasets, are more easily realized 

from LIDAR data but were not directly compared (or collected) in the total station surveys because of 

the marked differences in data density collected by the two methods. 

Future Directions 

Two new technologies, RTK GPS surveying and dual-mode terrestrial LIDAR image draping 

may provide solutions to the survey and impact limitations highlighted in this report. They are presented 

here as background for future studies. 

High-accuracy GPS surveying at the river level in Grand Canyon may be possible because of the 

increased number of available satellites for data collection; satellite capacity has increased, in part, as 

the result of the increased availability of signals from the Russian-based GLONASS satellites. To date, 

GPS reception has been spotty and inconsistent because of the height and steepness of the canyon walls 

and the lack of the needed line of sight to a sufficient number of satellites. After base-station set up is 

complete, GPS surveying has the advantage that only one person is required for data collection. In 

addition, the survey rod is kept low and is not impacted by windy conditions. Finally, multiple GPS 

rover units can be used simultaneously to further reduce survey time for sites with neighboring gullies. 

Although total survey time may decrease with GPS surveying, site impact can still be expected because 

of the need to occupy the gullies directly to make the required measurements. RTK GPS was used 

successfully in this study for georeferencing the LIDAR instrument and in collecting baseline datasets 

of five gully thalwegs in 2007. Whereas medium-range (2 to 5 minute) occupations were successful in 

generating accurate points for the LIDAR instrument locations, assessment of the accuracy of the gully 

data using short (1 to 10 seconds) occupation is currently in progress. 

Dual-mode terrestrial LIDAR image draping combines the high accuracy and point density of 

terrestrial LIDAR with the feature-recognition capabilities of digital photo analysis. Here, highresolution digital photos are orthometrically rectified to the LIDAR data, providing a method in which 

features can be simultaneously identified in the photo and measured in the LIDAR data. This may 

 49
provide a solution to the key limitations identified in this study concerning the ability to map the 

location of fine-scale geomorphic features such as gully thalwegs and knickpoints. The workflow in the 

field using this methodology is generally more time consuming, requiring the use of a network of 

reflector control points and the collection of both the LIDAR data and the photos. However, if wholesite topographic monitoring is not desired, the survey could focus completely on the gully system using 

potentially fewer locations. The postprocessing workflow time commitment is likely to be less than that 

required by this study. Using field-placed reflectors, registration time will decrease and the thalweg 

extraction will be more conclusive, thereby also reducing the postprocessing effort. The most promising 

improvement with this type of system may be with regard to data accuracy—the final measurements are 

likely to be an order of magnitude less than those presented in this evaluation because of the higher 

accuracy of these newer laser systems, the higher accuracy achieved by the reflector-dependent 

registration process, and the positive identification of the geomorphic feature in the photos. 

Conclusions 

This investigation evaluated the use of terrestrial LIDAR surveying to collect topographic data 

and map key geomorphic features while also minimizing site impacts at archeological sites in Grand 

Canyon National Park. The results of the topographic data evaluation show that although terrestrial 

LIDAR is capable of determining overall site topography and accurately mapping the general location 

of gully thalwegs in comparison to that provided by total station data, terrestrial LIDAR in its presented 

application did not map the entire plan and longitudinal profiles of the gullies to the required accuracy 

of this investigation. Further, it was not possible to identify fine-scale features such as knickpoints and 

check dams from the point cloud data. Although terrestrial LIDAR provides a higher point density, it is 

limited in its ability to identify the precise locations of subtle features because of the inherent 

differences between in-field feature identification by a trained eye and postprocessing feature extraction 

by computerized methods. It is noted for future reference that recent advances in technology may 

change these results. 

The results of the impact evaluation indicate that LIDAR and total station surveying are similar 

in their site impacts, as measured by the time spent in the sensitive area (defined at each survey site) and 

the number of footsteps taken within the sensitive area. The total survey time taken by LIDAR was 

approximately 15 percent less than total station surveying, but the postprocessing time was an order of 

magnitude greater. Several factors must be considered when making decisions based on these results, 

including the number, location, and training of personnel. With LIDAR, the number of personnel 

working inside the sensitive area may be higher; in total station surveying, the location of the personnel 

is more likely to directly affect the area of interest; and in both methods, highly trained personnel are 

required to maximize efficiency. 

Whether the goal of a project is to track key geomorphic features, as was evaluated in this study, 

or it is to track overall topographic change, as is being evaluated in a complementary study anticipated 

in early 2009, there will be key tradeoffs depending on the type of survey method selected. Making a 

choice must therefore be determined by selecting the method that is most suitable for the data to be 

collected. For geomorphic surveys of archeological sites, LIDAR is more time consuming given the 

postprocessing effort required but provides increased data density and considerably more spatial 

coverage for slightly less field effort. However, as discussed, the older generation of LIDAR technology 

applied in this study was not capable of fine-scale feature recognition—a significant limitation, 

considering that until now geomorphic feature mapping of gully thalwegs has been the most commonly 

used method for tracking archeological site erosion. Compared to total station surveying, RTK GPS 

 50
may provide an amenable solution to shortening site occupation time and increasing data accuracy for 

measuring these features. Similarly, different LIDAR postprocessing methodologies or newer LIDAR 

data collection techniques may also increase data accuracy and possibly lessen site impacts. However, 

these methods remain untested at such fine scale in Grand Canyon. 

Given the aims and goals of a particular project, the benefits and limitations of each survey 

methodology should be weighed accordingly. The following summary of the findings of this evaluation 

between terrestrial LIDAR surveying and total station surveying has been compiled to assist researchers 

and land managers involved in geomorphologic monitoring at archeological sites in Grand Canyon 

National Park. 

Clear benefits of terrestrial LIDAR surveying include 

• the ability to collect data remotely and, in some cases, outside of the erosion features; 

• the ability to collect the entire site topography for whole-site monitoring and for 

historical archiving purposes; and 

• the potential for increased accuracy with next-generation LIDAR systems. 

Issues posed by LIDAR surveying and potential solutions are listed below: 

• LIDAR surveying is unable to identify the precise location of fine-scale geomorphic 

features. The newest generation of lasers that combine digital photo draping on the 

collected topography may provide an amenable solution to this problem; this solution has 

not been tested. 

• Laser penetration through dense vegetation is limited. No specific solution is available, 

except extremely close-range scanning to penetrate the vegetative canopy. 

• Additional postprocessing time is required. The quantity of data obtained is the tradeoff 

for this limitation. 

Clear benefits of total station surveying include 

• the ability to collect data of the specific features of interest (that is, “feature coding”), 

including in-field recognition and identification of geomorphic features; 

• the ability to obtain results with minimal postprocessing, which allows for nearer realtime change detection; and 

• the ability to work in densely vegetated areas; 

Issues related to the use of total station surveying and possible solutions are listed below: 

• Wind affects the quality and accuracy of total station surveying results. A potential 

solution to this issue may be to use RTK GPS systems, which do not require survey rods 

to be extended. 

• Total station surveying concentrates site impacts on the features being monitored. This 

problem is inherent in the method and can be minimized by using trained personnel but 

cannot be eliminated. 

• It is not possible to map an entire site efficiently or with the level of detail necessary for 

tracking topographic change on a landscape scale using total station surveying. For this 

application, LIDAR surveying is preferable. 

 51
Acknowledgments 

Funding for this research was provided by a cooperative work agreement between the USGS 

Grand Canyon Monitoring and Research Center and the USGS Western Coastal Marine Geology Team. 

The authors would like to thank the following individuals for their assistance with the data collection 

effort: Robert Kayen and Diane Minasian, USGS; Jennifer Dierker and Lisa Leap, NPS; and Joel 

Pederson, Gary O’Brien, Ben Dejong, Mike Keller, Chris Tressler, and Tim Andrews, Utah State 

University. Thanks also to Carol Fritzinger, USGS GCMRC logistics coordinator, and the boatmen and 

women from Humphrey Summit Support Services who provided logistical support for this project. 

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California, USA: Geomorphology, v. 97, p. 483–501. 

Davis, P.A., 2004, Review of results and recommendations from the GCMRC 2000−2003 remotesensing initiative for monitoring environmental resources within the Colorado River ecosystem: U.S. 

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excavations, in Baltsavias and others, eds., Recording, modeling and visualization of cultural heritage: 

Taylor and Francis, p. 193–203. 

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along the Colorado River corridor in Grand Canyon National Park, Arizona: U.S. Geological Survey 

Professional Paper 1756, 71 p. 

Fairley, H.C., 2005, Cultural resources in the Colorado River corridor, in Gloss, S.P., Lovich, J.E., 

Melis, T., eds., The state of the Colorado River ecosystem in Grand Canyon, U.S. Geological Survey 

Circular 1282, p. 177–192. 

Fairley, H.C., Bungart, P.W., Coder, C.M., Huffman, J., Samples, T.L., and Balsom, J.R., 1994, The 

Grand Canyon river corridor survey project: Archaeological survey along the Colorado River between 

Glen Canyon Dam and Separation Canyon: Cooperative Agreement No. 9AA–40–07920, Grand 

Canyon National Park. Prepared in cooperation with the Bureau of Reclamation, Glen Canyon 

Environmental Studies, Flagstaff, Ariz. 

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monitoring and research development project, research proposal dated July 30, 2007, submitted to 

Grand Canyon National Park and National Park Service Research Permit Application System, March 

5, 2007. Copy on file at the U.S. Geological Survey, Grand Canyon Monitoring and Research Center, 

Flagstaff, Ariz., 149 p. 

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Hereford, R., 1993, Description of map units and discussion to accompany map showing surficial 

geology and geomorphology of the Palisades Creek archaeologic area, Grand Canyon National Park, 

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accompany map showing quaternary geology and geomorphology of the Nankoweap Rapids area, 

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erosion of archaeologic sites at four areas in eastern Grand Canyon National Park, Arizona—A 

preliminary analysis: Flagstaff, Ariz., Bureau of Reclamation, Glen Canyon Environmental Studies. 

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and erosion of archaeologic sites along the Colorado River, Eastern Grand Canyon, Grand Canyon 

National Park, Arizona: U.S. Geological Survey Open-File Report 93-517. 

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 54
Appendix A. Evaluation Metrics 

Evaluation 

characteristic 

Evaluation 

measurement Equipment needed Protocol 

Equipment and 

manpower cost U.S. dollars 

Equipment purchase 

quote or receipt and 

equipment manual 

Tally all dollar expenses associated with 

purchasing or leasing equipment, or 

contracting data collections services. 

Assess personnel requirements, that is, the 

minimum number of people required to use 

the equipment vs. the most efficient 

number of people. 

Time of survey effort Time Wrist watch 

Measure time spent in field, including 

mobilization, survey time, and 

demobilization. Measured on a per-person 

basis, including fixed survey personnel. 

Time of 

effort 

postprocessing Time Clock/stop watch 

Measure time spent postprocessing each 

dataset once raw data is collected (scans 

and point measurements). This effort 

includes time spent postprocessing the data 

either in the field or office. 

Wear and tear on 

equipment U.S. dollars Expense report 

Tally all dollar expenses associated with 

repairing or replacing equipment damaged 

in the field and with purchasing specialized 

equipment needed for Grand Canyon 

survey work. 

Impact on Land 

Distance Pedometer 

Measure total distance traveled upon 

entering the archeological study area via 

pedometer installed on each member of the 

survey team. Measured on a per-person 

basis. Does not include fixed survey 

personnel if base station is not located 

within the study area. 

Photograph Camera 

Photo document and tally significant 

impacts to a site (for example, stepping on 

cryptogrammic soil, etc). Significant 

impacts to be established by principal 

investigator (Helen Fairley). 

Quantity of data Number of 

points 

Laser/survey 

software 

Calculate the total number of points utilized 

in creating topographic surfaces of the 

study area. Does not include points filtered 

from the data (that is, scanned vegetation, 

cliffs, etc.). 

 55
Evaluation 

Characteristic 

Evaluation 

measurement Equipment needed Protocol 

Area of survey Plan area of 

collected data 

Laser/survey/GIS 

software 

Calculate the total 2-D plan area of data 

collection within the study area. 

Accuracy of point data Absolute error of 

equipment 

Laser/total station 

specifications 

Absolute equipment error is defined by the 

survey equipment manufacturer along with 

periodic calibration tests. Measurements 

within a single data scan are subject to this 

error. 

Accuracy of 

topographic model 

RMS error of 

final registration 

of individual 

scans (for 

LIDAR survey) 

Laser/GIS software 

The RMS error of the registration 

procedure defines the overall fit of the scan 

data within a particular model. 

Measurements within and between 

topographic surfaces are subject to this 

error. 

Product 

Reporting and 

compiling of results 

Publication or 

Open-File 

Report 

N/A 

Compile information collected for each 

evaluation characteristic from scanned 

topography and surveyed topography. 

Written report comparing the two 

techniques using graphs, tables, maps, and 

illustrations as needed. 

 56
Appendix B. Impact Evaluation Results 

Site name Survey method Total time of 

survey effort 

Total time in 

sensitive area⎯

indirect impact 

Footstep impact⎯

direct impact 

 (person-hours) (person-hours) (number of 

footsteps) 

LIDAR May 2006 12 3.2 1,316 

LIDAR May 2007 11.5 3 1,113 

Total station May 2006 13 7 2,269 

C:13:006 

Total station May 2007 13.5 8 3,196 

LIDAR May 2006 9 9 2,840 

LIDAR May 2007 5 3.2 2,329 

Total station May 2006 5.5 2.5 954 

C:13:336 

Total station May 2007 5.5 3 797 

LIDAR May 2006 5 5 2,071 

LIDAR May 2007 3 2 n/a1

Total station May 2006 6 4 962 

C:13:099 

Total station May 2007 n/a1 n/a1 n/a1

LIDAR May 2006 9 9 3,815 

LIDAR May 2007 13.7 9.5 4,298 

Total station May 2006 10.5 7.5 4,133 

C:13:348 

Total station May 2007 12 5.5 6,148 

LIDAR May 2006 12.5 8.5 2,231 

LIDAR May 2007 12 7 4,700 

Total station May 2006 19 11.5 7,384 

G:03:041 

Total station May 2007 12.5 7 3,105 

LIDAR May 2006 10 8.25 4,239 

LIDAR May 2007 11 7.5 3,650 

Total station May 2006 18 12 3,868 

G:03:002 

Total station May 2007 8.5 4 2,770 

LIDAR May 2006 5.25 4 947 

LIDAR May 2007 6.5 3 1,299 

Total station May 2006 8 4 2,910 

G:03:072 

Downstream 

Total station May 2007 4 2 1,374 

LIDAR May 2006 4.25 4.25 2,768 

LIDAR May 2007 8.25 4.5 3,040 

Total station May 2006 11 5.5 2,982 

G:03:072 

Upstream 

Total station May 2007 9 4.5 1,510 

1

 Site C:13:099 was not surveyed by total station in 2007, and the LIDAR footstep count was not measured. 

 57
 58

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Appendix C1. Variation Within Each Mapping Method for Total Time of 

Survey, Time in Sensitive Area, and Footstep Impact 

TOTAL TIME OF SURVEY EFFORT

-6

-4

-2

0

2

4

6

8

10

12

C:13:006

C:13:336

C:13:099

C:13:348

G:03:041

G:03:002G:03:072U

G:03:072D

Site Name

Total Person Hour Difference

(2006 - 2007)

Lidar

Total Station 

TIME IN SENSITIVE AREA

-2

-1

0

1

2

3

4

5

6

7

8

9

C:13:006

C:13:336

C:13:099

C:13:348

G:03:041

G:03:002

G:03:072U

G:03:072D

Site Name

Total Person Hour Difference

(2006 -2007)

Lidar

Total Station 

FOOTSTEP IMPACT DIFFERENCE

-3000

-2000

-1000

0

1000

2000

3000

4000

5000

C:13:006

C:13:336

C:13:099

C:13:348

G:03:041

G:03:002

G:03:072U

G:03:072D

Site Name

Cumulative Footstep Count Difference

(2006 - 2007)

Lidar

Total Station 

 59
Appendix C2. Variation Between Total Station and LIDAR Methods for Total 

Time of Survey, Time in Sensitive Area, and Footstep Impact 

TOTAL TIME OF SURVEY

-6

-4

-2

0

2

4

6

8

10

C:13:006

C:13:336

C:13:099

C:13:348

G:03:041

G:03:002

G:03:072U

G:03:072D

Site Name

Total Person Hour Difference

(Total Station - LIDAR)

May-06

May-07

TIME IN THE SENSITIVE AREA

-8

-6

-4

-2

0

2

4

6

C:13:006

C:13:336

C:13:099

C:13:348

G:03:041

G:03:002

G:03:072U

G:03:072D

Site Name

Total Person HourDifference

(Total Station - LIDAR)

May-06

May-07

FOOTSTEP IMPACT

-3000

-2000

-1000

0

1000

2000

3000

4000

5000

6000

C:13:006

C:13:336

C:13:099

C:13:348

G:03:041

G:03:002

G:03:072U

G:03:072D

Site Name

Cumulative Footstep Count Difference

(Total Station - LIDAR)

May-06

May-07

 60
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Open–File Report 2008–1384

Collins and others—Evaluation of Terrestrial LIDAR for Monitoring Geomorphic Change at Archeological Sites in Grand Canyon National Park, Arizona—