Review of Results and Recommendations from the GCMRC 2000-2003 Remote- Sensing 

Initiative for Monitoring Environmental Resources within the Colorado River Ecosystem

REVIEW COPY 

Philip A. Davis2

, Remote Sensing Coordinator 

Contributors (alphabetical order): 

Michael J. Breedlove2

Pat S. Chavez2

Donna M. Galuszka2

F. Mark Gonzales2

Joseph E. Hazel1

Jeffrey R. Johnson2

Matthew A. Kaplinski1

Michael J. C. Kearsley1

Keith A. Kohl2

Michael J. Liszewski2

William W. MacFarlane3

Christopher S. Magirl2

Mark F. Manone1

Theodore S. Melis2

Steven N. Mietz2

Joel L. Pederson3

Paul A. Petersen3

Jeffrey B. Plescia2

 Barbara E. Ralston2

 Mark R. Roseik2

 John C. Schmidt3

 Matthew I. Staid2

 William S. Vernieu2

1

Northern Arizona University 

Flagstaff, Arizona 86001 

2

U.S. Geological Survey 

Flagstaff and Tucson, Arizona 86001 

3

Utah State University 

Logan, Utah 84322 

September 17, 2003
2

Executive Summary 

In mid-2000, the Grand Canyon Monitoring and Research Center (GCMRC) 

began a remote-sensing initiative to evaluate all remote-sensing technologies and 

methods that had potential for providing improved data (capability) for its various 

programs that monitor the Colorado River ecosystem (CRE). The primary objective of 

the initiative was to determine the most cost-effective data collection protocols for 

GCMRC programs that (1) provide the accuracies required for currently measured 

parameters, (2) provide additional parameters for ecological monitoring, (3) reduce 

environmental impact by being less invasive than current methods, and (4) expand 

geographic extent of current ground approaches. The initial phase of the remote-sensing 

initiative determined the types of sampling parameters and their required accuracies for 

monitoring. This information was used to determine the most appropriate sensors for 

evaluation. The initiative evaluated 25 different data collections over a three-year period; 

many more remote-sensing instruments were considered, but were not evaluated because 

they could not meet the basic requirements on spatial resolution, wavelength, positional 

accuracy, or elevation accuracy. It was hoped that the evaluations would lead to a 

minimum set of technologies that would satisfy many program requirements. The results 

from all of our evaluations are reviewed in this report and are briefly summarized in the 

following paragraphs. 

Of the three research and monitoring programs within GCMRC, the cultural 

resources program presented the most difficult set of requirements on remote-sensing 

data due to the small size and obscuration of its resources. For example, very small, 

individual ethnobotanical stands require very high resolution imagery for monitoring, 

which is extremely expensive to acquire. Also, mineral resources within rock walls are 

obscured from aerial view and therefore cannot be approached using airborne remotesensing data. Cultural resources that were evaluated with remote-sensing technologies 

consisted of: (1) camping sites and beaches; (2) archaeological structures, (3) natural 

springs, and (4) arroyos and their effects of check dams and archaeological structures. Of 

these resources remote-sensing approaches proved useful for mapping camping sites and 

beaches. The composite/beach map produced in this evaluation includes more sediment 

sites than can reasonably be mapped by ground surveys, is a more rapid and more 

accurate product for change detection, and was produced at a fraction of the time and 

expense of the traditional ground surveys. Our evaluations for archeological structures 

and natural springs showed that (1) daytime thermal infrared (TIR) and 6-11 cm 

resolution visible imagery do not provide sufficient resolution or thermal differences to 

unambiguously identify or determine changes of the resources, and (2) although imagery 

at 3-cm resolution could only produce elevation accuracies near 20 cm, which is below 

the accuracies required to detect small changes in arroyos, the data do provide catchmentscale topography that allows geomorphic modeling of the potential effects of rainfall on 

arroyo development and on down-slope structure modification. With respect to the 

detection of archaeological structures and natural springs, previous TIR data collected 

just after dawn and after sunset were capable of mapping natural springs and should also 

detect archaeological structures. However, such data would require a separate data 
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collection for just these two resources and not all parts of the corridor would be 

illuminated during early morning collections. 

Remote-sensing technologies were evaluated for two important terrestrial aspects 

of the physical resource program: (1) mapping the distribution of sediment deposits and 

(2) mapping the topography and volumes of fine- and coarse-grained sediment deposits. 

We found remote-sensing technologies to be very useful for both of these aspects. Our 

evaluations showed that (1) digital CIR image data are more accurate than digital or film 

natural-color and panchromatic imagery for mapping terrestrial sediment deposits; (2) 

digital CIR image data provide a relatively rapid and accurate means for this mapping; 

and (3) these deposits can be accurately mapped using 22–44 cm resolution data. Our 

evaluations of different airborne approaches for monitoring terrestrial sediment volumes 

showed that (1) low-resolution LIDAR (one point every four to six meters) produced 26-

103 cm vertical accuracies on bare ground, whereas moderate-resolution LIDAR ( one 

point every one to two meters) produced 9-26 cm vertical accuracies; (2) although the 

moderate-resolution LIDAR did provide acceptable elevation accuracies on bare ground, 

these data were only acceptable after correction for a vertical offset that varied with river 

reach; (3) the accuracies at both LIDAR resolutions decreased (average 1.5-m error) in 

vegetated terrain; (4) the accuracy and precision of high-resolution LIDAR data (10 

points every meter) is 8 cm and 3 cm, respectively, with essentially no vertical offsets. 

Although the cost for the high-resolution LIDAR data is high ($6,200 per river km), it 

has wide applicability across many GCMRC programs for detailed, site-specific 

monitoring requirements; and (5) photogrammetric methods using 1:4,000 scale aerial 

photography provide acceptable elevation data on bare and vegetated ground (28-cm 

accuracy), but the cost for analysis is about $2,000 per river mile and the analysis 

requires control panels within the study sites and its accuracy is lower in winter months 

due to shadows. 

Remote-sensing technologies were evaluated for two major components of the 

terrestrial biologic resources: (1) estimating canopy volumes and (2) inventorying the 

vegetation. Remote sensing technologies were found to be useful for both program 

components with some qualifications. Manual photogrammetric methods using 1:4,000-

scale aerial photography provided about 85% accuracy in canopy height compared to 

ground surveys. However, manual photogrammetry requires the placement of ground 

panels and is both invasive and expensive. Automated photogrametric methods and very 

high resolution LIDAR data sets (15–30 points per square meter), which do not require 

ground control panels, are currently being evaluated and may provide similar accuracies 

to that provided by manual photogrammetry in a less invasive manner and at a lower cost. 

In terms of vegetation inventories, our evaluations showed that calibrated, digital fourband data with 30-cm resolution provide acceptable spectral and textural discrimination 

of CRE vegetation communities for developing inventory maps. However, certain 

vegetation species were not effectively discriminated, due to either (1) miscalibration of 

one of the sensor’s detectors, such that the two or three of the highest reflectance species 

appear similar in the data, or (2) the inherent inability of the four wavelength bands to 

distinguish these species. It may be that more spectral information at lower spatial 

resolution (without texture) might provide better species separation, or that correctly 
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calibrated four-band image data can provide adequate vegetation mapping. This issue 

needs to be resolved by an additional evaluation of existing or new airborne data that 

examines vegetation species instead of vegetation communities. 

Remote-sensing technologies were evaluated for several physical characteristics 

of the aquatic environment: (1) chlorophyll, (2) total suspended sediment, (3) turbidity, 

(4) warm backwater areas amenable to fish habitats, (5) sediment storage, and (6) 

substrate grain-size distribution. Remote-sensing technologies were found useful in 

mapping all of these characteristics. Digital (12-bit), high-gain CIR image data and 

thermal-infrared image data can provide large-area maps of chlorophyll, total suspended 

sediment, turbidity, warm backwater areas amenable to fish habitats, sediment storage, 

and substrate grain-size distribution. All of these characteristics can be mapped with 

airborne sensor having a few visible/near-infrared bands and a thermal-infrared band. 

However, the cost for this data in orthorectified form is about $850 per river mile. Thus, 

these remote sensing data cannot economically provide high-frequency data that are 

currently obtained by ground collection, but ground collection cannot achieve the rapid, 

wide-area coverage provided by the airborne data. For mapping channel substrate 

geomorphology, airborne technology easily surpasses side-scan sonar surveys in all 

aspects, but the water needs to be relatively clear for the airborne approach to work well 

and its depth of penetration in clear water is limited to about 20 m. An unanticipated 

result of our evaluations of low-resolution LIDAR elevation data was that the data 

actually mapped the elevation of the water’s surface over the main stem, which compared 

with historical elevation profiles of the main stem indicated changes in submerged cobble 

bars since the historical measurements. Remote sensing data are generally not useful for 

monitoring the chemical characteristics of running water because chemical 

concentrations are too low. 

Overall, the remote-sensing initiative has resulted in establishing the basic 

requirements on remote-sensing data and the technologies that can meet these 

requirements and has produced the most functional data sets to date. As a result, program 

scientists are making more and better use of these data each month, which has increased 

productivity and monitoring accuracy, made monitoring less invasive, and opened new 

avenues for improved ecological studies. Additional positive outcomes of this initiative 

are listed at the end of this report. Remote-sensing technology continually advances and 

improves in terms of capability and cost. CRE monitoring can benefit even more in the 

future from these improvements, but the technologies need to be thoroughly understood 

and evaluated for their potential benefits and against GCMRC monitoring requirements. 
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1.0 Introduction 

For the past two decades, monitoring and research teams in the physical, biological, and 

cultural resource programs within the Glen Canyon Environmental Studies (GCES), and now the 

Grand Canyon Monitoring and Research Center (GCMRC), have been monitoring and modeling 

the effects of the Glen Canyon dam flows on various ecological resources within the Colorado 

River ecosystem (CRE). The overall objective of these programs is to determine flow regimes 

that maintain the resources at recent levels, and possibly restore the resources to pre-dam 

conditions. The research and monitoring has been performed mostly by in-situ measurements, 

supplemented by annual airborne image data provided by the information technology (IT) 

program. The image data that were acquired generally consisted of analog, stereo black-andwhite photography (color-infrared or CIR photography in particular locations) at 11-cm spatial 

resolution. These data were point-perspective (unrectified) without pointing or camera 

information necessary to rectify (georeference) the data to make accurate maps or to perform 

photogrammetry to derive accurate topography. Correct use of these image data by scientists 

required a complex process to transform the distorted, point-perspective analog data into an 

undistorted (rectified), map-projected digital form so that accurate information could be obtained 

for any particular area. The complexity of the process did not encourage many scientists to use 

the image data to its fullest potential or accuracy. Therefore, the approaches that were used by 

GCMRC cooperators before the year 2000 were similar to approaches used by image scientists in 

the early 1970’s. 

The GCMRC monitoring and research programs (i.e., physical, biological, and cultural) 

were reviewed by external protocol evaluation panels (PEP) within the past few years (Wohl et 

al., 1999; Doelle et al., 2000; Urquhart et al., 2000; Anders et al., 2001; Jones et al., 2001). In 

general, these panels recommended that these programs conduct more integrated, corridor-wide 

monitoring in order to more accurately determine the effects of dam flow on ecosystem resources. 

In addition, the Remote Sensing PEP for the IT Program (Berlin et al., 1998) recommended that 

more modern, advanced remote-sensing technologies be examined to provide better data to the 

research programs. These two factors prompted GCMRC to establish a remote-sensing initiative 

whose purpose was to determine the most appropriate remote sensing technologies and 

approaches that could increase the capabilities and efficiencies of the research scientists in order 

to help them perform more integrated, less-invasive, corridor-wide studies. The first step in that 

initiative, which started in the fall of 2000, was a review of the types of ecological parameters 

being monitored, the collection methods being used, the precision required for each parameter, 

and alternative remote-sensing and GIS approaches for such monitoring. The latter aspect 

involved a review of published literature to determine technologies and approaches that produced 

useful results for problems analogous to those faced by GCMRC. The useful approaches were 

reviewed in Davis (2002a), which also includes a table of over 100 operational airborne and 

spaceborne sensor systems that lists relevant characteristics of the sensors. The sensor table was 

used to select appropriate sensors for consideration and possible evaluation, based on their 

capabilities for meeting the requirements for a particular program parameter. Although the 

review found that many resource parameters that are currently monitored could not be adequately 

approached using airborne remote-sensing technology, these being mostly chemical 

characteristics of water, the review also found an equal number of parameters that might be 

approached, if data of the correct type and format were collected and provided to the scientists. 

During this initial fact-finding process, we found that the level of detail recorded by previous 

airborne data collections was not being used during scientific analysis, despite initial claims by 

scientists that they needed the high resolution provided by historical data. This initial review also 

produced a table of CRE resource categories whose monitoring might be enhanced by remote 
6

sensing, along with the types of remote-sensing data that might satisfy required measurement 

accuracies; the types of data subject to investigation are listed in Table 1 in order of increasing 

complexity and generally increasing cost. Personnel involved in the remote-sensing initiative 

collected and analyzed remote-sensing data listed in the table, starting with the least complex and 

proceeding to the most complex data, until a particular data set was found to provide acceptable 

accuracies for a particular resource parameter. This approach was followed because cost of data 

is an issue. 

The remote-sensing initiative was supervised by Mike Liszewski (IT program manager) 

and coordinated by Philip Davis (research scientist at the U.S. Geological Survey). The initiative 

involved all of the IT personnel and many scientists from different disciplines, whose expertise 

was required for evaluation of specific remote-sensing technologies, data-provider performance, 

data-analysis methods, and resulting accuracies. This process is now near completion. This 

report reviews the GCMRC program objectives and measured parameters and the results from our 

remote-sensing investigations on those parameters that might benefit from improved data 

acquisition and/or analysis. The resources are discussed in order of the increasing capabilities 

found by remote-sensing approaches. Thus, the order of our discussions proceeds from the 

cultural resource program to the biologic resource program and then the physical resource 

program. 

Remote sensing of radiation on Earth is limited to the wavelength region from the visible 

to microwave energies and it is this broad energy region that we have investigated for monitoring 

applications within the CRE. The different types of remote-sensing data sets that were collected 

and investigated for the different environmental parameters are listed in Table 2. Radar data were 

not included in this evaluation because the aircraft used for radar data collections are large, 

impossible to maneuver in the canyon, and provide too low spatial resolutions (about 5 m) when 

flown above the canyon rim. In addition, the walls of the canyon can produce secondary radar 

reflections that interfere with the primary reflections and make the image data unintelligible. 

The collection of numerous wavelength bands by multi- and hyperspectral sensors limits 

the spatial resolution that can be achieved by such sensors because of data-rate limitations of 

current storage devices. Spectral resolution refers to the wavelength band width for a particular 

image, whereas spatial resolution refers to the surface dimension of a single picture element 

within that image. For example, multispectral sensors that record 12 wavelength bands with 50 

nm spectral resolution can obtain image data at a 1-m ground resolution, while hyperspectral 

sensors that record up to 220 wavelength bands with 10 nm spectral resolution can only obtain 

data at a 2-4 m ground resolution. The cost for image data (listed in Table 1) increases with the 

number of wavelength bands collected, which effects the benefit/cost ratio and makes use of more 

sophisticated data difficult to justify, unless these data provide information that cannot be 

obtained by more simple, less expensive data. Therefore, our evaluations proceeded from the 

simpler to the more complex data sets, until a viable data set was found for a particular resource 

parameter. The final section presents the team’s recommendations for future remote-sensing 

monitoring activities based on all of our investigations. 

2.0 Cultural/Socio-Economic Resources 

The primary goal of the cultural/socio-economic resource program is to monitor cultural 

and socio-economic resources with respect to Glen Canyon dam operations, so that ultimately a 

model can be constructed and used to predict and possibly mitigate the effects of dam operations 

on these resources. The primary resources for monitoring consist of camping beaches, prehistoric 

and historic sites, and traditional tribal resources, such as ethnobotanical, faunal and physical 
7

(springs, sediment deposits, and mineral deposits) resources. Four specific program objectives 

are: (1) to conserve downstream resources, (2) to design mitigation procedures where necessary, 

(3) to maintain physical access to cultural resources, and (4) to provide quality recreational 

resources that do not adversely affect natural/cultural resources. There are other socio-economic 

objectives associated with hydropower supply and water resources, but these are outside the 

realm of remote sensing. 

Many of the traditional tribal resources (such as ethnobotanical stands) are very small (< 

1 m) and difficult to discriminate from similar, surrounding materials using airborne approaches. 

Some other resources (such as mineral occurrences within wall adits) are obstructed from aerial 

view. Resource monitoring that might benefit from a remote-sensing approach include: (1) 

detecting of natural springs, (2) monitoring beach and camp-site changes through time associated 

with different flow regimes, (3) detecting and monitoring of archaeological structures (including 

those that are partly buried), and (4) evaluating the effectiveness of vegetation and earth check 

dams in mitigating erosion and degradation of historic and prehistoric resources. 

2.1 Natural springs 

The most identifiable characteristic of spring water in remote-sensing data is their colder 

temperature relative to the surrounding geologic materials or vegetation. This characteristic is 

best detected using thermal-infrared (TIR) image data. Thus, our investigation of natural springs 

centered on the ability of TIR imagery to detect the occurrence of small, less obvious springs. 

Thermal imagery used in this evaluation was collected at a spatial resolution of 1 meter in midafternoon. A multispectral instrument was used to collect 12 different wavelength bands, two of 

which were TIR bands. These data were collected for our evaluations of a number of GCMRC 

parameters, which included (1) mapping terrestrial vegetation, (2) mapping warm-water eddies 

and backwaters, (3) detection and monitoring of partly buried or degraded archaeological 

structures, and (4) detection of natural springs. The mid-afternoon collection time for these data 

was dictated by the period of maximum solar heating for quiescent water in order to detect warm 

backwaters and by full illumination of vegetation in order to map vegetation, but this was not the 

optimal time for data acquisition for detecting natural springs. 

Our analysis of the TIR data showed that the thermal contrast between the spring water 

and surrounding ground was too small during our daylight acquisition time to distinguish small 

springs from the surrounding geology on the walls of the canyon (Davis, 2002b). Only the larger 

springs (such as Vaseys Paradise) were detected. Previous TIR investigations using data 

collected just after sunset or just after sunrise seemed to be more successful at detecting warm 

springs along the Little Colorado River (Holroyd, 1995a, b) because thermal conductivity and 

emissivity differences between surface materials are most pronounced during these two time 

periods. However, these “detections” could not be confirmed by ground studies due to poor 

aircraft positional information. If springs are to be mapped within the canyon, it would require a 

separate, georeferenced TIR survey during post-sunrise hours; a post-sunset flight within the 

canyon would be unsafe. 

2.2 Archaeological structures 

 One of the priorities of the cultural resource program is monitoring historic and prehistoric structures and check dams at certain structures to determine (1) the degradation of these 

structures and (2) the effectiveness of the check dams in mitigating the effects of arroyo erosion 

and river flow stage. Thermal infrared is very sensitive to subtle differences (or changes) in 

density and grain size (Hussein, 1982; Johnson et al., 1998). Thus, minor disruptions of the 
8

surface will change the surface’s density and/or grain size, which may be detected in TIR imagery 

(Johnson et al., 1998). Thermal-infrared images can also distinguish a degraded, buried ruin from 

its surrounding alluvium, as long as the ruin and the alluvium have different compositions or 

densities (Berlin et al., 1977; Nash, 1985; Berlin et al., 1990). Thus, we investigated the use of 

TIR data, as well as visible and near-infrared (NIR) imagery of different spatial resolutions, for 

detection and mapping archaeological structures at Unkar Delta (Davis, 2002b). Our results 

showed that spatial resolution was the key factor in mapping these structures for data acquired 

during the middle of a day. We could not unambiguously identify the structures at Unkar Delta 

using the daytime TIR data at 1-m resolution or using any of the ten reflectance wavelength band 

images that were acquired at 1-m resolution with the TIR data (Figures 1 and 2). The ambient air 

temperature in the canyon during our mid-day July data acquisition was close to 38 oC. TIR 

sensors require liquid nitrogen cooling systems to maintain the sensor near absolute zero degrees, 

but the cooling system could not compensate for this high air temperature. Thus, the sensitivity 

of the TIR data was only 0.3 degrees, whereas a sensitivity of 0.1 degrees is required to detect 

thermal anomalies associated with geologic materials (such as the archaeological structure 

relative to its alluvial surroundings; Nash, 1985). Panchromatic (black-and-white) imagery at 18-

cm resolution was also found to be much less useful in uniquely identifying the structures than 

CIR (green, red, and NIR composite) imagery at 11-cm resolution, which is attributed to the lack 

of contrast between the alluvial and archaeological materials in black-and-white imagery. We 

found that the most unambiguous detection of these structures requires CIR or natural-color 

imagery with resolutions near 11 cm. The incipient stage of arroyo development may manifest 

itself more as subtle changes in surface materials due to recent exposure or transport than as 

changes in surface topography. However, the CIR imagery do not provide good discrimination of 

alluvial geologic materials that may indicate mass movement and arroyo development. In order 

to detect subtle changes in alluvial surface materials it would be better to acquire multispectral 

data that include at least one short-wave infrared wavelength band, where more distinctive energy 

absorptions occur for geologic minerals. Such systems cannot obtain spatial resolutions better 

than 0.5 m. Although this resolution will not detect morphometric changes in structures, the data 

could better map changes in surface materials that may indicate mass movement. 

For the detection of morphometric (dimensional) changes in archaeological structures and 

in the arroyos that affect these structures, even higher resolution imagery (2-3 cm or 1:1,600-scale 

photography) is required (MacFarlane et al., 2002; Petersen et al., 2002), especially for 

monitoring the very fine-scale (centimeter) changes in arroyos that could indicate potential 

impacts on such structures. Photogrammetric analyses of such extremely high-resolution image 

data, which were acquired within the remote-sensing initiative, produced an elevation accuracy of 

6-10 cm; the average vertical error at the 95% confidence level (for normally distributed errors) 

of 18 cm. These data could resolve sub-meter-scale changes, but the vertical error exceeds the 

change-detection threshold for the smaller, cm-scale gullies (MacFarlane et al., 2002). Field 

studies at four selected sites by Petersen et al. (2002) indicated that vegetation cover, soil 

permeability, and soil shear strength to be inversely related to gully erosion activity. These 

observed trends, plus the fact that gully head locations can be predicted by slope and drainage 

area relations derived from the photogrammetric data, suggest that up-catchment control of gully 

initiation and knick-point retreat are more critical factors in structural degradation than is baselevel changes related to Colorado River stage or Glen Canyon Dam operations (Petersen et al., 

2002). The stereo-image data that were collected did however provide a catchment DEM that 

allows geopmorphic modeling of potential impacts of rainfall on arroyo development and, in turn, 

of potential impacts of arroyo development on down-slope archaeological structures. One of the 

major drawbacks to this approach for long-term monitoring of archaeological site degradation is 

the large expense for collection of the very high-resolution imagery required to detect the finescale changes; image acquisition costs for just four 100-m-wide archaeological sites was $20,000. 
9

In addition, collection of such data requires a very low (240 m AGL) helicopter flight, which may 

become a noise issue within the Park. 

We have found photogrammetric elevation data produced from 1:4,000-scale 

photography to yield vertical RMSE values of 28 cm (Davis et al., 2002b) and average elevation 

errors of 34 cm (at the 95% confidence level) at a cost of about $3,000 per river km. Stereo 

image data at this scale may be obtained annually for the entire river corridor for only $180 per 

river km to support other GCMRC monitoring protocols. Although such data may be acquired 

for other resource needs, only a small fraction (35 km) of the collected data would be 

photogrammetrically processed and the photogrammetric analysis of the data represents a large 

fraction of the overall cost of $3,000 per river km. Thus, even this more conventional 

photogrammetric approach would be very expensive for monitoring the nearly one hundred 

archaeological sites within the Colorado River ecosystem (CRE). Alternatively, automated, 

digital photogrammetric technology (ISTAR; Table 2) provides 30-45 cm (RSME) vertical 

accuracy on bare ground, 1.3-m accuracy in dense vegetation, and corridor-wide topography, in 

addition to orthorectified imagery, at a cost of $625 per river km. The ISTAR system collects 

three panchromatic images simultaneously with different view angles, which allows derivation of 

elevation using automated softcopy photogrammetric technology. This system also 

simultaneously collects four color bands that are useful for mapping terrestrial physical and 

biologic resources. We will evaluate that elevation data during 2003 to determine its accuracy for 

canopy heights. If the ISTAR data provide relatively accurate canopy heights, then ISTAR 

technology may become the remote-sensing protocol for large-area requirements for the physical 

and biological resource programs and may also provide useful topographic data for geomorphic 

modeling of archaeological sites at no additional expense. We have found that low- and 

moderate-resolution LIDAR (Light Detection and Ranging) provides less accurate, less dense 

elevation data on bare ground than that provided by photogrammetric data. The density of 

photogrammetric elevation data is only limited by the resolution of the stereo imagery (assuming 

the data are collected with adequate viewing angles). Our recent evaluations of very highresolution LIDAR showed that the data provide an 8 cm vertical accuracy and a 4-5 cm vertical 

precision on bare ground, which is close to the requirements for monitoring the arroyos and check 

dams near archaeological sites. Such accuracies cost about $6,200 per river km, but also provide 

useful data to all GCMRC programs. 

2.3 Mapping Campsites and Beaches 

The quality of 20 main campsites and beaches along the corridor is currently evaluated 

annually by field surveys that map each site’s topography and amount of open space. The field 

surveys are performed by the fine-grained sediment team in the physical resource program, whose 

primary purpose is to map all storage sites for fine-grained sediment within the CRE. However, it 

is logistically impossible for ground surveys to obtain an accurate inventory within a given year 

for the entire CRE. This monitoring is better accomplished using remote-sensing approaches 

because (1) campsites are generally well exposed and the rather simple parameters that the 

ground surveys record for campsite habitability, (2) remote-sensing data can see most terrestrial 

storage sites for sediment, and (3) rather simple image-processing algorithms can be used to map 

the characteristics of these sites. Using the ISTAR digital elevation data and orthorectified, fourband color data that were acquired for the entire CRE during 2002, Mike Breedlove (personal 

communication, 2003) recently showed that these data could not only map all campsites and 

beaches, but could also accurately inventory all terrestrial fine-grained sediment storage sites, 

throughout the CRE in a period of a few months. His analysis used the color data to define the 

water’s edge (Figure 3) and to define areas of non-vegetation that have high levels of surface 

reflectance (Figure 4). Local near-infrared-band variance separated smooth (fine-grained) and 
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rough (coarse-grained) surfaces (a fine-grained sediment map is shown in Figure 5). Elevation 

data were used to restrict the mapping of campsites and beaches to areas that are 10 meters or less 

in height above the water’s edge, which is a criterion established for campsite inventories, but the 

analysis was actually performed up to the wall-rock/alluvium interface. These individual data 

sets were integrated to produce a CRE digital map of fine- and coarse-grained sediment deposits 

(Figure 6). Much of these analyses were largely automated computer processed, which was made 

possible by the calibrated, digital data provided by ISTAR. If this process was attempted using 

un-calibrated, aerial CIR photography, the time required to produce such a product would 

increase by a factor of 3-4 and the product would have higher positional errors making it less 

accurate for temporal analyses. This study found some limitations in that data, mainly due to the 

different viewing angles of the four ISTAR color detectors. For example, the different view 

angles produced non-coincident sun glint in the four bands over much of the water’s surface, 

resulting in random, unnatural colors for the water. Thus, mapping the water’s edge required a 

supervised approach, which is usually one of the simpler, automated remote-sensing analyses. 

ISTAR now uses a color-detector system in which three of the four color bands are collected 

through the same optics and therefore with the same view angle, but the NIR band (critical for 

mapping vegetation) is still collected at a different view angle. This improvement will make 

some aspects of camp-site/beach mapping more automated for rapid temporal analyses, but an 

optimal system would collect all band data with the same optics, which would not only make this 

type of mapping more unsupervised, more rapid, and less expensive, but would also produce 

more accurate temporal analyses. Despite these few problems, the resulting inventory map of 

campsites and beaches is the most complete and accurate database to date and provides a good 

foundation (database and approach) for improved monitoring in the future. 

3.0 Biologic Resources 

The primary objective of the biological resource program is to understand the 

cause/effect relations between Glen Canyon dam operations and the downstream aquatic and 

terrestrial biologic ecosystem, and to develop a model that can predict ecological effects for 

different dam operations. This objective is approached in three ways: (1) inventory of biologic 

resources and, together with related physical resource data, development of a conceptual model 

that links biotic and abiotic components; (2) research on and development of cause/effect 

relations between dam operations and the ecology and testing the validity of the observed 

relations under various dam operations; and (3) monitoring both long- and short-term ecosystem 

behavior to determine if models are predictive for both natural (tributary) and dam perturbations. 

Monitoring the biologic resources within the aquatic and terrestrial environments is a 

fundamental aspect of these objectives and therefore remote sensing approaches for detecting and 

mapping the various resource parameters were evaluated within this remote-sensing initiative. 

3.1 Aquatic Environment

 Parameters monitored in the aquatic environment are those deemed important for the 

survival of aquatic species. These parameters consist of the chemical and physical characteristics 

of the water, the aquatic foodbase, and the presence of warm backwater areas that serve as fish 

habitats. 

3.1.1 Water Parameters 

Water Resources Division of the USGS collects and analyzes data from water-gage 

stations and water samples at various locations within Lake Powell, the main channel, and within 

major tributaries. From the tailwaters to Lees Ferry, remote monitoring stations measure and 
11

transmit every four hours the sediment load, turbidity, and water temperature in the main channel. 

Monthly water samples are collected at river miles 0, -3, -6, -9, -11, and –16 and the samples are 

analyzed for chlorophyll, phytoplankton, and zooplankton. Remote water monitoring stations 

also measure the above parameters at the same frequency within several tributaries (Paria River, 

Shinumo Creek, Tapeats Creek, Spenser Creek, Havasu Creek, Kanab Creek, Bright Angel 

Creek, Little Colorado River, and Diamond Creek) and within the main channel above the Glen 

Canyon dam, at Lee’s Ferry, above the Little Colorado River confluence, near the Grand Canyon, 

above National Canyon, and above Diamond Creek. At Lake Powell, surveys are conducted 

quarterly (March, June, September, and December) to obtain profiles of water temperature and 

turbidity at approximately twenty stations north of Glen Canyon dam between river mile 2 to 263. 

Water samples are also collected at some of these sites and are analyzed for chlorophyll, 

phytoplankton, and zooplankton. 

Numerous remote-sensing studies conducted within the last decade have developed 

algorithms to measure sediment load (as total suspended sediment), turbidity, chlorophyll a and b, 

total chlorophyll, and total dissolved solids (specific conductance), but the depth of measurement 

is limited to the depth of light penetration and such measurements cannot determine variations 

with depth (e.g., Goodin et al., 1993; McFeeters, 1996; George, 1997; Sathyendranath et al., 

1997; Fraser, 1998a, b; Tassan, 1998). Most studies determined that multiple wavelength bands 

within the 0.420 μm and 0.710 μm wavelength region are necessary to obtain accurate estimates 

and that the algorithms to estimate the water parameters require periodic verification of their 

calibration. This latter requirement might suggest that remote sensing cannot benefit aquatic 

monitoring because it cannot replace in situ measurements that are necessary for remote-sensing 

calibration. However, the real strength of remote sensing is not the elimination of field 

verification, but rather the extrapolation of site-specific information to wide areas at a significant 

savings of time and cost. For example, the areal perspective provided by remote-sensing data can 

assist in determining the most representative sites for in situ measurement systems, which may 

not have been done for the existing water monitoring network. Chavez et al. (1997) used 

temporal remote-sensing data acquired under different environmental conditions within San 

Francisco Bay to help water resource personnel determine the most appropriate sites for their insitu monitoring systems. In addition, remote-sensing data can be used to produce maps that show 

the distribution of particular parameters over large areas for a particular time period. In-situ 

measurement systems provide only point-source information, which might miss or misrepresent 

an event that has spatial variation. Remote-sensing algorithms that have been developed for lakes 

can map chlorophyll concentrations with a sensitivity of 3 μg/l (George, 1997). Algorithms have 

been developed specifically for the Colorado River that relate spectral radiance of the water to the 

water’s chlorophyll content, turbidity, and suspended load, using water-gage data for calibration 

(Chavez et al, 2002a,b). The algorithms can now be used to produce water-parameter maps for 

parts or all of the CRE using the multispectral camera that Pat Chavez has purchased, whose 

bandwidths are optimized for mapping the aquatic environment, which was recommended by the 

remote-sensing PEP (Berlin et al., 1998). 

3.1.2 Aquatic Foodbase 

At 10-day intervals for 90 days each year, foodbase is determined within pool, cobbleriffle, shoreline, and nearshore environments. The foodbase surveys are performed at river miles 

-15.5, 0, 60, 64 , 138, and 205. For the pools, foodbase is examined at five locations along each 

of three transects; each transect is about 30 m apart. The five sampling locations along the 

transects include the thalweg, <28m3

/s, baseflow, lower varial, and upper varial zones. Cobble 

riffle sample collections occur within the deepest accessible zone, as well as the lower and upper 

varial zones. Population data are collected for five biotic classes. Associated data are also 
12

collected, consisting of water temperature, dissolved oxygen, pH, specific conductance, 

substratum type, microhabitat conditions, total P and N, Secchi depth, water velocity, depth, site, 

and time of day. Shoreline habitats are sampled to determine (1) invertebrates in emergent 

vegetation, (2) fine sediment volume, and (3) tychoplankton. Nearshore habitats are surveyed to 

obtain (1) temperature profiles at 5-cm depth intervals within shoreline vegetation and 0.5 m from 

the shoreline and (2) surface (≤ 0.5 m depth) drift samples of coarse- (500 micron mesh) and 

fine-particulate (0.5 micron mesh) organic matter. 

Although the remote-sensing PEP (Berlin et al., 1998) recommended that airborne 

multispectral image data be explored for mapping these parameters, most chemical constituents in 

water and other properties related to chemistry (pH) cannot be detected with airborne sensors. 

Even though certain elements and compounds do absorb in the visible through TIR wavelength 

region, their concentrations in water need to exceed 1 wt % for their detection. When aqueous 

chemical concentrations reach such high levels, such as in estuaries and lakes, it has been shown 

that their concentrations can be mapped (Chavez et al., 1997), but such techniques cannot provide 

depth-concentration profiles, which are obtained during GCMRC in-situ monitoring. In addition, 

an airborne remote-sensing approach for aquatic foodbase parameters would be limited to the 

clearer, shallower water areas, would provide only certain parameters (Alberotanza et al., 1999), 

such as algae, vegetation flotsum, plankton, organic matter, surface drift, total dissolved solids 

(specific conductance), and could not detect the biotic species that are monitored by GCMRC. 

Even though multispectral sensors exist that provide wavelength data appropriate for monitoring 

some of the foodbase parameters, obtaining such data through commercial vendors at the high 

frequency currently obtained by in-situ monitoring would cost so much that remote-sensing 

would not be a viable option for present foodbase monitoring. Pat Chavez tested this 

multispectral sensor by acquiring 3-cm image data within Glen Canyon to determine the ability of 

the data to map fish foodbase and nesting habitats. The results of this study will be available 

during the summer of 2003. If useful results are obtained, a similar sensor could be purchased by 

GCMRC ($15,000) and flown at relatively low cost to provide data for various monitoring 

requirements. However, orthorectified image data would be most useful, which requires 

expensive GPS and IMU instrumentation. Image data could be rectified using an existing 

controlled image base, but this georectification process is time consuming, would cost more than 

the data collection, and would provide at best only 1 m positional accuracy. 

Remote sensing can provide wide-area monitoring for two other aquatic parameters: 

water surface temperature and substrate type. Mapping water surface temperature is discussed in 

the following section on warm backwaters; substrate mapping is discussed in a section within the 

physical resource program in which channel substrate is a primary collection parameter. 

3.1.3. Warm Backwaters 

One of the objectives of the CRE monitoring is the preservation of the native Humpback 

Chub, whose population has diminished due to the cold, low-flow water releases since 

construction of the Glen Canyon dam. Chub prefer warm (18-22 oC), turbid, and sheltered water 

environments, such as return-current channels, shoreline embayments, and the mouths of 

tributaries. The presence and number of backwaters within or near return-current channels, 

shoreline embayments, and tributary mouths are monitored during quarterly, system-wide 

vegetation surveys, which map the presence of dry and wet marshes that contain different wetland 

vegetation. The temperatures within the backwater areas are monitored periodically by 

thermistor-string field surveys at selected backwater areas and by water gages at several sites 

within the CRE. By their nature, the surveys and gages sample a small portion of the CRE. 

Detection and mapping of these warm backwaters should be more easily accomplished using 
13

appropriate remote-sensing data. We investigated this possible remote-sensing application by 

collecting a multispectral (12-band) data set containing two TIR bands for a 44-mile segment of 

the CRE between river miles 30 and 74 (coincident with a ground survey in the area). This 44-

mile airborne survey took 20 minutes to complete using a helicopter flying at a 365 m AGL 

(above ground level). Even at this low AGL, the multispectral sensor could provide only 1-m 

spatial resolution. TIR image data record surface radiant temperature across the entire channel, 

which can augment site-specific thermistor surveys. Our TIR data were collected in early July 

2000 when the ambient air temperature was so high (38 ̊C) that the TIR cooling system could not 

reach the required absolute zero degrees (for a 0.1 degree sensitivity), which resulted in a 0.3 

degree sensitivity in the TIR image data, but even this sensitivity proved more than adequate to 

detect and map the warm backwater areas (Davis, 2002b). Derivation of water radiant 

temperature required calibration of the airborne sensor signals to water temperatures, which was 

accomplished using coincident water-gage temperature data. The airborne TIR data showed a 

linear relation with main-stem water temperature (Figures 7 and 8) and easily mapped all warm 

backwater areas within the 44-mile river segment, which included eddies with rather steep 

thermal gradients, tributary mouths with gradual and abrupt thermal gradients, eddies formed by 

reattachment bars, return-current channels, and isolated backwaters that are difficult to discern in 

visible-wavelength imagery (Figures 9 and 10). Although the 1-m spatial resolution (and 

possibly the 0.3 degree sensitivity) of the TIR imagery was insufficient for the detection of 

archaeological structures, it was found to be totally adequate for detection and mapping the 

warm-water areas within the 44-mile study area. Unfortunately, the 44-mile study area covered 

by the multispectral data only included wet marshes. Therefore, we could not test the capability 

of these data for discriminating and mapping dry and wet marsh areas based solely on the 1-m 

reflectance band data. 

At the present time, the limiting factor for multispectral sensors is their spatial resolution, 

which is not better than 50 cm. Additional protocols of the physical resource program, and 

possibly of the biologic resource program, require a higher spatial resolution (≤ 20 cm). These 

additional requirements are discussed in following sections. Without any additional applications 

of multispectral data at resolutions near 50-100 cm, the cost ($530 per river km) for such data for 

just mapping warm backwaters would be difficult to justify. However, there may be additional 

needs for this lower resolution, multispectral image data in order to more accurately and 

efficiently inventory the terrestrial vegetation resources within the CRE. This issue is addressed 

in the following section. 

3.2 Terrestrial Environment 

 The biological resource program periodically monitors the composition, area, and volume 

of vegetation habitats within the CRE each year. Composition is monitored to determine changes 

in plant populations due to the invasion of exotic (non-native) species and to dam flow operations 

during the year. Area and volume are primary indicators of the suitability of vegetation stands as 

faunal habitats. Monitoring of these characteristics is performed mostly by ground surveys. 

Parameters monitored in the terrestrial environment are those deemed important for bank 

stabilization, aquatic and terrestrial faunal habitats, tribal botanical resource, and recreation. Prior 

to 2002, vegetation type, area, and height were mapped annually at 11 sites (river miles, 6, 43.5, 

50.5, 55, 68.5, 71.5, 93, 123, 194, 209, and 243) using aerial photographs and field studies. In 

addition, downslope growth rates of equisetum, juncus, and phragmites were measured along 

selected transects on monthly basis over a four-month period. The monthly field studies also 

monitored growth or removal of exotic plants, such as tamarisk ramosissima and alhagi 

camelorum (camelthorn), and sample low-elevation areas for changes in seed abundance and 
14

type. Surveys now consist mostly of random samples throughout the CRE and of culturally 

significant plants (exotic/invasive species and ethnobotanical species) during April, May, and 

September. Vegetation of interest includes acacia, equisetum/sedge, redbud (Cercis occidentalis), 

tamarisk (tamarix ramosissima), arrowweed (tessaria sericea), bermuda and red brome monotypic 

grasses, hackberry (celtis reticulata), cliffrose, desert brome, mesquite, coyote willow (salix 

exigua), baccharis seepwillow (baccharis emoryi and salicifolia). Before 2000, photographic 

prints were used to manually trace the distribution of vegetation species, based on a visual 

interpretation of CIR color and texture. All derived polygons were then field checked. The 

collection of orthorectified imagery now makes this process more efficient and accurate. More 

advanced remote-sensing data and analysis methods should be able to increase the area covered 

and reduce the time required for field surveys, but this will depend on the capability of the 

remote-sensing data that can be acquired for the CRE within GCMRC’s remote-sensing budget. 

Several factors control the spectral reflectance of vegetation; these include water, 

chlorophyll a (absorbs at 0.430 μm and 0.662 μm) and b (absorbs at 0.453 μm and 0.642 μm), 

assessory pigments (e.g., Beta carotene and lycopene that absorb between 0.460 μm and 0.550 

μm), nitrogen, lignin (cell wall polymer), cellulose (40-60% of cell walls), and open pore space. 

Numerous studies have been performed over the past three decades to determine the most 

appropriate data and analysis methods to accurately detect and map these vegetation 

characteristics using remotely sensed data. Recent research has determined the following 

relations. 

1. There is a strong linear correlation between chlorophyll (a+b) and (1) the ratios 0.750 

μm/0.700 μm and 0.750 μm/0.550 μm and the green vegetation index (Gitelson and 

Merzlyak, 1997); (2) the 0.700 μm and 0.735 μm reflectance and their band ratio 

(Gitelson et al., 1999); (3) the first derivative of the green vegetation index (Elvidge and 

Chen, 1995); (4) the perpendicular vegetation index (Richardson and Wiegand, 1997); (5) 

the first derivative of the 0.721 μm band (Blackburn, 1999); and (6) the band ratios 0.836 

μm/0.817 μm and 0.969 μm/0.931 μm, the first derivative of the 0.750 μm band, and the 

second derivative of the 0.753 μm band (Blackburn and Steele, 1999). Blackburn and 

Steele (1999) also found good correlations between their wavelength band ratios and 

derivatives and the carotenoid content and that the derivative of 0.721 μm band correlated 

well with total chlorophyll, chlorophyll a, and chlorophyll b, but not so well with 

carotenoid content. 

2. Penuelas et al. (1997) found some correlation between the 0.900 μm/0.970 μm band 

ratio and plant water content, but it was very weak, but Hardy and Burgan (1999) found a 

good correlation between NDVI and plant moisture. 

3. Kokaly and Clark (1999) found good correlations between spectral reflectance 

centered at 1.730 μm, 2.100 μm, and 2.300 μm with nitrogen and cellulose, but not with 

lignin. 

4. Spectral reflectance from vegetation is affected by soil and litter cover, illumination 

angle, and shadows. Methods have been devised to mitigate these effects (Lee and 

Marsh, 1995; Garcia-Haro et al., 1996; Todd and Hoffer, 1998; Blackburn, 1999; Pinder 

and McLeod, 1999; Yu et al., 1999; and Quackenbush et al., 2000). 

5. Vegetation classification accuracies greater than 80% have been obtained using 

remotely sensed data (Butt et al., 1998; Purevdorj et al., 1998; Coulter et al., 2000). Use 

of seasonal data improves classification for deciduous vegetation (Grignetti et al., 1997; 
15

Mickelson et al., 1998). Additional and narrower wavelength bands increase 

classification accuracy (Elvidge and Chen, 1995; May et al., 1997; Green et al., 1998). 

Airborne imagery provides better accuracy than spaceborne imagery due to its higher 

resolution (Rowlinson et al., 1999; Zhu et al., 2000). 

All of this research points to the distinct possibility that terrestrial vegetation surveys can 

become more automated, extensive, and less expensive using remote-sensed data and imageprocessing algorithms. Therefore, the remote sensing initiative investigated various airborne 

technologies for mapping the community-level compositions and deriving accurate canopy 

elevations of habitats. 

3.2.1 Vegetation mapping 

 For vegetation composition at the community level, we evaluated different types and 

resolutions of image data that were acquired during the remote-sensing initiative (Davis et al., 

2002c). The data that were evaluated included 11-cm CIR film (July 2000), 30.5-cm CIR film 

(March 2000), 28-cm CIR film (acquired during overcast conditions in September 2000), 30.5-cm 

digital CIR imagery (September 1999), and 100-cm digital, 9-band multispectral (July 2000) 

image data. Vegetation texture was derived from each data set and used with the color 

information in various supervised image classifiers to produce vegetation maps at five study areas 

that were previously mapped by ground surveys (Kearsley and Ayers, 2000). The study areas 

were located at river miles 43.1, 51, 55.5, 68.2, and 71.4. The vegetation maps produced using 

the airborne image data were compared to the ground survey maps to determine the accuracies 

and relative merits of the different types and resolutions of image data for mapping CRE 

vegetation communities. The resulting classification maps produced for study area RM 68.2 are 

shown in Figures 11-17. The results of this investigation are summarized in the following items 

that were extracted from Davis et al. (2002c). 

1. The intrinsic reflectance of vegetation is an important factor in discrimination of the 

riparian vegetation within the CRE. Thus, digital sensors that record a large dynamic 

range and maintain radiometric fidelity provide higher mapping accuracies than 

photographic film. Although image data acquired under overcast sky conditions 

produced less shadowing within vegetation, the resulting lower reflectance of the 

vegetation reduced the classification accuracies from these data over that obtained 

from image data acquired under clear sky conditions. In addition, it is best to obtain 

image data near the summer solstice in order to minimize shadows within vegetation, 

but also shadows cast by the canyon walls. Even at the solstice, there are areas 

within the CRE that need to be acquired within one hour of noon in order to 

minimize shadows from steep canyon walls (Figures 18 and 19). 

2. Vegetation texture is an important factor in vegetation map accuracies. Texture was 

found to increase mapping accuracies by 20-30 absolute percent over accuracies 

obtained from vegetation mapping that used just color reflectance information. 

Mapping accuracy is the percentage of picture elements for a particular vegetation 

alliance or collection of alliances that are correctly identified. Texture is much better 

defined within the vegetation at higher spatial resolutions and is best derived from 

image resolutions near 20 cm or higher resolution. 

3. Calibrated, 9-band multispectral image data (1-m resolution) produced higher map 

accuracies than higher resolution (11-cm), un-calibrated CIR film, which shows the 

importance of radiometric calibration for vegetation mapping. Reducing the number 
16

of multispectral bands used to map vegetation to only four wavelength bands did not 

greatly reduce mapping accuracies over those obtained using the full 9-band set. The 

four most useful bands for mapping the CRE riparian vegetation were centered near 

the wavelengths 0.53-54 μm, 0.066-0.67 μm, 0.70 μm, and 0.79-0.82 μm. These are 

close to the wavelengths selected for the early Landsat Multispectral Scanner System 

(Landsat MSS), which was primarily designed for vegetation monitoring. 

4. The use of a Global Positioning System (GPS) and an Inertial Measuring Unit (IMU) 

that provides 30-cm positional accuracies should be used with DEM data to 

orthorectify the image data in order to provide accurate area and volume estimates. 

5. Phase angle during image data collection should be kept to a narrow range (≤ 10 ̊) 

because canopy reflectance values can change at different solar incidence angles and 

sensor viewing angles. 

6. Subsequent field work is required to reach an 80% mapping accuracy, but the field 

work would be much less intensive and invasive than current field surveys for 

vegetation. 

7. Remote sensing will not eliminate the need for the current random sampling of 

vegetation within the CRE because understory is a key component in these field 

surveys and remote-sensing data even at 6-cm resolution cannot identify, and in most 

cases see, the understory. 

We have not yet found an imaging system that can provide the four wavelength bands 

listed in item 3 above with acceptable spatial resolutions. We did locate a 4-band imaging system 

operated by ISTAR Americas that provides three of the four desired wavelength bands (not the 

band centered at 0.70 μm), in addition to a blue wavelength band, at 44-cm spatial resolution. 

This system was used to collect the 2002 annual image and DEM data for the entire CRE. The 

positional accuracy of the orthorectified image data was found to be about 30 cm, which is better 

than most other image data acquired by GCMRC to date and acceptable for biologic monitoring 

purposes. The 44-cm spatial resolution was less than desired, but our preliminary vegetation 

analyses using these data show that texture derived from this 44-cm data still added 10-25% to 

the classification accuracies for various vegetation alliances. We did encounter a problem with 

saturation in the critical NIR image data, which means that the recorded NIR brightness for some 

vegetation alliances were similar even though their NIR ground reflectance spectra show that they 

are distinctly different. This is a calibration issue and is the main reason that correct sensor 

calibration is a critical factor in the list above. 

Some vegetation alliances were found difficult to discern using the 4-band ISTAR data, 

which raises an important issue for future vegetation mapping. Is a sensor that provides 

additional useful wavelength bands at the expense of spatial resolution (and derived texture) 

better for future vegetation inventories than a sensor that provide a few bands but higher spatial 

resolution? Before the next data collection for vegetation inventory, this question needs to be 

answered so that mapping can be less time consuming and more accurate. Higher order 

multispectral data (referred to as hyperspectral data) were acquired for small areas within the 

CRE in order to evaluate these data. HYDICE (an Navy experimental sensor) acquired 206 bands 

at 1.5-m resolution within Glen Canyon, but the data proved to be too noisy (poorly calibrated) to 

use for vegetation classification. AVIRIS (a NASA instrument) acquired 220 bands at 4 m 

resolution, but occurrences of some important vegetation alliances are much smaller than an 

AVIRIS picture element. We hope that a well-calibrated, hyperspectral data set can be acquired 
17

in the near future at a few different resolutions in order to resolve this issue, preferably using a 

commercially available system so that we could confidently employ the system once collection 

specifications were determined. 

3.2.1 Canopy elevation and volume 

 Currently, vegetation stand volume is estimated from the stand’s area, which can be 

obtained from correctly orthorectified imagery, and from spot vegetation height measurements, 

which may be limited to the more accessible parts of a stand. Two remote-sensing approaches, 

photogrammetry and LIDAR, were investigated that potentially could produce more accurate 

(representative) stand volumes. For this evaluation, we examined photogrammetric data 

produced from 1:4,000-scale photography and two sets of LIDAR data acquired using different 

LIDAR sensors that collected points at a 1.5-m and 3.75-m spot spacings. The LIDAR data were 

evaluated as a potential method for mapping canopy volumes because neither LIDAR data set 

provided ground elevations within our CRE vegetated test areas. We therefore thought that these 

LIDAR data might at least provide canopy elevations. [Our assessment of LIDAR for ground 

elevation is discussed in the following section within the physical resource program.] Based on 

our assumption that stand volume could be under-/over-represented by 20% using current field 

sampling technique, we set this level of accuracy as the minimum accuracy for the remotesensing data. 

Our analyses of the photogrammetric and low-to-moderate resolution LIDAR elevation 

data (Davis et al., 2002a and submitted) showed that photogrammetry is much more accurate for 

mapping canopy elevations than are either of the two LIDAR surveys. We found that (1) 67% of 

the photogrammetric spot elevations were within 20% of ground-surveyed canopy elevations, (2) 

only 38% of the high-resolution (1-m spot spacing) LIDAR data met this accuracy, and (3) less 

than 5% of the moderate-resolution (3.75-m spot spacing) LIDAR data met this criterion (Figure 

20). The new ISTAR airborne technology that we employed in June of 2002 for the entire CRE 

produced a 1-m digital surface model (DSM), which is a digital elevation model for the reflected 

surface, that should include the vegetation canopy within dense stands. These data are currently 

being evaluated for canopy heights, which will require that the DSM data accurately represent 

both the canopy and surrounding bare ground. These DSM data were produced without human 

intervention using automated softcopy photogrammetry and therefore the cost is relatively low 

($625 per river km) compared to more conventional photogrammetric analyses. On the other 

hand, we are currently evaluating very high-resolution LIDAR data for topography and canopy 

height for vegetated areas. We have already found these data to be extremely accurate on bare 

surfaces (8 cm) and hope this also holds for the vegetation. However, the cost for these LIDAR 

data ($6,200 per river mile) may preclude its use for large-area volume estimates. A lessexpensive ($2,100 per river mile), high-resolution LIDAR sensor, which provided 17 cm vertical 

accuracy on bare ground, is also being evaluated and may provide an acceptable medium. 

4.0 Physical Resources 

The physical resources program provides information and assessments of dynamic 

hydrologic and geomorphic processes resulting from Glen Canyon dam operations that directly 

and indirectly affect the CRE. The overall objective of the program is ecosystem sustainability of 

hydrologic and geomorphic processes and interactions and long-term conservation of sediment in 

main-stem and riparian environments. The habitats of concern include channel environments 

(cobble and gravel bars, debris fans, and talus shorelines) where benthic organisms occur and 

which are used by spawning fish; aquatic near-shore habitats (sandy shorelines and backwaters) 

that are used by juvenile native fish and that provide substrates for plants; terrestrial habitats that 
18

support riparian flora and fauna; terrestrial substrates used by recreational visitors; and terrestrial 

substrates that support and preserve cultural resources up to the stage associated with pre-dam 

river terraces. This overall objective is being approached by three program elements, which also 

have implications for biological and cultural resources. 

1. Long-term monitoring of fine-grained sediments in key storage settings documenting 

system-wide changes in these deposits (morphology, volume, area distribution, 

and grain-size characteristics) relative to dam operations and natural tributary 

inputs. 

2. Long-term monitoring and evaluation of coarse-grained sediment inputs (with respect 

to volume, grain-size, and topographic changes within debris fans, eddies, cobble 

bars, and the channel substrate) from tributary debris flows and Glen Canyon 

dam operations relative to system-wide, coarse-sediment mass balance and 

distributions of aquatic and terrestrial habitats. 

3. Developing or refining existing stream-flow and suspended-sediment transport models, 

considering a subset of river reaches grouped by their common characteristics 

and behavior, to better predict average sand-bar deposition and erosion responses 

to varied discharge rates, fine-grained sediment supply, and thermal conditions, 

and to better understand coupled suspended-sediment and stream-flow processes 

along the main channel. 

Grain size is an important parameter, especially with respect to the 10 lowest size classes 

within the sand, silt, and clay categories. However, these fine grain sizes are also the most 

difficult to detect with remote sensing due to the small size of the particles. Past monitoring 

within this program concentrated on just 4-5 reaches within the first 100 river miles, on the 

assumption that these sites adequately represented corridor changes from dam operations. 

Monitoring within this program has now been expanded to 11 reaches that are distributed 

throughout 230 miles of the corridor. As in the biologic resource program, the parameters that 

are monitored by the physical resource program can be separated into two categories: aquatic and 

terrestrial. 

4.1 Aquatic Environment 

 Unlike the biologic resource program, the physical resource program is concerned mainly 

with the inorganic (geologic) components within the aquatic environment, which are easier to 

detect and monitor using remote-sensing data. 

4.1.1. Water parameters 

Water parameters monitored within this program consist of main-stem and tributary flow 

rate, sediment load, turbidity, temperature, and grain-size distribution of suspended sediment. 

The first four of these parameters are measured mainly with stream gages, whose data are 

transmitted by telemetry to the Flagstaff Field Center. The gaging stations are located at river 

miles 0, -3, -6, -9, -11, and -16; downstream within the Paria River, Shinumo Creek, Tapeats 

Creek, Spenser Creek, Havasu Creek, Kanab Creek, Bright Angel Creek, Little Colorado River, 

and Diamond Creek; and within the main channel above the Little Colorado River confluence, 

near the Grand Canyon, above National Canyon, and above Diamond Creek. In the recent past, 

grain-size distributions were only measured from collected water samples. The remote-sensing 

PEP suggested that in-stream optical devices be explored for measuring turbidity (Berlin et al., 
19

1998), but turbidity was already recorded by the gaging stations. However, grain-size distribution 

within the suspended sediment, which is not obtained by the conventional water-gaging stations, 

is now being continuously monitored using LISST (Laser In-Situ Scattering and 

Transmissometry) submersible instruments that measure particle concentration, particle size 

spectra, pressure, and temperature. These instruments are portable and are easily relocated. 

Spatial water temperature is measured with strings of thermistors that are deployed by boat crews. 

Our previous discussion on mapping warm backwater areas showed that airborne TIR data can 

provide rapid, wide-area, surface water temperature maps for the CRE, but the cost of these 

spatial data are high relative to the point-specific data provided by the in-situ detectors, especially 

high-frequency data collections. Use of remote-sensing data for mapping the water temperature 

will obviously be determined by the specific requirements of the program within any given year. 

Although stream instrumentation is the most accurate method for obtaining sediment 

load, and turbidity, the instruments provide only point-specific data and relatively few points 

within the CRE. Airborne image data, calibrated by these in-situ instrurments, have the potential 

for producing regional water-parameter maps for improved understanding of the spatial relations 

between sediment transport and deposition within the CRE. Multispectral, visible-wavelength 

image data have been used to estimate turbidity and total suspended sediment load using ground 

calibration data to relate spectral response to absolute water values (e.g., Whitlock et al., 1978; 

Goodin et al., 1993; Jerome et al., 1996; Sathyendranath et al., 1997; Fraser, 1998a, 1998b; 

Pozdoyakov et al., 1998; Tassan, 1998). Some of the better correlations between turbidity or total 

suspended sediment and spectral response have been obtained in the 0.695-0.720 micrometer 

wavelength region (Goodin et al., 1993; Tassan, 1998; Fraser, 1998a), but this wavelength region 

does not provide the greatest water penetration. 

Chavez et al. (2002a,b) correlated radiance-ratio measurements of the main stem at 

selected CRE locations with concentrations of total suspended sediment concentrations (TSSC; 

mg/l) and silt/clay ratios that were obtained from nearby water-gage stations. They found good 

correspondence (correlation coefficient of 0.95; Figure 21) for these two data sets, but not for 

sand concentration (correlation coefficient of 0.60; Figure 21). The observed relations were used 

to map TSSC concentrations and silt-clay ratios for segments of the CRE that were imaged with 

CIR sensors in September of 2000 (Figure 22). 

Spectral response due to total suspended load or turbidity is affected by mineral 

composition and quantity and dissolved organic matter, all of which affect the backscatter and 

absorption coefficients of water. Thus, the relations developed by Chavez may need to be 

established at various locations within the CRE for corridor-wide application. Chavez (personal 

communication, 2003) believes that just three locations within the CRE are necessary in order to 

capture the variations produced by different tributary inputs. The relations that he has developed 

for visible-wavelength image data can now be used to calibrate airborne sensors to produce maps 

of these parameters throughout the CRE. Relatively inexpensive ($15,000) digital camera 

systems (such as that used by Chavez) are available that can record these required wavelengths, 

which would allow data collection at any time for the cost of aircraft and pilot time (about $350 

per hour using a BOR aircraft and pilot). This helicopter-mounted instrument can image about 

100 miles of the river corridor in two hours because the aircraft can follow the course of the 

corridor, but care needs to be taken during turns so that the aircraft does not produce roll, which 

results in off-nadir imagery. Although data collection is inexpensive, post-processing of these 

data without GPS and IMU data will add to these costs. This factor is discussed in more detail in 

the next section. 

4.1.2. Channel substrate 
20

Channel substrate parameters consist of main-stem and tributary bathymetry, fine- versus 

coarse-grain-size distribution on beds, median grain-size distribution in vertical profiles of river 

beds, and thickness of beds. Only the first two of these parameters can be approached using 

airborne remote-sensing data. Bathymetry within the main stem is currently obtained using a 

backscatter multibeam instrument that provides fine-scale (3 cm) topography, as recommended 

by the physical resource PEP (Wohl et al., 1999). Bathymetry of shallow, limited near-shore 

environments that cannot be surveyed with this instrument is measured by ground-survey crews. 

Although the goal is to map the substrate bathymetry throughout the corridor every five years, 

processing of the backscatter data is time consuming and the monitoring is behind schedule. 

Alternative airborne remote-sensing techniques include the SHOALS LIDAR and optical 

image data. The physical resource PEP (Wohl et al., 1999) recommended that SHOALS 

(Scanning Hydrographic Operational Airborne Lidar Survey) be considered for bathymetric 

surveys. The SHOALS LIDAR system is a dual-beam laser system that obtains water depth by 

differencing the distances recorded from the green wavelength laser (substrate) and the nearinfrared laser (water surface). Recent studies have shown that the combination of SHOALS 

bathymetry and color aerial photography can greatly assist in the mapping of coastal substrate and 

coral reefs (Chavez and Field, 2000a, 2000b; Chavez et al., 2000a, 2000b), but those waters are 

relatively clear. The water penetration of SHOALS is constrained by turbidity and, therefore, 

will have limited application within the CRE (Irish and Lillycrop, 1999). Two optical-image 

approaches for mapping bathymetry derive (1) relative water depth from images acquired at two 

wavelengths and (2) absolute water depth from stereo-image pairs. The first technique has been 

used in a variety of clean, standing water bodies (Lyzenga, 1978, 1981; Bagheri et al., 1998; 

Bryant and Gilvear, 1999; Roberts and Anderson, 1999; Woodruff et al., 1999; Durand et a., 

2000). This technique requires two wavelength bands because reflectance from the substrate can 

change with the substrate composition and images of two different wavelengths can be used to 

separate and map water depth and bottom composition, as long as both wavelength signals are 

reflected from the substrate. The maximum water depth that can be determined using this method 

is limited by the maximum penetration depth of light in the longest wavelength region and by the 

optical properties of the water. The suspended sediment within the Colorado River will limit the 

application of this technique to a very small fraction of the CRE that generally has clear water. 

Sun glint from rapids will create problems in this approach for submerged cobble bars or debris 

flows. The photogrammetric approach using stereo-pair images, which was suggested by the 

remote-sensing PEP (Berlin et al., 1998), is extremely difficult to apply in water due to 

corrections for refraction and would be limited in its application to clear-water regions. We 

investigated the use of stereo image data to derive substrate elevations for shallow, calm-water 

areas that had ground truth bathymetry and found that the lack of texture on sandy substrates 

precluded image correlation between the stereo images, which is necessary for derivation of 

topography. Therefore, the backscatter multibeam approach appears to be the most viable 

approach for channel bathymetry despite its data processing limitation. 

Grain-size distribution on the channel substrate is currently mapped using a combination 

of side-scan sonar and underwater videography The sonar beam scans across the channel 

substrate producing a single image strip that has different perspective views on each side of the 

sonar image (centered on the boat position). Such imagery is extremely difficult to orthorectify 

and mosaic due to a lack of the sonar’s fish pointing characteristics (pitch, roll, and yaw) and 

point-perspective distortions. Consequently, side-scan sonar has not proven to be a productive 

tool for imaging the substrate. In addition, the resulting rectified sonar image data have 

positional accuracies of only 2-3 m at best. Videography is used in conjunction with the sidescan sonar surveys to record the surface characteristics of the substrate, which was initially 
21

recommended by the remote-sensing PEP (Berlin et al., 1998), but the panel subsequently 

recommended that videography be replaced with alternative sensors. New software (QTC 

Multiview) for processing backscatter multibeam data can supposedly map bed composition, but 

the software has not yet been fully evaluated to determine its mapping accuracy for grain-size 

distribution on the channel floor. 

 There are airborne imaging approaches that may provide good water penetration in 

order to image the channel substrate under clear water conditions. This approach is rather simple 

and rapid in its image processing, is cost-effective, and provides wide-area coverage with 

positional accuracies of at least 1 meter. Photographic film acquired with long exposures or 

digital image data acquired with a high-gain state provide maximum water penetration, but digital 

cameras that can record a larger range of radiance provide better image data for channel 

substrates. In August and September of 2000, an experiment was performed using a digital 

(CCD) panchromatic imaging system that acquired main-stem image data above Lees Ferry using 

a high-gain detector setting (equivalent to increasing the exposure time on photographic film). 

The two image acquisitions bracketed a spike flow in early September; the resulting image data 

clearly show morphologic detail on the channel substrate and clearly show changes in channel 

sand storage due to the spike-flow release (Figures 23-25; Chavez et al., 2002a). The image data 

appear superior to the image data produced by side-scan sonar. Although this technique requires 

relatively clear water for substrate imaging, there are periods when sediment input is quite low 

(such as 2002 and 2003). The present condition of low sediment input has allowed the physical 

resource program to extend this experiment to include the first 100 miles of the CRE using Pat 

Chavez’s CIR sensor whose wavelengths are optimized for water penetration. The three 

wavelength bands were acquired at different gain states. The blue-green band was acquired at 

high-gain for water penetration, the red band at moderate gain for moderate water penetration, 

and the NIR band at normal gain to provide land data for image registration. Data were acquired 

at 300 m AGL within the Grand Canyon producing CIR data with 15-cm spatial resolution. The 

high-gain image data clearly showed locations of various types of sediment storage within the 

channel, where the water depth allowed light penetration to the substrate. In fact, variation in 

grain size is also easily seen in that data, as evidenced by the image mosaic of a channel segment 

south of the LCR confluence (Figure 26). Although these data will be examined in detail this 

summer, our preliminary evaluation of these data show that it requires about one hour to 

georectify each image to an existing controlled image base. At 15-cm resolution, there are about 

1000 overlapping images that cover the first 100 miles of the CRE. Thus, without accurate GPS 

and IMU data for each image to allow automated rectification, the true cost of useable data needs 

to include one-half year of salary for the image rectification, but that will only produce 1 m 

positional accuracy. We are in the process of testing software that is especially designed for more 

automated image rectification, which is also relevant for rectifying some of the more useful 

historical data sets. 

4.1.3 Main-stem water elevation 

During 2002, water resources personnel (Tucson, Arizona) noticed a correlation between 

one of the LIDAR elevation data sets (acquired in 2000) over the CRE main stem and the 

historical main-stem elevations that were measured by a ground survey in the early 1900’s. They 

also noticed at some locations that the LIDAR main-stem elevations were at least 1 m higher than 

the historical water profile and that these locations coincided with riffles or rapids. The LIDAR 

data used in that comparison were acquired with a NIR laser system. Although LIDAR is not 

commonly used to measure water surface elevation, the SHOALS LIDAR does use a NIR laser 

source to measure water-surface elevation, as well as a green-wavelength laser to measure 

substrate elevation. Thus, water surface elevations might be obtained from conventional LIDAR 
22

data. In order to verify this observation, Davis et al. (2002b) compared the March 2000 LIDAR 

elevation data over the main stem channel with corresponding water-edge elevations obtained 

from coincident ground surveys at four long-term monitoring sites. They found a high degree of 

correlation between the LIDAR water elevations and the surveyed water-edge elevations. The 

LIDAR elevations over the main-stem channel showed a vertical root-mean-square error (RMSE) 

value of 30 cm (Figure 27), assuming that ground-surveyed water-edge elevations accurately 

reflected the water’s surface elevation at the center of the channel. Thus, the LIDAR data were 

found to closely represent water-surface elevations and may in fact be useful in monitoring 

changes in submergent debris flows or cobble bars. The majority of the LIDAR elevations (90%) 

at most sites were within 40-50 cm of the surveyed water-edge elevations (Figure 27) and, 

therefore, LIDAR elevation differences between any two time periods would have to exceed 40-

50 cm before being considered significant. 

4.2. Terrestrial Environment 

On land, the physical resource program monitors change in fine- and coarse-grained 

sediment storage, which is represented by debris flows, cobble bars, river terraces, and different 

types of sand bars. The basic parameters measured consist of geomorphology and topography. 

These parameters are monitored at active sand bars in the New High Water Zone, the returncurrent channels within fan/eddy complexes (backwaters), and the pre-dam river terraces with 

cultural resources, both on a historical (annual) basis and on a short-term, experimental-event 

basis. Study sites number about 35 between river mile -6 and 225, but many of these are camp 

sites and camping beaches; there are 11 main monitoring sites for sediment that average 2 miles 

in length. Geomorphology is interpreted and mapped using stereo imagery. Before 2000, 

scientists within the program used aerial photographic data that had no pointing or positional 

information, which made rectification and orthorectification very difficult, time-consuming, and 

error prone. The remote-sensing PEP recommended that future data be acquired with GPS 

(Global Positioning System) and IMU (Inertia Measurement Unit) data. This panel also 

recommended an image resolution 1-5 m for sand-bar mapping, but we have found that image 

resolution for the physical resource program was more a function of required vertical accuracy 

than resolving power for geomorphic mapping. 

 Before the remote-sensing initiative, all topographic data were acquired by field survey 

measurements, which is not only expensive but also time consuming, both of which limited the 

area that could be monitored within any given year. Since 2000, various alternative airborne 

approaches have been tested, the results of which are discussed below. As additional study sites 

were added or when more historical data were required to extend the period of observation, 

photogrammetry and geomorphic mapping were applied to historical photographic data. The 

historic photogrammetric analyses partly overlap time periods of land-based topographic surveys 

to provide ground truth data for photogrammetry. Before 2000, almost every annual image 

acquisition consisted of analog film and prints, which degrade over time, were lost or misplaced, 

and were extremely difficult to use by non-resident scientists. Thus, preservation and access of 

the historical image archive were two critical factors considered within the remote-sensing 

initiative. An operational airborne approach for topography must satisfy the most stringent 

vertical accuracy requirements within the GCMRC programs. This requirement resides within 

the physical resource program and is 25 cm, which is based on the minimum amount of change in 

sand bar height that is deemed “significant” and needs to be recorded (Schmidt et al., 1999). This 

elevation accuracy value was our targeted objective with all the various topographic remotesensing evaluations that we performed during the past two years (Table 2). 
23

Surficial geology of terraces and debris flows are mapped into units based on elevation 

(terrace level), hill slope, grain size, relative age, and composition. Historically, this information 

was extracted from (poorly rectified) aerial photographs using photointerpretation and field 

investigations. In general, debris flows are monitored and mapped on an annual, system-wide 

basis, whereas terrace deposits at long-term monitoring sites for fine-grained sediment are 

generally mapped once and monitored annually. Water resources personnel who monitor CRE 

debris flows have examined hyperspectral data to determine if mass movement could be predicted 

from the surficial geologic compositions provided by hyperspectral data. Although this is an 

intriguing problem, the remote-sensing initiative focused on a more fundamental issue to 

determine the type of data that is most appropriate for detecting and mapping debris flows. 

 One of the primary objectives of the remote-sensing initiative was to determine if there 

were better cost-effective, data-collection approaches for all the various parameters that are 

monitored by the physical resource program so that scientists could be more productive and their 

data would be at least as accurate as that obtained by past approaches. The most appropriate data 

would provide the highest mapping capability. Overall, there are four remote-sensing issues that 

needed to be resolved for this program and they are addressed in the following section. 

4.2.1. Preservation of GCMRC Image Archive 

 An initial step in the remote-sensing initiative was an inventory of all image data that had 

been collected by GCMRC. During this process, we discovered that the film for one annual 

image collection had been lost and could not be located. We also observed that the photographic 

print collection was deteriorating due to age (discoloration) and use (markings, tears, wrinkles). 

In addition, access to the archive required a physical presence into order to view, select and 

duplicate necessary data. The initiative therefore strongly recommended that all future image 

collections either be obtained with digital sensors or be immediately scanned to digital format. 

The initiative also strongly recommended that the original photographic film be converted to 

digital imagery and, in the case of lost film, that the prints be digitally scanned. Within the 

initiative, Davis et al. (2002b) determined by a series of scan tests that a scan resolution of 15 

microns per picture element was adequate to retain all of the information in the photographs 

(compare Figures 28 and 29); a scan resolution higher than 15 microns produces too much image 

noise to be useful in photogrammetry. In addition, evaluations of various data compression 

engines during the remote-sensing initiative determined that the best compression software was 

Gzip because it is lossless, free-ware, and not copyrighted. At this time, the film library is being 

converted to digital format by the IT program and, since the beginning of the initiative, all image 

data collections have been delivered in digital format and have been duplicated for archival 

preservation. An ArcIMS system has been implemented by the IT program which allows easy 

access to most of the collected digital data. 

4.2.2. Most suitable image data for geomorphology 

As far as future data collections, scientific review of image data collected during the 

remote-sensing initiative at different spatial resolutions showed that 15-20 cm resolution was 

sufficient for geomorphic mapping of physical resources. We found that digital, orthorectified 

panchromatic imagery with 18-cm resolution can be acquired at about one-half the cost of color 

imagery. However, evaluations of different types of image data for mapping physical resources 

showed that CIR imagery discriminates surface materials better than panchromatic data (compare 

Figures 30 and 31) and allows more accurate digital classification of sand bars and debris flows 

that does panchromatic imagery (Davis et al., 2002b). In fact, CIR imagery (Figure 31) was 

found to be better than true-color imagery (Figure 29) for mapping sand bars and debris flows, 
24

mainly because CIR imagery allows more complete removal of vegetation cover, which in turn 

allows more accurate determination of geologic surface textures (Davis et al., 2002b). Surface 

texture was found to be a very important characteristic for distinguishing smooth sand bars and 

rough debris flows (Davis et al., 2002b), which was also found in other surficial-geologic studies 

(Shih and Schowengerdt, 1983; Anys et al., 1994) 

4.2.3. Airborne approaches for ground topography 

In March and in August-September of 2000, GCMRC collected LIDAR data with 

different collection parameters to evaluate the ability of LIDAR to provide topography at 

acceptable vertical accuracies on bare surfaces and on vegetated surfaces at the GCMRC 

sediment long-term monitoring sites. The March data were collected with the ALMS (Aeroscan 

Laser Mapping System) sensor at a spot spacing of 3.75 m and a spot diameter of 1.5 m. The 

August-September data were collected with the RAMS (Remote Airborne Mapping System) 

sensor at a 1-m spot spacing and a 0.5-m spot diameter; these data were acquired both in August 

and in September over the same four long-term monitoring sites using the same collection 

parameters to evaluate the precision of LIDAR elevation data. Photogrammetric data were 

derived from stereo image data (1:4,000-scale photography) that were collected in September 

2000 for one of the four study areas. Ground-survey elevation transects were performed at the 

sites during the LIDAR surveys and stakeout surveys were conducted at selected LIDAR point 

locations after the LIDAR surveys on different types of bare surfaces and within vegetated 

terrain. The LIDAR data sets and the photogrammetry data set were compared to the ground 

survey data to determine each data set’s vertical accuracy. Various analyses were performed on 

these data over a two-year period (e.g., Davis et al., 2002a; Mietz et al., 2002), culminating in a 

final detailed evaluation of these data (Davis et al., submitted). 

 Our evaluations of high-resolution photogrammetry and of different resolution and 

replicate LIDAR data with respect to GCMRC monitoring requirements for sediment deposits 

provided the following conclusions: 

1. The low- and moderate-resolution LIDAR elevation data sets that we investigated on 

relatively flat, bare-sand surfaces were offset above the true ground surfaces at our four 

study areas (Figures 32-34). Without correction for these vertical offsets, almost all the 

LIDAR data sets have RMSE values greater than our desired 20 cm and greater than 

contractor specifications (15 cm); many of the data sets have RMSE values in the range of 

40-100 cm. Vertical offsets have also been reported in previously published evaluations 

of LIDAR data, which suggest that some LIDAR surveys are no more independent of 

ground control than photogrammetric surveys. 

2. After correction of the low- and moderate-resolution LIDAR data sets for their observed 

vertical offsets, the resulting bare-sand elevation data for the moderate-resolution LIDAR 

surveys produced better vertical accuracies (RMSE = 9-26 cm in August and RMSE = 13-

36 cm in September) than the lower resolution LIDAR survey (RMSE = 26-103 cm) and 

the photogrammetric survey (RMSE = 32 cm; Figure 35). However, the higher 

photogrammetric error was due to isolated editing errors by the photogrammetrist (Figure 

36). 

3. Photogrammetry produced more accurate ground elevations on the cobble bars (RMSE = 

16 cm) and on the vegetated sand surfaces (RMSE = 75 cm: Figure 37) than did either 

LIDAR survey. 
25

4. The low-resolution, March LIDAR survey produced better elevation accuracies within the 

vegetated terrain (RMSE = 14-189 cm; Figure 37) than the moderate-resolution, August 

LIDAR survey (RMSE = 58-279 cm; Figure 37). This is attributed to the March 

collection period during leaf-off conditions and to the smaller scan angle used during the 

lower resolution LIDAR survey. 

5. In terms of reproducibility, which is an important consideration in a monitoring program, 

our analyses of replicate collections of the moderate-resolution LIDAR data showed an 

average RMSE value of 29 cm for bare-ground surfaces in our four study areas (after 

correction for vertical offsets). In terms of vegetated terrain, the reproducibility of these 

data averaged 95 cm (RMSE). Thus, the RAMS moderate-resolution LIDAR data are 

both inaccurate and imprecise in CRE vegetated terrain, which is one reason we rank the 

performance of moderate-resolution LIDAR below that of photogrammetry for our 

requirements at this time. 

6. Our recent evaluations of high- (1.5 points/m2

) and very-high (10 points/m2) resolution 

LIDAR data on bare ground showed vertical accuracies of 17 cm and 8 cm, respectively, 

much higher accuracies than the photogrammetry data or lower resolution LIDAR data. In 

particular, the very high-resolution LIDAR data was found to have very high precision 

(reproducibility) of 4-5 cm and to have essentially no vertical offsets. Figure38 shows the 

correspondence between the very high-resolution LIDAR elevations and ground surveyed 

elevations for points on bare ground, near vegetation, and within vegetation. Within the 

densely vegetated areas, the accuracies of both high resolution LIDAR data sets decreased, 

but this may be a result of our processing. Visual inspection of these data sets within 

vegetation suggests that more rigorous (smarter) processing may be able to reduce the 

observed 0.7-1.3 m errors found in these data. An advantage of the very high-resolution 

LIDAR system is that it achieves very high accuracy without any ground control and is 

therefore non-invasive, except for the 100-m flight AGL required to obtain the high 

density data. Although the cost for the very high-resolution LIDAR data is $6,200 per 

river km, its high accuracy makes it useful for several monitoring requirements across all 

GCMRC programs and, therefore, may be quite cost effective. 

7. Overall, vertical accuracies from different airborne topographic mapping approaches 

increase with increasing cost and therefore decreasing surface area that can be mapped. 

ISTAR automated photogrammetry can cover the entire canyon system at 44 cm accuracy 

for $625/km, manual photogrammetry can map specific areas at 25-30 cm accuracy for 

$3,000/km, and high-resolution LIDAR can map specific areas at 8-17 cm accuracy for 

$2,100-$6,200/km. Sediment monitoring will require one of the latter two technologies, 

whereas system-wide resource monitoring will require the ISTAR system. 

4.2.4. Spatial resolution for terrestrial photogrammetry 

Stereo image data collections during 2000 included digital panchromatic (18-cm 

resolution), CIR film (10-cm resolution), and true-color (6-cm resolution) film. The true-color 

imagery was scanned at three different resolutions (resulting in 6, 8, and 16-cm resolution 

images) to determine the optimal minimum scan resolution to maintain high vertical accuracy. 

Photogrammetric analyses of these data were performed by the U.S. Geological Survey and by 

Pacific Western Technologies (PWT). Our evaluations of the resulting photogrammetric 

elevation data (Davis et al., 2002b) showed that image resolution needs to be near 6 cm or 

1:4,000-scale photography (Figures 39-40; Davis et al., 2002b) to produce elevation data with 

vertical accuracies of 25 cm or better, which is the smallest change in sand-bar elevation that is 

deemed “significant” (Schmidt et al., 1999). Digital panchromatic stereo imagery with 18-cm 
26

resolution produced very high elevation errors (RMSE = 53 cm; Figure 39; Davis et al., 2002b), 

but it is difficult to acquire higher resolution digital data using airborne digital sensors. In 

addition, the dimensions of CCD arrays need to be at least 10,000 by 10,000 to support accurate 

photogrammetric analysis. Industry is currently developing such cameras, but it will take a few 

years for the technology to be proven viable. In the meantime, data to support GCMRC 

photogrammetric needs will have to be acquired as film, which requires expensive post-collection 

scanning and rectification if orthorectified imagery is needed. 

5.0 Further Evaluations 

Although the remote-sensing initiative is officially over, we still need to evaluate some 

remaining data and some analysis systems that will answer unresolved issues. The types of data 

for further evaluations consist of photogrammetric data from ISTAR and the two high-resolution 

LIDAR data sets. This analysis will resolve the accuracies of these data for topography within 

vegetated areas and for canopy volume. GCRMC should proceed to establish a set of fixed, 

photo-identifiable points within the CRE (with accurate N, E, and elevation values) in order to 

verify future airborne topographic surveys and to develop a photogrammetric method that is 

based on these points instead of control panels. We will also evaluate the ability of the Chavez 

multispectral camera for mapping main-stem sediment storage within the first 100 miles of the 

CRE. The QTC software should also be examined to determine if it can map sediment 

characteristics from backscatter multibeam data, but there is no time for this evaluation this fiscal 

year. An additional evaluation should also be performed on a well-calibrated, multispectral or 

hyperspectral data set for mapping vegetation species within the CRE to determine the accuracy 

tradeoffs between number of spectral bands and spatial resolution. 

6.0 Summary of Remote-Sensing Protocols 

Protocols for remote-sensing data collection are divided into three categories based on 

their different collection requirements. The categories include terrestrial image data, aquatic 

image data, and digital surface models (both ground and canopy elevations). 

Protocols 

Terrestrial 

Image Data 

Aquatic 

Image Data 

Digital 

Surface Models 

Wavelength 

bands 

At a minimum, green 

(0.55 μm), red (0.65 

μm) , and 1st NIR (0.80 

μm ) [for vegetation, 

2nd NIR near 0.7 μm] 

High-gain 

blue-green (0.50 μm) 

and TIR 

ISTAR DSM for wide-area 

surveys; high-resolution 

LIDAR or 

photogrammetry for 

specific areas 

Resolution 

or scale ≤ 20 cm ≤ 20 cm 

1 m (ISTAR); 

≥ 10 points/m2

 (LIDAR); 

or 1:4,000-scale film 

Positional 

accuracy ≤ 30 cm ≤ 30 cm ≤ 30 cm 

Vertical 

accuracy n.a. n.a. ≤ 25 cm 

Time of year Summer solstice Summer solstice 

Summer solstice (ISTAR); 

any time for LIDAR 

Time of day 10:30 AM – 2 PM 

(more restricted in 

10:30 AM – 2 PM 

 (more restricted in 

10:30 AM – 2 PM 

(more restricted in certain 
27

certain areas) certain areas) 

(TIR = 1-2:30 PM) 

areas) 

Format 

Calibrated digital; 

orthorectified or at least 

with image φ, κ, α

Calibrated digital; 

orthorectified or at least 

with image φ, κ, α Calibrated x, y, z text files 

Radiometric 

calibration ≤ 3% ≤ 3% (TIR ≤ 0.1°) n.a. 

Bit size of 

digital data 

8-bit minimum 

11 or 12-bit preferred 

8-bit minimum 

11 or 12-bit preferred 32-bit 

7.0 Positive Effects of the Remote Sensing Initiative 

1. Established detailed scope of work (SOW) for airborne data collections, including 

image, photogrammetric, and LIDAR data. The SOW should only require minor 

modifications for future data collections based on particular data requirements for a 

given year. 

2. Established the image data characteristics that need to be obtained for overall GCMRC 

protocols. These characteristics include spatial resolution, band number and 

wavelengths, positional accuracy, preference for digital sensors that can record a large 

dynamic range, and calibrated sensors with 2-3% radiometric accuracy (see above table). 

3. Established the optimal period for airborne image data collection. This was found to be 

the summer solstice when wall shadows are minimal, but also includes time-of-day 

restrictions on data collection (Figures 19-20). 

4. Established the best airborne approach for terrestrial topography. Very high-resolution 

LIDAR for specific areas and ISTAR DSM data for wide-area analyses seems to be the 

most appropriate approaches for non-invasive topographic and canopy volume mapping. 

5. Established the protocols for digital archiving the GCMRC photographic archive. The 

photographic library records the changes that have occurred within the CRE over the 

past 20 years. It is in serious danger of further degradation due to misuse and 

misplacement of photos. This library is also one of the least accessible data sets due to 

its format. Conversion of this library to digital format has now commenced. 

6. Because all data are now delivered in digital format and are stored in an accessible online archive, cooperators are now making more and better use of airborne data. When 

these data provided in rectified form, cooperators are able to perform more accurate 

analyses in a much shorter time, thus increasing their productivity. Because of this, use 

of airborne data is ever increasing in all the monitoring programs. 

8.0 Future Challenges for the GCMRC Remote Sensing Program 

1. Finding a good contracting vehicle. This is by far the most difficult and frustrating 

challenge facing GCMRC airborne data collection. The existing remote-sensing QBS 

(Quality Based Selection) contract within the USGS has cumbersome management 

controls with a large overhead charge (17%). In addition, the existing QBS contract 

personnel appear to be more sympathetic to commercial profit margins than to USGS 

science requirements. GCMRC needs to establish its own (QBS) contract for their 

monitoring needs. 

2. Enforcement of established standards for delivered data and delivery schedules. This 

may never get easier unless future data are collected by a set of contractors who become 

familiar with GCMRC standards and expectations. 
28

3. Enforcement of the statement of work for airborne data collections. This needs attention 

during each data collection so that contractors do not relax the specifications. 

4. Keeping up with latest remote-sensing technologies for GCMRC protocols, which change 

within a given year. This is a never-ending process, but also a critical process in order to 

ensure that the best (or acceptable) data are collected for GCMRC protocols and at a 

reasonable price. Performing this function for GCMRC requires that personnel within 

GCMRC (preferably Information Technology) actually research new data to better 

understand its true capabilities and limitations. In many instances we have found the 

claims made by remote-sensing firms to be overly optimistic or not applicable to the 

CRE. Only after interrogating data and questioning commercial firms on specific issues 

did we receive more accurate or realistic statements from commercial firms. 

5. Maintaining the ever-increasing volume of collected data so that it is safe and accessible. 

The most critical factor here is preservation of data. Duplicate copies of data need to be 

maintained where one copy is never used except to restore a damaged shelf copy. It is 

not critical that original data be kept on-line, as long as data can be viewed (browse files) 

and requested for digital transfer. 

6. Historically, the river stage for collection of remote-sensing data has been at a low steady 

flow rate of 8,000 cfs. With increasing energy costs, we have made every effort to 

reduce the time required for data collections in order to minimize loss of dam revenue 

from the low flows. In 2003, we found that even the most simple, conventional data 

collection using a framing camera and film can fail (due to a shutter malfunction). In 

order to reduce the financial risk of possible failure, and to allow a second data 

collection, it would be prudent to institute a liability clause in the remote-sensing 

contract, requiring companies to compensate BOR for its lost revenue in case of 

equipment or personnel failure. Such a clause will undoubtedly increase the cost for data 

collections. 
29

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32

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Table 1. Remote-sensing technologies tested or investigated based on their potential to provide desired ecosystem parameters at requir

and some environmental parameters are not listed because current technologies cannot provide that information, based on published rescannot (NO) measure a parameter, can measure a parameter with limitations (LIMITED), or was not tested (NO TEST). The middle r

processing data to provide the ecosystem parameter. 

 Aquatic Resources Terrestrial Resource Para

Sensor 

Data 

Suspended 

Materials 

Water 

Temperature 

Substrate 

Unit 

Mapping Bathymetry

Vegetation 

Type 

Vegetation 

Area 

Vegetation 

Volume 

G

1-band, B&W NO NO LIMITED NO NO LIMITED LIMITED LPanchromatic mod. depth/clear ambiguous ISTAR/film a

200 50 6251/3,000 

3-band LIMITED NO LIMITED NO NO YES YES Natural Color susp. Sed. mod. depth/clear only film 

?? 200 50 3,000 

3-band LIMITED NO LIMITED NO YES YES YES Color Infrared susp. Sed. shallow Suitable Best only film 

?? 200 400 50 3,000 

Multispectral NO TEST2 NO LIMITED NO YES NO TEST NO TEST (≤ 10 bands) mod. depth/clear 4-band Best

 400 

Hyperspectral NO TEST2 NO LIMITED NO NO TEST NO TEST NO TEST N(>10 bands) mod. depth/clear Thermal NO YES NO NO NO NO NO 

TOD4 

200 

Near-IR NO NO NO NO NO NO LIMITED 

only dense veg.? 

200 

Green LIDAR NO NO NO NO NO NO NO 

1 These costs also provide orthorectified CIR image data.

2 Funding limitations precluded further testing. 

3 Includes both collection and analysis costs. 4 TOD = time of day.
Table 2. Image and topographic data collected and evaluated during the GCMRC Remote Sensing Initiative. 

Image Data Type 

Spatial 

Resolution Rectified 

Horizontal 

Accuracy 

GPS 

IMU 

Collection 

Date Conditions 

HYDICE digital 206- band HS 150 cm No n.a. yes Aug., 1998 clear Emerge digital CIR 30 cm Yes 6-8 m yes Sept., 1999 clear enCIR film 30 cm Yes 1-2 m yes Mar., 2000 p. cloudy enED digital B&W 30 cm No n.a. yes Mar., 2000 p. cloudy enCIR film 11 cm No n.a. yes June, 2000 clear, windy enBechtel 12-band MS 100 cm Yes 2-3 m yes July, 2000 clear EQ digital B&W 18 cm Yes 30 cm yes Aug., 2000 clear EQ digital B&W 18 cm No n.a. yes Aug., 2000 clear CIR film 28 cm No n.a. no Sept., 2000 overcast EQ digital B&W 18 cm Yes 30 cm yes Sept., 2000 clear EQ digital B&W 18 cm No n.a. yes Sept., 2000 clear EQ digital B&W and Emerge CIR 18 cm Yes n.a. yes May, 2001 clear enISTAR digital B&W and CIR 22 and 44 cm Yes 25 cm yes May, 2002 clear enB&W film 6 cm No n.a. no May, 2003 clear enChavez digital CIR 8 and 15 cm No n.a. no May, 2003 clear 

Topographic 

Data Type 

Spatial 

Resolution 

Unadjusted 

Vertical 

Accuracy 

Adjusted 

Vertical 

Accuracy 

GPS 

IMU 

Collection 

Date 

Vegetation 

Conditions 

ED LIDAR 3.75 m 44-103 cm 26-103 cm yes Mar., 2000 Leaf-off enEQ LIDAR 1.5 m 14-33 cm 9-26 cm yes Aug., 2000 Leaf-on 5 LNatural-color film 6 cm 28 cm n.a. no (panels) Sept., 2000 Leaf-on 2 LEQ LIDAR 1.5 m 22-53 cm 13-36 cm yes Sept., 2000 Leaf-on 5 LB&W film 3 cm 6-10 cm n.a. no (panels) Mar., 2002 n.a. 4 ISTAR B&W 22 cm 45 cm 30 cm yes June, 2002 Leaf-on enB&W film 6 cm 25-30 cm n.a. yes (panels) June, 2002 Leaf-on B&W film 3 cm 20 cm n.a. yes (panels) Oct., 2002 n.a. 4 3Di LIDAR 0.8 m 17 cm n.a. yes Nov., 2002 Leaf-off 4 LChance LIDAR 0.3 m 8 cm n.a. yes May, 2003 Leaf-on 2 L
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