U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2008–1216

In cooperation with Pinnacle Mapping Technologies, Inc. and Northern Arizona University 

A Vegetation Database for the Colorado River Ecosystem 

from Glen Canyon Dam to the Western Boundary of Grand 

Canyon National Park, Arizona
This page intentionally left blank
A Vegetation Database for the Colorado River Ecosystem 

from Glen Canyon Dam to the Western Boundary of Grand 

Canyon National Park, Arizona

By Barbara E. Ralston, Philip A. Davis, Robert M. Weber, and Jill M. Rundall

In cooperation with Pinnacle Mapping Technologies, Inc. and Northern Arizona 

University

Open-File Report 2008–1216

U.S. Department of the Interior

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

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Mark D. Myers, Director

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

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

reproduce any copyrighted materials contained within this report.

Suggested citation:

Ralston, Barbara E., Davis, Philip A., Weber, Robert M., and Rundall, Jill M., 2008, A vegetation database for the 

Colorado River ecosystem from Glen Canyon Dam to the western boundary of Grand Canyon National Park, Arizona: 

U.S. Geological Survey Open-File Report 2008–1216, 37 p.
iii

Contents

Executive Summary.......................................................................................................................................1

Introduction.....................................................................................................................................................2

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

Criteria for Vegetation Class Selection .............................................................................................5

Methods...........................................................................................................................................................6

Preliminary Vegetation Classes..........................................................................................................6

Verifying Vegetation Classes ..............................................................................................................7

Digital Data Acquisition .......................................................................................................................7

Supervised Classification Analyses of the Groundtruth Regions.................................................8

Accuracy Assessment of Vegetation Classification.......................................................................9

Fuzzy Accuracy Assessment ..............................................................................................................9

Mapping Scale ......................................................................................................................................9

Results. .............................................................................................................................................................9

Revision of Alliance-Level Classification..........................................................................................9

Revised Vegetation Classes and Related National Vegetation Classification Alliances........10

Vegetation Classification Accuracies .............................................................................................12

Electronic Map Organization ............................................................................................................12

Summary of Vegetation Cover Within the Colorado River Ecosystem in 2002 .........................14

Vegetation Distribution and Areal Cover Patterns ........................................................................14

Discussion.....................................................................................................................................................14

Vegetation Classification and Remote-Sensing Accuracies.......................................................14

Image Quality.......................................................................................................................................17

General Changes in Vegetation........................................................................................................18

Map Utility for Other Resources.......................................................................................................21

Conclusions...................................................................................................................................................21

Acknowledgments.......................................................................................................................................22

References....................................................................................................................................................22

Appendix A. Image Processing..............................................................................................................27

Figures

1. Map of study area from Glen Canyon Dam to Lake Mead with geomorphic reaches 

indicated by name and distances from the Glen Canyon Dam indicated by kilometer 

markers or river kilometers.........................................................................................................4

2. Zonation of vegetation according to river stage.....................................................................5

3. Index map of flight lines used in ISTAR airborne data collection in late May–early June 

2002 .................................................................................................................................................8

4. Example of vegetation map from RK 107.3–107.7 with National Vegetation Classification 

Alliance classes color coded in the legend ...........................................................................15

5. Vegetation map classes ............................................................................................................17

6. Total vegetated area (ha) within each geomorphic reach ..................................................18

7. Areal cover of each vegetation class within each geomorphic reach .............................19
iv

8. Vegetation cover between RK 124.9 and 127.9 ......................................................................20

Tables

1. Preliminary vegetation classes and number of surveyed groundtruth sites containing 

these classes.................................................................................................................................6

2. Modified Daubenmire scale for cover values and corresponding percentages of 

ground cover .................................................................................................................................7

3. Divisions, groups, and corresponding alliance groups identified through TWINSPAN 

analysis...........................................................................................................................................7

4. Revised vegetation classes for the Colorado River ecosystem .........................................11

5. Accuracy assessment of revised vegetation classes reflecting accuracies using fuzzy 

logic...............................................................................................................................................13

6. Percent commission and omission, rank of accuracy, and explanation of the classification results for the aggregated vegetation classes ..............................................................14

7. Area (in hectares) of mapped classes, of total vegetation mapped, and of total land 

mass within each geomorphic reach......................................................................................16

A1. Preliminary vegetation classes and number of ground spectroradiometric readings 

taken in the field..........................................................................................................................27

A2. ISTAR color-band data and ground spectroradiometric data for various materials that 

occur within the flight line zones 1 and 2 ...............................................................................28

A3. ISTAR color-band data and ground spectroradiometric data for various materials that 

occur within the flight line zones 4 and 5 ...............................................................................29

A4. ISTAR color-band data and ground spectroradiometric data for various materials that 

occur within the flight line zones J, K, and L..........................................................................31

A5. ISTAR color-band data and ground spectroradiometric data for various materials that 

occur within the flight line zones 8, B, C, and D ....................................................................32

A6. Calibration results for the ISTAR..............................................................................................34

A7. Calculated gains and offsets for ISTAR image data determined from ground reflectance data for various vegetation and inorganic surface materials acquired during 

2000 ...............................................................................................................................................35
A Vegetation Database for the Colorado River Ecosystem 

from Glen Canyon Dam to the Western Boundary of Grand 

Canyon National Park, Arizona

Executive Summary

A vegetation database of the riparian vegetation located 

within the Colorado River ecosystem (CRE), a subsection of 

the Colorado River between Glen Canyon Dam and the western 

boundary of Grand Canyon National Park, was constructed 

using four-band image mosaics acquired in May 2002. A digital 

line scanner was flown over the Colorado River corridor in Arizona by ISTAR Americas, using a Leica ADS–40 digital camera 

to acquire a digital surface model and four-band image mosaics 

(blue, green, red, and near-infrared) for vegetation mapping. 

The primary objective of this mapping project was to develop 

a digital inventory map of vegetation to enable patch- and 

landscape-scale change detection, and to establish randomized 

sampling points for ground surveys of terrestrial fauna (principally, but not exclusively, birds). The vegetation base map was 

constructed through a combination of ground surveys to identify 

vegetation classes, image processing, and automated supervised 

classification procedures. Analysis of the imagery and subsequent supervised classification involved multiple steps to evaluate band quality, band ratios, and vegetation texture and density. 

Identification of vegetation classes involved collection of cover 

data throughout the river corridor and subsequent analysis using 

two-way indicator species analysis (TWINSPAN).

Vegetation was classified into six vegetation classes, 

following the National Vegetation Classification Standard, 

based on cover dominance. This analysis indicated that total 

area covered by all vegetation within the CRE was 3,346 ha. 

Considering the six vegetation classes, the sparse shrub (SS) 

class accounted for the greatest amount of vegetation (627 ha) 

followed by Pluchea (PLSE) and Tamarix (TARA) at 494 and 

366 ha, respectively. The wetland (WTLD) and Prosopis-Acacia

(PRGL) classes both had similar areal cover values (227 and 

213 ha, respectively). Baccharis-Salix (BAXX) was the least 

represented at 94 ha. Accuracy assessment of the supervised 

classification determined that accuracies varied among vegetation classes from 90% to 49%. Causes for low accuracies were 

similar spectral signatures among vegetation classes. Fuzzy 

accuracy assessment improved classification accuracies such 

that Federal mapping standards of 80% accuracies for all classes 

were met.

The scale used to quantify vegetation adequately meets the 

needs of the stakeholder group. Increasing the scale to meet the 

U.S. Geological Survey (USGS)-National Park Service (NPS)

National Mapping Program’s minimum mapping unit of 0.5 ha 

is unwarranted because this scale would reduce the resolution 

of some classes (e.g., seep willow/coyote willow would likely 

be combined with tamarisk). While this would undoubtedly 

improve classification accuracies, it would not provide the 

community-level information about vegetation change that 

would benefit stakeholders. The identification of vegetation 

classes should follow NPS mapping approaches to complement the national effort and should incorporate the alternative 

analysis for community identification that is being incorporated into newer NPS mapping efforts. National Vegetation 

Classification is followed in this report for association- to 

formation-level categories. 

Accuracies could be improved by including more environmental variables such as stage elevation in the classification 

process and incorporating object-based classification methods. 

Another approach that may address the heterogeneous species 

issue and classification is to use spectral mixing analysis to 

estimate the fractional cover of species within each pixel and 

better quantify the cover of individual species that compose 

a cover class. Varying flights to capture vegetation at different times of the year might also help separate some vegetation 

classes, though the cost may be prohibitive. Lastly, photointerpretation instead of automated mapping could be tried. Photointerpretation would likely not improve accuracies in this case, 

however, because the image quality that was obtained prevented 

visual differentiation of several major vegetation classes like 

tamarisk, mesquite, and seep willow. In addition, the time and 

associated costs necessary to photointerpret the river corridor 

would be prohibitive.

By Barbara E. Ralston1, Philip A. Davis1, Robert M. Weber2, and Jill M. Rundall3

1 U.S. Geological Survey, Southwest Biological Science Center, Flagstaff, 

Ariz.

2

 Pinnacle Mapping Technologies, Inc., Flagstaff, Ariz.

3

 Northern Arizona University, Center for Environmental Science and 

Education, Flagstaff, Ariz. 
2 Vegetation Database for the Colorado River Ecosystem

The vegetation base map for the CRE provides an initial 

inventory of the area covered by vegetation and an indication of 

which species dominate the river corridor. The map is a critical 

element in assessing the status and trends of riparian vegetation 

for the CRE at a large scale. It contributes systemwide information that can promote a far-reaching understanding of how dam 

operations affect biological resources. Beyond helping to assess 

vegetated area change and vegetation class changes over time, 

map data can be linked to ecosystem function and structure 

questions. Specifically, questions can begin to be addressed 

about how changes in riparian vegetation affect other resources 

like campsite area, exotic species expansion and control, and 

food web development in the terrestrial and aquatic system. A 

long-term systemwide approach to vegetation area inventories, 

in addition to local-scale measures of change, is a critical tool 

in monitoring change in riparian vegetation along the river corridor associated with dam operations.

Introduction

Monitoring vegetation at multiple temporal and spatial 

scales is proving to be the most informative approach for 

detecting change and inferring causal agents of change (Herbel and others, 1972; Ares and others, 2003; Bestelmeyer and 

others, 2006). Riparian communities that are tightly linked 

to annual stream hydrology, ground water elevations, sediment transport, and accompanying disturbance of varying 

magnitudes over time (Stromberg and others, 1996; Montgomery and MacDonald, 2002; Naiman and others, 2005) 

are particularly strong candidates for multiscale monitoring. 

In the arid southwest, where riparian habitats are rare but 

play an important role in animal sustainability and overall 

biodiversity (Johnson and others, 1977; Farley and others, 

1994), knowing the extent and composition of the riparian 

community and potential trajectories of change is critical for 

resource managers.

In 1996, signing of a Record of Decision for the operation of Glen Canyon Dam (Department of the Interior [DOI], 

1996) initiated the Glen Canyon Dam Adaptive Management Program and the process of long-term monitoring and 

assessment of resources affected by dam operations. Previous 

vegetation research and monitoring in the Colorado River 

ecosystem (CRE) had focused primarily on local, and to some 

extent, geomorphic reach-based effects on vegetation relative to dam operations (Anderson and Ruffner, 1987; Stevens, 

1989; Waring and Stevens, 1986; Stevens and Ayers, 1993; 

Stevens and others, 1995; Waring, 1995; Stevens and Ayers, 

1997; Kearsley and Ayers, 1999; Howe, 2001; Porter, 2002). 

Smaller scale evaluations that consider the entire river corridor 

were limited by the time and cost required for ground surveys. 

These studies included general plant collections for species 

lists (Clover and Jotter, 1944; Phillips and others, 1987; Ayers 

and others, 1994), descriptions of marsh community assemblages relative to dam operations (Stevens and others, 1995), 

and the operational effects of Glen Canyon Dam on riparian 

vegetation (Carothers and Aitchison, 1976; Turner and Karpiscak, 1980; Kearsley and Ayers, 1999; Stevens and others, 

2001; Ralston, 2005). Work since 1997 has been conducted to 

support information needs identified by the Glen Canyon Dam 

Adaptive Management Program.

The only previous quantitative description of vegetation distribution for the length of the river corridor from Glen 

Canyon Dam to the boundary of Lake Mead was completed 

by Phillips and others (1977). The map exists only in hard 

copy, which has limited utility for change detection studies. Since 1977, Glen Canyon Dam operations have included 

a flood release at 2,747 m3/s in 1983 (Fradkin, 1984), and 

reduced peaking operations in association with Record of 

Decision operation criteria (DOI, 1996). With the initiation 

of long-term monitoring associated with the Glen Canyon 

Dam Adaptive Management Program (DOI, 1996), a GISbased vegetation inventory map was identified as a basic 

need to conduct long-term, large-scale change detection of 

vegetation and to increase the statistical rigor associated with 

ground-based surveys and monitoring (Urquhart and others, 

2000). The information needs of the adaptive management 

program regarding riparian vegetation include quantifying the 

area change of dominant vegetation classes that occupy areas 

below and above the postdam high-water zone (approximates 

1,416 m3/s). Changes in riparian vegetation associated with 

Glen Canyon Dam operations are of the greatest interest.

The primary objective of this mapping project was 

to develop a digital inventory map of vegetation to enable 

patch- and landscape-scale change detection, and to establish 

randomized sampling points for ground surveys of terrestrial fauna (principally, but not exclusively, birds). Digital 

orthorectified imagery and computer-aided classification 

provide for larger scale inventory and assessment of riparian 

resources along the river corridor that complements local or 

smaller scale monitoring. In 2003, the development of a digital 

vegetation base map for the CRE was initiated. The advanced 

state of digital and image-processing technologies suggested 

that automated classification using digital multiband imagery could be an efficient and feasible option for mapping the 

corridor’s extent. The effort considered the objectives of the 

mapping project relative to monitoring needs, previous mapping efforts (Phillips and others, 1977; Kearsley and Ayers, 

1996), the national vegetation classification standards for the 

system of National Vegetation Classification (NVC; Grossman and others, 1998), Federal mapping standards (Federal 

Geographic Data Committee Vegetation Subcommittee, 1997), 

and the limitations imposed on mapping by the characteristics 

of the imagery (e.g., spatial resolution, radiometric calibration, 

and atmospheric corrections). This project utilized two-way 

indicator species analysis (TWINSPAN; Hill, 1979) to identify 

major vegetation classes for mapping the river corridor. Image 

signatures of these classes were used to automate the mapping 

process, which resulted in a systemwide digital inventory of 

the riparian vegetation along the river corridor.
Introduction 3

Study Area

The riparian zone within the CRE encompasses portions 

of Glen Canyon Recreational Area, Grand Canyon National 

Park, and The Hualapai Nation. The corridor from Glen Canyon 

Dam to the boundary of Lake Mead Recreational Area spans a 

distance of 474 km and includes pre- and postdam riparian areas 

approximately 75 m wide along each side of the river corridor, 

resulting in a total land area of approximately 69,750 km2. The 

river channel passes through 13 geomorphic reaches (fig. 1) that 

vary in width and depth because of differences in the underlying bedrock lithology (Schmidt and Graf, 1990). Within Grand 

Canyon there are 740 tributaries that produce debris-fan eddy 

complexes (Schmidt, 1990), which form constrictions and associated rapids along the river. Most tributaries are intermittent 

rather than permanent sources of water entering the mainstem 

Colorado River. The debris fans, eddies, and channel margins 

form the substrate for riparian vegetation. The density and areal 

coverage of vegetation are influenced by the geographic orientation controlling solar shading and slope aspect (Yard and others, 

2005), the type of exposed bedrock, the frequency of debris 

flows from tributaries that form debris fan-eddy complexes 

(Schmidt, 1990), flood event variables (e.g., duration, magnitude, and frequency), and other factors (Naiman and Decamps, 

1990; Naiman and others, 2005).

The predam riparian zone was subject to annual floods 

of 1,416 m3/s, biannual floods of 2,407 m3/s, and 6-year return 

floods of up to 3,398 m3/s (fig. 2; Topping and others, 2003). 

The largest water volumes through the canyon were associated 

with spring runoff from watersheds of the Green, Colorado, 

and San Juan Rivers. Construction of Glen Canyon Dam began 

in 1956 as a part of the Colorado River Storage Project Act of 

1956 (43 United States Code 620) and the dam became operational in 1963. A consequence of water-flow regulation is that 

the yearly natural disturbance associated with annual runoff was 

replaced with daily fluctuations associated with dam operations. Summer monsoonal and fall storms still contribute some 

disturbance to the downstream tributaries in the system. Primary 

tributaries that contribute to downstream disturbance are the 

Paria and Little Colorado Rivers, located 15 and 125 km downstream of Glen Canyon Dam, respectively (fig 1).

Besides the loss of a seasonal disturbance pattern, the 

annual peak discharges along the river were reduced following regulation. Since regulation began in 1963, median annual 

discharge has varied between 268 and 450 m3/s. In 1996 (DOI, 

1996), operational constraints were placed on Glen Canyon 

Dam, and the median annual discharge measured at the USGS 

gage at Lees Ferry (USGS gage number 09380000) through 

September 2000 was 378 m3/s (Topping and others, 2003). Glen 

Canyon Dam is a hydropower dam and discharge fluctuates on 

a daily basis according to power demand; since 1996, the range 

of these daily fluctuations has been 138 m3/s.

The effect of regulation on riparian vegetation was an 

expansion of vegetation from the predam annual stage elevation

shoreward (Carothers and Aitchison, 1976; Turner and Karpiscak, 1980; Waring, 1995; Ralston, 2005). Analysis of selected 

areas within the corridor that were mapped (Waring, 1995) 

indicated that vegetated area below the annual flood volume 

increased by more than 50%. Marsh communities that had limited distribution in Glen Canyon and in Western Grand Canyon 

prior to regulation expanded throughout the corridor. Today, 

riparian and marsh vegetation exist throughout the corridor from 

the shoreline to the predam 6-year return flood stage elevation. The vegetation composition follows a moisture gradient 

with marsh species and obligate riparian species adjacent to the 

shoreline and facultative riparian and xerophytic species located 

upslope. Daily and monthly operations tend to affect vegetation up to the 991-m3/s stage elevation, while local precipitation 

affects species composition of areas above and below the 991-

m3/s stage elevation (Kearsley, 2006). Since regulation began, 

the riparian zone affected by dam operations generally occupies 

areas below the 1,274-m3/s stage elevation (Kearsley, 2006; Ralston, 2005), although a spill as high as 2,747 m3/s did occur in 

1983 in association with high spring inflows and resulting high 

reservoir elevations.

The vegetation composition along the corridor follows a 

north-south gradient; includes elements from the Great Basin, 

Mohavean, and Sonoran Desert Scrub Communities (classification per Brown, 1982); and includes exotic species like 

tamarisk (Tamarix ramosissima Ledeb.), camelthorn (Alhagi 

maurorum Medik.), and Bermuda grass (Cynodon dactylon

[L.] Pers). Prior to regulation, vegetation below the 1416-m3/s 

stage elevation (approx. annual return flood) was sparse (Clover and Jotter, 1944) and was composed mostly of seep willow 

(Baccharis emoryi A. Gray), longleaf brickellbush (Brickelia 

longifolia S. Watson), coyote willow (Salix exigua Nutt.) 

and arrowweed (Pluchea sericea [Nutt.] Coville), and other 

herbaceous vegetation. Gooding’s willow (Salix gooddingii

Ball) was present, but sparsely distributed and predominantly 

associated with flood plain geomorphology like that found at 

Lees Ferry or in western Grand Canyon.

Vegetation above the 1,416-m3/s stage elevation 

included trees and shrubs such as mesquite (Prosopis glandulosa Torr.), net leaf hackberry (Celtis laevigata Willd. var. 

reticulata [Torr.] L.D. Benson), and tamarisk; small shrubs 

were also present such as four-wing salt bush (Atriplex confertifolia [Torr. and Frém.] S. Watson), brittlebush (Encelia farinose A. Gray ex Torr.), and Mormon tea (Ephedra 

nevadensis S. Watson), as well as bunch grasses including 

sand dropseed (Sporobolus contractus Hitchc.), purple three 

awn (Aristida purpurea Nutt.), and old man’s beard (Bothriochloa barbinodes [Laq.] Herter).

Since the dam became operational, marsh habitat along 

the CRE has increased (Clover and Jotter, 1944; Turner and 

Karpiscak, 1980; Stevens and others, 1995) and includes, but 

is not limited to, common reed (Phragmites australis [Cav.] 

Steud.), cattail (Typha latifolia L.), water sedge (Carex aquatilis Wahlenb.), rabbitfoot grass (Polypogon viridis [Gauan] 

Breistr.), and Torrey’s rush (Juncus torreyi Coville) (Phillips 

and others, 1987).
Figure 1. Map of study area from Glen Canyon Dam to Lake Mead with geomorphic reaches indicated by name and distances from the Glen Canyon markers or river kilometers.
 5

Criteria for Vegetation Class Selection

The first corridorwide vegetation mapping was conducted 

by Phillips and others (1977) using 13 vegetation types. 

Vegetation types were categorized following a physiognomic 

classification scheme and included forest, woodland, scrub, 

and herbaceous categories, further modified to include leaf 

type descriptors (e.g., deciduous, evergreen, sclerophyllous, 

orthophyllous). Their mapping units are comparable to the 

formation-level units in the NVC. Included in their map key 

are common species associated with each vegetation type, but 

an absolute correspondence of vegetation type to dominant 

species is lacking in this map. Mapped areas included vegetation into the xeric zone (e.g., Agave, Larrea).

Subsequent mapping efforts did not attempt to cover the 

river corridor, but focused either on specific sites to document plant community change for relatively small areas (e.g., 

50 ha; Kearsley and Ayers, 1996) or on segments of the river 

corridor (approx. 8 km in length) to map the riparian vegetation at a landscape scale (Waring, 1995). In this latter case, 

vegetation was classified as either pre- or postdam vegetation 

and referred to as old or new high-water classes, based on the 

1,416-m3/s stage elevation (Waring, 1995). Kearsley and Ayers 

(1996) used relevé plots to record vegetation cover and species 

composition in combination with TWINSPAN (Hill, 1979) to 

delineate community types. Of the two studies, Kearsley and 

Ayers’ (1996) results were more applicable to the mapping 

objectives described in this report for selecting preliminary 

vegetation classification units. Though Kearsley and Ayers cautioned that the results of their classification analyses were not 

applicable to Southwestern regions, their results were useful in 

formulating the initial vegetation classification units used here 

because their census included locations throughout the CRE.

This study expands vegetation classification far beyond 

previous site-specific studies (Stevens and others, 1995; 

Kearsley and Ayers, 1996) to include riparian shrubs and 

woodlands along the channel margins and up to the predam 

6-year return flood of 3,398-m3/s stage elevation. To monitor 

community change not only for riparian vegetation but also for 

faunal constituents, a finer classification scale was used in this 

study than that used by Phillips and others (1977). The intention was to develop a base map at the alliance or association 

level following the NVC definition of these categories (Grossman and others, 1998). These levels of classification better 

represent community assemblages than higher order levels 

such as the formation level. For questions regarding largescale vegetation change, such as the change in all vegetation 

below the new high-water stage, the higher level classification 

would be appropriate and formation classes could be extracted 

from the more detailed classes by aggregation. Additionally, 

because the areal coverage was extensive (474 river kilometers 

[RK]), the use of unsupervised and supervised classification 

on recently collected digital imagery was investigated. This 

report discusses the capabilities of these approaches relative to 

the objectives and provides summary statistics for all vegetation classes along the CRE.

Figure 2. Zonation of vegetation according to river stage. Reduced flood frequencies, determined by releases from Glen Canyon 

Dam, have resulted in the development of three well-defined vegetation zones: shoreline habitats below the 700-m3/s stage, postdam 

riparian habitats (“new high-water zone”) below the 1460-m3/s stage, and predam riparian habitats (“old high-water zone”) above that 

(Carothers and Aitchison, 1976).

Introduction 
6 Vegetation Database for the Colorado River Ecosystem

a vegetation class. Associated herbaceous vegetation including 

horsetails (Equisetum sp.), water sedge, common reed, and 

brome grasses (Bromus sp.) were also identified as dominant 

or associated understory species. The Kearsley and Ayers 

analysis did not include areas above the 1,274-m3/s stage 

elevation; additional classes for these higher elevations had to 

be identified.

The Natureserve Web site (www.natureserve.org) was 

used to identify alliances that met the species composition of the groups identified by Kearsley and Ayers (1996). 

Information was also used from Davis and others (2002), 

who had remapped some of the Kearsley and Ayers sites 

using a supervised classification to determine which of 

the Kearsley/Ayers vegetation classes could be adequately 

separated using digital imagery. In addition, a special 

status species was included for Grand Canyon National 

Park (Brian, 2000), satintail (Imperata brevifolia Vasey), to 

determine whether it could be accurately mapped. Nonvegetated classes to address sand and bedrock were added, and 

it was noted that dried grass and dead brush (i.e., ground 

litter) produced a widespread signature that needed to be 

identified. From these sources of information, a preliminary 

classification was developed of 15 vegetation classes and 

3 nonvegetated classes (table 1). Training sites along the 

corridor that were considered to be representative of these 

vegetation classes were identified, including those identified by Kearsley and Ayers (1996), for use in the supervised 

classification process.

Methods

Preliminary Vegetation Classes

Preliminary alliance classes were developed from three 

sources:

1. Previous classification work of Kearsley and Ayers (1996)

2. A combination of existing vegetation classes defined by 

Natureserve with vegetation classes that were considered 

to be appropriate for change detection questions identified 

by adaptive management stakeholders

3. Consideration of remote-sensing and classification capabilities for riparian habitats

Kearsley and Ayers (1996) used cover data from relevé 

plots and TWINSPAN analysis (Hill, 1979) to characterize 

nine sites distributed along the river corridor. Their work 

focused on the communities within these sites, which were 

at a resolution considered to be appropriate for a base map to 

monitor riparian habitat.

Kearsley and Ayers (1996) identified four to seven 

classes per site. Their analysis showed coyote willow cooccurring with tamarisk and seep willow, or, alternatively, any 

of these species being the single dominant species. Arrowweed 

was often a monotypic dominate shrub and was designated as 

Table 1. Preliminary vegetation classes and number of surveyed groundtruth sites containing these classes.

Vegetation class Species name or description of class Number of sample sites

ATCA Atriplex canescens 22

BAXX Baccharis spp. 36

CERE Celtis reticulata 32

EQFE Equisetum ferrisii 16

IMBR Imperata brevifolia 29

LATR Larrea tridentata 19

PHAU Phragmites australis 28

PLSE Pluchea sericea 17

PRGL Prosopis glandulosa 28

QUTU Quercus turbinella 34

SAEX Salix exigua 27

SAGO Salix goodingii 39

TARA Tamarix ramossisima 28

TYDO Typha domingensis 36

WTLD Wetland grasses and sedges 29

BDRX/SAND Bedrock/sand 10

Dead grass Litter 4

Dead brush Litter 17
Methods 7

Verifying Vegetation Classes

To verify the preliminary alliance classes, ground surveys were conducted at randomly generated points along the 

CRE. The points were generated in ENVI (ITT Visual Information Solutions©, 2006). Relevé plots with a radius of 3 m 

(approx. 28 m2) were used to record cover dominant species 

and to compare with the supervised classification results. 

Plot size was determined by referring to Westhoff and van 

der Maarel (1978) and was similar to the size used by Kearsley and Ayers (1996). The plot size also corresponded to 

areal density calculations described in appendix A. For each 

plot, cover dominant species (following a modified Daubenmire scale, table 2) were recorded, as well as the geomorphic 

reach of the species, the physiognomic leaf character, and 

plant heights of dominant plants in the plot. Plant heights 

were estimated to the nearest 0.5-m increments and recorded. 

All plants occurring in the plot were listed. In the event that 

a single or rare species occurred in the plots, the species was 

noted as a “trace” or “t” on the data sheet and given a value 

of 0.01 in the spreadsheet.

For most classes, at least 25 plots were surveyed. For 

rarer vegetation types, at least 10 plots were surveyed. The 

study began with 578 points; however, some sites were inaccessible or out of the study area. The final database included 

451 sample points.

Cover data and associated species were entered into 

an Access (MicrosoftTM) database. The vegetation cover 

data were analyzed using ordination software, PC-ORD v.4 

(McCune and Mefford, 1999). TWINSPAN analysis was done 

for cover values of 3 (25%–50% cover) and 4 (50%–75% 

cover; table 3A, B). TWINSPAN was used because it was the 

standard approach used by the National Mapping Program 

when this project was initiated. The maximum group size 

per division, maximum number of indicators, and maximum 

number of species in the final table were set at 3, 3, and 25, 

respectively, for all runs.

Digital Data Acquisition

During a 10-day period starting May 30, 2002, a digital 

line scanner was flown over the Colorado River corridor in 

Arizona by ISTAR Americas, using a Leica ADS–40 digital camera to acquire a digital surface model and four-band 

image mosaics (blue, green, red, and near-infrared [NIR]) for 

vegetation mapping. The timing of the mission was selected 

to reduce the likelihood of storms, clouds, and shadows; to 

minimize phase angle changes during image acquisition; and 

to capture most vegetation with full foliage, although some 

species leaf out slightly later in the growing season (e.g., common reed and cattails).

Table 3. Divisions, groups, and corresponding alliance groups 

identified through TWINSPAN analysis. A, Grouping using cover 

values 3. B, Grouping using cover values 4. 

A

B

Table 2. Modified Daubenmire scale for cover values and 

corresponding percentages of ground cover.

Cover values Percent of ground cover

Trace <1%

1 <5%

2 5%–25%

3 25%–50%

4 50%–75%

5 75%–95%

6 >95%

Group Alliance

Division 0

00000 Phragmites/Scirpus americanus

00001 Typha domingensis/Carex aquatilis

00001 Baccaharis emoryi/Salix exigua

010 Tamarisk ramosissima/Aster spinosa

011 Pluchea sericea

Division 1

1 Prosopis glandulosa/Acacia. greggii/Baccharis sarothroides

Group Alliance

Division 0

00000 Phragmites australis

00001 Typha domingensis/Carex aquatilis

00001 Baccharis emoryi/Salix exigua

0001 Equisetum ferrisii

001 Polypogon monspeliensis

0100 Baccharis salicifolia

0101 Tamarix ramossisima/Brickelia longifolia

011 Pluchea sericea/Aster spinosa

Division 1

100 Bromus rubens

1010 Acacia greggi/Fallugia paradoxa

1011 Prosopis glandulosa/Larrea tridentate/

atriplex canescens

11 Baccharis sarothroides
8 Vegetation Database for the Colorado River Ecosystem

Each river reach required multiple, overlapping flight 

lines; each set of parallel flight lines for a particular river 

reach was assigned a number or letter designation (fig. 3). 

The digital surface model (DSM) was derived from three 

charge-coupled device (CCD) line scanners that recorded 

ground reflectance within the 520–760-nm wavelength range 

at a ground resolution of 22 cm. This broadband is referred 

to as a panchromatic band. The three CCDs looked fore, aft, 

and nadir along each flight line. Flight lines overlapped by as 

much as 50%, which provided multiple looks for each ground 

element along the entire corridor.

Photogrammetric computations applied to these stereo 

databases produced surface elevation at a 1-m cell spacing with 

a root-mean-square vertical error of 45 cm. The DSM represents 

the elevation of visible surface regardless of surface cover, and 

as such it may be used to derive vegetation heights using nearby 

bare-ground elevations. The vegetation heights have additional 

uncertainties due to variable depth of canopy reflection.

Four additional wavelength-band images were collected 

with the same sensor, using four additional CCD line scanners 

that collected ground reflectance in the blue (450–510 nm), 

green (530–576 nm), red (642–682 nm), and NIR (770–814 

nm) regions of the electromagnetic spectrum. These data were 

collected at a ground resolution of 44 cm. The wavelength 

ranges for the green, red, and NIR bands are close to the 

suggested ranges for three of the four bands that were judged 

to be optimum for discriminating vegetation within the CRE 

(Davis and others, 2002).

Supervised Classification Analyses of the 

Groundtruth Regions

Following image processing procedures that evaluated band quality, band ratios, and vegetation texture and 

density (see more detailed information in appendix A), 15 

possible databases were identified for use in vegetation 

classification: four bands, their six-band ratios, four-texture measures, and vegetation density. Different supervised 

classifiers were run on these data for the various groundtruth regions within the CRE to determine the type of classifier that produced the highest accuracy. For these analyses, all groundtruth areas were mosaicked into a single 

15-band image and designated pixels representative of the 

various vegetation alliances. The types of classifiers tested 

included parallelepiped, minimum distance, Mahalanobis 

distance, maximum likelihood, and spectral angle mapper. 

The maximum likelihood produced the highest mapping 

accuracies, which was also found by Davis and others 

(2002). Maximum likelihood classifications were then 

performed on each groundtruth region using different combinations of the 15 possible databases. Analyses of image 

classification results indicated that the combination of the 

four-band data and four-texture measures most accurately 

reproduced the groundtruth vegetation maps, and this 

combination was used in the final vegetation classification 

processing (see more detailed information in appendix A).

Figure 3. Index map of flight lines used in ISTAR airborne data collection in late May–early June 2002. Sets of lines with the same color 

represent repetitive data collections with 50% overlap; each set of flight lines is labeled with a number or letter.
Results 9

Accuracy Assessment of Vegetation 

Classification

Confusion matrices (Congalton, 1991) were constructed 

for the identified vegetation and unvegetated classes to 

determine classification accuracies. For these assessments, 

groundtruth vegetation polygons were compared with supervised classification polygons with areas greater than 0.01 ha. 

Groundtruth vegetation polygons were delineated in October 

2003 and June 2004 with a minimum mapping scale of 0.01 

ha. Groundtruth polygons were designated using the identified 

mapping classes.

Fuzzy Accuracy Assessment

Comparing accuracies between pixel-based classified 

imagery and groundtruth polygons presented scale problems. 

Fuzzy accuracy assessment was also employed to assess map 

classification accuracies. Fuzzy accuracy assessment was first 

described by Gopal and Woodcock (1994) as an alternative 

assessment approach that allows for degrees of membership 

to particular classes. Accuracies are assessed in categories 

of agreement rather than in a binary assessment such that a 

class might be “mostly correct” instead of simply “correct” 

or “incorrect.” Three criteria were used in the fuzzy sets, 

including the strictly “correct” value associated with binary 

assessment. These criteria have been used by the USGS-NPS 

National Vegetation Mapping Program (Klopfer and others, 

2002; Hansen and others, 2004; Salas, unpub. data). The three 

criteria are as follows: criterion 5 (exact match), when the 

groundtruth and pixel-based map-class data match exactly; 

criterion 4 (acceptable error), when the majority (>50%) of 

the pixel-based map class match the groundtruth polygon; and 

criterion 3 (understandable error), when the pixel-based map 

signature of the vegetation may be similar, possibly due to the 

density of the dominant vegetation, but was misidentified. If 

adjacent map classes permitted the likely identification of the 

map class to the exclusion of the assigned map class, this was 

also taken into consideration (for example Prosopis classified as 

Tamarix, but the plant is too high in elevation to be Tamarix).

Mapping Scale

The groundtruth for this study was mapped at .01 ha 

based on habitat surveys for birds and ground transects for 

small mammals (Kearsley and others, 2006). The minimum 

mapping unit for this map was smaller than the conventional 

mapping unit used in the USGS-NPS National Mapping Program, which is 0.5 ha (The Nature Conservancy, 1994). Filters 

used in the supervised classification of the imagery ranged in 

size from 4 to 10 m (appendix A). Mapping at the scale used 

in the National Mapping Program for the national parks would 

have resulted in a dilution of community change information 

that the Glen Canyon Dam Adaptive Management Program 

identified in their information needs for riparian vegetation. Vegetation information for a larger minimum mapping 

unit can be obtained by using formation-level categories; by 

combining alliances, such as tamarisk and seep willow classes, 

within an ArcMapTM (ESRI©, ArcMap 9.2) framework; or by 

reanalyzing the imagery at a coarser scale.

Results

Revision of Alliance-Level Classification

TWINSPAN analysis revealed 12 and 6 interpretable vegetation classes for cover values 3 and 4, respectively (table 3A

and B). The first division separated higher elevation xeromorphic and facultative riparian vegetation from lower elevation 

facultative and obligate riparian and marsh vegetation. Within 

group “0,” species fell out along a hydrologic gradient that 

separated obligate wetland and riparian species from facultative species. The greatest difference in groups between cover 

values 3 and 4 is the identification of more herbaceous groups 

in division 1 for cover value 3. These herbaceous groups tend 

to grow in monotypic patches, such as red brome (Bromus 

rubens L.), common reed, or annual rabbit’s foot grass (Polypogon monospeliensis [L.] Desf).

The identified classes also varied slightly from the initial 

classification that was used here. The greatest disagreements 

between the TWINSPAN results and the initial classification 

are differentiation among Baccharis species and the absence 

of a separate Salix exigua class (it is combined with Baccharis emoryi) in the TWINSPAN results. For cover value 3, 

Baccharis emoryi and B. salicifolia (Ruiz and Pav.) Pers. are 

separate classes. Baccharis salicifolia becomes a subordinate 

species for cover value 4. The initial classification in this 

study lumped both of these species into a general category 

of Baccharis (BAXX) and thus agreed with the TWINSPAN 

analysis for cover value 4. Catclaw acacia (Acacia greggii A. 

Gray) and mesquite were also separated in cover value 3, but 

these species co-occur in cover value 4. Catclaw acacia was 

not initially identified as a separate class.

Based on the TWINSPAN results for cover value 4 and 

the accuracies obtained from the supervised classification 

results, classes that had similar signatures and corresponded 

to the results of the TWINSPAN group analyses (table 1) 

were aggregated. The limitations of the imagery also required 

aggregating wetland species groups into a single class called 

“wetland.” “Seasonal dry grass” was collapsed into the wetland class because this signature at times represented common reed or other nearshore herbaceous species like Bermuda 

grass. A sparse shrub (SS) class was also included composed 

of perennial grasses and forbs with small leaf areas, similar 

reflectance values, and low ground cover percentages (<25%; 

cover values of 1 or 2), like four-wing saltbush and creosote 

bush (Larrea tridentata [DC.] Coville), Mormon tea, and 
10 Vegetation Database for the Colorado River Ecosystem

brittlebush. Lastly, litter, sand, and rock were combined into 

a single “nonvegetated” class. The resulting seven classes 

and corresponding National Vegetation Classification (NVC) 

categories for vegetation are listed in table 4. Subsequent 

accuracy assessments were performed on these seven classes 

using groundtruth data collected in October 2003 and June 

2004. The attribute table appearing in the digital vegetation 

map includes the preliminary vegetation classes (table 1), the 

aggregated vegetation classes, and the corresponding NVC 

alliance and formation classes (table 4).

Revised Vegetation Classes and Related 

National Vegetation Classification Alliances

The six revised vegetation classes (with abbreviations) 

and their corresponding NVC alliance names that were used in 

the final vegetation map are described below. The nonvegetation class is also described with less detail.

1. Wetland (WTLD)—Phragmites australis/Scirpus americanus combined with Typha latifolia/Carex aquatilis—A 

patchily distributed assemblage that occurs throughout 

the river corridor. It is usually found at ca. 266 m3/s (base 

flow) through the 566 m3/s stage elevation. Substrate is 

silt to fine sand associated with low-velocity channel 

margin habitats (e.g., return channels) along rocky and 

vegetated shorelines, rarely associated with talus slopes. 

Typha, Carex, or Phragmites often dominate with cover 

ranging from 50% to 90% of the area. Associated species are sparsely interspersed or along the edges of the 

community. This class can be obscured in imagery by 

overhanging shrubs like Tamarix or Baccharis. Vegetation heights vary from prostrate herbs to Phragmites and 

Typha reaching heights of 2 m. Associated species include 

Polypogon monosplenis, Veronica americana, Conyza 

canadensis, Euthamea occidentalis, Gnaphalium chinensis, Agrostis semiverticilate, Cynodon dactylon, Elymus 

canadensis, Muhlenbergia asperifolia, Juncus balticus, 

and J. torreyii.

Wetland (WTLD)

NVC Formation Class Herbaceous Vegetation

NVC Formation Name Semipermanently Flooded Temperate or 

Subpolar Grassland

NVC Alliance Typha (angustifolia, latifolia)—

(Schoenoplectus spp.) Semipermanently Flooded Herbaceous Alliance 

NVC Association Typha (angustifolia, domingensis, 

latifolia)—(Schoenoplectus americanus) Herbaceous Vegetation 

2. Baccharis emoryi/Salix exigua (BAXX)—A very heterogeneous community found throughout the river corridor 

in sparse to dense patches. Both dominant species can 

be found singly, or they can codominate and intermingle 

with tamarisk. Baccharis emoryi often forms dense thick 

patches with canopy cover values up to 75% interspersed 

with Salix exigua. The shrubs reach heights of 2–3 m. The 

associated understory is often sparse with ground cover 

<10%. The community is found close to the shoreline 

at stage elevations between approximately 566 and 877 

m3/s. It is found along channel margins and the downriver 

side of debris fans and can be overhanging the wetland 

community. The substrate varies from fine to coarse sand 

occasionally interspersed with cobbles and boulders. 

Rarely associated with talus slopes or, if so, then cover is 

minimal. Associated species include shrubs such as Salix 

goodingii (rare), Baccharis salicifolia, and Brickellia 

longifolia, and herbs such as Equisetum ferrisii, Conyza 

canadensis, and Alhaghi maurorum. 

Baccharis emoryi/Salix exigua (BAXX)

NVC Formation Class Shrubland

NVC Formation Name Seasonally Flooded Cold-Deciduous 

Shrubland

NVC Alliance Baccharis emoryi Seasonally Flooded 

Shrubland Alliance 

NVC Association Baccharis emoryi-Salix exigua Shrubland

3. Tamarix ramosissima/Aster spinosa (TARA)—A ubiquitous community found throughout the CRE that can 

be variable with respect to the density and age of stands. 

Older individuals within stands can be very tall (up to 

7 m) and have a dense canopy of over 75% with little 

understory, while younger individuals in newer stands 

may be more dispersed with cover varying from 25% to 

50% and interspersed with annual grasses and forbs and 

other shrubs like Baccharis or Pluchea. Found in dense 

patches along channel margins and in more dispersed 

stands or patches on debris fans and in talus slopes from 

226 m3/s (base flow) to 1,274 m3/s. Substrate is mostly 

sandy though individuals can be found interspersed in 

cobbles. Associated species include shrubs such as Prosopis glandulosa, Pluchea sericea, Baccharis emoryi, 

Brickellia longifolia, and Salix exigua, and herbs such 

as Lepidium latifolia, Bromus rubens, Bromus tectorum, 

Cynodon dactylon, and Equisetum ferrisii. 

Tamarix ramosissima/Aster spinosa (TARA)

NVC Formation Class Shrubland

NVC Formation Name Temporarily Flooded Microphyllous 

Shrubland

NVC Alliance Tamarix spp. Seminatural Temporarily 

Flooded Shrubland Alliance 

NVC Association Tamarix spp. Temporarily Flooded 

Shrubland

4. Pluchea sericea (PLSE)—A homogeneous community in 

which cover varies from 25% to 75%. When cover is low, 

the signature for this community is easily confused with 

sparse shrub. Plants can grow to 1.5 m tall with sparse 

understory or associated species. Found along chan-
Group analysis NVC association NVC alliance Unique identifier Formation name 

Phragmites/Scirpus, combined with Typha domingensis/Carex aquatilis(WTLD)

Typha (angustifolia, domingensis, 

latifolia)—(Schoenoplectus americanus) Herbaceous Vegetation

Typha (angustifolia, latifolia)—

(Schoenoplectus spp.) Semipermanently Flooded Herbaceous Alliance

CEGL002010 V.A.5.N.I—Semipermanently Flooded Temperate or 

Subpolar Grassland

Baccharis emoryi/Salix 

exigua

(BAXX)

Baccharis emoryi-Salix exigua

Shrubland

Baccharis emoryi Seasonally 

Flooded Shrubland Alliance

CEGL005947 III.B.2.N.e—Seasonally 

Flooded Cold-Deciduous Shrubland

Tamarix ramosissima/Aster 

spinosa

(TARA)

Tamarix ramosissima Seminatural Temporarily Flooded 

Shrubland

Tamarix ramossisima Seminatural Flooded Shrubland 

Alliance

CEGL003114 III.A.4.N.c—Temporarily 

Flooded Microphyllous Shrubland

Pluchea sericea(PLSE)

Pluchea sericeaSeasonally Flooded Shrubland

Pluchea sericea Seasonally 

Flooded Shrubland Alliance

CEGL003080 III.A.2.N.h—Seasonally 

Flooded Shrubland

Prosopis glandulosa/Acacia 

greggii/Baccharis sarothroides

(PRGL)

Prosopis glandulosa-Acacia 

greggii Temporarily Flooded Woodland

Prosopis glandulosa Temporarily Flooded Woodland CEGL004934 II.B.2.N.b—Temporarily Flooded Cold-Deciduous 

Woodland

Sparse shrub(SS)

Encelia farinosa-Ephedra 

nevadensis-Ephedra viridisShrubland or Gutierrezia sarothrae Dwarf Shrubland

Encelia farinosa Shrubland 

Alliance or Gutierrezia

Dwarf Shrubland

CEGL001252 or 

A2528

III. A.5.N.b—Facultatively 

Deciduous Extremely Xeromorphic Subdesert Shrubland or Caespitose Cold-Deciduous Dwarf Shrubland

Nonvegetated (NV)

Table 4. Revised vegetation classes for the Colorado River ecosystem.
12 Vegetation Database for the Colorado River Ecosystem

nel margins occupying habitat upslope of tamarisk and 

seep willow. Associated with stage elevations between 

708 and 1,274 m3/s in sand or alluvial gravel substrates. 

Often encroaches on campsites. Associated species: Aster 

spinosa and Bromus tectorum. 

Pluchea sericea (PLSE)

NVC Formation Class Shrubland

NVC Formation Name Seasonally Flooded Shrubland

NVC Alliance Pluchea sericea Seasonally Flooded 

Shrubland Alliance

NVC Association Pluchea sericea Seasonally Flooded 

Shrubland [Placeholder]

5. Prosopis glandulosa/Acacia greggii (PRGL)—A sparsely 

vegetated and variable community assemblage with 

canopy cover often <50%. Dominant species represent the 

relictual predam riparian community. Substrate is finegrained alluvial terraces associated with predam fluvial 

deposits with gravels interspersed. Often growing on 

slopes and at stage elevations above 1,274 m3/s. Dominant 

species occur singly or form codominant patches. Trees 

grow to 5 m tall with wide canopies. Understory species 

cover is low. Individuals of these species, particularly 

Prosopis, can also be found associated with tamarisk 

closer to the river channel. Associated species: Atriplex 

canescens, Ephedra nevadensis, Fallugia paradoxa, Lepidium montana, and Bromus tectorum. 

Prosopis glandulosa/Acacia greggii (PRGL)

NVC Formation Class Woodland

NVC Formation Name Temporarily Flooded Cold-Deciduous 

Woodland

NVC Alliance Prosopis glandulosa Temporarily 

Flooded Woodland 

NVC Association Prosopis glandulosa-Acacia greggii 

Temporarily Flooded Woodland

6. Sparse shrub (SS)—A variable community dispersed 

throughout the CRE composed of low-density perennial 

herbs and bunch grasses and occurring across all stage 

elevations above 566 m3/s. Ground cover is <25%, and 

plants vary in height from <.5 to 1 m tall. Substrate is 

variable and includes fine alluvial predam flood deposits, desert gravel, debris fans, talus slopes, and shoreline 

sand deposits. Primarily represented by dwarf shrub 

species (e.g., Gutierrezia sarothrae) and xeromorphic 

forbs (e.g., Encelia farinosa, Opuntia spp.), though it 

can include single tamarisk or willow seedlings found 

along the shoreline. Associated species: Ephedra viridis, 

E. nevadensis, Acacia greggii, Eriogonum inflatum, 

Sporobolus airiodes, Aristida purpurea, Hesperostipa 

comata, and Bromus tectorum. 

Sparse shrub (SS)

NVC Formation Class Bland or Dwarf Shrubland

NVC Formation Name Facultatively Deciduous Extremely 

Xeromorphic Subdesert Shrubland or 

Caespitose Cold-Deciduous Dwarf 

Shrubland 

NVC Alliance Encelia farinosa Shrubland Alliance or 

Gutierrezia Dwarf Shrubland Alliance

NVC Association Encelia farinosa—Ephedra nevadensis—

Ephedra viridis Shrubland or Gutierrezia sarothrae Dwarf Shrubland

7. Nonvegetated (NV)—Open areas occurring throughout the CRE. Generally absent of live vegetation, or if 

vegetation is present, it is of such low density that image 

analysis could not separate vegetation from sand, rocks, 

or litter. Substrate varies from silt, sand, and gravels to 

boulders on debris fans, talus slopes, and shorelines.

Vegetation Classification Accuracies

Class accuracies for the seven classes based on criterion 

5 were quite variable, with an overall accuracy of 49.2% 

(table 5A). Two classes, SS and mesquite/acacia (PRGL), had 

≥80% user and producer accuracies, respectively. Accuracies 

improved with fuzzy set criteria applied to the standard dataset. Criterion 4 produced much higher accuracies (≥50%) for 

all classes (tables 5B, 6); BAXX, TARA, PRGL, and WTLD 

classes were assigned “acceptable error” with user accuracies 

>80%, and SS, PLSE, and WTLD have producer accuracies 

≥80% (tables 5B, 6). Overall accuracy for the seven classes 

under criterion 4 increased to 79.5%. Criterion 3 resulted in 

all classes with ≥80% producer and user accuracies (tables 5C, 

6). Overall accuracy for the seven classes under criterion 3 

increased to 95%.

Electronic Map Organization

The groundtruth vegetation surveys and the vectorized 

classification maps are in the GCVEG_2006 database that 

was created using ArcMap. The database has Federal Geographic Data Committee compliant metadata. The database is 

divided into three personal geodatabases (Microsoft Access 

files with spatial information created by ESRI with the release 

of ArcGIS) due to the amount of data: segment I, RK 0–124; 

segment II, RK 124–267; and segment III, RK 267–474. Each 

geodatabase has the groundtruth data and associated zones 

that correspond with the flight lines of the 2002 imagery (fig. 

3). An example of the vegetation map and attribute table is 

provided in figure 4. The map is posted on the Grand Canyon 

Monitoring and Research Center (GCMRC) Internet map 

server: http://www.gcmrc.gov/website/vegmap2002/Run.htm.
Results 13

Table 5. Accuracy assessment of revised vegetation classes reflecting accuracies using fuzzy logic. A, criterion 5 (exact agreement). 

B, criterion 4 (acceptable). C, criterion 3 (understandable). BAXX = Baccharis emoryi/Salix exigua, PRGL = Prosopis glandulosa/Acacia 

greggii/Baccharis sarothroides, TARA = Tamarix ramosissima/Aster spinosa, PLSE = Pluchea sericea, SS = sparse shrub, and WTLD = 

Phragmites/Scirpus, combined with Typha domingensis/Carex aquatilis.

A

B

C

Vegetation class BAXX PRGL TARA PLSE SS WTLD Total User’s 

accuracy

BAXX 5 0 5 7 0 7 24 0.208

PRGL 1 20 9 9 6 6 51 0.392

TARA 2 2 18 8 0 1 31 0.581

PLSE 0 3 0 17 0 7 27 0.629

SS 0 0 0 3 14 0 17 0.824

WTLD 3 0 2 3 12 19 39 0.487

Total 11 25 34 47 32 40 189

Accuracy

Producer’s accuracy 0.454 0.800 0.529 0.362 0.438 0.475 93

Overall accuracy 0.492

Vegetation class BAXX SS PRGL TARA PLSE WTLD Total User’s 

accuracy

BAXX 12 0 0 4 3 5 24 0.500

SS 0 17 0 0 0 0 17 1.000

PRGL 0 4 34 2 3 0 43 0.791

TARA 1 0 0 24 6 0 31 0.774

PLSE 0 2 0 0 25 0 27 0.926

WTLD 0 4 0 0 3 32 39 0.820

Total 13 27 34 30 40 37 181

Accuracy

Producer’s accuracy 0.923 0.630 1.000 0.800 0.625 0.865 144

Overall accuracy 0.796

Vegetation class BAXX SS PRGL TARA PLSE WTLD Total User’s

accuracy

BAXX 21 0 0 1 2 0 24 0.875

SS 0 17 0 0 0 0 17 1.000

PRGL 0 0 41 0 2 0 43 0.954

TARA 0 0 0 31 0 0 31 1.000

PLSE 0 0 0 0 27 0 27 1.000

WTLD 0 4 0 0 0 37 41 0.902

Total 21 21 41 32 31 37 183

Accuracy

Producer’s accuracy 1.000 0.809 1.000 0.969 0.871 1.000 174

Overall accuracy 0.951
14 Vegetation Database for the Colorado River Ecosystem

Colorado River ecosystem 

vegetation class

Percent comission Percent omission Explanation for error

Phragmites/Scirpus, combined 

with Typha domingensis/Carex 

aquatilis (WTLD)

82.0% acceptable 86.4% acceptable Confused with sparse shrub when vegetation is in low 

density. Overstory of Baccharis and tamarisk may 

obscure signature.

Baccharis emoryi/Salix exigua

(BAXX)

87.5% understandable 92.3% acceptable Confused with Pluchea and tamarisk when in sparse 

form. Can intergress with tamarisk and be codominant.

Tamarix ramosissima/Aster 

spinosa (TARA)

100% understandable 80% acceptable Most often confused with Pluchea, particularly when 

tamarisk is in low density. Ground reflectance 

signature influences misclassification.

Pluchea sericea (PLSE) 92.5% acceptable 87.1% understandable Most often confused with sparse shrub class. Density 

gradient in combination with stage elevation information may help identification of this species.

Prosopis glandulosa/Acacia 

greggii/Baccharis sarothroides

(PRGL)

80% exact 100% acceptable Confused most often with tamarisk due to similar 

color signature and occasional canopy architecture. Can also be confused with Pluchea if in low 

density. Stage elevation information can be used to 

help discriminate this class.

Sparse shrub (SS) 80.9% understandable 82.3% exact Most often confused with Pluchea. Density gradient 

for this class difficult to define.

Table 6. Percent commission and omission, rank of accuracy, and explanation of the classification results for the aggregated 

vegetation classes. 

Summary of Vegetation Cover Within the 

Colorado River Ecosystem in 2002

The total area covered by all vegetation within the CRE 

was 3,346 ha (table 7). Considering the six vegetation classes 

(table 7, figs. 5A, B), the SS class accounts for the greatest 

amount of vegetation (627 ha) followed by PLSE and TARA at 

495 and 367 ha, respectively. WTLD and PRGL had similar 

areal cover values (227 and 213 ha, respectively). BAXX is 

least represented at 94 ha. When the vegetated area is compared 

between reaches (figs. 6, 7A–F), a general trend of increasing 

vegetated area with distance from the dam is observed.

Vegetation Distribution and Areal Cover Patterns

The lower Marble Canyon, Lower Canyon, and western Grand Canyon reaches (table 7, figs. 1, 6, 7A–F) show 

the greatest area cover for each vegetation class. The SS 

category has the greatest areal cover (figs. 5A, B) and could 

include single tamarisk and willow saplings as well as other 

species. The SS category gives an indication of the patchy 

nature of vegetation within the corridor. The data also indicate that Pluchea covers a larger area than Tamarix (fig. 7C, 

D). Wetlands cover only 7% less area than tamarisk, though 

tamarisk individuals are also likely to be included as part of 

the SS category. Thus, area for Tamarix may be greater than 

is represented by the classified values.

Discussion

Vegetation Classification and Remote-Sensing 

Accuracies

Vegetation along the CRE can be categorized into six 

classes that represent single or codominant species. The vegetation classes identified in this mapping effort agreed with 

existing associations and alliances that were defined by the 

NVC and are available on the Natureserve Web site. Alternative methods of identifying vegetation classes could include 

hierarchical agglomerative cluster analysis, nonmetric multidimensional scaling, and indicator species analysis, which 

is being used by the USGS-NPS mapping program (Vanderhorst and others, 2007). The TWINSPAN method, which 

was previously used in the National Mapping Program, has 

been subject to criticism in recent years (McCune and Grace, 

2002). Subsequent mapping efforts will employ the alternative approaches described above. Changes in classification 

of CRE vegetation related to the use of these alternative 

approaches instead of the TWINSPAN method are unlikely. 

The problems with TWINSPAN classifications have been 

associated with the grouping of rare species (McCune and 

Grace, 2002), whereas the six vegetation classes presented 

here are for dominant cover species.
 15

Figure 4. Example of vegetation map from RK 107.3–107.7 with National Vegetation Classification Alliance classes color coded in the 

legend.

Discussion 
Table 7. Area (in hectares) of mapped classes, of total vegetation mapped, and of total land mass within each geomorphic reach. Vegetation Class Glen Canyon Permian Supai Gorge Redwall Lower Marble Furnace Flats Upper Granite Aisles Middle Granite Muav Gorge Lowe

Canyo

PRGL/ACGR 7.48 0.18 0.00 0.20 16.16 30.32 1.54 1.23 1.09 0.52 33.

SS 52.25 9.36 0.13 13.10 63.87 21.12 59.82 20.89 21.00 32.43 172.2

BAXX/SAEX 3.02 0.79 3.26 0.40 8.72 1.69 1.80 0.91 0.90 0.75 12.2

TARA 21.83 10.00 10.94 4.24 37.39 9.16 11.94 2.89 2.56 2.40 50.

PLSE 30.81 24.11 0.00 9.98 88.32 31.93 56.36 12.60 12.11 17.65 82.

NV 49.90 10.29 12.59 26.75 82.34 20.99 113.36 33.37 33.69 22.12 94.

WTLD 17.71 2.18 2.62 3.60 17.75 10.50 16.78 8.16 5.40 4.45 28.

Area Total area mapped (ha) 183.00 56.92 29.54 58.27 314.56 125.71 261.59 80.05 76.76 80.32 474.

Total vegetated area (ha) 133.10 46.62 16.95 31.52 232.22 104.72 148.24 46.68 43.07 58.21 380.

Percent vegetated area 73 82 56 55 74 83 56 59 56 72 80 
Discussion 17

Hectares covered by vegetation class

213

627

367 94

494

227

Mesquite/Acacia

Sparse shrub

Baccharis/Salix

Tamarisk

Arrowweed

Wetlands

Percent cover of vegetated classes

10%

32%

18% 5%

24%

11%

Mesquite/Acacia

Sparse shrub

Baccharis/Salix

Tamarisk

Arrowweed

Wetlands

Figure 5. Vegetation map classes. A, Percent areal cover of 

each class for the Colorado River ecosystem (CRE). B, Total area 

of each class for the CRE.

Image Quality

The combination of vegetation density, variable color 

band reflectance values within and among species, and saturation of the NIR color band often caused signatures to be 

misclassified in all classes. The reflectance values for Tamarix

varied widely depending on its density and location along the 

river, and its signature was confused with most other classes 

(table 5A; appendix A, tables A2–A5). Similarly, the ability to 

differentiate between types of wetland species (e.g., Phragmites vs. Typha) was hampered by similar signatures among 

all wetland species. Others (Ehleringer, 1981; Mooney and 

others, 1977) have determined that spectral similarity among 

arid species can occur for multiple reasons, including reduced 

leaf absorption in the visible range by arid-adapted species; 

individual response of plants to small precipitation events, 

which affect spectral variability among individuals of single 

species (Duncan and others, 1993); and different canopy 

structures within a species, which affect reflectance (Huete 

and Jackson, 1988; Hurcom and Harisson, 1998). All of these 

variables likely affected the spectral patterns for individual 

species in this study.

Determining whether the overlapping reflectance values 

observed among species are accurate involves multiple steps 

associated with sensors and reflectance values. The first step 

is calibrating the sensor before data acquisition to the brightest 

anticipated reflectance values. Second, ground reflectance data 

should be collected simultaneously with overflight acquisition 

to compare image reflectance values with true reflectance values. Finally, a comparison with other imagery of comparable 

resolution (e.g., imagery obtained in 2004 and 2005) should be 

done to determine whether it was the sensor or inherent environmental factors that contributed to variability in reflectance 

values. These last comparisons can be done before additional 

acquisition of four-band data.

Alternative image processing approaches might also 

be pursued to improve image classification accuracy. Using 

alternative vegetation indices such as the soil-adjusted vegetation index (which incorporates soil reflectance and normalized 

difference vegetation index [NDVI]) might further constrain 

values and help discriminate among signatures (Huete, 1988; 

Elvidge and Chen, 1995). Because two different NDVI filters 

were necessary for dark and light alluvial surfaces (appendix 

A), pursuing additional filters for vegetation may prove profitable. The clumped and patchy nature of vegetation along the 

river corridor contributes to the variability in class discrimination. When contrast and co-occurrence filters for textures were 

applied, which proved to be helpful in discriminating between 

vegetation classes, filter size had to be varied depending on 

vegetated stand size (appendix A). The larger dimension for 

texture (23-by-23 boxcar [10-m dimension]) smoothed the 

variations within large stands more effectively, but degraded 

the obvious texture of small stands by including adjacent alliance variations in the small alliance classes.

Heterogeneous plant assemblages (common in riparian 

communities) and scale differences between the groundtruth 

and image resolution also presented problems with accuracies. The 44-cm-pixel resolution provides high-resolution 

information that can potentially identify single individuals, 

but can also obscure the larger scale information on a particular cover class that may encompass 100 m2 and include 

other interspersed species. This may be more problematic 

in arid environments, where cover values are low compared 

to eastern deciduous forests. Another mapping approach 

could be spectral mixture analysis (SMA), which takes into 

account the vegetative mosaic and may more accurately 

report vegetative components of the riparian community. The 

concept behind SMA is that the relative proportion of a few 

spectrally distinct elements dominates the variance observed 

in a remotely sensed area (Roberts and others, 1998; Elmore 

and others, 2000). SMA works best when the number of 

individual spectral signatures is smaller than the number of 

useful bands. For Colorado River imagery, which is limited 

to four bands, SMA may have limited utility. Acquiring multiple infrared bands may be necessary to improve mapping 

accuracy. Costs increase with the number of bands acquired, 

but these costs may be offset by decreasing the resolution 

A

B
18 Vegetation Database for the Colorado River Ecosystem

Vegetated area by geomorphic reach

0

100

200

300

400

500

600

700

800

Glen Canyon

Permian

Supai Gorge

Redwall

Lower Marble

Furnace Flats

Upper Granite

Aisles

Middle Granite

Muav Gorge

Lower Canyon

Lower Granite Gorge

Western GC

Geomorphic reach

Area (ha)

Figure 6. Total vegetated area (ha) within each geomorphic reach.

to 1 m. This needs further analysis and consideration before 

acquiring future imagery.

Supervised classification approaches have been effective 

for mapping large areas of vegetation (Eder, 1989; Mickelson 

and others, 1998 ; Driese and others, 2004), but these studies 

have also identified low accuracies for particular vegetation 

classes. Driese and others (2004) identified spectral similarity 

between beech and maple and overall accuracies of 28% for a 

vegetation map using multitemporal Landsat imagery. Accuracies in their study improved through aggregation of classes 

and use of a fuzzy accuracy assessment. Accuracies in another 

study (Laba and others, 2002) were higher when classes were 

aggregated. In this study, aggregation improved accuracy for 

some classes, but fuzzy dataset assessment was also used to 

improve the map accuracy (tables 5A–C). In a mathematical 

sense, the vegetation map meets national standards for mapping accuracy. The accuracy of the map for long-term change 

detection may be problematic beyond looking at larger scale 

questions about vegetated areas above a certain discharge or 

associated with a geomorphic feature (e.g., channel margins, 

debris fans).

General Changes in Vegetation

The general pattern of more vegetation in the Glen 

Canyon, lower Marble Canyon, Lower Canyon, and western 

Grand Canyon reaches (figs. 5 and 6A–F) is likely due to 

geomorphic and sun-aspect effects. These reaches are some of 

the widest and longest in the river corridor (Schmidt and Graf, 

1990) and are oriented in such a manner that sun availability 

is also greater in these reaches than in others (Yard and others, 2005). Both available area and sunlight affect vegetation 

densities and areal cover. In Glen Canyon, the larger vegetated 

areas may also be a function of perched beaches that are less 

subject to erosion because of channel incision since closure of 

the dam (Grams and others, 2007).

The ability to compare changes in vegetation between 

the 2002 dataset and previous vegetation studies is limited. 

Although no historic data are available to determine whether 

this study’s current areal measurement of arrowweed is an 

increase over time, distributional maps from Turner and 

Karpiscak (1980) can still be used to compare distribution 

data. Comparisons of distributions indicate that the range of 
Discussion 19

Figure 7. Areal cover of each vegetation class within each geomorphic reach. A, Wetlands. B, Baccharis/Salix. C, Tamarix. D, Pluchea. 

E, Prosopis/Acacia. F, Sparse shrubs. 

Area Cover of Wetland Species by Geomorphic Reach

0

10

20

30

40

50

60

70

80

90

100

110

Glen Canyon

Permian

Supai Gorge

Redwall

Lower Marble

Furnace Flats

Upper Granite

Aisles

Middle Granite

Muav Gorge

Lower Canyon

Lower Granite Gorge

Western GC

Geomorphic reach

Area (ha)

Area Cover of Baccharis/Salix by Geomorphic Reach

0

10

20

30

40

50

60

Glen Canyon

Permian

Supai Gorge

Redwall

Lower Marble

Furnace Flats

Upper Granite

Aisles

Middle Granite

Muav Gorge

Lower Canyon

Lower Granite Gorge

Western GC

Geomorphic Reach

Area (ha)

Area Cover of Tamarix sp. by Geomorphic Reach

0

50

100

150

200

250

Glen Canyon

Permian

Supai Gorge

Redwall

Lower Marble

Furnace Flats

Upper Granite

Aisles

Middle Granite

Muav Gorge

Lower Canyon

Lower Granite Gorge

Western GC

Geomorphic Reach

Area (ha)

Area Cover of Pluchea sericea by Geomorphic Reach

0

50

100

150

200

250

Glen Canyon

Permian

Supai Gorge

Redwall

Lower Marble

Furnace Flats

Upper Granite

Aisles

Middle Granite

Muav Gorge

Lower Canyon

Lower Granite Gorge

Western GC

Geomorphic Reach

Area (ha)

Area Cover of Prosopis/Acacia by Geomorphic Reach

0

20

40

60

80

100

120

140

Glen Canyon

Permian

Supai Gorge

Redwall

Lower Marble

Furnace Flats

Upper Granite

Aisles

Middle Granite

Muav Gorge

Lower Canyon

Lower Granite Gorge

Western GC

Geomorphic Reach

Area (ha)

Area Cover of Sparse Shrubs by Geomorphic Reach

0

20

40

60

80

100

120

140

160

180

200

Glen Canyon

Permian

Supai Gorge

Redwall

Lower Marble

Furnace Flats

Upper Granite

Aisles

Middle Granite

Muav Gorge

Lower Canyon

Lower Granite Gorge

Western GC

Geomorphic Reach

Area (ha)

A B

C D

E F
20 Vegetation Database for the Colorado River Ecosystem

A B

C

Figure 8. Vegetation cover between RK 124.9 and 127.9. A, 1992. B, 2002. C, The difference in vegetation between 1992 and 2002.
Conclusions 21

arrowweed has expanded upstream into Glen Canyon and is 

distributed more evenly within upper Marble Canyon above 

RK 70. These observations can be made for arrowweed, but 

similar observations about expansion or contraction of other 

species are not possible because the distribution data from 

1980 were fairly general.

Areal cover comparisons with previous data are also 

somewhat limited because few studies identified specific vegetation classes. However, it was possible to compare mapping 

of a 3-km area below the Little Colorado River (RK 124–127) 

in 1992 with the 2002 imagery (Waring, 1995). The sparse 

vegetation category was not included in order to focus on 

large patches that provide some comparability. Using extraction programs in ArcMap, it was determined that vegetated 

area had increased by 6 ha in the 3-km length of river (figs. 

8A–C). Rough comparisons show that the greatest increases in 

vegetation occurred along the shorelines of channel margins, 

although some previously vegetated area did expand within 

the debris fan area and downstream eddy bars. Waring (1995) 

showed that channel margins tend to gain in vegetated area 

over time. This comparison corroborates this pattern (fig. 8C), 

but distinct geomorphic features need to be identified to verify 

where vegetated area is greatest.

Admittedly, this analysis needs to go into much further 

detail. A better analysis may be possible if 2002 data are combined with geomorphic information previously developed by 

the Utah State University physical science program (principally by Jack Schmidt). In this study, only a very superficial 

analysis is presented to show how the 2002 vegetation map 

could be used to assess changes in vegetation since the last 

large area change detection study was conducted by Waring 

(1995) (these analyses are beyond the scope of this report). 

The limited analysis in this study indicates that riparian 

vegetation continues to expand within the corridor, and that 

systemwide vegetation inventories are critical in monitoring 

change in this environment. Long-term approaches for monitoring riparian vegetation should include large-scale as well as 

local-scale assessment of change. Further analysis is needed to 

determine the extent of change for other portions of the river 

corridor, and the types of species that have changed the most.

Map Utility for Other Resources

While the vegetation base map for the CRE provides 

an inventory of the area covered by vegetation, the map also 

has utility for integrated questions about resource change and 

functional linkages. Linking this map to other GIS coverages 

or location data provides information to stakeholders about 

how changes in riparian vegetation affect other resources 

like wildlife, campsite area, and food web linkages between 

terrestrial and aquatic systems. The map’s initial use has been 

to provide annual aboveground biomass estimates within the 

fluctuating zone for riparian vegetation. This information can 

improve understanding of the contribution that local marsh 

and riparian vegetation make within the aquatic food webs 

(Kennedy and Ralston, unpub. data). Datasets associated with 

wildlife can also be used to begin to look at riparian vegetation 

and wildlife linkages. Mortenson and others (in press) made 

linkages between tamarisk and coyote willow occupancy and 

beaver locations, bringing up questions about beavers structuring riparian areas through species selection, as cited in studies 

outside of the CRE (Johnston and Naiman, 1990; Rosell and 

others, 2005). Lastly, vegetated areas can be superimposed onto 

campsite area maps to determine where vegetation is changing 

most rapidly and which riparian species are affecting camping 

areas the most. Kaplinski and others (2005) and Kearsley and 

others (1994) have pointed out that vegetation encroachment 

is a primary cause of campsite area decrease. Identifying these 

two variables can help managers target particularly vulnerable 

campsites and those plant species that may need to be managed through removal or trimming. Subsequent mapping could 

help to evaluate the success or failure of management actions, 

including those associated with vegetation removal.

Conclusions

The vegetation database is a critical element in assessing 

the status and trends of riparian vegetation for the CRE. The 

scale used to quantify vegetation adequately meets the needs 

of the stakeholder group. Increasing the scale to meet the 

USGS-NPS National Mapping Program’s minimum mapping 

unit of 0.5 ha is unwarranted because this scale would reduce 

resolution of some classes (e.g., seep willow/coyote willow 

would likely be combined with tamarisk). While this would 

undoubtedly improve classification accuracies, it would not 

provide the community-level information about vegetation 

change that would benefit stakeholders.

Classification accuracy, which was low for some vegetation classes in this study, may be improved by increasing the 

spectral range of the imagery. The 2002 imagery was limited 

because color-infrared (green, red, and NIR) bands are limited 

in their ability to distinguish riparian plant species. Ensuring that image data are coregistered and that band data can 

be collected and accurately calibrated to capture the entire 

dynamic reflectance range of all plant species should improve 

classification. Along a similar vein, collecting multitemporal 

imagery may provide a means to distinguish species that have 

similar signatures (e.g., mesquite and salt cedar) but different 

phenology with respect to leaf drop (Driese and others, 2004; 

Schriever and Congalton, 1995). Flying two separate data 

collection missions, however, would likely be financially prohibitive because it would require holding releases from Glen 

Canyon Dam steady for the period of data acquisition (which 

can be several days). The identification of vegetation classes 

should follow NPS mapping approaches to complement the 

national effort and should incorporate the alternative analysis 

for community identification noted by Vanderhorst and others 
22 Vegetation Database for the Colorado River Ecosystem

(2007). Ensuring quality control with the contractor who is 

acquiring the data is also critical for the success of subsequent 

image processing and classification.

The sparse nature of semiarid to arid environments and 

the heterogeneous nature of riparian habitats pose additional 

mapping challenges. Vegetation classes like shrubs and bunch 

grasses that may be ecologically distinct could not be separately identified with confidence using only species assemblage data. The small leaf areas and sparse occurrences of 

these communities make them difficult to distinguish digitally. 

On the other hand, the dense and somewhat heterogeneous 

nature of riparian vegetation made it equally difficult to digitally distinguish the boundaries between Tamarix and other 

riparian overstory classes like Baccharis emory-Salix exigua. 

Other researchers (Ohmann and Gregory, 2002; Schmidtlein 

and Sassin, 2004) have used remotely sensed data in combination with environmental parameters to improve mapping 

accuracies. Within the CRE, additional parameters that could 

be explored to improve accuracies of heterogeneous species 

classification are stage elevation data (e.g., marsh vegetation 

would generally be below 708 m3/s) to restrict classification choices in an automated process; inclusion of ecological 

parameters; the use of SMA to estimate the fractional cover of 

species within each pixel (Roberts and others, 1998) and better 

quantify the cover of individual species composing a cover 

class; slope aspect with respect to solar illumination to further 

define plant distributions in combination with remotely sensed 

data (Walton and others, 2005); and object-based classification 

software used in combination with these suggestions (Lindsey 

and Donnelly, 2006). Photo-interpretation may not be feasible 

for use due to time and cost constraints. Geographic distribution may be another parameter to consider, but if plant ranges 

are contracting or expanding, limiting geographic distribution 

may exclude the identification of this phenomenon.

This map database provides a base layer associated with 

vegetative cover that can be used in combination with other 

existing GIS coverages to make linkages among terrestrial and 

aquatic resources. The linkage between campsites and vegetation encroachment can be monitored over time using this and 

subsequent vegetation area maps. Habitat and terrestrial vertebrate and invertebrate linkages can also begin to be made with 

this map. The vegetation base map also provides shoreline 

vegetation information that may be used in fisheries projects 

associated with shoreline habitat. The additional benefits of 

this product will become more evident as other resources use 

the information and as subsequent vegetation databases are 

produced for monitoring long-term change.

Acknowledgments

Funding for this work was provided through the Glen 

Canyon Dam Adaptive Management Program. Cooperative agreements between USGS/GCMRC and Northern 

Arizona University (99HQAG0175 cooperative project 

award 99175HS021) and between USGS/GCMRC and 

Pinnacle Mapping Technologies, Inc (Agreement Number 

03WRAG0004) ensured the completion of data collection and 

image processing. We thank Carol Fritzinger, GCMRC, for her 

logistic coordination, and river guides Dave Baker, Jeff Behan, 

Steve Jones, Dirk Pratley, Lynn Roeder, J.P. Running, and 

Brian Smith (provided through Humphrey Summit Support) 

for their assistance and patience as they navigated our way 

to the sampling sites and through the canyon without incident. Matthew Andersen, David Salas, and two anonymous 

reviewers provided constructive comments that improved 

this document. Patty McCredie provided valuable editorial 

assistance that improved the appearance and readability of this 

document.

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Appendix A. Image Processing 27

Appendix A. Image Processing

Image Band Calibration

Ground reflectance data obtained in the year 2000 were 

used to determine the gain (digital number [DN]/reflectance) 

and offset or bias (DN) for each color band. Reflectance is the 

ratio of the reflected solar energy to the incident solar energy; 

a reflectance value of 1.0 indicates a perfect reflector. The vegetation alliances used in these calibrations are listed in table 

A1; sand, alluvium, litter, concrete, asphalt, and tin were also 

used, where they existed within the ISTAR data for the Colorado River ecosystem (CRE). The recorded image values and 

ground reflectance values used in this study for calibrations 

are listed in tables A2–A5 for the four regions where ground 

information was collected. The resulting calibrations for sets 

of flight zones are summarized in tables A6A–F. Examination 

of these DN-reflectance relations shows that the near-infrared 

(NIR) band data are saturated at high DN at about 40%–45% 

reflectance (table A6E). Thus, certain vegetation alliances that 

have high reflectance values in the NIR probably have similar 

DN, which makes their discrimination more difficult. The 

other three color bands and the panchromatic band display an 

unsaturated linear relation with ground reflectance.

The radiometric calibrations derived for each color band 

within four sets of flight zones showed a fairly narrow range 

in gain (table A7), with the gains for the blue, green, and 

panchromatic bands varying by 4.0% or less, and those for 

the red and NIR bands varying 8.1%–9.8%. The gain value 

for the panchromatic band is much larger than those for the 

color bands because the panchromatic band data are 12-bit 

values (0–4,095), whereas the color bands were recorded as 

8-bit (0–255) values. These average gain values were used to 

determine each band’s offsets within each set of flight zones 

by least squares analysis. The offsets represent the DN value 

at zero reflectance, which indicates the amount of atmospheric 

scattering at a particular wavelength and flight zone. The 

derived offset values within the four sets of flight zones have a 

narrow range (table A7) and were considered to be essentially 

constant throughout the corridor.

Image Data Masks

Several data masks were produced and used in the 

analysis of the image data to map vegetation. Two primary 

masks were generated to restrict the study area within the 

CRE to the region between the water’s edge and the landward 

limit of 80,000-cfs (2,265-m3/s) river flow level. The water’s 

edge was manually digitized using the ISTAR image data and 

used to exclude water. Normally, a water mask can easily be 

produced using the distinctive low, flat reflectance spectra 

Table A1. Preliminary vegetation classes and number of ground spectroradiometric readings taken in the field.

Vegetation class Species name or description of class Number of samples

ATCA Atriplex canescens 22

BAXX Baccharis spp. 36

CERE Celtis reticulata 32

EQFE Equisetum ferrisii 16

IMBR Imperata brevifolia 29

LATR Larrea tridentata 19

PHAU Phragmites australis 28

PLSE Pluchea sericea 17

PRGL Prosopis glandulosa 28

QUTU Quercus turbinella 34

SAEX Salix exigua 27

SAGO Salix goodingii 39

TARA Tamarix ramossisima 28

TYDO Typha domingensis 36

WTLD Wetland grasses and sedges 29

BDRX/SAND Bedrock/sand 10

Dead grass Litter 4

Dead brush Litter 17
28 Vegetation Database for the Colorado River Ecosystem

Table A2. ISTAR color-band data and ground spectroradiometric data for various materials that occur within the flight line zones 1 and 

2. Ground reflectance data were convolved to the band wavelengths of the ISTAR CCD sensors.

Zones 1b and 2 B1 B2 B3 B4 PAN

Image (digital number)

LITTER_2 79.0 67.7 70.3 73.6 897.3

ASPHALT 88.8 83.6 85.0 78.2 1091.1

LITTER_2 114.1 109.6 106.2 104.9 1363.0

8_6 TERR ALLUV 135.9 150.9 161.0 167.3 2009.0

ATCA 106.5 111.4 100.9 155.9 1449.8

TARA 71.5 90.2 70.5 168.4 1177.6

CERE 67.8 84.1 73.1 223.4 1265.2

CONCRETE 213.8 222.0 208.2 163.1 2675.0

20_1 TERR ALLUV_2 191.1 198.0 206.9 200.5 2636.6

SAEX 73.4 86.4 72.4 238.0 1319.9

SAND_2 222.0 232.1 226.7 223.8 2976.9

8_5 BEACH SAND 221.1 236.1 231.9 233.1 2895.1

14_3 BEACH SAND 221.7 239.1 229.7 224.8 2887.6

PHAR 68.4 80.9 62.5 241.5 1222.7

PHAR 64.2 83.0 74.5 251.6 1437.0

SEDGES 65.9 82.3 74.6 235.7 1416.1

CERE_2 73.0 96.0 78.0 241.0 1493.0

Ground (reflectance)

LITTER_2 0.0425 0.0471 0.0548 0.0671 0.0538

ASPHALT 0.0588 0.0723 0.0901 0.1040 0.0844

LITTER_2 0.0776 0.1000 0.1390 0.1941 0.1321

8_6 TERR ALLUV 0.1198 0.1662 0.2376 0.2781 0.2206

ATCA 0.0704 0.1090 0.1148 0.3002 0.1451

TARA 0.0541 0.0954 0.0856 0.3191 0.1298

CERE 0.0544 0.1044 0.4840 0.3380 0.1210

CONCRETE 0.2157 0.2525 0.3165 0.3514 0.2966

20_1 TERR ALLUV_2 0.1682 0.2276 0.3207 0.3683 0.2984

SAEX 0.0717 0.1063 0.0796 0.3864 0.1395

SAND_2 0.1828 0.2574 0.3750 0.4551 0.3497

8_5 BEACH SAND 0.1828 0.2574 0.3750 0.4551 0.3497

14_3 BEACH SAND 0.1828 0.2574 0.3750 0.4551 0.3497

PHAR 0.0459 0.0987 0.0582 0.5215 0.1432

PHAR 0.0459 0.0987 0.0582 0.5215 0.1432

SEDGES 0.0272 0.0899 0.0379 0.5770 0.1432

CERE_2 0.0648 0.1208 0.0692 0.5874 0.1722
 29

Table A3. ISTAR color-band data and ground spectroradiometric data for various materials that occur within the flight line zones 4 and 

5. Ground reflectance data were convolved to the band wavelengths of the ISTAR CCD sensors.

Zones 4 and 5 B1 B2 B3 B4 PAN

Image (digital number)

LITTER_4 96.0 92.0 95.0 100.0 1172.0

LITTER_5a 90.0 87.0 86.0 93.0 1247.0

LITTER_5b 88.3 82.4 90.6 93.6 1279.1

PLSE_DARK_5a 95.3 87.6 90.3 99.4 1343.3

PLSE_RED_5a 96.7 95.7 94.0 132.1 1476.2

ATCA?_5a 112.1 108.1 115.6 162.1 1715.7

ATCA2?_5a 95.6 102.7 113.0 176.9 1737.1

PRGL_5b 58.3 66.3 68.9 178.5 1177.4

PRGL_DARK_5a 72.8 81.6 71.6 211.5 1359.4

NANK DARK BROUND CHANNEL_5a 194.5 204.6 235.0 231.6 2785.6

NANK RED SAHLYDIRT_5a 202.7 211.2 234.6 232.4 2664.3

SADDLE RED WADI_5a 134.9 158.4 229.2 242.6 2630.4

PLSE_4 102.7 108.5 95.3 219.6 1445.9

SAEX_4 79.5 91.3 77.6 244.5 1338.9

SAEX_5a_DARK 120.4 132.6 114.8 187.3 1804.9

NANK SAND_5a 237.0 237.5 239.7 234.5 2944.5

SAND_4 237.9 244.1 245.2 241.6 2997.0

SAND_5a 232.8 237.1 243.8 240.8 3045.1

SAND_5b 232.0 235.4 240.5 236.0 2986.4

TARA_4 80.3 89.7 76.8 237.8 1230.1

TARA_5a 66.0 81.3 62.8 226.6 1356.0

PRGL RED_5a 61.3 67.7 49.5 209.9 1163.4

SAEX_5a-RED 79.8 92.0 80.5 222.3 1544.2

SAEX-HIGHER_5b 63.1 88.0 77.0 253.0 1533.0

BASL_5b 60.7 73.8 72.3 223.7 1327.0

PHAR_4 79.4 92.1 79.5 242.8 1317.2

PHAR_5a 60.8 78.9 63.7 244.7 1405.5

PHAR_5b 53.9 70.5 68.7 250.2 1341.1

TYDO_5a 60.4 77.2 59.6 250.2 1371.0

TYDO_5b 63.6 79.5 72.1 252.3 1428.1

WATERCRESS 88.0 112.0 80.0 254.0 1659.0

Appendix A. Image Processing 
30 Vegetation Database for the Colorado River Ecosystem

Zones 4 and 5 B1 B2 B3 B4 PAN

Ground (reflectance)

LITTER_4 0.0645 0.0804 0.1063 0.1393 0.1017

LITTER_5a 0.0645 0.0804 0.1063 0.1393 0.1017

LITTER_5b 0.0645 0.0804 0.1063 0.1393 0.1017

PLSE_DARK_5a 0.0645 0.0804 0.1063 0.1393 0.1017

PLSE_RED_5a 0.0669 0.0918 0.1097 0.2037 0.1250

ATCA?_5a 0.0704 0.1090 0.1148 0.3002 0.1600

ATCA2?_5a 0.0704 0.1090 0.1148 0.3002 0.1600

PRGL_5b 0.0487 0.0830 0.0583 0.3325 0.1116

PRGL_DARK_5a 0.0497 0.0977 0.0602 0.3484 0.1263

NANK DARK BROUND CHANNEL_5a 0.1682 0.2276 0.3207 0.3683 0.2984

NANK RED SAHLYDIRT_5a 0.1682 0.2276 0.3207 0.3683 0.2984

SADDLE RED WADI_5a 0.1098 0.1593 0.3135 0.3841 0.2758

PLSE_4 0.0714 0.1060 0.0795 0.3854 0.1392

SAEX_4 0.0717 0.1063 0.0796 0.3864 0.1395

SAEX_5a_DARK 0.0841 0.1376 0.0889 0.3980 0.1632

NANK SAND_5a 0.1972 0.2675 0.3683 0.4173 0.3434

SAND_4 0.1972 0.2675 0.3683 0.4173 0.3434

SAND_5a 0.1972 0.2675 0.3683 0.4173 0.3434

SAND_5b 0.0438 0.2675 0.3683 0.4173 0.3434

TARA_4 0.0438 0.0911 0.0581 0.4235 0.1304

TARA_5a 0.0427 0.0911 0.0581 0.4235 0.1304

PRGL RED_5a 0.0476 0.0797 0.0453 0.4238 0.1185

SAEX_5a-RED 0.0476 0.1154 0.0852 0.4271 0.1588

SAEX-HIGHER_5b 0.0335 0.1154 0.0852 0.4271 0.1588

BASL_5b 0.0459 0.0768 0.0470 0.4377 0.1205

PHAR_4 0.0459 0.0987 0.0582 0.5215 0.1432

PHAR_5a 0.0459 0.0987 0.0582 0.5215 0.1432

PHAR_5b 0.0383 0.0987 0.0582 0.5215 0.1432

TYDO_5a 0.0383 0.0870 0.0436 0.5423 0.1370

TYDO_5b 0.0000 0.0870 0.0436 0.5423 0.1370

WATERCRESS 0.0527 0.1196 0.0710 0.5664 0.1767

Table A3. ISTAR color-band data and ground spectroradiometric data for various materials that occur within the flight line zones 4 and 

5. Ground reflectance data were convolved to the band wavelengths of the ISTAR CCD sensors.—Continued.
 31

Zones Jd, K, La B1 B2 B3 B4 PAN

Image (digital number)

LITTER_K 83.1 74.9 76.4 80.6 978.4

LATR_K 73.6 68.6 76.1 92.8 984.9

LATR_La 94.3 85.8 88.4 104.8 1066.6

LITTER 104.8 100.0 105.0 105.8 1139.1

LATR 104.3 102.0 105.5 126.9 1257.1

PRGL 66.0 71.8 62.9 193.1 934.5

CERE 70.9 78.8 64.6 213.1 1007.6

SAEX 77.6 93.8 72.7 236.7 1209.5

SAND 239.3 241.8 243.1 241.2 2849.9

SAND_K 241.9 244.1 246.7 246.2 2612.4

SAND_La 243.8 247.1 248.8 247.0 2869.5

SAEX_K 156.6 169.4 162.2 236.3 1775.7

PLSE_La 77.4 108.3 80.7 253.7 1407.2

BASL 45.9 90.5 65.7 232.9 1113.0

BASL_La 81.0 85.7 64.2 231.8 1055.1

PRGL_K 59.7 67.6 57.2 216.4 1003.7

TARA 70.7 85.9 61.8 237.8 1067.2

TARA_K 55.6 74.4 56.9 247.8 1108.2

BASL_K 53.3 68.3 58.5 224.4 1045.5

TYDO 73.5 78.0 68.1 198.7 1019.3

SAEX_La 105.2 131.8 111.9 237.6 1485.5

SAGO_K 56.7 74.5 60.0 227.0 1083.6

PHAR 65.6 82.7 62.0 244.5 1113.0

Ground (reflectance)

LITTER_K 0.0645 0.0804 0.1063 0.1393 0.1017

LATR_K 0.0626 0.0806 0.1105 0.1482 0.1048

LATR_La 0.0655 0.0838 0.1139 0.1517 0.1082

LITTER 0.0764 0.0988 0.1379 0.1931 0.1309

LATR 0.0740 0.1067 0.1050 0.2140 0.1300

PRGL 0.0487 0.0830 0.0583 0.3225 0.1116

CERE 0.0544 0.1044 0.0484 0.3380 0.1210

SAEX 0.0717 0.1063 0.0796 0.3864 0.1395

SAND 0.2041 0.2645 0.3542 0.3955 0.3320

SAND_K 0.2041 0.2645 0.3542 0.3955 0.3320

SAND_La 0.2041 0.2645 0.3542 0.3955 0.3320

SAEX_K 0.1325 0.1881 0.1961 0.3980 0.2314

PLSE_La 0.0643 0.1242 0.0929 0.4039 0.1627

BASL 0.0508 0.0957 0.0708 0.4066 0.1350

BASL_La 0.0508 0.0957 0.0708 0.0407 0.1350

Table A4. ISTAR color-band data and ground spectroradiometric data for various materials that occur within the flight line zones J, K, 

and L. Zones are further divided into segments defined by lower case letters (eg., Ja, La). Ground reflectance data were convolved to 

the band wavelengths of the ISTAR CCD sensors.

Appendix A. Image Processing 
32 Vegetation Database for the Colorado River Ecosystem

Zones Jd, K, La B1 B2 B3 B4 PAN

Ground (reflectance)—Continued

PRGL_K 0.0427 0.0797 0.0453 0.4238 0.1185

TARA 0.0462 0.0932 0.0603 0.4243 0.1322

TARA_K 0.0462 0.0932 0.0603 0.4243 0.1322

BASL_K 0.0335 0.0768 0.0470 0.4377 0.1205

TYDO 0.0387 0.0777 0.0386 0.4486 0.1158

SAEX_La 0.0966 0.1407 0.1295 0.4759 0.1844

SAGO_K 0.0374 0.0835 0.0467 0.4952 0.1309

PHAR 0.0459 0.0987 0.0582 0.5215 0.1432

Table A4. ISTAR color-band data and ground spectroradiometric data for various materials that occur within the flight line zones J, K, 

and L. Zones are further divided into segments defined by lower case letters (eg., Ja, La). Ground reflectance data were convolved to 

the band wavelengths of the ISTAR CCD sensors.—Continued.

Zones 8, Ba, Cb, and Da B1 B2 B3 B4 PAN

Image (digital number)

tar roof 85.5 76.5 71.3 76.1 730.8

LITTER_8a 80.2 72.3 73.0 89.9 1234.3

Tin_roof1 127.4 118.8 90.7 92.7 965.9

Tin_roof2 130.4 122.7 116.4 131.4 1134.0

med brown soil 117.2 113.6 158.4 166.2 1900.8

PLSE_8a 99.6 107.9 88.1 162.5 1521.5

light brown soil_8a 152.1 156.7 191.4 199.3 2212.3

light brown soil_8b 139.9 148.5 193.8 205.0 2214.0

TARA_Ba 67.6 98.8 72.0 213.5 766.8

PRGL_8a 50.0 60.6 51.5 173.0 1155.1

PRGL_8b 49.4 57.6 50.4 187.5 1141.3

PRGL_Cb 67.8 75.5 59.8 167.4 846.0

SAEX_Cb 85.7 109.9 76.4 229.5 1241.9

SAND_8a 239.5 240.8 240.8 238.6 3090.1

SAND_Cb 237.9 241.9 244.7 244.1 2579.9

SAND_Da 243.5 246.0 246.8 245.8 2782.0

TARA_8a 55.7 67.2 51.5 223.1 1233.7

TARA_Cb 67.1 90.8 62.8 228.9 1038.1

BASL_8a 54.6 64.8 51.1 202.2 1205.2

BASL_8b 43.8 56.9 42.2 238.8 1179.8

BASL_Cb 56.4 74.1 55.0 227.5 897.7

TYDO_8a 45.5 63.0 50.8 235.5 1303.6

TYDO_8b 46.3 55.1 44.0 242.1 1181.8

PHAR_8a 53.4 69.7 50.0 245.4 1326.0

Table A5. ISTAR color-band data and ground spectroradiometric data for various materials that occur within the flight line zones 8, B, 

C, and D. Ground reflectance data were convolved to the band wavelengths of the ISTAR CCD sensors.
 33

Table A5. ISTAR color-band data and ground spectroradiometric data for various materials that occur within the flight line zones 8, B, 

C, and D. Ground reflectance data were convolved to the band wavelengths of the ISTAR CCD sensors.—Continued.

Zones 8, Ba, Cb, and Da B1 B2 B3 B4 PAN

Ground (reflectance)

tar roof 0.0611 0.0729 0.0853 0.0981 0.0829

LITTER_8a 0.0645 0.0804 0.1063 0.1393 0.1017

Tin_roof1 0.1046 0.1177 0.1317 0.1413 0.1280

Tin_roof2 0.1220 0.1373 0.1537 0.1649 0.1493

med brown soil 0.1065 0.1446 0.2131 0.2466 0.1966

PLSE_8a 0.0800 0.1200 0.0500 0.2500 0.1700

light brown soil_8a 0.1331 0.1808 0.2664 0.3082 0.2458

light brown soil_8b 0.1331 0.1808 0.2664 0.3082 0.2458

TARA_Ba 0.0296 0.0685 0.0429 0.3221 0.0992

PRGL_8a 0.0487 0.0830 0.0583 0.3225 0.1116

PRGL_8b 0.0487 0.0830 0.0583 0.3225 0.1116

PRGL_Cb 0.0487 0.0830 0.0583 0.3225 0.1116

SAEX_Cb 0.0717 0.1063 0.0796 0.3864 0.1395

SAND_8a 0.2041 0.2645 0.3542 0.3955 0.3320

SAND_Cb 0.2041 0.2645 0.3542 0.3955 0.3320

SAND_Da 0.2041 0.2645 0.3542 0.3955 0.3320

TARA_8a 0.4620 0.0932 0.0603 0.4243 0.1322

TARA_Cb 0.0462 0.0932 0.0603 0.4243 0.1322

BASL_8a 0.0335 0.0768 0.0470 0.4377 0.1205

BASL_8b 0.0335 0.0768 0.0470 0.4377 0.1205

BASL_Cb 0.0335 0.0768 0.0470 0.4377 0.1205

TYDO_8a 0.0387 0.0770 0.0386 0.4486 0.1158

TYDO_8b 0.0387 0.0770 0.0386 0.4486 0.1158

PHAR_8a 0.0459 0.0987 0.0582 0.5215 0.1432

Appendix A. Image Processing 
34 Vegetation Database for the Colorado River Ecosystem

Zone N Multiple R R square R square adj. S.E. F Significance F

1b and 2 w/o CEMENT 17 0.9767 0.9540 0.9509 14.22 311.20 1.9 x 10–11

4 and 5 31 0.9907 0.9815 0.9808 8.29 1539.6 1.09 x 10–26

Jd, K, and La 23 0.9908 0.9818 0.9810 8.24 1137.6 8.93 x 10–20

8, Cb and Da 24 0.9881 0.9764 0.9753 9.90 910.45 2.14 x 10–19

Table A6. Calibration results for the ISTAR. A, Blue. B, Green. C, Red. D, Near-infrared-band data with reflectance values ≤0.455. E, 

Near-infrared-band data with reflectance values ≤0.400. F, Panchromatic image data for the four regions having ground reflectance 

data.

Zone N Multiple R R square R square adj. S.E. F Significance F

1b and 2 w/o CEMENT 17 0.9855 0.9713 0.9693 11.35 507.75 5.56 x 10–13

4 and 5 31 0.9865 0.9732 0.9723 9.68 1053.88 2.37 x 10–24

Jd, K, and La 23 0.9943 0.9887 0.9881 6.30 1840.19 6.14 x 10–22

8, Cb and Da 24 0.9748 0.9503 0.9480 13.52 420.83 7.83 x 10–16

Zone N Multiple R R square R square adj. S.E. F Significance F

1b and 2 w/o CEMENT 17 0.9949 0.9899 0.9892 6.99 1473.53 2.15 x 10–16

4 and 5 31 0.9898 0.9797 0.9790 9.90 1400.55 4.22 x 10–26

Jd, K, and La 23 0.9900 0.9801 0.9791 9.05 1034.96 2.36 x 10–19

8, Cb and Da 24 0.9880 0.9763 0.9752 10.99 906.43 2.24 x 10–19

Zone N Multiple R R square R square adj. S.E. F Significance F

1b and 2 w/o CEMENT 17 0.9463 0.8955 0.8860 19.57 94.30 9.90 x 10–07

4 and 5 31 0.9545 0.9116 0.9073 16.23 235.90 1.39 x 10–13

Jd, K, and La 23 0.9807 0.9618 0.9594 12.61 403.35 8.97 x 10–13

8, Cb and Da 24 0.9439 0.8910 0.8858 17.65 171.69 1.41 x 10–11

Zone N Multiple R R square R square adj. S.E. F Significance F

1b and 2 w/o CEMENT 17 0.9333 0.8711 0.8550 21.61 54.09 7.95 x 10–05

4 and 5 31 0.9559 0.9137 0.9071 17.47 137.78 2.72 x 10–08

Jd, K, and La 23 0.9918 0.9837 0.9825 8.74 788.24 5.08 x 10–13

8, Cb and Da 24 0.9676 0.9363 0.9318 14.43 206.11 9.07 x 10–10

Zone N Multiple R R square R square adj. S.E. F Significance F

1b and 2 w/o CEMENT 17 0.9916 0.9832 0.9821 97.38 882.19 9.63 x 10–15

4 and 5 31 0.9881 0.9764 0.9755 100.53 1200.50 3.76 x 10–25

Jd, K, and La 23 0.9898 0.9797 0.9788 86.67 1017.31 2.82 x 10–19

8, Cb and Da 24 0.9696 0.9401 0.9374 163.50 345.78 6.06 x 10–15

A

B

C

D

E

F
 35

Table A7. Calculated gains and offsets for ISTAR image data determined from ground reflectance data for various vegetation and 

inorganic surface materials acquired during 2000. Gain is in units of DN/ground reflectance; offset is in DN units.

Flight zone set Blue 

(gain)

Blue 

(offset)

Green 

(gain)

Green 

(offset)

Red 

(gain)

Red 

(offset)

NIR 

(gain)

NIR 

(offset)

Pan 

(gain)

Pan 

(offset)

1b, 2 1002.3 17 860.7 3 506.5 28 435.1 14 7384.4 270

4, 5a, 5b 1081.6 22 879.4 3 576.3 37 485.8 10 7711.3 353

Jd, K, La 1098.2 17 931.4 1 619.4 31 569.5 3 8021.2 101

8a, 8b, Ba, Cb, Da 1096.9 12 911.6 –1 623.3 28 449.9 –2 8218.6 184

Average 1069.8 17 895.8 2 581.4 31 534.1 5 7833.9 227

39.5 4 27.4 2 47.1 4 52.3 9 316.3 109

of water, but the different look directions of the four-color 

charge-coupled devices produced noncoincident sun glint in 

the four color images, causing the water’s surface to appear 

multicolored (peacock colored), which made it impossible to 

determine a consistent, unique signature for water in order to 

easily remove it.

The next mask needed for image analysis was the vegetation mask. This mask was used to restrict the calculation of 

spatial texture to pixels that are most likely vegetation. This 

mask was also used to determine the areal density of vegetation at any given location and to restrict the area considered by 

the final vegetation classifier to make the classification processing faster and more accurate. Several vegetation indices 

were examined that were shown to have superior accuracy for 

distinguishing vegetation by Elvidge and Chen (1995), as well 

as the Spectral Angle Mapper developed by Yuhas and Goetz 

(1993), and Kruse and others (1993).

Visual examination of bright and dark vegetation units 

within the training areas showed that the normalized difference vegetation index (NDVI) best separated vegetation from 

bare ground without omitting dark litter and dark vegetation. 

However, only the brightest alluvium was eliminated in the 

attempt to include the dark patches of vegetation, which produced anomalously high texture values along the margins of 

vegetated and nonvegetated areas. It was difficult to exclude 

alluvium and include dark vegetation using just the NDVI 

ratio image. Investigations of methods to exclude the alluvium 

while not excluding the sparse, dark vegetation (whose flat 

reflectance pattern is similar to that of alluvium) indicated that 

the blue band would be effective because alluvium is higher 

in the blue band than is the dark vegetation. It was also found 

that an NDVI produced from the NIR and red bands without 

haze subtraction better isolated the bright and dark vegetation 

from the alluvium.

Ambiguities for vegetation growing within the alluvium 

were produced when haze was subtracted from the red and 

NIR bands without converting the resulting DN to reflectance. 

Thus, the NDVI ratios were recomputed without subtracting 

haze, and a blue band parameter was added for the dark NDVI 

index to separate dark vegetation from alluvium. Both bright 

and dark vegetation masks were produced because vegetation 

density was based solely on the density of bright vegetation. 

These two vegetation masks were used to separate bright and 

dark vegetated ground within the ISTAR color image data, 

which was relevant to digital analyses of vegetation characteristics in this study and subsequent classification based on the 

characteristics. The bright vegetation NDVI ratio is ≥0.153; 

the dark-vegetation NDVI ratio range is 0.00 to 0.152, if the 

blue band DN is ≤133.

Vegetation Texture Data

Vegetation at the Genus or Family level have different 

branch and leaf structure which, viewed from above, appears 

as textural differences. Manual photographic interpretation 

uses these textural differences intuitively to map vegetation, 

but digital classifications of imagery require the image texture 

to be quantified. There are two categories of digital filters 

used to detect and quantify texture within image data: occurrence and co-occurrence. Occurrence filters were developed 

first and represent a pixel’s texture by some statistical measure of image data within a specified area centered on that 

pixel. Co-occurrence filters were later developed by Haralick 

and others (1973); this category of texture filter looks at the 

correspondence between a designated image area (boxcar) 

and an adjacent image area shifted an x and y amount and, as 

such, represents the spatial dependence of texture (Ferro and 

Warner, 2002). This type of filter amplifies moderate levels of 

texture, but has little to no noticeable effect for very smooth 

or very rough surfaces. There are several types of occurrence 

and co-occurrence filters; many were tried and it was found 

that the occurrence variance filter and the co-occurrence 

contrast and variance filters better separate the vegetation 

alliances. The contrast (or inertia) filter is the squared difference between the unshifted and shifted pixel values. Filter 

algorithms within commercial software do not compensate 

for nondata and, as a result, produce anomalously high texture 

values along borders between data and nondata. Therefore, 

new texture algorithms were produced that exclude nondata 

in their calculation of texture. A textural filter with a circular 

shape was also produced and tested. This filter was intended 

Appendix A. Image Processing 
36 Vegetation Database for the Colorado River Ecosystem

to approximate the appearance of vegetation by excluding 

internal pixels whose distance from the normal boxcar’s center 

was greater than the boxcar radius plus 0.4.

Davis and others (2002) found that a filter dimension of 

10 m worked best for the CRE vegetation. This dimension 

seemed too large, especially for small vegetation patches. 

Thus, a range of areal dimensions was examined to determine 

the most appropriate for alliance separation. The optimum 

areal dimension for the texture filters was determined by 

applying a range of filter dimensions to the training areas 

(locations shown in fig. 10) and determining the filter dimension that best separated the different vegetation alliances. 

Variance filters applied to the NIR and panchromatic bands 

showed that (1) the NIR band provided a larger range in 

texture values within the vegetation because NIR values vary 

more than panchromatic values in vegetation, and (2) the panchromatic band produced anomalously high texture values on 

some vegetation borders, which was attributed to the misregistration mentioned previously. Thus, the NIR band data were 

used for texture.

Examination of the results using co-occurrence and 

circular occurrence texture filters showed that the contrast and 

variance co-occurrence filters better separated (≥5% accuracy) the alliances than did other co-occurrence filters and the 

occurrence variance filter. The circular filters did not significantly improve the results. It was also found that information 

within alliance delineations was different for two sizes of the 

co-occurrence filters, a 9-by-9 boxcar (4-m dimension) and 

a 23-by-23 boxcar (10-m dimension). The larger dimension 

for texture better smoothed the variations within large stands, 

but degraded the obvious texture of small stands by including 

adjacent alliance variations in the small alliances. It is common in classification efforts for forests to use semivariograms 

of texture showing change in texture with increasing filter 

dimension. This approach was also investigated using the 

texture results from this study, but the changes observed were 

equally represented only by the two co-occurrence dimensions 

listed above, and only these two dimensions of the variance 

and contrast filters were used in this classification in order to 

reduce the processing time.

Vegetation Areal Density

Certain vegetation alliances may be separated by the 

areal density of vegetation in the areas where they commonly 

occur. In order to test and use this parameter, an areal vegetation density database was constructed using an adaptive filter 

developed by Schwartz and Soha (1977) that uses only the 

image data around the DN of a particular pixel. This filter 

minimizes border smearing between densely and sparsely 

vegetated areas. Two filters of different sizes were employed. 

The smaller filter examines the very local areal density around 

a pixel; the larger filter examines the data produced by the 

smaller filter to determine pixels with similar densities and 

uses only these pixels within its areal dimension to calculate 

vegetation density. The bright vegetation mask produced 

previously was used by these filters to determine the presence 

of vegetation. It was found that a small 5-by-5 filter (2.2-m 

dimension) and a large 17-by-17 filter (7.5-m dimension) provided good separation between some vegetation alliances with 

respect to their areal vegetation density.

Preclassification Results

In preparation for the corridorwide data classification, the 

accuracies obtained on groundtruth regions using various combinations of the above databases were examined. It appeared 

that (1) four-band data produced higher mapping accuracies than their ratios, (2) inclusion of texture with band data 

increased mapping accuracies by 10%–25%, (3) the use of all 

four-texture measures produced higher mapping accuracies 

than any subset of the four-texture measures, (4) vegetation 

density did not increase mapping accuracy over that provided 

by the four ISTAR bands and four-texture measures, and (5) 

mapping accuracies for the groundtruth regions using the 

band and texture data ranged from 65%–95% within various 

groundtruth areas. The lower accuracies were primarily due 

to misclassification of Tamarix (TARA) and Prosopis-Acacia

(PRGL), even though bright and dark training areas were separated for these two classes to decrease classifier confusion. 

This step showed that acceptable overall producer accuracies 

could be obtained from any particular training site, but accuracy might degrade when all training areas are considered in a 

single corridorwide classification.

Image Data Classification

Maximum likelihood classification was then performed 

on the same combinations of data used above, but operated on all groundtruth areas using the image mosaic of all 

groundtruth regions to determine which data provided the 

highest mapping accuracy for corridorwide mapping. This 

total groundtruth classification analysis produced the same 

conclusions using the different database combinations as those 

obtained from individual training sites. Thus, only the four 

ISTAR bands and the four-texture measures were used in the 

final vegetation classification processing. The overall producer 

mapping accuracy resulting from the integrated classification 

was only 45%–50%, but many of the vegetation alliances had 

accuracies of 75%–90%.

Results from confusion matrix analysis were used to 

determine the sources of the inaccuracies (misclassification) 

for specific alliances. The training data for those alliances 

were revised using the n-D visualization tool provided by 

ENVI (ITT Visual Information Solutions©, 2006), which 

allows interactive editing of alliances clusters in n-space. This 

editing was performed in a stepwise manner, changing the 

groundtruth area for just a single alliance before rerunning 

the classifier and examining its results. This stepwise process minimized changes in groundtruth and provided a better 
 37

understanding of the results produced by each change. The 

overall producer accuracy after groundtruth editing was 84%, 

but this accuracy is only related to the groundtruth regions. 

Mapping accuracy needs to be established using randomized 

field verification of the results. Thus, a set was developed of 

stratified random sample locations for each vegetation unit 

within each subzone of the CRE, and the classification results 

at these points were field checked.

References

Davis, P.A, Staid, M.I, Plescia, J.B., and Johnson, J.R., 2002, 

Evaluation of airborne image data for mapping riparian vegetation within the Grand Canyon: U.S. Geological Survey 

Open-File Report 02-470, 65 p.

Elvidge, C.D., and Chen, Z., 1995, Comparison of broad-band 

and narrow-band red and near-infrared vegetation indices: 

Remote Sensing of Environment, v. 54, p. 38–48.

ENVI Image Analysis Software, 2006, v4.2, ITT Visual Information Solutions©: Boulder, Colo.

Ferro, C.J.S., and Warner, T.A., 2002, Scale and texture in 

digital image classification: Photogrammetric Engineering 

and Remote Sensing, v. 68, p. 51–63.

Haralick, R.M., Shanmugam, K., and Dinstein, I., 1973, 

Textural features for image classification: IEEE Trans. On 

Systems, Man and Cybernetics SMC–3, p. 610–621.

Kruse, F.A., Lefkoff, A.B., Boardman, J.G., Heidebrecht, 

K.B., Shapiro, A.T., Barloon, P.J., and Goeta, A.F.H., 1993, 

The Spectral Image Processing System (SIPS)—interactive 

visualization and analysis of imaging spectrometer data: 

Remote Sensing of Environment, v. 44, p. 145–163.

Schwartz, A.A., and Soha, J.M., 1977, Variable threshold 

zonal filtering: Applied Optics, v. 16, p. 1779–1781.

Yuhas, R.H., and Goetz, A.F.H., 1993, Comparison of airborne 

(AVIRIS) and spaceborne (TM) imagery data for discriminating among semi-arid landscape endmembers: Proceedings, Ninth Thematic Conference on Geologic Remote 

Sensing, Pasadena, Calif., Feb. 8–11, Environmental 

Research Institute of Michigan, p. 503–511.

Appendix A. Image Processing 
Ralston and others—A Vegetation Database for the Colorado River Ecosystem from Glen Canyon Dam to the Western Boundary of Grand Canyon National 

Park, Arizona—Open-File Report 2008–1216