i

Development of a High-Resolution Binational Vegetation 

Map of the Santa Cruz River Riparian Corridor and 

Surrounding Watershed, Southern Arizona and Northern 

Sonora, Mexico 

By Cynthia S.A. Wallace, Miguel L. Villarreal, and Laura M. Norman 

Open-File Report 2011–1143 

U.S. Department of the Interior 

U.S. Geological Survey 
ii

U.S. Department of the Interior 

KEN SALAZAR, Secretary 

U.S. Geological Survey 

Marcia K. McNutt, Director 

U.S. Geological Survey, Reston, Virginia: 2011 

For more information on the USGS—the Federal source for science about the Earth, 

its natural and living resources, natural hazards, and the environment—visit 

http://www.usgs.gov or call 1–888–ASK–USGS 

For an overview of USGS information products, including maps, imagery, and publications, 

visit http://www.usgs.gov/pubprod

Suggested citation: 

Wallace, C.S.A., Villarreal, M.L., and Norman, L.M., 2011, Development of a high-resolution binational vegetation map 

of the Santa Cruz River riparian corridor and surrounding watershed, southern Arizona and northern Sonora, Mexico: 

U.S. Geological Survey Open-File Report 2011-1143, 22 p. [http://pubs.usgs.gov/of/2011/1143/]. 

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

endorsement by the U.S. Government. 

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

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

Contents

Conversion Factors ........................................................................................................................................iv

Abbreviations..................................................................................................................................................iv

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

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

Objective .........................................................................................................................................................4

Model Inputs....................................................................................................................................................5

Independent Data ........................................................................................................................................5

Training Data .............................................................................................................................................10

Methods.........................................................................................................................................................11

Independent Data Preparation...................................................................................................................11

Training Data Preparation..........................................................................................................................11

Vegetation Classification ...........................................................................................................................13

Accuracy Assessment ...............................................................................................................................14

Results.......................................................................................................................................................15

Conclusions...................................................................................................................................................19

References ....................................................................................................................................................20

Figures

1. Southwest Regional Gap Analysis Project (SWReGAP) map for the analysis area 

surrounding the Santa Cruz Watershed..........................................................................................3

2. Mapped riparian vegetation classes ....................................................................................................4

3. Study area............................................................................................................................................5

4. Spring Landsat.....................................................................................................................................7

5. Summer Landsat..................................................................................................................................8

6. Fall Landsat .........................................................................................................................................8

7. Digital Elevation Model (DEM) of the study area and its derivatives ...................................................9

8. Landforms of the Santa Cruz Watershed...........................................................................................10

9. Flow Accumulation and derivatives....................................................................................................10

10. Southwest Regional Gap Analysis Project (SWReGAP) core areas .................................................12

11. Training points ...................................................................................................................................13

12. Santa Cruz Watershed map of the riparian corridor and surrounding watershed..............................16

13. Vegetation classifications in three maps of the riparian corridor........................................................16

14. Selected Terrestrial Ecological Systems units in the Santa Catalina Mountains ...............................17

Tables

1. Error matrix for CART modeled vegetation classes...........................................................................18

2. Classification cross-walk....................................................................................................................19

Map Raster Metadata and Data 

Metadata (TXT file, 25 kB) 

Raster data (ZIP file, 1.4 MB compressed; 19 MB expanded) 
iv

Conversion Factors 

Multiply By To obtain 

kilometer (km) 0.6214 mile (mi) 

meter (m) 3.281 foot (ft) 

Abbreviations 

AOI area of interest 

B blue 

CART Classification And Regression Tree 

DEM Digital Elevation Model 

DOQQ Digital Orthophoto Quarter Quadrangle 

EPM Ecosystem Portfolio Model 

EROS Earth Resources Observation and Science 

G green 

GAP Gap Analysis Program 

GIS geographic information system 

GLOVIS Global Visualization Viewer 

MIR middle-infrared 

NAIP National Agricultural Imagery Program 

NDVI Normalized Difference Vegetation Index 

NED National Elevation Dataset 

NIR near-infrared 

NLCD National Land Cover Database 

NM–GAP New Mexico GAP 

PCA Principal Components Analysis 

R red 

SCWEPM Santa Cruz Watershed Ecosystem Portfolio Model 

SWIR shortwave infrared 

SWReGAP Southwest Regional Gap Analysis Project 

TES Terrestrial Ecological Systems 

TM Thematic Mapper 

TOA top of atmosphere 

USGS U.S. Geological Survey 
1

Development of a High-Resolution Binational 

Vegetation Map of the Santa Cruz River Riparian 

Corridor and Surrounding Watershed, Southern 

Arizona and Northern Sonora Mexico 

By Cynthia S.A. Wallace1, Miguel L. Villarreal2, and Laura M. Norman1

Abstract 

This report summarizes the development of a binational vegetation map 

developed for the Santa Cruz Watershed, which straddles the southern border of Arizona 

and the northern border of Sonora, Mexico. The map was created as an environmental 

input to the Santa Cruz Watershed Ecosystem Portfolio Model (SCWEPM) that is being 

created by the U.S. Geological Survey for the watershed. The SCWEPM is a map-based 

multicriteria evaluation tool that allows stakeholders to explore tradeoffs between valued 

ecosystem services at multiple scales within a participatory decision-making process. 

Maps related to vegetation type and are needed for use in modeling wildlife habitat and 

other ecosystem services. Although detailed vegetation maps existed for the U.S. side of 

the border, there was a lack of consistent data for the Santa Cruz Watershed in Mexico. 

We produced a binational vegetation classification of the Santa Cruz River riparian 

habitat and watershed vegetation based on NatureServe Terrestrial Ecological Systems 

(TES) units using Classification And Regression Tree (CART) modeling. Environmental 

layers used as predictor data were derived from a seasonal set of Landsat Thematic 

Mapper (TM) images (spring, summer, and fall) and from a 30-meter digital-elevationmodel (DEM) grid. Because both sources of environmental data are seamless across the 

international border, they are particularly suited to this binational modeling effort. 

Training data were compiled from existing field data for the riparian corridor and data 

collected by the NM–GAP (New Mexico Gap Analysis Project) team for the original 

Southwest Regional Gap Analysis Project (SWReGAP) modeling effort. Additional 

training data were collected from core areas of the SWReGAP classification itself, 

allowing the extrapolation of the SWReGAP mapping into the Mexican portion of the 

watershed without collecting additional training data. 

                                                           

1

U.S. Geological Survey. 2

University of Arizona.
2

Introduction 

A modeling tool created by the U.S. Geological Survey (USGS), the Ecosystem 

Portfolio Model (EPM; Labiosa and others, 2009), is being developed for the Santa Cruz 

Watershed (Norman and others, 2010), which straddles the southern border of Arizona 

and the northern border of Sonora, Mexico. The EPM is a map-based multicriteria 

evaluation tool that stakeholders can use to explore tradeoffs between valued ecosystem 

services at multiple scales within a participatory decision-making process. Maps related 

to vegetation type and condition that span the international border are needed for input to 

the Santa Cruz Watershed Ecosystem Portfolio Model (SCWEPM) for use in modeling 

wildlife habitat and other ecosystem services. Although detailed vegetation maps exist for 

the U.S. side of the border, there is a lack of consistent data for the Santa Cruz Watershed 

in Mexico. This report summarizes the development of a binational vegetation map. 

The Southwest Regional Gap Analysis Project (SWReGAP) vegetation map for 

the U.S. portion of the Santa Cruz Watershed was used as a starting point for this 

mapping effort (fig. 1; Lowry and others, 2006). The SWReGAP is part of the USGS Gap 

Analysis Program (GAP) that couples animal habitat models with land stewardship to 

identify conservation opportunities in the form of gaps in the spatial extent of important 

habitat for various common species (Lowry and others, 2006). The SWReGAP modeled 

vegetation, which was used as input to the habitat models, by using a Classification And 

Regression Tree (CART) algorithm (Hansen and others, 1996; Lawrence and Wright, 

2001), with several environmental data layers (including seasonal Landsat images and 

topography) used as predictors and field data used to train the classifier. For the 

SWReGAP mapping effort, NatureServe (http://www.natureserve.org/) developed the 

Terrestrial Ecological Systems (TES) Classification (Grossman and others, 1998; Lowry 

and others, 2006) that serves as a regionally consistent mesoscale land-cover 

classification between the more general formation (physiognomically defined) and the 

more detailed alliance (floristically defined) mapping levels (Grossman and others, 

1998). This mesoscale classification is sufficiently general to be applicable on regional 

scales while providing enough detail to be useful for supporting wildlife-habitat mapping 

efforts. 

Ecological systems are defined as “groups of plant community types that tend to 

co-occur within landscapes with similar ecological processes, substrates and/or 

environmental gradients” (Comer and others, 2003). As such, the TES definition 

emphasizes dominant vegetation type but also incorporates physical characteristics such 

as topographic position, hydrology, climate, and substrate. These characteristics also 

make TES units accessible for mapping using synoptic satellite imagery with ancillary 

data, including digital elevation models (DEMs) and derivatives (Lowry and others, 

2006). Additional information about the TES Classification for the United States is 

available at http://www.natureserve.org/publications/usEcologicalsystems.jsp (accessed 

October 21, 2010). 
3

Figure 1. Southwest Regional Gap Analysis Project (SWReGAP) map for the analysis area 

surrounding the Santa Cruz Watershed (modified from Lowry and others, 2006). 

In addition to the existing SWReGAP map, detailed vegetation mapping for a 

portion of the Santa Cruz River riparian corridor was used to refine riparian vegetation 

units of interest (Sonoran Institute, 2008; Drake and others, 2009; Villarreal, 2009; 

Villarreal and others 2011b). Previous studies had recognized problems in the SWReGAP 

depiction of riparian vegetation types for this area, including overmapping Mesquite 

Bosque (Fonseca, 2008). Mesquite Bosque is a gallery forest found along the riparian 

flood plains of stream and river banks in the Sonoran Desert. The overmapping of this 

association is evident in figure 2, where Villarreal’s detailed vegetation mapping (2009; 

fig. 2A) is compared with the National Land Cover Database (NLCD; fig. 2B; Homer and 

others, 2004; Fry and others, 2009), SWReGAP (fig. 2C; Lowry and others, 2006), and 

the new map of standardized ecosystems of the conterminous United States (fig. 2D; 

Sayre and others, 2008). The Santa Cruz River map of riparian vegetation and land cover 

developed by Villarreal (2009) for the National Park Service has a map accuracy of 83–

95 percent for individual riparian vegetation classes. 

ARIZONA

EXPLANATION

Selected land-cover classes

Base from SWreGAP vegetation map,

clipped to analysis area of interest

/ 0 25 50 12.5 Kilometers

0 2 10 0 40 Miles

Agriculture

Apacherian-Chihuahuan Mesquite Upland Scrub

Apacherian-Chihuahuan Piedmont Semi-Desert Grassland

Barren Lands, Non-specific

Chihuahuan Creosotebush, Mixed Desert and Thorn Scrub

Chihuahuan Mixed Salt Desert Scrub

Chihuahuan Sandy Plains Semi-Desert Grassland

Chihuahuan Stabilized Coppice Dune and Sand Flat Scrub

Chihuahuan Succulent Desert Scrub

Chihuahuan-Sonoran Desert Bottomland and Swale Grassland

Colorado Plateau Mixed Bedrock Canyon and Tableland

Developed, Medium - High Intensity

Developed, Open Space - Low Intensity

Madrean Encinal

Madrean Juniper Savanna

Madrean Pine-Oak Forest and Woodland

Madrean Pinyon-Juniper Woodland

Madrean Upper Montane Conifer-Oak Forest and Woodland

Mogollon Chaparral

North American Arid West Emergent Marsh

North American Warm Desert Bedrock Cliff and Outcrop

North American Warm Desert Lower Montane Riparian

North American Warm Desert Pavement

North American Warm Desert Riparian Mesquite Bosque

North American Warm Desert Riparian Woodland and Shrubland

North American Warm Desert Volcanic Rockland

North American Warm Desert Wash

Open Water

Recently Mined or Quarried

Rocky Mountain Aspen Forest and Woodland

Rocky Mountain Ponderosa Pine Woodland

Sonora-Mojave Creosotebush-White Bursage Desert Scrub

Sonoran Mid-Elevation Desert Scrub

Sonoran Paloverde-Mixed Cacti Desert Scrub
4

Figure 2. Mapped riparian vegetation classes. A, 2004 Santa Cruz River map showing riparian 

vegetation and land cover (Villarreal, 2009). B, 2001 National Land Cover Dataset (NLCD) 

map; Homer and others, 2004; Fry and others, 2009. C, Southwest Regional Gap Analysis 

Project (SWReGAP) map. D, U.S. Geological Survey Standardized Terrestrial Ecosystems 

map. 

The small segment of the Santa Cruz River riparian corridor shown in figure 2 

contains a dense mix of Riparian Forest and Woodland and Mesquite Bosque. Most of 

these classes were unmapped in the standardized ecosystems map and significant portions 

were missed in the NLCD and the SWReGAP. The NLCD does not split out the 

Mesquite Bosque, and the SWReGAP maps all as Mesquite Bosque at the expense of 

Riparian Forest and Woodland. These distinctions between riparian vegetation types 

represent important differences in habitat for many wildlife species. 

Objective 

The main objective of this study is to produce a detailed vegetation map of the 

Santa Cruz Watershed based on NatureServe’s Terrestrial Ecological Systems (TES) 

mesoscale vegetation mapping units that seamlessly spans the international border for 

supporting ecosystem services evaluation and wildlife habitat modeling. An additional 

objective is to enhance the existing SWReGAP TES map, produced for the U.S. portion 

of the watershed (Lowry and others, 2006), by incorporating details of Santa Cruz River 

riparian corridor vegetation mapped by Villarreal (2009). 

A B C

/

D

EXPLANATION

Riparian Forest and Woodland

Mesquite Bosque

012 0.5 Mi

0123 0.5 Km

Base is a mosaic of 2007 DOQQs from USGS
5

Model Inputs 

The CART modeling environment requires “ground truth” data used to train the 

classifier and independent data used as predictors. The training data are typically field 

data, for which the vegetation classes to be modeled are known to occur at specific 

locations on the ground. The independent data are typically environmental data layers 

that capture various characteristics of the landscape. One benefit of the CART modeling 

is that independent data can be either continuous, such as elevation or satellite 

reflectance, or categorical, such as landforms or soils. 

The environmental datasets used for this project were Landsat-derived and DEMderived, chosen on the basis of an inspection of existing SWReGAP modeling results and 

because they span the international border. Although other datasets, such as surficial 

geology, could be useful in the classification, a seamless dataset with suitable detail was 

not available for the binational area. Furthermore, the inclusion of Landsat Thematic 

Mapper (TM) data, with six reflectance bands ranging from visible to shortwave infrared, 

captures information about the soils and surficial geology (Jensen, 1996). 

The study area is the Santa Cruz Watershed, but the modeling was performed 

within a larger area of interest (AOI) containing the watershed (fig. 3). This shape is the 

intersection of nine Landsat image footprints (spring, summer, and fall images) and a 

rectangular DEM subset from the seamless National Elevation Data (NED) 30-meter 

raster data available through the USGS at http://gisdata.usgs.gov/website/seamless/. 

Figure 3. Study area. The area of interest (AOI) for modeling is the polygonal shape representing 

the intersection of the nine Landsat image footprints used in the model and a rectangular 

Digital Elevation Model (DEM) subset for the binational area. 

Independent Data 

The Landsat images used by the SWReGAP modelers (http://fwsnmcfwru.nmsu.edu/swregap/, accessed October 21, 2010) were downloaded from the 

/ ARIZONA

EXPLANATION

Santa Cruz Watershed (Study Area)

Area of Interest (AOI) for modeling

United States

Mexico

Location Map

0 250 500 1,000 Miles

0 375 750 1,500 Kilometers
6

USGS Global Visualization Viewer (GLOVIS) Web site (http://glovis.usgs.gov/) and 

radiometrically corrected to top of atmosphere (TOA) reflectance using models created in 

Erdas Imagine 9.1 (Erdas, 2006). These images are from 1999 and 2000, coincident with 

the collection of the field data by the New Mexico GAP (NM–GAP) modeling team. 

After radiometric correction, the three contemporaneous Landsat images (P35R38, 

P36R37, and P36R38) for each of three seasons (spring, summer, and fall) were mosaiced 

in Erdas Imagine and clipped to the AOI. 

A seasonal set of Landsat images is used to capture the dynamics of vegetation 

greenness throughout the year. These dynamics reflect the vegetation phenology, or 

seasonally recurring ecological events, such as vegetation green up, leafing or flowering, 

and senescence. The seasonal Landsat set captures information related to phenology and 

helps discriminate between vegetation communities on the basis of their distinctive 

dynamics. The Landsat images and derivatives used for modeling are as follows: 

1. Landsat Mosaics. The seasonal mosaics are the original Landsat images,

corrected to TOA, mosaiced using Erdas Imagine (specifying P36R38 as the

reference image with no color balancing and no feathering), and clipped to the

AOI.

1. Spring Landsat Bands (six bands: blue (B), green (G), red (R), near‐infrared

(NIR), middle‐infrared (MIR), and shortwave infrared (SWIR); fig. 4)

2. Summer Landsat Bands (six bands: B,G,R,NIR,MIR,SWIR; fig. 5)

3. Fall Landsat Bands (six bands: B,G,R,NIR,MIR,SWIR; fig. 6)

2. Tasseled Cap Indices. The Tasseled Cap algorithm transforms the original

multiband TM data onto three orthogonal axes that represent overall

“Brightness,” “Greenness,” and “Wetness” (Crist and Kauth, 1986). The seasonal

tasseled cap images were calculated in Erdas Imagine (specifying the output

stretched to 8‐bit) from the mosaiced images and then clipped to the AOI.

a. Spring Tasseled Cap (three bands: Brightness, Greenness, and Wetness)

b. Summer Tasseled Cap (three bands: Brightness, Greenness, and Wetness)

c. Fall Tasseled Cap (three bands: Brightness, Greenness, and Wetness)

3. Normalized Difference Vegetation Index (NDVI) Images. The NDVI is an index

derived from reflectance values of the R and NIR regions of the electromagnetic

spectrum and is sensitive to various biophysical vegetation characteristics, such

as biomass and percent cover (Huete and Jackson, 1987; Duncan and others,

1993). The formula is NDVI= (NIR–R)/(NIR+R). NDVI values range from –1 to 1;

non‐land surfaces (such as water and snow) typically assume negative values and

land surfaces typically assume positive values. As landscapes become more

densely vegetated, the NDVI trends to 1. For this modeling, NDVI images were

calculated in Erdas Imagine (specifying the output stretched to 8‐bit) from the

mosaiced images and then clipped to the AOI. Although NDVI images were not
7

used directly in the model, since the Tasseled Cap Greenness is an analogous

metric, they were used to derive the texture images (number 4, this section).

a. Spring NDVI

b. Summer NDVI

c. Fall NDVI

4. Texture Images. These were calculated in Erdas Imagine (Interpreter, Spatial

Enhancement, Texture, 3x3 Variance) from the mosaiced images and then

clipped to the AOI.

a. April NDVI Texture: This derivative was included to capture information

about the pattern of vegetation on the landscape.

b. October Red Band Texture: This derivative was included to capture

information about the pattern of brightness on the landscape, especially to

highlight urban/non‐urban areas.

Figure 4. Spring Landsat. Mosaic of three Landsat Enhanced Thematic Mapper Plus (ETM+) 

images from spring 2000 (P35R38-April 13,2000; P36R37-April 12, 2000; and P36R38-April 

12, 2000), displayed as: ETM band 7 (short-wave infrared) = Red, ETM band 4 (near-infrared) 

= Green, ETM band 1 (visible blue) = Blue. With this color combination, vegetation appears 

green, urban and barren areas appear blue, and sparsely-vegetated soils appear in shades of 

tan, pink and purple. The red patch visible to the west of the watershed is a recent fire scar. 

/ ARIZONA

EXPLANATION

Mosaic of April 2000 

Landsat Enhanced 

Thematic Mapper images

Spring Landsat

Santa Cruz Watershed (Study Area)

0 245 490 980 Miles

0 370 740 1,480 Kilometers
8

Figure 5. Summer Landsat. Mosaic of three Landsat Enhanced Thematic Mapper Plus (ETM+) 

images from summer 2000 (P35R38-June 16,2000; P36R37-June 15, 2000; and P36R38-June 

15, 2000), displayed as: ETM band 7 (short-wave infrared) = Red, ETM band 4 (near-infrared) 

= Green, ETM band 1 (visible blue) = Blue. With this color combination, vegetation appears 

green, urban and barren areas appear blue, and sparsely-vegetated soils appear in shades of 

tan, pink and purple. 

Figure 6. Fall Landsat. Mosaic of three Landsat Enhanced Thematic Mapper Plus (ETM+) 

images from fall 2000 (P36R37-October 19, 2000; P36R38-October 19, 2000; the October 

image P35R38 was too cloudy so the image used in the mosaic is an average of the two 

images: September 12, 2000 and November 13, 2000), displayed as: ETM band 7 (short-wave 

infrared) = Red, ETM band 4 (near-infrared) = Green, ETM band 1 (visible blue) = Blue. With 

this color combination, vegetation appears green, urban and barren areas appear blue, and 

sparsely-vegetated soils appear in shades of tan, pink and purple. 

/ ARIZONA

EXPLANATION

Mosaic of April 2000 

Landsat Enhanced 

Thematic Mapper images

Spring Landsat

Santa Cruz Watershed (Study Area)

0 250 500 1,000 Miles

0 370 740 1,480 Kilometers

/ ARIZONA

EXPLANATION

Mosaic of Fall 2000 

Landsat Enhanced 

Thematic Mapper images

Fall Landsat

Santa Cruz Watershed (Study Area)

0 250 500 1,000 Miles

0 370 740 1,480 Kilometers
9

An additional suite of independent variables was created from topographic data 

for the region. A rectangular subset of the 30-meter resolution DEM containing the Santa 

Cruz Watershed study area was obtained through the USGS Earth Resources Observation 

and Science (EROS) Center (http://edc.usgs.gov/; fig. 7). The DEM-based raster data 

used for modeling and their derivations are as follows: 

5. DEM derivatives

a. DEM: Original data, from the National Elevation Dataset (NED) 30‐meter

raster data available at: http://gisdata.usgs.gov/website/seamless/ (fig. 7A).

b. Aspect: Derived in Erdas Imagine (Interpreter, Topographic Analysis, Aspect)

from the DEM and then clipped to the AOI (fig. 7B).

c. Slope: Derived in Erdas Imagine (Interpreter, Topographic Analysis, Slope)

from the DEM and then clipped to the AOI (fig. 7C).

d. Landform: A stratification of the DEM into 10 landform classes as developed

by Tagil and Jenness (2008; fig. 8). The algorithm that produced the Landform

layer was accessed in the ArcGIS toolbox (Esri, 2010).  

e. Flow Accumulation: Derived using ArcGIS Spatial Analyst Hydrology tools. The

DEM was first “Filled,” then “Flow Direction” was calculated, and finally

“Flow Accumulation” was derived. Although the Flow Accumulation (fig. 9A)

was not used directly in the modeling, it was used to derive the next layer.

f. Flow Accumulation Difference of Focal Max and Mean: Calculated in Erdas

Imagine using the Model Maker from the mosaiced images and then clipped

to the AOI. Several variations on this type of neighborhood calculation were

explored. The calculation that seemed to produce the desired result is

subtracting the mean Flow Accumulation value within a 25x25 window from

the maximum Flow Accumulation value within a 5x5 window. This difference

was designed to pull out the areas within and near the topographic drainages

to effectively constrain classification of riparian vegetation types. Figure 9B

shows this derivative image.

Figure 7. Digital Elevation Model (DEM) of the study area and its derivatives. A, DEM. B, Aspect. 

C, Slope. Elevation derivatives included in the model are Aspect (B) and Slope (C), both of 

which were calculated in Erdas Imagine (Erdas, 2006). 

A

LOW (442) HIGH (2,825)

B C

Meters 0 (N) Degrees 360 (N) LOW (4) Degrees HIGH (70)

EXPLANATION EXPLANATION EXPLANATION

0 20 40 10 Mi

0 30 60 15 Km

0 20 40 10 Mi

0 30 60 15 Km

0 20 40 10 Mi

Watershed 0 30 60 15 Km Boundary Watershed

Boundary

Watershed

Boundary

 ̄ ̄ ̄
10

Figure 8. Landforms of the Santa Cruz Watershed. 

Figure 9. Flow Accumulation (A) and derivatives (B). 

Training Data 

Training data, which are the “ground truth” data used to train the classifier, are 

from three sources: 

1. NM–GAP field data: SWReGAP training data for the New Mexico SWReGAP (NM–

GAP) group’s ecoregion NM–3. These consist of polygons identified within their

NM–3 mapping unit that intersect the AOI for this study.

2. Santa Cruz River riparian corridor mapping. The training data used for this map

included field data collected between 2006 and 2008 (Villarreal, 2009), Quickbird

multispectral satellite imagery, and historical aerial photographs.

/

EXPLANATION

Santa Cruz Watershed (Study Area)

Canyons, Deeply Incised Streams

Midslope Drainages, Shallow Valleys

Upland Drainages, Headwaters

U-shaped Valleys

Plains Small

Open Slopes

Upper Slopes, Mesas

Local Ridges/Hills in Valleys

Midslope Ridges, Small Hills in Plains

Mountain Tops, High Ridges

Detail

0 25 50 12.5 Mi

0 30 60 15 Km

Base is USGS DEM stratified into landforms

using Jenness algorithm in ArcGIS

A B

c c

LOW HIGH

EXPLANATION Santa Cruz Watershed

0 3 15 0 60 Mi

0 5 25 0 100 Km

Base modified from USGS National Elevation Dataset
11

3. SWReGAP classification. Initial CART modeling trials revealed that the available

field data from SWReGAP field sites and Villarreal (2009) field sites were too

limited within the AOI to produce a reasonable classification. Since our goal was

to expand and refine the SWReGAP vegetation mapping to the full binational

watershed, and we did not have the resources to collect additional field data,

additional training data were extracted from the SWReGAP classification itself.

Methods 

Independent Data Preparation 

The environmental data layers described in the section “Model Inputs” were all 

clipped to the AOI and collected in a single folder for modeling. Although additional 

layers were derived, tested and considered, the layers shown are the ones used in the final 

model. An example of other layers considered is a Principal Components Analysis (PCA) 

of the original image bands, but this transformation is analogous to a Tasseled Cap 

transformation (they are both an orthogonalization of the bands into new uncorrelated 

bands). Furthermore, in contrast to PCA, the Tasseled Cap bands have an interpreted 

physical meaning of Brightness, Greenness, and Wetness. 

Training Data Preparation 

To simplify the classification process and enhance processing speed, we 

converted all training data into points. For the NM–GAP training sites, random points 

were gathered within the polygons by using a stratified-random sampling algorithm in 

Erdas Imagine specifying a minimum of 10 points per class. The points were then 

inspected on a 2006 Digital Orthophoto Quarter Quadrangle (DOQQ) and a few were 

deleted if they appeared to be unrepresentative of their labeled land-cover type (for 

example, the random point was located near a polygon edge). All of the riparian points 

were deleted because another data set (Villarreal, 2009) provided points within the Santa 

Cruz riparian corridor. A total of 447 training points were generated in this manner. 

The training data Villarreal (2009) collected for 2006 (field data) and 1996 

(interpreted aerial photos) were subset to those that were mapped identically for both 

time periods, to identify field data that existed as labeled in our intermediate time period 

of 1999–2000. The remaining training site polygon arcs were buffered by 60 meters, to 

exclude mixed-pixel edge areas, and random points were gathered within the reduced 

polygons by using a stratified-random sampling algorithm in Erdas Imagine, again 

specifying a minimum of 10 points per class. The points were then inspected on a 2006 

DOQQ and a few were deleted if they appeared to be unrepresentative of their labeled 

land cover type. A total of 237 points were generated in this manner. 

Mapped vegetation classes for the Santa Cruz River map were cross-walked to the 

SWReGAP TES units, requiring generalization of some classes. For example, “Riparian 

Woodland,” “Shrub Savanna,” “Shrubland,” and “Tree Savanna” were all merged into 

the SWReGAP “North American Warm Desert Riparian Woodland and Shrubland.” To 
12

preserve ecological detail we considered important for wildlife habitat, we added a 

“Riparian Forest” class to the existing SWReGAP TES units. 

Additional training points were collected within the core areas of the SWReGAP 

polygons by using a stratified-random sampling algorithm in Erdas Imagine, again 

specifying a minimum of 10 points per class. The core areas of the polygons were 

extracted using the Erdas Imagine model maker by first calculating a Focal Diversity 

within a 3x3 window for the thematic image and then eliminating (setting to zero) all 

resulting values greater than 1 (fig. 10). A total of 2,912 points were generated in this 

manner. 

Figure 10. Southwest Regional Gap Analysis Project (SWReGAP) core areas. 

We removed all “Agriculture” points from the SWReGAP core data, for three 

reasons. First, SWReGAP did not use CART to model agriculture—it had been captured 

through heads-up digitizing or from other agency geographic information system (GIS) 

data. Second, the only agriculture evident in the Santa Cruz Watershed occurs along the 

riparian corridor and the SWReGAP includes this agriculture as well as upland centerpivot agriculture; these are distinctly different types of agriculture and would likely 

confound the CART classifier. Third, the Villarreal (2009) data include field points for 

the Santa Cruz riparian corridor agriculture, which are the data we preserved for training 

purposes. Figure 11 shows the locations and provenance of all the points used as training 

data. 

/ Base from SWreGAP vegetation map

012 0.5 Mi

0123 0.5 Km
13

Figure 11. Training points from New Mexico Gap Analysis Project (NM–GAP) field data, Villarreal 

(2009) field data, and random points from within the Southwest Regional Gap Analysis Project 

(SWReGAP) core areas. 

Vegetation Classification 

The vegetation classification of the Santa Cruz Watershed was accomplished by 

using Classification And Regression Tree (CART) software (Quinlan, 1993, 1996). We 

accessed the CART tool developed by the USGS–NLCD for Erdas Imagine that is 

available free for download at http://www.mrlc.gov/. For each modeling run, we first used 

the “NLCD sampling tool” to create a “.data” file by inputting the independent data 

layers (all clipped to the AOI) and specifying the “training data” as a tab-delimited text 

file containing the three columns X, Y, and CODE (input to the tool as a .txt file without 

a header). The See5 program (installed as a stand-alone program outside of the Erdas 

toolbar) was then run, with the “.data” file specified as input and “construct classifier” 

executed to produce the output “.names” file. Several options are offered in the 

“construct classifier” menu. After some trial and error, we chose to “boost” 10 trials and 

chose Global Pruning (specifying Pruning CF 25 percent, minimum two cases) for the 

final classification runs. Once See5 is run, the NLCD tool “See5 classifier” is activated, 

choosing the See5 created “.names” file as input and specifying a “tree” classifier. The 

output is a classified image. 

The final classification was the result of an iterative process. Early attempts, using 

only the field data from NM–GAP and Villarreal’s mapping, were unsuccessful and 

prompted the addition of SWReGAP core-area training sites. The next series of 

classifications produced an overmapping of riparian vegetation types outside of 

prominent drainage areas. This was controlled by creating the Flow Accumulation 

Difference of Focal Max and Mean (fig. 9B) for use as an additional independent data 

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/

EXPLANATION

DEM background

Meters

Training Data Source

! NM-GAP field data 

(447 points)

! SWReGAP core areas 

(2,912 points)

! Santa Cruz River map 

(237 points)

Santa Cruz Watershed

High : 2825.32

Low : 441.667

0 4 20 0 80 Mi

Base map from USGS DEM 0 30 60 120 Km
14

layer and by digitizing additional training points from the SWReGAP classification in the 

areas incorrectly classified (234 points were added at this stage). 

All TES classes were initially modeled, including anthropogenic and disturbed 

landscapes, such as Developed (urban) and Recently Mined or Quarried. Many of these 

nonvegetation classes tend to be quite heterogeneous, and although the CART 

environment produced reasonable results with the suite of independent layers we 

included, small patches of these types were identified by the CART classifier in 

improbable landscapes. Because of this, we followed the protocol of the original 

SWReGAP modelers, who captured these nonvegetation classes separately and applied 

them as an overlay on the CART-modeled vegetation classification. The SWReGAP 

modelers used a variety of methods, such as heads-up digitizing of Urban and Agriculture 

classes from high-resolution imagery and accessing other agency GIS data. 

For our product, we accessed and overlaid Developed classes (high, medium, and 

low density) from the Land Cover product for 1999 produced under a parallel effort for 

this project (Villarreal and others, 2011a). In addition, the Recently Mined or Quarried 

class was accessed from the original SWReGAP product. Inspection of our model that 

included all TES classes revealed no obvious Recently Mined or Quarried landscapes in 

the Mexican portion of the watershed, so this overlay of the U.S. side only was 

considered reasonable. 

The water class was produced as a combination of modeling and overlay. 

Training sites representing water were used in the CART classification to model a water 

class, but modeling resulted in clearly misclassified pixels in the interiors of some water 

bodies. To better delineate the water class, we accessed and overlaid the SWReGAP 

water class as an additional source of classified water pixels. 

Further inspection of the vegetation classification revealed an abundance of 

mapped Chihuahuan Mixed Salt Desert Scrub in the foothills of the mountains as a 

transition between Apacherian-Chihuahuan Mesquite Upland Scrub and the Madrean 

Encinal. This did not seem reasonable on the basis of our field knowledge of the area and 

did not fit our interpretation of the vegetation as revealed in high resolution National 

Agricultural Imagery Program (NAIP) imagery. Only two field training sites of this 

vegetation type were collected in the watershed by the NM–GAP team (AZ071202ES13 

and AZ071202ES13), and inspection of the photographs from the SWReGAP archives 

suggest these two sites do not represent a classic Chihuahuan Mixed Salt Desert Scrub 

assemblage. Given that this salt desert type is typical of saline basins in the Chihuahuan 

Desert and the CART model has placed it in the alluvial fans within our watershed, we 

consider it more accurate to classify these landscapes as an ecotone of ApacherianChihuahuan Mesquite Upland Scrub. We have, therefore, relabeled it as ApacherianChihuahuan Mesquite Upland Scrub in our final classification. 

In summary, the CART tool was used to model only natural and vegetated 

landscapes. A 3x3 majority filter was applied to the output classification to eliminate 
15

small isolated pixels that are generally considered noise. The Developed, Recently Mined 

or Quarried, and Water classes were then overlain, the modeled Chihuahuan Mesquite 

Upland Scrub class was relabeled Apacherian-Chihuahuan Mesquite Upland Scrub, and 

the final product was clipped to the Santa Cruz Watershed study area. 

Accuracy Assessment

Accuracy was quantified from a classification based on 80 percent of the training 

data and evaluated with the remaining 20 percent withheld as “testing” data. Following 

the protocol of the SWReGAP team, the final classification was created using 100 

percent of the training data. A random sample to separate data into training and testing 

sets was generated for each data provenance (NM–GAP, Villarreal, SWReGAP core 

areas). A total of 641 points were withheld for testing: 89 from NM–GAP, 48 from 

Villarreal (2009), and 504 from SWReGAP core areas. After the elimination of 

nonvegetation training sites from the SWReGAP and NM–GAP sets and the addition of 

training sites in areas of misclassified riparian vegetation, a total of 3,448 training sites 

were used in the final classification. 

Results

The final high-resolution binational map of the Santa Cruz River riparian habitat and 

watershed vegetation based on NatureServe TES units (Santa Cruz Watershed map) is 

shown in figure 12. The map is available for download as a georeferenced raster dataset 

in Erdas Imagine image format (http://pubs.usgs.gov/of/2011/1143/of2011-

1143_data.zip) with associated metadata (http://pubs.usgs.gov/of/2011/1143/of2011-

1143_metadata.txt). Visual comparison of the detailed Santa Cruz River map, the Santa 

Cruz Watershed map, and the SWReGAP map for a portion of the river near Tumacacori 

is shown in figure 13. The Santa Cruz Watershed map appears to be effective in 

representing units mapped by Villarreal (2009) in the Santa Cruz River map, and 

provides enhanced detail of the riparian vegetation units compared to the original 

SWReGAP product. The inclusion of the additional Riparian Forest class is especially 

striking and will provide useful data for wildlife habitat modelers. There are differences 

noted, such as the extent of agriculture, but some of these may be attributable the fact that 

the Santa Cruz River map represents conditions in 2006, whereas the data input to our 

model are from 1999 to 2000. 

Figure 14 shows the Santa Cruz Watershed map and the SWReGAP for the Santa 

Catalina Mountains north of Tucson; the Santa Cruz Watershed map honors the overall 

vegetation patterns of the SWReGAP, which is reassuring given that the SWReGAP map 

was used as a major source of training data. For the riparian corridor area of Figure 13, 

the main difference between the Santa Cruz Watershed map and the SWReGAP map is 

the introduction of Sonoran Paloverde-Mixed Cacti Desert Scrub (light green), replacing 

some of the Apacherian-Chihuahuan Piedmont Semi-Desert Grassland (pale pink). 

Inspection of 1996 and 2005 aerial photographs shows shrub canopies are dominant in 

this area, supporting the classification as a desert scrub community (Santa Cruz 

Watershed map, this study) rather than a grassland community (SWReGAP). 
16

Figure 12. Santa Cruz Watershed map of the riparian corridor and surrounding watershed. 

Figure 13. Vegetation classifications in three maps of the riparian corridor. 

/

ARIZONA

Agriculture

Apacherian-Chihuahuan Mesquite Upland Scrub

Apacherian-Chihuahuan Piedmont Semi-Desert Grassland

Barren Lands, Non-specific

Chihuahuan Creosotebush, Mixed Desert and Thorn Scrub

Chihuahuan Sandy Plains Semi-Desert Grassland

Chihuahuan Stabilized Coppice Dune and Sand Flat Scrub

Chihuahuan Succulent Desert Scrub

Chihuahuan-Sonoran Desert Bottomland and Swale Grassland

Colorado Plateau Mixed Bedrock Canyon and Tableland

Developed, Medium - High Intensity

Developed, Open Space - Low Intensity

Madrean Encinal

Madrean Juniper Savanna

Madrean Pine-Oak Forest and Woodland

Madrean Pinyon-Juniper Woodland

Madrean Upper Montane Conifer-Oak Forest and Woodland

Mogollon Chaparral

North American Arid West Emergent Marsh

North American Warm Desert Bedrock Cliff and Outcrop

North American Warm Desert Lower Montane Riparian Woodland and Shrubland

North American Warm Desert Pavement

North American Warm Desert Riparian Mesquite Bosque

North American Warm Desert Riparian Woodland and Shrubland

North American Warm Desert Volcanic Rockland

North American Warm Desert Wash

Open Water

Recently Mined or Quarried

Riparian Forest

Rocky Mountain Aspen Forest and Woodland

Rocky Mountain Ponderosa Pine Woodland

Sonora-Mojave Creosotebush-White Bursage Desert Scrub

Sonoran Mid-Elevation Desert Scrub

Sonoran Paloverde-Mixed Cacti Desert Scrub

EXPLANATION

Vegetation and Land-cover types

0 6 12 18 3 Mi

0 10 20 5 Km

Santa Cruz 

River map

Santa Cruz

Watershed Map SWReGAP

(Villarreal) (this study) (Lowry et al.)

EXPLANATION

[

[

[

[

[ Classes visible in Santa Cruz River Map (on left)

/

Selected vegetation and land-cover types

Agriculture

Chihuahuan Mixed Salt Desert Scrub

Developed, Medium - High Intensity

Developed, Open Space - Low Intensity

Madrean Encinal

Riparian Forest

Sonoran Mid-Elevation Desert Scrub

Apacherian-Chihuahuan 

Mesquite Upland Scrub

Apacherian-Chihuahuan Piedmont 

Semi-Desert Grassland

Chihuahuan Creosotebush, 

Mixed Desert and Thorn Scrub

Chihuahuan Stabilized Coppice 

Dune and Sand Flat Scrub

North American Warm Desert Bedrock 

Cliff and Outcrop

North American Warm Desert 

Riparian Mesquite Bosque

North American Warm Desert 

Riparian Woodland and Shrubland

Sonoran Paloverde-Mixed Cacti 

Desert Scrub

024 1 Mi

036 1.5 Km
17

Figure 14. Selected Terrestrial Ecological Systems (TES) units in the Santa Catalina Mountains 

shown in the Santa Cruz Watershed map and the Southwest Regional Gap Analysis Project 

(SWReGAP) map. 

The overall accuracy of all vegetation classes modeled with the random 80-

percent subset of the training points and evaluated with the withheld 20-percent test 

points was 78 percent (see tables 1 and 2). Because the final classification was created 

using all the training and testing points, the final map accuracy is expected to be better 

than the accuracy for this intermediate classification. Column totals of table 1 show 

producer’s accuracies and row totals show user’s accuracies. Units modeled with large 

amounts of training data have good validation results, for example Sonoran PaloverdeMixed Cacti Desert Scrub has a user’s accuracy of 97 percent. Poor accuracy results for 

the Sonoran Mid-Elevation Desert Scrub likely reflect both the fact that it is an 

ecologically generalized class as well as the lack and quality of training data. There were 

no field-based data for this class in our analysis area, so all training data were derived 

exclusively from the original SWReGAP classification. SWReGAP map Santa Cruz Watershed map

Selected vegetation and land-cover types

5

Kilometers /

EXPLANATION

Agriculture

Apacherian-Chihuahuan Mesquite Upland Scrub

Chihuahuan Creosotebush, Mixed Desert and Thorn Scrub

Chihuahuan Mixed Salt Desert Scrub

Developed, Medium - High Intensity

Developed, Open Space - Low Intensity

Madrean Encinal

Madrean Juniper Savanna

Madrean Pine-Oak Forest and Woodland

Madrean Pinyon-Juniper Woodland

Madrean Upper Montane Conifer-Oak Forest and Woodland

Mogollon Chaparral

North American Warm Desert Bedrock Cliff and Outcrop

Rocky Mountain Aspen Forest and Woodland

Sonoran Mid-Elevation Desert Scrub

Sonoran Paloverde-Mixed Cacti Desert Scrub

Apacherian-Chihuahuan Piedmont Semi-Desert Grassland
18

CLASS 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 T

1 3 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 

2 0 2 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 3 1 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 1 27 2 2 1 0 0 0 1 0 0 0 1 0 0 0 0 5 0 0 0 4 9 1 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 97 11 3 1 0 6 0 0 0 0 1 1 0 0 7 0 0 0 0 0 4 33 8 0 0 5 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 156 4 0 0 1 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 8 28 0 0 0 0 0 0 0 1 0 0 

10 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 11 0 0 0 2 0 9 6 4 0 0 78 2 0 0 0 0 0 0 0 12 0 0 0 0 0 3 0 4 0 0 0 11 0 0 0 1 0 1 0 13 0 0 0 0 0 0 0 2 0 0 0 0 8 1 0 0 0 0 0 14 0 0 0 0 0 1 0 0 0 0 2 1 1 7 0 0 0 0 0 15 0 0 2 1 3 3 1 0 0 0 0 0 0 0 8 0 0 0 0 16 0 0 0 1 0 5 0 0 0 0 0 0 1 0 0 0 0 0 0 17 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 1 0 0 18 0 0 0 0 0 0 0 3 0 0 0 1 0 0 0 0 0 6 0 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 3 

Total 4 2 6 36 15 126 52 188 36 4 95 16 10 8 9 2 3 8 3 

Table 1. Error matrix for CART modeled vegetation classes. 

[All classes with at least four testing points are shown. See table 2 for colors and descriptions of classes]
19

CLASS Map Color DESCRIPTION 

1 North American Warm Desert Bedrock Cliff and Outcrop

2 North American Warm Desert Volcanic Rockland

3 Madrean Pine‐Oak Forest and Woodland

4 Madrean Encinal

5 Mogollon Chaparral

6 Apacherian‐Chihuahuan Mesquite Upland Scrub

7 Chihuahuan Creosotebush, Mixed Desert and Thorn Scrub

8 Sonoran Paloverde‐Mixed Cacti Desert Scrub

9 Sonora‐Mojave Creosotebush‐White Bursage Desert Scrub

10 Sonora‐Mojave Mixed Salt Desert Scrub

11 Apacherian‐Chihuahuan Piedmont Semi‐Desert Grassland and Steppe

12 North American Warm Desert Riparian Woodland and Shrubland

13 North American Warm Desert Riparian Mesquite Bosque

14 Chihuahuan‐Sonoran Desert Bottomland and Swale Grassland

15 Madrean Pinyon‐Juniper Woodland

16 Sonoran Mid‐Elevation Desert Scrub

17 Barren Lands, Non‐specific

18 Agriculture

19 Riparian Forest

Table 2. Classification cross-walk 

[Number on left is “Class” shown in table 1. Colors and descriptions are those used in figures 12,

13, and 14]

Error matrix tables were also generated for only the field data testing points (not 

shown). Accuracy for only NM–GAP test points was 76 percent and only Villarreal test 

points was 60 percent. The combined accuracy for only the test points based on field data 

(NM–GAP and Villarreal) was 71 percent. Highest accuracy for Villarreal test points are 

for those classes that mapped into the classification scheme one-to-one, which are the 

North American Warm Desert Riparian Mesquite Bosque (100 percent) and Riparian 

Forest (75 percent) Lowest accuracies are for the North American Warm Desert Riparian 

Woodland and Shrubland (53 percent) and the Chihuahuan-Sonoran Desert Bottomland 

and Swale Grassland (50 percent), both of which were a many-to-one mapping, 

suggesting that the some of the Villarreal classes that were collapsed into single 

SWReGAP classes may not have been representative of the TES unit as defined by the 

NM–GAP mappers. The mapping accuracy may be improved by reevaluating the 

classification cross-walk and by eliminating Villarreal map units that are uncharacteristic 

of the NatureServe-defined TES units. 
20

Conclusions

Using a CART modeling environment, we produced a binational vegetation 

classification of the Santa Cruz River riparian habitat and watershed vegetation based on 

NatureServe TES units. The map is available for download as a georeferenced raster 

dataset in Erdas Imagine image format (http://pubs.usgs.gov/of/2011/1143/of2011-

1143_data.zip) with associated metadata (http://pubs.usgs.gov/of/2011/1143/of2011-

1143_metadata.txt). Environmental layers used as independent, predictor data were 

derived from a seasonal set of Landsat TM images (spring, summer, and fall) and from a 

30-meter DEM grid. Because both sources of environmental data are seamless across the 

international border, they are particularly suited to this binational modeling effort. 

Training data were compiled from existing field data collected for a recent map produced 

by Villarreal (2009) for the Santa Cruz riparian corridor and data collected by the NM–

GAP team for the original SWReGAP modeling effort. Additional training data were 

collected from core areas of the SWReGAP classification itself, allowing the 

extrapolation of the SWReGAP mapping into the Mexican portion of the watershed 

without collecting additional training data—an expensive and logistically challenging 

endeavor. 

We assumed that the training data, which are exclusively from the U.S. portion of 

the watershed and region, are representative of the vegetation south of the border. For the 

relatively small study area of the Santa Cruz Watershed, this is a reasonable assumption. 

Although particular details of the vegetation mapping at a TES-level of resolution will 

benefit from inclusion of more field-verified data to train the model, this map serves as a 

reasonable representation of the vegetation patterns and communities within the Santa 

Cruz Watershed for the intended purpose of informing hydrologic modeling and wildlife 

habitat modeling within the Santa Cruz Watershed Ecosystem Portfolio Model 

(SCWEPM). 

References

Comer, P., Faber-Langendoen, D., Evans, R., Gawler, S., Josse, C., Kittel, G., Menard, 

S., Pyne, M., Reid, M., Schulz, K., Snow, K., and Teague, J., 2003, Ecological 

systems of the United States—a working classification of U.S. terrestrial systems: 

NatureServe, Arlington, Va., available online at 

http://www.natureserve.org/library/usEcologicalsystems.pdf. 

Crist, E.P., and Kauth, R.J., 1986, The tasseled cap de-mystified: Photogrammetric 

Engineering and Remote Sensing, v. 52, no. 1, p. 81–86. 

Drake, S., Buckley, S., Villarreal, M.L., Studd, S., and Hubbard, A.J., 2009, Vegetation 

classification, distribution and mapping report—Tumacácori National Historical 

Park, Natural Resource Report NPS/SODN/NRR–2009/148: National Park 

Service, Fort Collins, Co. 

Duncan, J., Stow, D., Franklin, J., and Hope, A., 1993, Assessing the relationship 

between spectral vegetation indices and shrub cover in the Jornado Basin, New 

Mexico: International Journal of Remote Sensing, v. 14, no. 18, p. 3,395–3,416. 

Erdas, 2006, ERDAS IMAGINE 9.1, Leica Geosystems Geospatial Imaging, LLC, 

Georgia USA. August 26, 2006. 

ESRI, 2010, Topography tools for ArcGIS (9.3, 9.2, 9.1/9.0), accessed October 21, 2010, 

at http://arcscripts.esri.com/details.asp?dbid=15996. 
21

Fonseca, J., Jones, C., and Regan, J., 2008, Change in wetland cover, 1992–2001, Pima 

County, Arizona: 5th Annual Research Insights in Semiarid Ecosystems (RISE) 

Symposium, The University of Arizona, Tucson, October 11, 2008, poster 

presentation. 

Fry, J.A., Coan, M.J., Homer, C.G., Meyer, D.K., and Wickham, J.D., 2009, Completion 

of the National Land Cover Database (NLCD) 1992–2001 Land Cover Change 

Retrofit product: U.S. Geological Survey Open-File Report 2008–1379, 18 p., 

available online at http://pubs.usgs.gov/of/2008/1379/. 

Grossman, D.H., Faber-Langendoen, D., Weakley, A.S., Anderson, M., Bourgeron, P., 

Crawford, R., Goodin, K., Landaal, S., Metzler, K., Patterson, K.D., Pyne, M., 

Reid, M., and Sneddon, L., 1998, International classification of ecological 

communities—terrestrial vegetation of the United States: The Nature 

Conservancy, Arlington, Va. 

Hansen, M., Dubayah, R., and DeFries, R., 1996, Classification trees—an alternative to 

traditional land cover classifiers: International Journal of Remote Sensing, v. 17, 

p. 1,075–1,081. 

Homer, C.G., Huang, Chengquan, Yang, Limin, Wylie, B.K., and Coan, M.J., 2004, 

Development of a 2001 National Land Cover Database for the United States: 

Photogrammetric Engineering and Remote Sensing, v. 70, no. 7, p. 820–840. 

Huete, A.R., and Jackson, R.D., 1987, Suitability of spectral indices for evaluating 

vegetation characteristics on arid rangelands: Remote Sensing of Environment, v. 

23, p. 213–232. 

Jensen, J.R., 1996, Introductory digital image processing—a remote sensing perspective 

(2d ed.): Upper Saddle River, N.J., Prentice-Hall, 316 p. 

Labiosa, W.B., Bernknopf, R., Hearn, P., Hogan, D., Strong, D., Pearlstine, L., Mathie, 

A.M., Wein, A.M., Gillen, K., and Wachter, S., 2009, The South Florida 

Ecosystem Portfolio Model—a map-based multicriteria ecological, economic, and 

community land-use planning tool: U.S. Geological Survey Scientific 

Investigations Report 2009–5181, 41 p. 

Lawrence, R.L., and Wright, A., 2001, Rule-based classification systems using 

Classification and Regression Tree (CART) analysis: Photogrammetric 

Engineering & Remote Sensing, v. 67, p. 1,137–1,142. 

Lowry, J.H, Jr., Ramsey, R.D., Thomas, K.A., Schrupp, D., Kepner, W., Sajwaj, T., 

Kirby, J., Waller, E., Schrader, S., Falzarano, S., Langs, L., Manis, G., Wallace, 

C., Schulz, K., Comer, P., Pohs, K., Rieth, W., Velasquez, C., Wolk, B., Boykin, 

K., O’Brien, L., Prior-Magee, J.S., Bradford, D., and Thompson, B., 2006. Land 

cover classification and mapping, chap. 2 of J.S. Prior-Magee and others, eds., 

Southwest Regional Gap Analysis Final Report: U.S. Geological Survey, Gap 

Analysis Program, Moscow, Idaho. 

Norman, L., Tallent-Halsell, N., Labiosa, W., Weber, M., McCoy, A., Hirschboeck, K., 

Callegary, J., van Riper III, C., and Gray, F., 2010, Developing an ecosystem 

services online decision support tool to assess the impacts of climate change and 

urban growth in the Santa Cruz Watershed—where we live, work, and play: 

Sustainability, v. 2, no. 7, p. 2,044–2,069, available online at 

http://www.mdpi.com/2071-1050/2/7/2044/. 
22

Quinlan, J.R., 1993, C4.5—programs for machine learning: San Mateo, Calif., Morgan 

Kaufmann, 302 p. 

Quinlan, J.R. 1996, Bagging, boosting and c4.5: Thirteenth National Conference on 

Artificial Intelligence, Portland, Oreg., 1996, Proceedings, v. 1, AAAI Press, p. 

725–730. 

Sonoran Institute, 2008, Santa Cruz River Riparian Vegetation Mapping Project, Santa 

Cruz County, Arizona: Sonoran Institute, University of Arizona, 68 p., available 

online at http://sonoraninstitute.org/library/recoreading/doc_download/724-

santa-cruz-river-riparian-vegetation-map-final-report.html.

Sayre, R., Comer, P., Warner, H., and Cress, J., 2009, A new map of standardized 

terrestrial ecosystems of the conterminous United States: U.S. Geological Survey 

Professional Paper 1768, 17 p. 

Tagil, Sermin, and Jenness, Jeff, 2008, GIS-based automated landform classification and 

topographic, landcover and geologic attributes of landforms around the Yazoren 

Polje, Turkey: Journal of Applied Sciences, v. 8, p. 910–921. 

Villarreal, M.L., 2009, Land use and disturbance interactions in dynamic arid systems—

multiscale remote sensing approaches for monitoring and analyzing riparian 

vegetation change: Tucson, Arizona, University of Arizona, Ph.D. dissertation, 

167 p. 

Villarreal, M.L., Norman, L.M., Wallace, C.S.A., and van Riper, Charles, III, 2011, A 

multitemporal (1979-2009) land-use/land-cover dataset of the binational Santa 

Cruz Watershed: U.S. Geological Survey Open-File Report 2011-1131, 26 p. and 

database [http://pubs.usgs.gov/of/2011/1131/]. 

Villarreal, M.L., Drake, S., Marsh, S.E and A.L. McCoy. 2011b. The influence of 

wastewater subsidy, flood disturbance and neighbouring land use on current and 

historical patterns of riparian vegetation in a semi-arid watershed. River Research 

and Applications, doi:10.1002/rra.1510.