A High-Resolution Land-Use 

Map—Nogales, Sonora, 

Mexico 

Open-File Report 2010-1156

U.S. Department of the Interior 

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

KEN SALAZAR, Secretary 

U.S. Geological Survey 

Marcia K. McNutt, Director 

U.S. Geological Survey, Reston, Virginia 2010

For product and ordering information: 

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

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Suggested citation: 

Norman, L.M., Villarreal, M.L., Wallace, C.S.A., Gil Anaya, C.Z., Diaz Arcos, Israel and 

Gray, Floyd, 2010, A high-resolution land-use map; Nogales, Sonora, Mexico: U.S. 

Geological Survey Open-File Report 2010-1156, 21 p. [http://pubs.usgs.gov/of/2010/1156/].

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 

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

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

Procedures .................................................................................................................................. 3

Accuracy Assessment ................................................................................................................... 11

Results .......................................................................................................................................... 13

Conclusions .................................................................................................................................. 14

Acknowledgments ......................................................................................................................... 14

References Cited .......................................................................................................................... 14

Appendix. Metadata ..................................................................................................................... 16

Figures 

Figure 1. Locality map depicting streams, washes, and arroyos, the cities of Nogales, 

Arizona, and Nogales, Sonora; the Ambos Nogales Watershed boundary and the 

Nogales, Sonora Watershed boundary. .............................................................................. 2

Figure 2. National Geospatial-Intelligence Agency (NGA) high-resolution 

orthoimagery boundaries for the Nogales, Sonora Watershed. ................................. 4

Figure 3. National Land Cover Dataset (NLCD) 1992 Land-Cover Color Classification 

Legend. .............................................................................................................................. 5

Figure 4. Image 12RVV965605; RGB composite, red band, and reclassification. ............................ 7

Figure 5. Pictorial evolution of classification process, including: A, the supervised 

classification process on the RGB bands, B, transportation layer infusion, C, the 

urban zone information outlined in black, and D, the final product of these 

processes........................................................................................................................... 8

Figure 6: Pictorial display of transforming the visualized ponds into the land use map, 

including: A, the aerial photo of pond, B, the digitized water polygon, and C, the 

final raster depiction at that site. ......................................................................................... 9

Figure 7. Final land-use map for Nogales, Sonora. ....................................................................... 10

Figure 8. Random-point distribution within the Watershed boundary on orthophotos. .................... 11

Tables 

Table 1: Description of the National Land Cover Dataset 1992 classification system for 

classes used in Nogales, Sonora, subwatershed................................................................ 6

Table 2. Table of conditional Kappa for each category/class. ........................................................ 12

Table 3. Reference data for accuracy assessment of each class. ................................................. 12

Table 4. Classification accuracy totals. .......................................................................................... 12

Table 5. Selected classes from the AGWA associated look-up table for K2 modeling 

derived from Woolhiser, Smith, and Goodrich (1990). ...................................................... 13
A High-Resolution Land-Use Map—Nogales, Sonora, 

Mexico 

By Laura M. Norman1, Miguel L. Villarreal1, Cynthia S. A. Wallace1, Claudia Zulema Gil 

Anaya2, Israel Diaz Arcos2, and Floyd Gray1

Abstract 

The cities of Nogales, Sonora, and Nogales, Arizona, are located in the Ambos Nogales 

Watershed, a topographically irregular bowl-shaped area with a northward gradient. Throughout 

history, residents in both cities have been affected by flooding. Currently, the primary method 

for regulating this runoff is to build a series of detention basins in Nogales, Sonora. 

Additionally, the municipality also is considering land-use planning to help mitigate flooding. 

This paper describes the production of a 10-meter resolution land-use map, derived from 2008 

aerial photos of the Nogales, Sonora Watershed for modeling impacts of the detention basin 

construction and in support of an “Early Warning Hazard System” for the region.

Introduction 

The Ambos Nogales Watershed is 93.98 square miles (243 km2), and is bisected almost evenly 

by the east-west International Boundary between the United States and Mexico (fig. 1). The 

Nogales Wash originates 6.72 miles (10.83 km) south of the border and flows north another 8.88 

miles (14.29 km) before joining with the Santa Cruz River in Arizona. The main branch of the 

Nogales Wash has a contributing area in Nogales, Sonora, of 25.79 square miles (66.8 km2) that 

discharges into Nogales, Arizona. Actions to control and regulate dangerous flows into the 

urban areas of Ambos Nogales are being carried-out primarily upstream in Mexico within this 

subwatershed. 

Locations for the installation of gabion-type flood detention features to reduce flood-stage 

discharges in washes are based on the recommendations of the USACE report (2005). Norman 

and others (2010) implement the KINEROS2 (K2) model using the Automated Geospatial 

Watershed Assessment 2.0 geographic information system interface to evaluate the Ambos 

Nogales Watershed’s vulnerability to flooding. The K2 model is used to identify flood-prone 

areas, to simulate the impact of land-use change, and to evaluate the impact of potential floodcontrol interventions. A heterogeneous land-cover dataset was developed by Norman and 

Wallace (2008) for the Ambos Nogales Watershed to support input needed for calculating the 

hydrological parameters of a small Watershed. The data were derived from imagery acquired on 

October 7, 1992, and the pixel size for the raster land-use map is 60 m. 

 

1U.S. Geological Survey, 520 N. Park Avenue, Tucson, AZ, USA 85719-5035. 

2

Instituto Municipal de Investigación y Planeación, Av. Alvaro Obregón # 61 Altos, Oficina 107; 

Col. Centro, C. P. 84030. H. Nogales, Sonora, Mexico.
2

Figure 1. Locality map depicting streams, washes, and arroyos, the cities of Nogales, Arizona, 

and Nogales, Sonora; the Ambos Nogales Watershed boundary and the Nogales, Sonora 

Watershed boundary. 

The demonstration of this model’s utility to simultate these impacts prompted the International 

Boundary and Water Commission (IBWC) to implement a higher-resolution study on the 

impacts of the detention basins that have been constructed in Nogales, Sonora, to provide a more 

accurate understanding of the volume of water being detained by these features and the 

magnitude of discharge for all the return periods studied, 10-YR, 25-YR, 50-YR, using the 

KINEROS2 (K2) model . 

Nogales, Sonora, offers employment opportunities for people in Mexico. The city is growing 

without much regard to slope, location of washes, or infrastructure capacity. Within Nogales, 

Sonora the 2000 census recorded a population of 159,787, with an annual growth rate of 4.9 

percent. In 2009, unofficial estimates suggest the population is closer to 300,000. Based on 

urban growth scenarios predicted by Norman and others (2009) the SLEUTH model predicted 
3

that Nogales, Sonora, urban area would grow to almost 3.5 times its 2002 size by 2030. In order 

to characterize the impacts of increasing impervious surfaces on modeling results, an updated 

higher-resolution land-use map is warranted. 

The following sections describe the procedure employed to develop and test the accuracy of a 

10-meter resolution land-use map, using recent orthophotos of the Nogales, Sonora Watershed. 

The product will assist in modeling impacts of the detention basin construction for the IBWC 

and also as a part of a larger project to develop an “Early Warning Hazard System” for the region 

in cooperation with the U.S. Department of Defense’s Northern Command (NORTHCOM). 

Procedures 

High-resolution orthoimagery was acquired October 13, 2008, by the National GeospatialIntelligence Agency (NGA). The orthoimages were produced at 0.3-meter pixel resolution 

(approximately 1-foot) in three color bands represented by the red, green, and blue (RGB) 

portions of the spectrum. Pixel values range from 0 to 255. Seventy-two images comprised the 

study area (fig. 2). 

The National Land Cover Dataset (USGS Land Cover Institute, 1992) classification schema (fig. 

3; table 1) was employed to classify the imagery because that type of dataset was used previously 

in modeling the Watershed runoff (Norman and Wallace, 2008; Norman and others, 2010). 

Vegetation can be identified using aerial photography; in the shorter, visible wavelengths, the 

reflectance of vegetation demonstrates a peak at the green region and is low in both the blue and 

red regions of the electromagnetic spectrum (Jensen, 2007). A level-slice classifier was applied 

to the data layers after evaluating the relationship between image histogram distributions and 

observed land cover. Each TIFF was reclassified in ESRI ArcMap 9.3 based on the longest 

visible red wavelength. The histogram of these red band data were boxed into three classes: 

those with values 0-50, were classified as 43: Mixed Forest, 50-100, were classified as 51: 

Shrubland, 100-255, were classified as 31: Bare Rock/Sand/Clay (fig. 4). 

These raster datasets were mosaicked into a new raster dataset and resampled to a 10-meter 

resolution using the nearest neighbor assignment (fig. 5A). The transportation vector data layer 

of the Municipal Institute of Research and Planning was merged with linear transportation data 

(roads, streets, highways, and railroad) from the Mexican National Institute of Statistics and 

Geography (Instituto Nacional de Estadística y Geografía; INEGI) to develop a raster grid 

represention with the class value of 23: Commercial/Industrial/Transportation, which was 

overlain onto the land-use map (fig. 5B). Land-use vector data developed by Municipal Institute 

of Research and Planning (Instituto Municipal de Investigación y Planeación; IMIP) in 2009 was 

then substituted in the urban area (fig. 5C). Specifically, the population density and services, 

trade, and industrial locations as specified by 31: Bare Rock/Sand/Clay were substituted with 

either 21: Low Intensity Residential, 22: High Intensity residential, or 23: 

Commercial/Industrial/Transportation, according to the corresponding polygonal reference data 

(fig. 5D). 
4

Figure 2. National Geospatial-Intelligence Agency (NGA) high-resolution orthoimagery 

boundaries for the Nogales, Sonora Watershed. 
5

Figure 3. National Land Cover Dataset (NLCD) 1992 Land-Cover Color Classification Legend.
6

Table 1: Description of the National Land Cover Dataset 1992 classification system for classes 

used in Nogales, Sonora, subwatershed (http://landcover.usgs.gov/classes.php).

Land-Cover Classification Description

11. Open Water

All areas of open water, generally with less than 25

percent cover of vegetation/land cover.

21. Low Intensity Residential

Includes areas with a mixture of constructed 

materials and vegetation. Constructed materials 

account for 30-80 percent of the cover. Vegetation 

may account for 20-70 percent of the cover. These 

areas most commonly include single-family 

housing units. Population densities will be lower 

than in high-intensity residential areas.

22. High Intensity Residential

Includes highly developed areas where people 

reside in high numbers. Examples include 

apartment complexes and row houses. Vegetation 

accounts for less than 20 percent of the cover. 

Constructed materials account for 80 to100 percent 

of the cover.

23. Commercial/ Industrial/

Transportation

Includes infrastructure (for example, roads and 

railroads) and all highly developed areas not 

classified as High Intensity Residential.

31. Bare Rock/ Sand/ Clay

Perennially barren areas of bedrock, desert 

pavement, scarps, talus, slides, volcanic material, 

glacial debris, beaches, and other accumulations of 

earthen material.

43. Mixed Forest 

Areas dominated by trees where neither deciduous 

nor evergreen species represent more than 75 

percent of the cover present.

51. Shrubland

Areas dominated by shrubs; shrub canopy accounts 

for 25-100 percent of the cover. Shrub cover 

generally is greater than 25 percent when tree cover 

is less than 25 percent. Shrub cover may be less 

than 25 percent in cases when the cover of other 

life forms (for example, herbaceous or tree) is less 

than 25 percent and shrub cover exceeds the cover 

of the other life forms. 
7

Figure 4. Image 12RVV965605; RGB composite, red band, and reclassification. 
8

Figure 5. Pictorial evolution of classification process, including: A, the supervised classification process on the RGB bands, B,

transportation layer infusion, C, the urban zone information outlined in black, and D, the final product of these processes.

A B 

C D
9

Upon visual inspection, we identified some very green looking, turbid pond features that had not 

been mapped correctly by the classification procedure. These were digitized into a “water” 

shapefile to represent these locations, converted to a raster dataset, and overlain into the land-use 

map in the class 11: Open Water (fig. 6). 

A B C 

Figure 6: Pictorial display of transforming the visualized ponds into the land use map, including: A, the 

aerial photo of pond, B, the digitized water polygon, and C, the final raster depiction at that site.

Classes used in this land-use map are described in table 1. Metadata were developed (appendix), and 

the final dataset is available as a raster download through this online publication (fig. 7). 
10

Figure 7. Final land-use map for Nogales, Sonora
11

Accuracy Assessment 

An accuracy assessment was performed on the final product, which revealed an overall 

classification accuracy of 91.43 percent. An accuracy assessment was completed to determine if 

the mapped land cover would match the field data for 300 random points; 50 points per class 

(except water) were stratified across classes. Random points were generated using Leica 

Geosystems Erdas Imagine 9.1 and assigned to the distribution of thematic layer classes. The 

orthophotos that comprise the subwatershed boundaries were mosaicked, and 175 of the 300 

random points occurred on the mosaicked watershed-boundary image. These points were used 

for the accuracy assessment (fig. 8). 

Figure 8. Random-point distribution within the Watershed boundary on orthophotos. 
12

While the overall classification accuracy of this map is 91.43 percent, the overall kappa value is 

0.8961, which is characterized by Landis and Koch as almost perfect agreement (1977). 

Individual class Kappas are reported in table 2. The reference data for accuracy assessment (table 

3) and the classification-accuracy totals are reported in table 4. 

Table 2. Table of conditional Kappa for each category/class. 

Classified name Category description Conditional Kappa

Class 21 Low Intensity Residential 0.9615

Class 22 High Intensity Residential 0.9012

Class 23 Commercial/Industrial/ Transportation 0.7871

Class 31 Bare Rock/ Sand/ Clay 0.9550

Class 43 Mixed Forest 0.8408

Class 51 Shrubland 0.9587

Table 3. Reference data for accuracy assessment of each class. 

Classified Data Class 21 Class 22 Class 23 Class 31 Class 43 Class 51

 Class 21 31 0 0 0 0 1

 Class 22 0 21 0 2 0 0

 Class 23 0 0 35 7 0 0

 Class 31 0 0 1 27 0 0

 Class 43 2 0 1 0 18 0

 Class 51 0 0 1 0 0 28

Column total 33 21 38 36 18 29

Table 4. Classification accuracy totals. 

Class name

Reference

totals

Classified

totals

Number

correct

Producers 

accuracy, in 

percent

Users

accuracy, in 

percent

Class 21 33 32 31 93.94 96.88

Class 22 21 23 21 100.00 91.30

Class 23 38 42 35 92.11 83.33

Class 31 36 28 27 75.00 96.43

Class 43 18 21 18 100.00 85.71

Class 51 29 29 28 96.55 96.55

Totals 175 175 160
13

Discrepancies are noted in the accuracy assessment. Primarily, Class 31: Bare Rock/Sand/Clay is 

mislabeled as either Class 22: High Intensity Residential or Class 23: 

Commercial/Industrial/Transportation. This mislabeling resulted from the constraints of using 

IMIP data to support the supervised classification since the classes associated with the zoning 

polygons have a coarser aerial resolution compared to the more discrete spectrally derived classes. 

In addition, the IMIP data may represent land-use classes that were in planning stages when the 

imagery was originally flown. Other errors include the shadows inherent in the photos being 

misclassified or resulting from the resampling from less than 1-meter data to 10-meter data. 

Results 

A detailed land-use map has been produced to be used as an input into the K2 model, which will 

be used to support flood control efforts in the Ambos Nogales Watershed. As demonstrated, the 

majority of Low Intensity Residential; High Intensity Residential; 

Commercial/Industrial/Transportation; Bare Rock/Sand/ Clay; Mixed Forest; and Shrubland are 

classified very accurately. The small number of misclassified pixels should not disturb the 

suitability of the map for use as input to the K2 model. Derived land-use parameters for K2 

include the fraction of surface covered by intercepting cover, Manning’s roughness coefficient, 

and percentage covered by impervious materials. Manning’s roughness coefficient, n, represents 

the resistance to flood flows in channels and flood plains. Respective runoff values should not be 

greatly affected by the small number of misclassified pixels. Note in table 5 that the runoff 

parameters assigned for the classes in this map demonstrate that runoff characteristics of bare 

rock, soil, and sand are very similar to those of commercial, industrial, and transport classes. 

Table 5. Selected classes from the AGWA associated look-up table for K2 modeling derived from 

Woolhiser, Smith, and Goodrich (1990).

Class Name A B C D Cover Int N Imperv

11 Open Water 100 100 100 100 0 0.00 0.000 0.00

21 Low-Intensity Residential 77 85 90 92 15 0.10 0.150 0.40

22 High-Intensity Residential 81 88 91 93 10 0.08 0.120 0.75

23 Commercial/Industrial/Transport 89 92 94 95 2 0.05 0.010 0.80

31 Bare Rock/Sand/Clay 96 96 96 96 2 0.00 0.010 0.00

43 Mixed Forest 55 55 75 80 50 1.15 0.015 0.00

51 Shrubland 63 77 85 88 25 3.00 0.055 0.00
14

Conclusions 

Remote-sensing data coupled with field-information and geographic information systems have 

been recognized as an effective methodology in identifying spatial patterns for mapping land use. 

The hybrid procedure described is not a new approach; however, we were fortunate to be able to 

recognize a pattern in the natural color imagery from which to classify the vegetation structure 

from the green band data that was available. Additionally, the input of existing city zoning data 

and transportation data compliments the output by providing real-world data to describe the 

current or intended use of the land that can be fitted into the hydrological model and used to 

develop scenarios; however, using the vegetation classification overlay, allows for the shrubland 

or mixed forest classification to be identified within. The resulting map will be used by local 

managers to examine the impacts of constructed flood-detention features and locate priority areas 

more vulnerable to flooding hazard. It will also be used in combination with cooperative 

raingage data to stream live precipitation data into the live model to simulate runoff scenarios for 

a flood warning system that will benefit both communities living in the Ambos Nogales 

Watershed. 

Acknowledgments 

The U.S. Section of the International Boundary and Water Commission, El Paso, Texas (IBWC) 

has funded the implementation of developing more accurate predictions of the detention basins 

and the updated land-use map to support it. In a cooperative effort, NORTHCOM is 

implementing the linked emergency flood response system that will ultimately run off of this 

combined modeling effort. The developed model integration project described in this paper was 

supported by the USGS Geographic Analysis and Monitoring (GAM) Program to contribute 

towards an understanding of the land-surface changes occurring and the consequences of these 

changes. 

This project is in collaboration with many integral partners, including La Sección mexicana de la 

Comisión Internacional de Límites y Aguas, Nogales, Sonora (CILA); the National Water 

Commission in Mexico (CONAGUA); the Nogales, Sonora Wastewater Utility (OOMAPASNS); the Arizona Department of Environmental Quality (ADEQ); the U.S. Department of 

Agriculture, Tucson, Arizona (USDA-ARS-SWRC); the University of Arizona, Tucson, Arizona 

(UA); Sonora’s Water Commission, Hermosillo, Sonora (CEA); and the City of Nogales, Sonora, 

Mexico—Department of Emergency Response. 

The authors wish to thank Hans Huth (ADEQ) and James Callegary and Mike Gishey (USGS) for 

their reviews of this material. 

References Cited 

Jensen, J.R., 2007, Remote sensing of the environment—an Earth resource perspective, (2nd 

ed.), Prentice Hall. ISBN 0-13-188950-8. 

Landis, J.R., and Koch, G.G., 1977, The measure of observer agreement for categorical data. 

Biometrics, 33: 159-174. 
15

Norman, Laura M., Feller, Mark, and Guertin, D. Phillip, 2009, Forecasts of Cross-border Urban 

Growth Using the SLEUTH Model; Ambos Nogales Watershed, United States-Mexican 

Border, Computers, Environment, and Urban Systems, 33: pp. 150-159. 

Norman, Laura M., Huth, Hans, Levick, Lainie, Burns, I. Shea, Guertin, D. Phillip, LaraValencia, Francisco, and Semmens, Darius, 2010, Flood Hazard Awareness and 

Hydrologic Modeling at Ambos Nogales, US-Mexico Border, Journal of Flood Risk 

Management, Volume 3: Issue 2, 15 p. 

Norman, L.M., and Wallace, C.S.A., 2008, Mapping land use/land cover in the Ambos Nogales 

study area: U.S. Geological Survey Open-File Report 2008-1378, 42 p. 

[http://pubs.usgs.gov/of/2008/1378/].

USACE, 2005, Ambos Nogales Special Flood Damage Reduction Study, Los Angeles, CA: US 

Army Corps of Engineers. 

USGS Land Cover Institute, 1992, NLCD land-cover class definitions: U.S. Geological Survey 

Website, accessed July 6, 2010, at http://landcover.usgs.gov/classes.php. 

Woolhiser, D.A., Smith, R.E., Goodrich, D.C., 1990. KINEROS, a kinematic runoff and erosion 

model: Documentation and user manual. U.S. Department of Agriculture, Agricultural 

Research Service, ARS-77, 130p. SWRC Reference No.: 703. 
16

Appendix. Metadata 

Identification_Information: 

 Citation: 

 Citation_Information: 

 Originator: Laura M. Norman , Miguel Villarreal, Cynthia S.A. Wallace, Claudia Zulema Gil 

Anaya, Israel Diaz Arcos, and Floyd Gray 

 Publication_Date: Unknown 

 Title: A High-Resolution Land-Use Map: Nogales, Sonora, Mexico 

 Geospatial_Data_Presentation_Form: raster digital data 

 Series_Information: 

 Series_Name: Open-File Report 

 Publication_Information: 

 Publisher: USGS 

 Online_Linkage: \landuse 

 Description: 

 Abstract: This is a 10-meter resolution land-use map derived from 2008 aerial photos and vector data 

for the Nogales, Sonora, Mexico part of the Ambos Nogales Watershed. 

 Purpose: The results will be used for modeling impacts of detention basin construction and also as a 

part of a larger project to develop an "Early Warning Hazard System" for the region. This dataset was 

created to be used as input to the Automated Geospatial Watershed Assessment (AGWA) Tool, in order 

to predict runoff in this urbanizing Watershed. 

 Time_Period_of_Content: 

 Time_Period_Information: 

 Single_Date/Time: 

 Calendar_Date: 7-Oct-2008 

 Time_of_Day: unknown 

 Currentness_Reference: ground condition 

 Status: 

 Progress: Complete 

 Maintenance_and_Update_Frequency: As needed 

 Spatial_Domain: 

 Bounding_Coordinates: 

 West_Bounding_Coordinate: -111.036791 

 East_Bounding_Coordinate: -110.894884 

 North_Bounding_Coordinate: 31.332976 

 South_Bounding_Coordinate: 31.224569 

 Keywords: 

 Theme: 

 Theme_Keyword_Thesaurus: None 

 Theme_Keyword: Land uset. 

 Theme_Keyword: Land cover 

 Place: 

 Place_Keyword_Thesaurus: None 

 Place_Keyword: Nogales, Sonora 

 Place_Keyword: Nogales, Arizona 
17

 Temporal: 

 Temporal_Keyword_Thesaurus: None 

 Temporal_Keyword: 2008 

 Access_Constraints: None 

 Use_Constraints: There is no guarantee concerning the accuracy of the data. Users should be aware 

that temporal changes may have occurred since this data set was collected and that some parts of this 

data may no longer represent actual surface conditions. Users should not use this data for critical 

applications without a full awareness of its limitations. Acknowledgement of the originating agencies 

would be appreciated in products derived from these data. Any user who modifies the data is obligated 

to describe the types of modifications they perform. User specifically agrees not to misrepresent the 

data, nor to imply that changes made were approved or endorsed by the U.S. Geological Survey. Please 

refer to <http://www.usgs.gov/privacy.html> for the USGS disclaimer. 

 Point_of_Contact: 

 Contact_Information: 

 Contact_Person_Primary: 

 Contact_Person: Laura M. Norman 

 Contact_Organization: USGS 

 Contact_Position: Research Scientist

 Contact_Address: 

 Address_Type: mailing address 

 Address: 520 N. Park Ave., Ste #102K 

 City: Tucson 

 State_or_Province: AZ 

 Postal_Code: 85719 

 Country: USA 

 Contact_Voice_Telephone: 5206705510 

 Contact_Electronic_Mail_Address: lnorman@usgs.gov 

 Native_Data_Set_Environment: Microsoft Windows XP Version 5.1 (Build 2600) Service Pack 3; 

ESRI ArcCatalog 9.3.0.1770 

Data_Quality_Information: 

 Attribute_Accuracy: 

 Attribute_Accuracy_Report: The Overall Kappa (K^) Statistics was .8961--the classification process 

is avoiding 89.61 percent of the errors that a completely random classification generates and the overall 

classification accuracy is 91.43percent . The area is dominated by both mixed forest (trees) and 

shrubland around bare areas of transportation and urban area, which is represented in our map. 

 Logical_Consistency_Report: The accuracy of the dataset is based on the software's ability to detect 

land use signatures and the analysts's interpretation of features on the ground. Additional inaccuracy 

could occur in the original image it was processed from, because even slight measurement inaccuracies 

of the ground features selected for ortho control can affect the final accuracy. 

 Completeness_Report: Data are limited to areas included in Nogales, Sonora, Mexico subWatershed of 

the Ambos Nogales Watershed, as defined by a minimum bounding rectangle around the subWatershed. 

 Lineage: 

 Source_Information: 

 Source_Citation: 

 Citation_Information: 

 Originator: National Geospatial-Intelligence Agency (NGA) 
18

 Publication_Date: 2009 

 Title: Border Imagery 

 Geospatial_Data_Presentation_Form: raster digital data 

 Type_of_Source_Media: digital tape media 

 Source_Information: 

 Source_Citation: 

 Citation_Information: 

 Originator: Instituto Municipal de Investigación y Planeación 

 Publication_Date: Unpublished Material 

 Title: Nogales Land Use Cover 

 Geospatial_Data_Presentation_Form: vector digital data 

 Type_of_Source_Media: electronic mail system 

 Source_Time_Period_of_Content: 

 Time_Period_Information: 

 Single_Date/Time: 

 Calendar_Date: 2009 

 Source_Currentness_Reference: ground condition 

 Source_Information: 

 Source_Citation: 

 Citation_Information: 

 Originator: The Mexican Insituto Nacional de Estadistica, Geografia, e Informatica (INEGI) 

 Publication_Date: Unknown 

 Title: INEGI Transportation in Nogales, Sonora 

 Geospatial_Data_Presentation_Form: vector digital data 

 Online_Linkage: www.inegi.gob.mx

 Type_of_Source_Media: electronic mail system

 Source_Time_Period_of_Content: 

 Time_Period_Information: 

 Single_Date/Time: 

 Calendar_Date: 1995 

 Source_Currentness_Reference: publication date 

 Source_Contribution: The Mexican Insituto Nacional de Estadistica, Geografia, e Informatica is the 

main agency in Mexico that provides mapping and census data for the country. This 1:250,000-scale 

dataset provides medium resolution detail of the transportation road network of the country 

 Process_Step: 

 Process_Description: Each TIFF was reclassified based on the RGB composites. Those with values 

0 - 50, were classified as "43: Mixed Forest", 50-100, were classified as "51: Shrubland", 100-255, were 

classified as "31: Bare Rock/Sand/Clay". These raster datasets were mosaiced into a new raster dataset 

and resampled to a 10-meter resolution using the nearest neighbor assignment 

 Process_Date: 2010 

 Process_Step: 

 Process_Description: The transportation vector data layer of the Municipal Institute of Research and 

Planning was merged with linear transportation data (roads, streets, highways, and railroad) from INEGI 

to develop a raster grid represention with the class value of "23: Commercial/Industrial/Transportation" 

which was overlain onto the land use map 

 Process_Date: 2010 
19

 Process_Step: 

 Process_Description: Land-use vector data was then substituted in the urban area having been 

developed by Municipal Institute of Research and Planning for 2009. Specifically, the population 

density and services, trade, and industrial locations as specified with the class "31: Bare 

Rock/Sand/Clay" were substituted with either "21: Low Intensity Residential", "22: High Intensity 

residential", or "23: Commercial/Industrial/Transportation", according to the polygonal reference data it 

corresponded with. 

 Process_Date: 2010 

 Process_Step: 

 Process_Description: An accuracy assessment was performed on the final product, which revealed 

an overall classification accuracy of 91.43percent 

 Process_Date: 2010 

 Process_Step: 

 Process_Description: Upon manual inspection, we realized some very green looking pond features 

that had not been recognized by the classification procedure. These were digitized into a "water" 

shapefile to represent these locations, converted to a raster dataset, and overlain into the landuse map in 

the class "11: Open Water". 

 Process_Date: 2010 

Spatial_Data_Organization_Information: 

 Direct_Spatial_Reference_Method: Raster 

 Raster_Object_Information: 

 Raster_Object_Type: Grid Cell 

 Row_Count: 1201 

 Column_Count: 1350 

 Vertical_Count: 1 

Spatial_Reference_Information: 

 Horizontal_Coordinate_System_Definition: 

 Planar: 

 Grid_Coordinate_System: 

 Grid_Coordinate_System_Name: Universal Transverse Mercator 

 Universal_Transverse_Mercator: 

 UTM_Zone_Number: 12 

 Transverse_Mercator: 

 Scale_Factor_at_Central_Meridian: 0.999600 

 Longitude_of_Central_Meridian: -111.000000 

 Latitude_of_Projection_Origin: 0.000000 

 False_Easting: 500000.000000 

 False_Northing: 0.000000 

 Planar_Coordinate_Information: 

 Planar_Coordinate_Encoding_Method: row and column 

 Coordinate_Representation: 

 Abscissa_Resolution: 10.000000 

 Ordinate_Resolution: 10.000000 

 Planar_Distance_Units: meters 

 Geodetic_Model: 

 Horizontal_Datum_Name: North American Datum of 1983 
20

 Ellipsoid_Name: Geodetic Reference System 80 

 Semi-major_Axis: 6378137.000000 

 Denominator_of_Flattening_Ratio: 298.257222 

Entity_and_Attribute_Information: 

 Detailed_Description: 

 Entity_Type: 

 Entity_Type_Label: landuse.vat 

 Attribute: 

 Attribute_Label: Rowid 

 Attribute_Definition: Internal feature number. 

 Attribute_Definition_Source: ESRI 

 Attribute_Domain_Values: 

 Unrepresentable_Domain: Sequential unique whole numbers that are automatically generated. 

 Attribute: 

 Attribute_Label: VALUE 

 Attribute_Definition: Land Cover Class 

 Attribute_Definition_Source: The National Land Cover Dataset (NLCD 1992) classification schema 

was employed to classify the imagery 

 Attribute_Domain_Values: 

 Enumerated_Domain: 

 Enumerated_Domain_Value: NLCD 1992 Classification System 

 Enumerated_Domain_Value_Definition: Classes used in Nogales, Sonora subWatershed 

 Enumerated_Domain_Value_Definition_Source: 

(http://www.epa.gov/mrlc/definitions.html#1992). 

 Attribute: 

 Attribute_Label: COUNT 

 Attribute_Definition: # of times this value occurs 

 Attribute_Definition_Source: ESRI 

Distribution_Information: 

 Distributor: 

 Contact_Information: 

 Contact_Organization_Primary: 

 Contact_Organization: U.S. Geological Survey 

 Contact_Person: Laura Norman 

 Contact_Position: Research Physical Scientist 

 Contact_Voice_Telephone: 5206705510 

 Hours_of_Service: 8am-5pm, M-F 

 Resource_Description: Downloadable Data 

 Distribution_Liability: Although these data have been processed successfully on a computer system at 

the USGS, no warranty expressed or implied is made by the USGS regarding the use of the data on any 

other system, nor does the act of distribution constitute any such warranty. Data may have been 

compiled from various outside sources. Spatial information may not meet National Map Accuracy 

Standards. This information may be updated without notification. The USGS shall not be liable for any 

activity involving these data, installation, fitness of the data for a particular purpose, its use, or analyses 

results. 

 Standard_Order_Process: 
21

 Digital_Form: 

 Digital_Transfer_Information:

 Format_Name: ERDAS 

 Format_Version_Number: GRID

 File_Decompression_Technique: no compression applied 

 Transfer_Size: 1.432 

 Fees: None 

 Ordering_Instructions: Data are available online at no charge via Internet download. 

Acknowledgement of the U.S. Geological Survey would be appreciated in products derived from these 

data.

Metadata_Reference_Information: 

 Metadata_Date: 20100513 

 Metadata_Contact: 

 Contact_Information: 

 Contact_Organization_Primary: 

 Contact_Organization: USGS 

 Contact_Person: Laura M. Norman 

 Contact_Position: Researrch Physical Scientist 

 Contact_Address: 

 Address_Type: mailing address 

 Address: 520 N. Park Ave., Ste 102K 

 City: Tucson 

 State_or_Province: AZ 

 Postal_Code: 85719 

 Country: USA 

 Contact_Voice_Telephone: 520-670-5510 

 Metadata_Standard_Name: FGDC Content Standards for Digital Geospatial Metadata 

 Metadata_Standard_Version: FGDC-STD-001-1998 

 Metadata_Time_Convention: local time 

 Metadata_Extensions: 

 Online_Linkage: http://www.esri.com/metadata/esriprof80.html 

 Profile_Name: ESRI Metadata Profile