MAPPING LAND USE/LAND COVER IN THE
AMBOS NOGALES STUDY AREA
By Laura M. Norman and Cynthia S.A. Wallace
Open File Report 2008-1378
U.S. Department of the Interior
U.S. Geological Survey
U.S. Department of the Interior
DIRK KEMPTHORNE, Secretary
U.S. Geological Survey
Mark D. Myers, Director
U.S. Geological Survey, Reston, Virginia 2008
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Suggested citation:
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.
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.
2
CONTENTS
ABSTRACT ............................................................................................................................................ 5
INTRODUCTION ................................................................................................................................... 5
LAND USE/LAND COVER.................................................................................................................... 8
PROCEDURES..................................................................................................................................... 11
ACCURACY ASSESSMENT REPORT ..............................................................................................................................................20
CONCLUSIONS................................................................................................................................... 34
ACKNOWLEDGMENTS ..................................................................................................................... 34
REFERENCES ...................................................................................................................................... 35
APPENDIX A—METADATA............................................................................................................. 37
List of Figures
Figure 1. Location map of the Ambos Nogales watershed along the International border of
Arizona, United States, and Sonora, Mexico. .................................................................................................7
Figure 2: National Land Cover Data (NLCD) data for Nogales, Arizona, in the Ambos Nogales
watershed. ...................................................................................................................................................................9
Figure 3. Binational Land‐cover dataset for Ambos Nogales. Arizona, United States, and
Sonora, Mexico........................................................................................................................................................10
Figure 4. North American Landscape Characterization (NALC) image over Ambos Nogales,
Arizona, United States, and Sonora, Mexico, acquired on October 7, 1992, by using Landsat
Multi‐Spectral Scanner data. .............................................................................................................................11
Figure 5. ERDAS IMAGINE 9.1 AOI tool editor and example of samples in Nogales, Arizona.
.......................................................................................................................................................................................13
Figure 6. Signature Editor in ERDAS IMAGINE 9.1 and 8 signatures..............................................14
Figure 7. National Land Cover Data Maps. A., Ambos Nogales watershed land cover (60‐m
resolution), Arizona, United States, and Sonora, Mexico; B., Ambos Nogales watershed land
cover (30‐m resolution), Nogales, Arizona. ................................................................................................15
3
Figure 8: DOQQ of area south of the U.S.‐Mexico border. ....................................................................17
Figure 9. Residential area in Nogales, Sonora, that was classified as Bare Rock/Sand/Clay.
.......................................................................................................................................................................................19
Figure 10: Picture of colonias at the U.S.‐Mexico border in Ambos Nogales showing lack of
pavement on roads in this watershed. .........................................................................................................20
Figure 11. Screengrab of ERDAS IMAGINE Signature Editor with 6 classes. ..............................25
Figure 12. Image of property at the U.S.‐Mexico border between Marisposa Rd. and I‐19 in
Nogales, Arizona.....................................................................................................................................................29
Figure 13. Photo of development land taken near Highway 82 in Nogales, Arizona...............30
Figure 14. MRLC look‐up table available in AGWA2..............................................................................31
Figure 15. New “Class” attribute assigned to binational map............................................................31
Figure 16. Final attribute table for the raster binational land‐cover input of Ambos Nogales,
Arizona, United States, and Sonora, Mexico. ..............................................................................................32
Figure 17. Final binational land‐cover map of Ambos Nogales, Arizona, United States, and
Sonora, Mexico, for input to AGWA2. ............................................................................................................33
List of Tables
Table 1. Error Matrix ..............................................................................................................................................22
Table 2. Accuracy Totals.....................................................................................................................................23
Table 3. Conditional Kappa for each Category. .........................................................................................24
Table 4. Error Matrix............................................................................................................................................26
Table 5. Accuracy Totals.....................................................................................................................................27
Table 6. Errors of omission, commission, and percent correct. .........................................................28
4
Mapping Land Use/Land Cover in the Ambos Nogales Study Area
By Laura M. Norman and Cynthia S.A. Wallace
Abstract
The Ambos Nogales watershed, which surrounds the twin cities of Nogales, Arizona,
United States and Nogales, Sonora, Mexico, has a history of problems related to flooding.
This paper describes the process of creating a high-resolution, binational land-cover
dataset to be used in modeling the Ambos Nogales watershed. The Automated Geospatial
Watershed Assessment tool will be used to model the Ambos Nogales watershed to
identify focal points for planning efforts and to anticipate ramifications of implementing
detention reservoirs at certain watershed planes.
Introduction
Watersheds located along the Arizona-Sonora border of the United States and Mexico
are especially susceptible to flooding and erosion during the summer monsoon season.
Soils in this semi arid region typically have high caliche content (hard deposit of calcium
carbonate), making them relatively impermeable and leading to enhanced runoff and
increased risk of flash floods and debris flows. Homes, livelihoods, and even lives are
threatened by these hazards. In Ambos Nogales (fig. 1), landslides and erosion of roads
and hillslopes threaten surface water quality, contaminating streams with sediments and
included toxins. Sewers in Nogales, Sonora, are not equipped to handle some loads and
have caused fecal contamination of ground-water supplies in times of flood. In the face of
climate change and imminent urban growth, scientists can offer prediction scenarios of
what might happen during extreme events.
5
In order to determine the effects of flooding scenarios and urban growth for future
planning, a model will be applied. The Kinematic Runoff and Erosion (KINEROS2)
Model was developed by the U.S. Department of Agriculture-Agricultural Research
Service (USDA-ARS) to simulate runoff, infiltration, interception, and erosion based on
precipitation events (Woolhiser and others, 1970). This model can be applied within a
GIS to represent spatial distribution within a watershed using the Automated Geospatial
Watershed Assessment (AGWA2) Tool (Miller and others, 2002; Semmens and others,
2008). Four inputs are required to run the model (1) watershed elements (for example,
topography and slope), (2) soil types, (3) precipitation information, and (4) land-cover
data. Land-cover data is not readily available at a high enough resolution to simulate
processes within this small watershed.
6
Figure 1. Location map of the Ambos Nogales watershed along the International border of Arizona, United States, and Sonora, Mexico.
7
Land Use/Land Cover
An integer grid dataset representing the distribution of land-cover classes across the study
area is required for input to AGWA2. Several datasets are supported by AGWA2 natively,
including the North American Landscape Characterization (NALC; Lunetta and Sturdevant,
1993), Multi-Resolution Land Cover Characterization (MRLC; Loveland and Shaw, 1996), and
Gap Analysis Program (GAP; Gaydos, 1996) land covers. The National Land Cover Data
(NLCD; Vogelmann and others, 2001) is a dataset that maps the conterminous United States into
21-classes of land cover. The spatial resolution is 30-meters derived from Landsat Thematic
Mapper (TM) satellite imagery. We acquired NLCD data for Nogales, Arizona (fig. 2).
8
Nogales, Arizona
‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐
Nogales, Sonora, Mexico
Figure 2: National Land Cover Data (NLCD) data for Nogales, Arizona, in the Ambos Nogales
watershed.
The U.S. Geological Survey’s (USGS) Border Environmental Health Initiative combined
Mexico’s Instituto Nacional de Estadística, Geografía, e Informática (INEGI) 1:250,000 Uso de
Suelo (Land Use) dataset (1993) with NLCD (1992) to make a border-wide Binational Land
Cover dataset (http://borderhealth.cr.usgs.gov/index.html, last accessed December 21, 2008).
Eight land-cover classes were mapped to a generalized, modified Anderson Level I (Anderson
and others, 1976) binational classification system to which both countries’ Land Use/Land Cover
(LULC) data could be reclassified (Parcher and others, 2006; Wilson, 2006; fig. 3).
9
Nogales, Arizona
‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐
Nogales, Sonora, Mexico
EXPLANATION
Developed
Agriculture
Forest
Shrubland
Water
Barren
Grassland/Pasture
Wetland/Halophillic
vegetation
Figure 3. Binational Land-cover dataset for Ambos Nogales. Arizona, United States, and Sonora,
Mexico.
The binational data has been reclassified and was derived entirely from Landsat interpretations;
however, the original datasets were captured through different processes. The Mexican data were
digitized polygons of land use, while the NALC data were classified using automated techniques
resulting in a raster dataset. The LULC data represented by the polygons tend to present a more
homogeneous picture of the landscape; the raster data represent more heterogeneity (Parcher and
others 2006, Wilson 2006). While the binational dataset does provide a good qualitative
representation of regional patterns in LULC, a more heterogeneous dataset is desirable to support
the complexity needed for calculating hydrological parameters of a small watershed.
10
Procedures
The NALC data are Landsat Multi-Spectral Scanner (MSS) time-series triplicates that
were acquired in 1973, 1986, and 1991 (+/- one year). Pixel size for all images is 60 meters.
NALC triplicates were acquired for Path 36, Row 38. The dataset from October 7, 1992 were
used for this processing (fig. 4).
Figure 4. North American Landscape Characterization (NALC) image over Ambos Nogales,
Arizona, United States, and Sonora, Mexico, acquired on October 7, 1992, by using Landsat MultiSpectral Scanner data.
Using ERDAS IMAGINE 9.1 software, we extracted forty-five samples of land cover,
based on dead-reckoning, and compared them with the classification scheme available from the
NLCD using the Area of Interest (AOI) tool editor (fig. 5), to represent the full range of land
11
cover in the watershed. The AOI feature allows the user to draw polygons around distinct
features and relate the signatures back to a known reference.
12
Figure 5. ERDAS IMAGINE 9.1 AOI tool editor and example of samples in Nogales, Arizona.
13
From the 45 samples identified, we merged the samples into 8 signatures that correlate with the
NLCD classes occurring in the Ambos Nogales watershed: (1) Deciduous Forest, (2) Bare
Rock/Sand/Clay, (3) Quarries/Strip Mines/Gravel Pit, (4) Grassland/Herbaceous, (5) Urban/Recreational
Grasses, (6) Shrubland, (7) Low Intensity Residential, and (8) Commercial/Industrial/Transport (fig. 6).
Figure 6. Signature Editor in ERDAS IMAGINE 9.1 and 8 signatures.
We applied a supervised classification, using these 8 signatures and the minimum distance rule, to map
each pixel in the MSS scene into one of the 8 classes to create a new map of land cover in the watershed
(fig. 7).
14
A.
B.
Figure 7. National Land Cover Data Maps. A., Ambos Nogales watershed land
cover (60-m resolution), Arizona, United States, and Sonora, Mexico; B.,
Ambos Nogales watershed land cover (30-m resolution), Nogales, Arizona.
15
To check the accuracy of the newly created binational land-cover map, we
developed a stratified sampling regime by assigning random points (reference pixels) to
the classified image. ERDAS IMAGINE 9.1 uses a square window to select the reference
pixels and the number of points is stratified to the distribution of thematic layer classes.
Congalton (2001) and Congalton and Green (1999) suggest using a minimum sample size
of 50 per class.
This creates a CellArray that lists two sets of class values for the randomly
selected points from the classified map file. One set of class values is the land-cover class
from the new map, and the other set of class values (reference values) is determined from
higher resolution images by the analyst.
For each randomly selected point, we manually compared the classification on the
NCLD map of Nogales, Arizona, the 1995-1996 Digital Orthophoto Quarter Quadrangles
(DOQQs) of Nogales, Arizona, from the USGS, and of Nogales, Sonora, from INEGI to
identify the reference points. These orthophotos are taken at a 1-m resolution and can be
zoomed in on to determine actual composition of land use. Some points were not
accurately classified.
A large area south of the International border was classified as Quarries/mines,
but according to the DOQQ, reference point #98 is located in areas of widely dispersed
mesquite or creosote surrounded by bare soil, a land cover reclassified as Shrubland and
Bare rock/Sand/Clay (fig. 8).
16
Figure 8: DOQQ of area south of the U.S.-Mexico border.
17
Additionally, in Nogales, Sonora, a lot of area that is residential is classified as Bare (fig.
9) because the neighborhood design (bare soils, raw housing materials) varies from the
design normally seen in the U.S. We left these Sonora residential areas classified as bare
because it most accurately represents the terrain, especially since this map will be used as
input to a hydrological model (not particularly created for urban planning purposes; fig.
10).
18
Figure 9. Residential area in Nogales, Sonora, that was classified as Bare
Rock/Sand/Clay.
19
Figure 10: Picture of colonias at the U.S.-Mexico border in Ambos Nogales showing lack
of pavement on roads in this watershed.
(http://www.worldproutassembly.org/archives/2006/12/children_cross.html, last accessed
December 21, 2008).
Accuracy Assessment Report
An error matrix was created to evaluate the new map’s accuracy, to compare the
reference class values to the assigned class values, and to identify errors of inclusion
(commission errors) and exclusion (omission errors) present in the map (Congalton and
20
Green, 1999; table 1). A commission error occurs when an area is misclassified to an
incorrect category. An omission error occurs when an area is excluded from the class to
which it belongs. In addition to showing errors of omission and commission, the error
matrix can be used to compute overall accuracy (table 2). Finally, the Kappa coefficient
expresses the proportionate reduction in error generated by a classification process
compared with the error of a completely random classification (table 3).
21
Table 1. Error Matrix
Commercial/
Industrial/
Transport
Commercial/ Industrial/
Transport
Deciduous
Forest
Grassland/
Herbaceous
Bare
Rock/
Sand/
Clay
Quarries/
Strip Mines/
Gravel Pit
Shrubland
Low
Intensity
Residential
Urban/
Recreational
Grasses
Classified
total
46
0
0
1
0
3
0
0
50
Deciduous Forest
0
46
1
0
0
3
0
0
50
Grassland/ Herbaceous
0
0
33
0
0
17
0
0
50
Bare Rock/ Sand/ Clay
1
1
0
47
0
1
0
0
50
Quarries/ Strip Mines/
Gravel Pit
1
0
3
10
6
28
2
0
50
Shrubland
1
1
0
0
0
48
0
0
50
Low Intensity Residential
0
0
0
1
0
0
49
0
50
Urban/ Recreational
Grasses
0
2
18
2
0
21
1
6
50
49
50
55
61
6
121
52
6
400
Reference total
22
Table 2. Accuracy Totals.
Reference
totals
Classified
totals
Number
correct
Producers
accuracy, in
percent
Users
accuracy, in
percent
Commercial/
Industrial/
Transport
49
50
46
93.88
92
Deciduous Forest
50
50
46
92.00
92
Grassland/
Herbaceous
55
50
33
60.00
66
Bare Rock/ Sand/
Clay
61
50
47
77.05
94
Quarries/ Strip
Mines/ Gravel Pit
6
50
6
100.00
12
121
50
48
39.67
96
Low Intensity
Residential
52
50
49
94.23
98
Urban/
Recreational
Grasses
6
50
6
100.00
12
400
400
281
Shrubland
Totals
23
Table 3. Conditional Kappa for each Category.
Class Name
Kappa
Commercial/ Industrial/ Transport
0.9088
Deciduous Forest
0.9086
Grassland/ Herbaceous
0.6058
Bare Rock/ Sand/ Clay
0.9292
Quarries/ Strip Mines/ Gravel Pit
0.1066
Shrubland
0.9427
Low Intensity Residential
0.9770
Urban/ Recreational Grasses
0.1066
The Overall Kappa (K^) Statistic was 0.66. This implies that the classification
process is avoiding 66 percent of the errors that a completely random classification
generates (Congalton, 2001). The overall classification accuracy was calculated to be
70.25 percent.
We were not satisfied with these results and so went back to the drawing board.
The majority of error appeared to be with the Quarries/Strip Mine/Gravel Pit class and
the Urban/Recreational Grasses class. The samples were merged into 6 signatures from
the original 8 (1) Deciduous Forest, (2) Shrubland, (3) Low Intensity Residential, (4)
Commercial/Industrial/Transport, (5) Combo Bare Rock/Quarries, and (6) Grasses (fig.
11).
24
Figure 11. Screengrab of ERDAS IMAGINE Signature Editor with 6 classes.
In the modified Anderson Level I binational classification scheme, the Anderson Level I
rangeland class is sometimes split into two classes: shrubland and grassland/pasture. We
considered further combining classes to merge grasslands and shrubs, but felt that given
the hydrological modeling application, it would be more accurate to keep 2 separate
classes.
We applied a supervised classification using these 6 signatures and the minimum
distance rule to acquire a second pass cross-border land-cover map. Accuracy statistics
were computed for the new map (Tables 4-6).
25
Table 4. Error Matrix
Commercial/
Industrial/
Transport
Commercial/
Industrial/ Transport
Deciduous
Forest
Shrubland
Low Intensity
Residential
Combination
Grasses
Combination
Bare
Classified
Total
46
0
3
0
0
1
50
Deciduous Forest
0
46
3
0
1
0
50
Shrubland
1
1
48
0
0
0
50
Low Intensity
Residential
0
0
0
49
0
1
50
Combination Grasses
0
2
38
1
57
2
100
Combination Bare
2
1
29
2
3
63
100
49
50
121
52
61
67
400
Reference Total
26
Table 5. Accuracy Totals.
Reference
totals
Classified
totals
Producers
accuracy, in
percent
Number
correct
Users
accuracy,
in percent
Commercial/ Industrial/ Transport
49
50
46
93.88
Deciduous Forest
50
50
46
92.00
92
121
50
48
39.67
92
Low Intensity Residential
52
50
49
94.23
96
Combination Grasses
61
100
57
93.44
98
Combination Bare
67
100
63
94.03
57
400
400
309
Shrubland
Totals
27
Table 6. Errors of omission, commission, and percent correct.
Errors of
omission, in
percent
Errors of
commission, in
percent
Percent correct
Commercial/ Industrial/
Transport
6.12
8.16
93.88
Deciduous Forest
8.00
8.00
92.00
60.33
1.65
39.67
Low Intensity
Residential
5.77
1.92
94.23
Combination Grasses
6.56
70.49
93.44
Combination Bare
5.97
55.22
94.03
22.75
22.75
77.25
Shrubland
Totals
The Overall Kappa (K^) Statistic was 0.7275—the classification process is
avoiding 72.75 percent of the errors that a completely random classification generates
and the overall classification accuracy was calculated to be 77.25 percent. The Ambos
Nogales area is dominated by both shrubland and grassland around bare areas of
transportation and urban sprawl (figs. 12 and 13), which is now represented in our new
map.
28
Figure 12. Image of property at the U.S.-Mexico border between Marisposa Rd. and I-19
in Nogales, Arizona (http://www.loopnet.com/Arizona/Nogales-commercial-realestate/?LinkCode=18400, last accessed June 21, 2008).
29
Figure 13. Photo of development land taken near Highway 82 in Nogales, Arizona
(http://www.loopnet.com/Arizona/Nogales-commercial-real-estate/?LinkCode=18400, last
accessed June 21, 2008).
In order to make the dataset acceptable as input to AGWA2, some further
manipulation of the dataset was necessary. AGWA2 accepts NLCD datasets as input
using a look-up table for the MRLC (fig. 14).
30
Figure 14. MRLC look-up table available in AGWA2.
Classes in the new image were assigned class numbers according to this table (fig. 15).
Figure 15. New “Class” attribute assigned to binational map.
31
The image was converted to GRID format, to polygon format, and back to GRID format, to
replace the Value field with the new CLASS numbers. The new GRID, “mrlc_Nogales” is
appropriate for use in the AGWA model (figs. 16 and 17).
Figure 16. Final attribute table for the raster binational land-cover input of Ambos
Nogales, Arizona, United States, and Sonora, Mexico.
32
Figure 17. Final binational land-cover map of Ambos Nogales, Arizona, United States, and Sonora, Mexico, for input to AGWA2.
33
Conclusions
Environmental modeling across international borders can be challenging due to differences
in language, nomenclature, scale, style, and priorities. Remotely sensed images can be used
to create seamless data across administrative boundaries for input into models. This
research paper describes procedures used to create a binational land use/land‐cover map
of use in the AGWA KINEROS2 model.
Acknowledgments
The authors would like to thank Darius Semmens and Lainie Levick for their reviews of
this report; also Theresa Mathiasmeier for her review of the metadata.
34
REFERENCES
Anderson, J.R., Hardy, E.E., Roach, J.T. and Witmer, R.E., 1976, A land use and land
cover classification system for use with remote sensor data: U.S. Geological Survey
Professional Paper 964. [http://landcover.usgs.gov/pdf/anderson.pdf, last accessed
December 21, 2008].
Congalton, R., 2001, Accuracy assessment and validation of remotely sensed and other
spatial information: The International Journal of Wildland Fire. V. 10. p. 321-328.
Congalton, R. and Green, K., 1999, Assessing the accuracy of remotely sensed data—
Principles and practices: Boca Raton, Fla., CRC/Lewis Press, 137 p.
Gaydos, L., 1996, Today’s land cover mapping, in Scott, J.M., Tear, T.H., and Davis, F.,
eds., Gap analysis—A landscape approach to biodiversity planning: Bethesda, Md.,
American Society for Photogrammetry and Remote Sensing, p. 67-70.
Instituto Nacional de Estadística, Informática e Geografía (INEGI), 1993, Guía Para la
Interpretación de Información Cartográfica Impresa y Digital de Uso de Suelo.
Loveland T.R., and Shaw, D.M., 1996, MultiResolution land characterization—Building
collaborative partnershipsi, in Scott, J.M., Tear, T.H., and Davis, F., eds., Gap analysis—
A landscape approach to biodiversity planning, Proceedings of the ASPRS/GAP
Symposium: Charlotte, N.C., National Biological Service, Moscow, ID, p. 83-89.
Lunetta, R.S., and Sturdevant, J.A., 1993, The North American landscape
characterization Landsat Pathfinder project, in Pettinger, L.R., ed., Pecora 12 symposium,
land information from space-based systems, Proceedings: Bethesda, Md., American
Society of Photogrammetry and Remote Sensing, p. 363-371.
Miller, S.N., Semmens, D.J., Miller, R.C., Hernandez, M., Miller, W.P., Goodrich, D.C.,
Kepner, W.G., and Ebert, D., 2002, GIS-based hydrologic modeling—The automated
geospatial watershed assessment tool, Proceedings 2nd Federal Interagency Conf. on
Hydrologic, July 29-Aug. 1, Las Vegas, Nev.
Parcher, J.W., Norman, L.M., Papoulias, D.M., Stefanov, J.E., Wilson, Z.D., Page, W.R.,
and Gary, R.H., 2006, Developing a binational geodatabase to examine environmental
health and quality-of-life issues along the U.S.-Mexico border: GSDI-9 Conference
Proceedings, 6-10 November, Santiago, Chile.
35
Semmens, D.J., Goodrich, D.C., Unkrich, C.L., Smith, R.E., Woolhiser, D.A. and Miller,
S.N., 2008, KINEROS2 and the AGWA modeling framework, in Wheater, H.,
Sorooshian, S., and Sharma, K.D., eds., Hydrological Modelling In Arid and SemiArid Areas, New York, Cambridge University Press, 206 p.
U.S. Geological Survey, 2000, National land cover dataset: U.S. Geological Survey Fact
Sheet 108-00 [http://erg.usgs.gov/isb/pubs/factsheets/fs10800.html, last accessed
December 21, 2008].
Vogelmann, J.E., Howard, S.M., Yang, L., Larson, C.R., Wylie, B.K., and Van Driel,
J.N., 2001, Completion of the 1990’s National Land Cover Data Set for the conterminous
United States, Photogrammetric Engineering and Remote Sensing v. 67, p. 650-662.
Wilson, Z.D., 2006, Binational integration of national land use/land cover datasets in the
United States-Mexico border region
[http://borderhealth.cr.usgs.gov/PDFs/website_methods_LandCoverIntegration_2006063
0.pdf, last accessed December 21, 2008].
Woolhiser, D.A., Hanson, C.L., and Kuhlman, A.R., 1970, Overland flow on rangeland
watersheds: Journal of Hydrology, v. 9, no. 2, p. 336-335.
36
Appendix A—Metadata
Identification_Information:
Citation:
Citation_Information:
Originator: Laura Norman and Cynthia Wallace
Publication_Date: Unknown
Title: Land Use/Land Cover in the Ambos Nogales Watershed; Nogales, Arizona,
United States and Nogales, Sonora, Mexico
Geospatial_Data_Presentation_Form: raster digital data
Online_Linkage: TBD
Larger_Work_Citation:
Citation_Information:
Originator: Laura Norman and Cynthia Wallace
Publication_Date: Unknown
Title: Mapping Land Use/Land Cover in the Ambos Nogales Study Area
Geospatial_Data_Presentation_Form: raster digital data
Series_Information:
Series_Name: Open File Report
Publication_Information:
Publisher: U.S. Geological Survey
Description:
Abstract: An integer GRID dataset representing the distribution of landscape classes
across the Ambos Nogales Watershed was created. Six signatures that correlate with the
Multi-Resolution Land Characteristics (MRLC) Consortium classes were identified using
image processing techniques in ERDAS IMAGINE 9.1 software to develop a binational
land cover dataset similar to the National Land Cover Dataset (NLCD). Data resolution is
60 m., based on the source Landsat MSS imagery in 1992.
Purpose: 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: 10/07/92
Currentness_Reference: ground condition
Status:
Progress: Complete
Maintenance_and_Update_Frequency: None planned
Spatial_Domain:
Bounding_Coordinates:
West_Bounding_Coordinate: -111.080090
East_Bounding_Coordinate: -110.886233
North_Bounding_Coordinate: 31.446346
South_Bounding_Coordinate: 31.228667
Keywords:
37
Theme:
Theme_Keyword: Land use
Theme_Keyword: Land Cover
Place:
Place_Keyword: Nogales
Place_Keyword: Sonora
Place_Keyword: Arizona
Place_Keyword: Mexico
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 Norman
Contact_Organization: US Geological Survey
Contact_Position: Research Scientist
Contact_Address:
Address_Type: mailing and physical address
Address: 520 N. Park Ave., Ste #355
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 2; ESRI ArcCatalog 9.2.2.1350
Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report: The Overall Kappa (K^) Statistics was 0.7275-the
classification process is avoiding 72.75 percent of the errors that a completely random
classification generates and the overall classification accuracy was calculated to be
77.25%. The area is dominated by both shrubland and grassland around bare areas of
transportation and urban sprawl , which is now 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
groud. Additional inaccuracy could occur in the original image it was processed from,
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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 the Ambos Nogales study
area as defined by a minimum bounding rectangle around the watershed.
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator: National Aeronautics and Space Administration (NASA) Landsat
Pathfinder Program
Publication_Date: Unknown
Title: North American Landscape Characterization
Online_Linkage: http://GloVis.usgs.gov/
Type_of_Source_Media: remote sensing image
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 19921007
Source_Citation_Abbreviation: NALC dataset from 10/07.1992 for Path 26, Row 38.
Source_Contribution: North American Landscape Characterization (NALC) data are
Landsat Multi-Spectral Scanner (MSS) time-series triplicates that were acquired in 1973,
1986, and 1991 (+/- one year). Pixel size for all images is 60 meters. The data has been
cast to the Universal Transverse Mercator (UTM) projection and is referenced to the
North American Datum of 1927 (NAD27).
Process_Step:
Process_Description: Applied supervised classification, using signatures calculated
from the NLCD of Nogales, Ariz., using the minimum distance rule, to acquire cross
border signatures.
Process_Date: 20080501
Process_Step:
Process_Description: Created a CellArray that listed two sets of class values for 400
randomly selected points in the classified .img file. One set of class values was
automatically assigned to these random points as they are selected (hidden in figure
below-in order to get unbiased reference samples), and the other set of class values
(reference values) was input .
Process_Date: 20080501
Process_Step:
Process_Description: Checked accuracy of classification using DOQQs and created
accuracy report. We were not satisfied with these results and so we merged signatures.
We applied a supervised classification using these 6 signatures and the minimum distance
rule, to acquire a more accurate cross-border land cover map
Process_Date: 20080501
Spatial_Data_Organization_Information:
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:
Raster_Object_Type: Grid Cell
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Row_Count: 402
Column_Count: 307
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: 60.000000
Ordinate_Resolution: 60.000000
Planar_Distance_Units: meters
Geodetic_Model:
Horizontal_Datum_Name: North American Datum of 1983
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: mrlc_nogales.vat
Entity_Type_Definition: Land use classes
Entity_Type_Definition_Source: Multi-Resolution Land Cover Characterization
(MRLC)
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: Internal ID # from image processing steps.
Attribute:
Attribute_Label: COUNT
40
Attribute_Definition: Number of GRID cells assigned to this value.
Attribute:
Attribute_Label: CLASS
Attribute_Definition: Class created to correspond with the NLCD data.
Attribute_Definition_Source: Multi-Resolution Land Cover Characterization (MRLC)
Distribution_Information:
Distributor:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Laura M. Norman
Contact_Organization: U.S. Geological Survey
Contact_Address:
Address_Type: mailing and physical address
Address: 520 N. Park Ave., Ste #104
City: Tucson
State_or_Province: AZ
Postal_Code: 85719
Country: USA
Contact_Electronic_Mail_Address: lnorman@usgs.gov
Resource_Description: Downloadable Data
Standard_Order_Process:
Digital_Form:
Digital_Transfer_Information:
Format_Name: GRID
Transfer_Size: 0.091
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: 20080903
Metadata_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Laura M. Norman
Contact_Organization: US Geological Survey
Contact_Address:
Address_Type: mailing and physical address
Address: 520 N. Park Ave, Ste #355
City: Tucson
State_or_Province: AZ
Postal_Code: 85719
Contact_Voice_Telephone: 5206705510
Metadata_Standard_Name: FGDC Content Standards for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001-1998
Metadata_Time_Convention: local time
Metadata_Extensions:
41
Profile_Name: ESRI Metadata Profile
Metadata_Extensions:
Online_Linkage: http://www.esri.com/metadata/esriprof80.html
Profile_Name: ESRI Metadata Profile
42