Location of Irrigated Land Classified from Satellite Imagery—High Plains Area, Nominal Date 1992

Metadata:

• Identification_Information
• Data_Quality_Information
• Spatial_Data_Organization_Information
• Spatial_Reference_Information
• Entity_and_Attribute_Information
• Distribution_Information
• Metadata_Reference_Information

Identification_Information:

Citation:

Citation_Information:

Originator:

Sharon L. Qi, Alexandria Konduris, David W. Litke, and Jean Dupree

Publication_Date: 2002

Title:

Location of Irrigated Land Classified from Satellite Imagery—High Plains Area, Nominal Date 1992

Edition: 1.0

Geospatial_Data_Presentation_Form: raster digital data

Series_Information:

Series_Name: Open File Report

Issue_Identification: 02-XXX

Publication_Information:

Publication_Place: Lakewood, Colorado

Publisher: U.S. Geological Survey

Online_Linkage: Here

Description:

Abstract:

Satellite imagery from the Landsat Thematic Mapper (nominal date 1992) was used to classify and map the location of irrigated land overlying the High Plains aquifer. The High Plains aquifer underlies 174,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. The U.S. Geological Survey is conducting a water-quality study of the High Plains aquifer as part of the National Water-Quality Assessment Program. To help interpret data and select sites for the study, it is helpful to know the location of irrigated land within the study area. To date, the only information available for the entire area is 20 years old. To update the data on irrigated land, 40 summer and 40 spring images (nominal date 1992) were acquired from the National Land Cover Data set and processed using a band-ratio method (Landsat Thematic Mapper band 4 divided by band 3) to enhance the vegetation signatures. The study area was divided into nine subregions with similar environmental characteristics, and a band-ratio threshold was selected from imagery in each subregion that differentiated the cutoff between irrigated and nonirrigated land. The classified images for each subregion were mosaicked to produce an irrigated-land map for the study area. The total amount of irrigated land classified from the 1992 imagery was 13.1 million acres, or about 12 percent of the total land in the High Plains. This estimate is approximately 1.5 percent greater than the amount of irrigated land reported in the 1992 Census of Agriculture (12.8 millions acres).

Purpose:

The purpose of this data set is to classify irrigated land in the High Plains using satellite data acquired for a nominal date of 1992. Additionally, it was used to compare the amount and location of irrigated land determined in the early 1980’s (Thelin and Heimes, 1987) to determine if the amount of irrigated land has changed. This information was used to help in the analysis of water-quality data, modeling efforts, and future planning and design of the High Plains Groundwater (HPGW) study.

Supplemental_Information:

This data set is associated with and is the basis for the interpretations made in the report "Classification of Irrigated Land Using Satellite Imagery—High Plains Aquifer, Nominal Date 1992", U.S. Geological Survey Water-Resources Investigations Report 02-4236.

The High Plains aquifer underlies 174,000 square miles in parts of eight States (Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming). The aquifer is an important national resource, providing water for about 27 percent of the irrigated land in the United States and about 30 percent of the groundwater used for irrigation in the United States (Dennehy, 2000). Irrigation is the dominant water use in the High Plains, accounting for withdrawals during 1995 of more than 15 billion gallons per day (U.S. Geological Survey National Water Information System database). Substantial pumping of the High Plains aquifer for irrigation since about the 1940’s has resulted in water-level declines in some parts of the aquifer of more than 100 feet (McGuire and Sharpe, 1997). Concern about these declines led the U.S. Congress in 1984 to institute a water-level monitoring program for the aquifer. Water quality of the aquifer is a more recent concern. There have been local studies of water quality, but no large-scale, comprehensive assessment has been made of the entire aquifer system. Knowledge of the quality of water resources is important because of the implications to human and aquatic health. In 1991, the U.S. Geological Survey (USGS) began full implementation of the National Water-Quality Assessment (NAWQA) Program. The long-term goals of the NAWQA Program are to describe the status and trends in the quality of the Nation’s surface- and ground-water resources and determine the natural and anthropogenic factors affecting the water quality (Gilliom and others, 1995). The HPGW study began in October 1998 and represents a modification of the traditional NAWQA design in that the ground-water resource is the primary focus of the investigation. The HPGW study requires detailed and current information about the location of irrigated land for analyzing water-quality results with respect to land use and the selection of new study sites and for use in ground-water vulnerability modeling. The only existing information on irrigated land for the entire High Plains area is approximately 20 years old (Thelin and Heimes, 1987), and it only provides the percentage of irrigated land in 4-square-kilometer grid cells across the High Plains, not the actual locations of irrigated fields.

Acknowledgments: The timely effort of the U.S. Department of Agriculture’s (USDA) Farm Service Agency (FSA) in providing the historical ground-reference information for approximately 1,000 square miles of the High Plains is gratefully acknowledged. The authors also acknowledge the USGS National Mapping Discipline Earth Resources Observation Systems (EROS) Data Center for providing the original Landsat Thematic Mapper (TM) imagery.

References: Congalton, Russ, and Green, Cass, 1999, Assessing the accuracy of remotely sensed data—Principles and practices: Boca Raton, Fla., Lewis Publishers, 131 p.

Dennehy, K.F., 2000, High Plains regional ground-water study: U.S. Geological Survey Fact Sheet FS-091-00, 6 p.

Frankforter, J.D., 1996, Nebraska wetland resources, in Fretwell, J.D., Williams, J.S., and Redman, P.J. compilers, National water summary on wetland resources: U.S. Geological Survey Water-Supply Paper 2425, p. 261-266.

Gilliom, R.J., Alley, W.M., and Gurtz, M.E., 1995, Design of the National Water-Quality Assessment Program—Occurrence and distribution of water-quality conditions: U.S. Geological Survey Circular 1112, 33 p.

High Plains Regional Climate Center, 2002, Regional historical monthly precipitation data, accessed February 4, 2002, at URL <http://hprcc.unl.edu/wrcc/states/ne.html>

McGuire, V.L., and Sharpe, J.B., 1997, Water-level changes in the High Plains aquifer—Predevelopment to 1995: U.S. Geological Survey Water-Resources Investigations Report 97-4081, 2 plates.

Nebraska Agricultural Statistics Service, 2002, 1995-1996 Annual bulletin, accessed February 14, 2002, at URL <http://www.agr.state.ne.us/agstats/nebrpubs.htm>

Richards, John A., 1993, Remote sensing and digital image analysis—An introduction: New York, Springer-Verlag, 340 p.

Thelin, G.P., and Heimes, F.J., 1987, Mapping irrigated cropland from Landsat data for determination of water use from the High Plains aquifer in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming: U.S. Geological Survey Professional Paper 1400-C, p. C1-C38.

University of Nebraska, 1986, The groundwater atlas of Nebraska: Lincoln, University of Nebraska, resource atlas No. 4, 32 p.

U.S. Geological Survey, National Water Information System, accessed May 14, 2002, at URL <https://waterdata.usgs.gov/nwis/>

Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 1992

Currentness_Reference: ground condition

Status:

Progress: Complete

Maintenance_and_Update_Frequency: None planned

Spatial_Domain:

Bounding_Coordinates:

West_Bounding_Coordinate: -106.044017

East_Bounding_Coordinate: -96.210435

North_Bounding_Coordinate: 43.826921

South_Bounding_Coordinate: 31.636121

Keywords:

Theme:

Theme_Keyword_Thesaurus: None

Theme_Keyword: Irrigated land

Theme_Keyword: Landsat

Theme_Keyword: Imagery

Theme_Keyword: Satellite

Theme_Keyword: Agriculture

Place:

Place_Keyword_Thesaurus: None

Place_Keyword: High Plains aquifer

Place_Keyword: Colorado

Place_Keyword: Kansas

Place_Keyword: Nebraska

Place_Keyword: New Mexico

Place_Keyword: Oklahoma

Place_Keyword: South Dakota

Place_Keyword: Texas

Place_Keyword: Wyoming

Place_Keyword: Great Plains region

Place_Keyword: western U.S.

Access_Constraints: None

Use_Constraints:

The data set has a resolution of 30 meters and should not be used for more detailed analysis.

Point_of_Contact:

Contact_Information:

Contact_Person_Primary:

Contact_Person: District Chief - Colorado District

Contact_Organization: U.S. Geological Survey

Contact_Position: District Chief

Contact_Address:

Address_Type: mailing address

Address: Box 25046, MS 415, Denver Federal Center

City: Lakewood

State_or_Province: Colorado

Postal_Code: 80225

Country: USA

Contact_Voice_Telephone: (303)236-4882

Contact_Facsimile_Telephone: (303)236-4912

Contact_Electronic_Mail_Address: dc_co@usgs.gov

Browse_Graphic:

Browse_Graphic_File_Name:

<https://water.usgs.gov/lookup/getspatial?ofr02-XXX_hpirrlnd/browse.gif>

Browse_Graphic_File_Description: An example of what the data set looks like

Browse_Graphic_File_Type: GIF

Security_Information:

Security_Classification_System: Public

Security_Classification: Unclassified

Security_Handling_Description: None

Native_Data_Set_Environment:

Microsoft Windows 2000 Version 5.0 (Build 2195) Service Pack 3; ESRI ArcCatalog 8.1.1.649

Cross_Reference:

Citation_Information:

Originator:

Qi, Sharon L., Konduris, Alexandria, Litke, David W., and Dupree, Jean

Publication_Date: 2002

Title:

Classification of Irrigated Land Using Satellite Imagery—High Plains Aquifer, Nominal Date 1992

Geospatial_Data_Presentation_Form: document

Series_Information:

Series_Name: Water Resources-Investigations Report

Issue_Identification: 02-XXXX

Publication_Information:

Publication_Place: Lakewood, Colorado

Publisher: U.S. Geological Survey

Data_Quality_Information:

Attribute_Accuracy:

Attribute_Accuracy_Report: See Entity Attribute information

Logical_Consistency_Report: Not applicable for raster data.

Completeness_Report:

This data set represents irrigated land classified from Landsat TM imagery with a nominal date of 1992. Dates for the imagery used for this data set ranged from 1986 to 1996. It must be noted that the density of irrigated land in the eastern Nebraska part of the study area is problematic. The satellite imagery dates for this region range from 1991 to 1993, which was a time of above-average rainfall (High Plains Regional Climate Center, 2002) for eastern Nebraska with 1993 being a time of flooding in the Midwest. Due to large areas of dead and damaged crops, there was a substantial underestimate (mean of 46 percent) in the density of irrigated land south of the Platte River in the counties of Phelps, Adams, Webster, Hamilton, Clay, Polk, York, Fillmore, and Saline compared to State agricultural statistics (Nebraska Agricultural Statistics Service, 2002). The area in the far northeast of the study area in the counties of Antelope, Boone, Nance, Platte, Madison, Pierce, Wayne, Stanton, Colfax, Cumins, and Dodge show a substantial overestimate (mean of 33 percent) in the density of irrigated land than may actually have been present (Nebraska Agricultural Statistics Service, 2002). The area to the south of the Platte River is part of the Rainwater Basin wetlands complex characterized by nearly flat and gently rolling loess plains. Surface drainage is poorly developed, and the soils are more clay rich (Frankforter, 1996; University of Nebraska, 1986). The area to the north of the Platte River is characterized by rolling hills and dissected plains where infiltration is moderate and runoff is high (University of Nebraska, 1986). Therefore, nonirrigated fields to the north of the Platte River would appear very healthy and green (very bright white in the ratio-classified image) to the satellite while areas to the south of the Platte River were substantially wetter with more saturated soils and therefore appeared more damaged or drowned (darker gray or black in the ratio-classified image). Pixel brightness for irrigated fields (ones that were not damaged or dead) and many nonirrigated fields were relatively bright and made choosing a threshold very difficult. A lower threshold was required to avoid misclassifying many dark (wet) fields (center pivots) as nonirrigated. Therefore, fields that were healthy and may have been nonirrigated but very green, perhaps due to above-normal rainfall, were also included in the classification.

Positional_Accuracy:

Horizontal_Positional_Accuracy:

Horizontal_Positional_Accuracy_Report: Landsat TM satellite imagery is 30-meter resolution

Vertical_Positional_Accuracy:

Vertical_Positional_Accuracy_Report: None

Lineage:

Source_Information:

Source_Citation:

Citation_Information:

Originator: Multi-Resolution Land Characteristics Consortium (MRLC)

Publication_Date: 2000

Title: National Land Cover data set (NLCD)

Geospatial_Data_Presentation_Form: raster digital data

Other_Citation_Details:

See: <http://www.epa.gov/mrlc/> for details about Federal entities involved in the MRLC.

Online_Linkage: <http://landcover.usgs.gov/natllandcover.html>

Type_of_Source_Media: Landsat TM imagery

Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 1988

Ending_Date: 1996

Source_Currentness_Reference: ground condition

Source_Citation_Abbreviation: NLCD

Source_Contribution:

Raw Landsat imagery from NLCD was used in ERDAS Imagine to classify irrigated land.

Process_Step:

Process_Description:

The imagery data were received from EROS Data Center as a band sequential (BSQ) generic binary format containing 4 bands (3, 4, 5, and 7). There were two files associated with each image: the compressed image data file (.IMG file; not to be confused with the .IMG format for ERDAS Imagine) and the georeferencing information file (.PRT file). The data were imported into ERDAS Imagine and converted to an Imagine .IMG format by using the Import menu and information about number of rows and columns in the data set from the .PRT file. To import the bands in the order required for the band-ratio method of classification, the bands were reordered during the import process to 7, 5, 3, and 4. The imported Imagine file was then georeferenced using information from the associated .PRT file that came with each image.

Process_Date: 2000

Process_Step:

Process_Description:

Because the satellite sensor can record differences in soil moisture, plant health, atmospheric conditions, and many other factors, the imagery was divided into nine subregions. The extent of the subregions was based on a visual inspection of several overlays of environmental data sets such as crop patterns, precipitation, and ecoregions and also was based on a need to limit file size for processing purposes. This would help ensure that the data being processed in a given subregion would have similar spectral signatures and that the selected threshold to distinguish irrigated from nonirrigated land would be appropriate within that subregion.

Process_Date: 2001

Process_Step:

Process_Description:

In addition to the unclassified raw TM data, the classified NLCD data for the same scenes were retrieved. Because the MRLC had already spent a great amount of time classifying the TM data into land-cover classes with the use of extensive ancillary data, it was decided that the agricultural classes would be used to mask out pixels that were not considered agricultural land and could interfere with the results of the ratio (such as the spectral overlap between riparian pixels and irrigated agriculture pixels). However, not all rangeland or riparian pixels were masked out completely due to being misclassified in the NLCD. The masking procedure produced files containing mainly agricultural pixels—the subject of the study.

Process_Date: 2001

Process_Step:

Process_Description:

Multispectral data can be transformed, or enhanced, to generate new sets of image components or bands. The new band or bands represent an alternative description of the original data that may enhance certain features not formerly visible. Image arithmetic, such as a ratio of pixel brightness values (digital numbers [DN]), can reduce effects of topography, create a vegetation index, or enhance differences in the spectral characteristics of rocks and soils (Richards, 1993). A band ratio was used in this study and was defined as the TM near-IR band (band 4) divided by the visible-red band (band 3) which created a vegetation index. Each image in the nine subregions was processed using this ratio. The resulting files were a single grayscale band with irrigated agriculture pixels indicated as bright white pixels and all other variations of vegetation a range of gray. Riparian pixels or other wet, green vegetation pixels that were not masked out were also bright white pixels.

Process_Date: 2001

Process_Step:

Process_Description:

Applying a threshold to an image is another way of categorizing an image. In this case, the reason for choosing a threshold for the image was to create a binary file containing values of 1 to represent irrigated agriculture and 0 to represent nonirrigated agriculture. This file could then be input into a formula to calculate the number of irrigated agriculture pixels per unit area. The approach selected to determine the best threshold was to collect several samples of DN values from areas that the analyst thought to be irrigated (center-pivot irrigation systems) and areas thought to be nonirrigated. The pixels were selected by using a seed tool to click on a pixel in the area of interest, and the tool selected surrounding contiguous pixels that were within a specified geographic distance and/or a specified spectral distance (similar brightness). This group of pixels was considered a sample. The analyst then looked at the minimums, maximums, means, and ranges of the various samples of DN values to determine the best threshold value that would separate brighter, irrigated agriculture pixels from darker, nonirrigated agriculture pixels. The statistics (minimum, maximum, mean, and range of DN values) were gathered solely from the grayscale ratio image. There are certain guidelines to consider when using statistics to threshold or create classes in an image. Generally, 50 samples per class (land-cover type) is a good balance between statistical validity and practicality (Congalton and Green, 1999). The statistics were used to determine how to group the pixels in the ratio-classified image into their respective land-cover types. The brightness ranges for each sample were plotted on a graph to determine where along the brightness scale the various samples would group for each of the classes (irrigated and nonirrigated). For example, samples from the brighter pixels, considered irrigated agriculture, have different minimums, maximums, and ranges but generally overlap each other in the graph. Samples of darker pixels, considered nonirrigated agriculture, also grouped together and have noticeably lower ranges of DN values but still overlap slightly with the brighter pixel samples. The ideal DN ranges would not have any overlap, but in this study this never occurred. Any threshold value chosen would always misclassify some nonirrigated pixels as irrigated and some irrigated pixels as nonirrigated. Therefore, a threshold was chosen that achieved the best balance between misclassified irrigated and nonirrigated pixels.

Process_Date: 2001

Process_Step:

Process_Description:

Once the initial classification was completed, the estimates of irrigated land classified by the TM imagery were refined using ground-reference information to adjust threshold values. Ground-reference information on irrigation status was used to determine the initial error estimates for the land-cover classification. The polygons in the ground-reference data set were classified as being either irrigated or nonirrigated, and this information was overlaid with the classified TM imagery to determine the initial accuracy of the classification for each scene. The error estimates were calculated in Arc by converting the ground-reference polygons into a raster data set where irrigated areas were assigned a value of "20" and nonirrigated as "10." The classified image was also converted from an Imagine .IMG file to an Arc grid file where irrigated land had an assigned value of "1" and nonirrigated land a value of "0." The two data sets were then added together to create an error data set with the following values: no ground reference available = 0; nonirrigated - correctly identified = 10; nonirrigated - incorrectly identified (underestimated irrigated land) = 20; irrigated - correctly identified = 21; and irrigated - incorrectly identified (overestimated irrigated land) = 11. The cell counts for each of the values were compared to the total cell count for each data set to calculate the percentage of correctly classified pixels and incorrectly classified pixels. To refine the threshold values, the ground-reference data were randomly split in half to produce calibration and verification data sets. The calibration data set was used to refine the thresholds to optimize the amount of irrigated land correctly classified (percentage correct) and the amount of irrigated land correctly located. The errors and the percentage correct were then verified using the verification data set. The percentage correct for each subregion was weighted on the basis of the percentage of the total agricultural land in each subregion. The overall weighted percentage of pixels correctly identified by the ratio-threshold process was 77.5 percent. This number increased to 79.8 percent when the effects of subregion 2 were removed. Subregion 2 encompassed eastern Nebraska and included imagery dates from the 1992 and 1993 growing seasons. This is the time period when Midwest flooding occurred and extremely wet conditions existed for many Midwestern States. Ground-reference information was obtained only for the leaf-on dates (summer) of the imagery in order to limit the amount of work required by the Farm Service Agency since this is when most of the irrigation takes place.

Process_Date: 2001

Process_Step:

Process_Description:

After error estimations were completed the leaf-on imagery, the imagery was mosaicked together for the entire High Plains area. The leaf-off imagery (early spring) was also mosaicked together in a separate file. For this area the irrigated land in the leaf-off imagery was predominantly irrigated winter wheat. The leaf-on data set and the leaf-off data set were then merged to represent the total amount of irrigated land in the High Plains area. To determine if the amount of irrigated land added by merging the leaf-off data set was correct, a comparison was made with ground-reference information. Irrigated wheat comprised approximately 5 percent of the ground-reference information. The amount of irrigated land added from the leaf-off imagery was approximately 6 percent of the irrigated land in the leaf-on imagery, consistent with the amount of irrigated wheat grown in the High Plains area.

Process_Date: 2001

Process_Step:

Process_Description:

Because irrigated fields are not perfectly homogeneous in terms of plant health, pixels within irrigated land that were below the brightness threshold were left out of the irrigated classification and appeared as holes in many fields. Conversely, many small areas of pixels and individual pixels that were nonirrigated agriculture were above the brightness threshold and were included in the irrigated classification. These problem pixels were cleaned up by using a series of eliminate-and- fill filter passes using Imagine. First, the eliminate was done using a minimum area of 10 to 15 acres (approximately 49 to 74 contiguous pixels)—smaller than most any irrigated field. Second, the fill process was used to fill in gaps in otherwise contiguous irrigated fields. This was generally done using a 5x5 neighborhood (group of pixels) definition with a majority function (each pixel would be classified on the basis of the majority value of a defined set of pixels within the neighborhood). The shape of the neighborhood was changed depending on the general pattern of irrigated land: densely irrigated areas that contained many section roads were processed using a "+"-shaped neighborhood so that the roads would be preserved, and irrigated areas defined largely by circular center-pivot irrigation systems were processed using a more circular neighborhood.

Process_Date: 2001

Cloud_Cover: 0

Spatial_Data_Organization_Information:

Direct_Spatial_Reference_Method: Raster

Raster_Object_Information:

Raster_Object_Type: Grid Cell

Row_Count: 44246

Column_Count: 26264

Vertical_Count: 1

Spatial_Reference_Information:

Horizontal_Coordinate_System_Definition:

Planar:

Map_Projection:

Map_Projection_Name: Albers Conical Equal Area

Albers_Conical_Equal_Area:

Standard_Parallel: 29.500000

Standard_Parallel: 45.500000

Longitude_of_Central_Meridian: -96.000000

Latitude_of_Projection_Origin: 23.000000

False_Easting: 0.000000

False_Northing: 0.000000

Planar_Coordinate_Information:

Planar_Coordinate_Encoding_Method: row and column

Coordinate_Representation:

Abscissa_Resolution: 30.000000

Ordinate_Resolution: 30.000000

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: grid cell

Entity_Type_Definition: value of cell indicates irrigated or nonirrigated

Entity_Type_Definition_Source: ERDAS Imagine

Attribute:

Attribute_Label: ObjectID

Attribute_Definition: internal ID number

Attribute_Definition_Source: ArcGIS

Attribute_Domain_Values:

Range_Domain:

Range_Domain_Minimum: 0

Range_Domain_Maximum: 9999999

Attribute:

Attribute_Label: Value

Attribute_Definition: Irrigation Status

Attribute_Definition_Source: Authors

Attribute_Domain_Values:

Enumerated_Domain:

Enumerated_Domain_Value: 1

Enumerated_Domain_Value_Definition: Land is irrigated

Enumerated_Domain_Value_Definition_Source: Author

Enumerated_Domain:

Enumerated_Domain_Value: 0

Enumerated_Domain_Value_Definition: Land is not irrigated

Enumerated_Domain_Value_Definition_Source: Author

Attribute:

Attribute_Label: Count

Attribute_Definition: how many cells have value

Attribute_Definition_Source: ArcGIS

Attribute_Domain_Values:

Range_Domain:

Range_Domain_Minimum: 0

Range_Domain_Maximum: 9999999

Overview_Description:

Entity_and_Attribute_Overview:

The attribute .vat file for this data set only contains a value and count. The value is 1 (irrigated) or 0 (nonirrigated). The count value is how many cells contain each value.

Entity_and_Attribute_Detail_Citation: None

Distribution_Information:

Distributor:

Contact_Information:

Contact_Organization_Primary:

Contact_Organization: U.S. Geological Survey

Contact_Address:

Address_Type: mailing address

Address: 12201 Sunrise Valley Drive, MS 440

City: Reston

State_or_Province: VA

Postal_Code: 20192

Country: USA

Contact_Voice_Telephone: 1-800-426-9000

Contact_Electronic_Mail_Address: h2oteam@usgs.gov

Hours_of_Service: 8 AM to 4 PM Eastern Time Zone

Contact_Instructions: Please use email

Resource_Description: Open-File Reports on FTP site. ofr02-XXXX.

Distribution_Liability:

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 data set has been used by the U.S. Geological Survey, U.S. Department of the Interior, no warranty expressed or implied is made by the U.S. Geological Survey as to the accuracy of the data and related materials.

The act of distribution shall not constitute any such warranty, and no responsibility is assumed by the U.S. Geological Survey in the use of this data, software, or related materials.

Standard_Order_Process:

Digital_Form:

Digital_Transfer_Information:

Format_Name: ARCE, SDTS, and Shapefile

Transfer_Size: 17.304

Digital_Transfer_Option:

Online_Option:

Computer_Contact_Information:

Network_Address:

Network_Resource_Name: <https://water.usgs.gov/lookup/getspatial?ofr02-XXX_hpirrlnd>

Fees: None

Ordering_Instructions: Available via ftp.

Metadata_Reference_Information:

Metadata_Date: 20030226

Metadata_Review_Date: 20021213

Metadata_Contact:

Contact_Information:

Contact_Person_Primary:

Contact_Person: Sharon L. Qi

Contact_Organization: U.S. Geological Survey

Contact_Position: Hydrologist

Contact_Address:

Address_Type: mailing address

Address: Box 25046, MS 415, Denver Federal Center

City: Lakewood

State_or_Province: Colorado

Postal_Code: 80225

Country: USA

Contact_Voice_Telephone: (303)236-4882 x310

Contact_Facsimile_Telephone: (303)236-4912

Contact_Electronic_Mail_Address: slqi@usgs.gov

Metadata_Standard_Name: FGDC Content Standards for Digital Geospatial Metadata

Metadata_Standard_Version: FGDC-STD-001-1998

Metadata_Time_Convention: local time

Metadata_Access_Constraints: None

Metadata_Use_Constraints: None

Metadata_Extensions:

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

Profile_Name: ESRI Metadata Profile