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Surface water data for isolated stock ponds in southern Arizona, USA and northern Sonora, Mexico

Data for journal manuscript: Using Full and Partial Unmixing Algorithms to Estimate the Inundation Extent of Small, Isolated Stock Ponds in the southwest USA and northern Mexico

Dates

Publication Date
Time Period
2007-06-27
Time Period
2014-05-22

Citation

Jarchow, C.J., and Sigafus, B.H., 2019, Surface water data for isolated stock ponds in southern Arizona, USA and northern Sonora, Mexico: U.S. Geological Survey data release, https://doi.org/10.5066/P95ZFPT1.

Summary

These data were compiled to compare the ability of matched filtering (MF) and linear spectral mixture analysis (LSMA) to map isolated wetland sites using Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) data. We analyzed 81 stock ponds corresponding to a 2007 Landsat 5 image and 73 sites corresponding to a 2014 Landsat 8 image in southern Arizona and northern Sonora, Mexico, with ponds ranging from completely dry to ~17,000 m2 surface water. Both Landsat images we used were Tier 1 Level-1 terrain corrected scenes acquired from Earth Explorer (https://earthexplorer.usgs.gov), which were provided as individual bands represented by digital numbers (DNs). The inundation extent of stock ponds in the San Rafael [...]

Contacts

Point of Contact :
Brent H Sigafus
Originator :
Brent H Sigafus, Christopher J Jarchow
Metadata Contact :
Brent H Sigafus
Publisher :
U.S. Geological Survey
Distributor :
U.S. Geological Survey - ScienceBase
SDC Data Owner :
Southwest Biological Science Center
USGS Mission Area :
Ecosystems

Attached Files

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Stock_Pond_Metadata.xml
Original FGDC Metadata

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Data_Metadata.zip 16.68 MB

Purpose

The purpose of these data are to detect and estimate surface water area of stock ponds in southern Arizona, USA and northern Sonora, Mexico. These water bodies can be difficult to locate, especially in areas were little is known. A full (linear spectral mixture analysis; LSMA) and partial (matched filtering; MF) spectral unmixing algorithm was applied to a 2007 Landsat 5 and a 2014 Landsat 8 satellite image to determine the ability of a time-intensive (i.e., more spectral input; LSMA) vs. a more efficient (less spectral input; MF) spectral unmixing approach to detect and estimate surface water area of stock ponds in this area.

Rights

The author(s) of these data request that data users contact them regarding intended use and to assist with understanding limitations and interpretation. Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty.

Additional Information

Identifiers

Type Scheme Key
DOI https://www.sciencebase.gov/vocab/category/item/identifier doi:10.5066/P95ZFPT1

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