Linear Deconvolution Results For Site DS-1 (2-component-model-1)
Dates
Publication Date
2018-02-28
Time Period
2014-05-26
Citation
Hubbard, B.E., Hooper, D.M., Mars, J.C., and Solano, Federico, 2018, Linear Deconvolution Mineral Maps of Compositionally Variable Dune Fields in the Western United States and Alaska: U.S. Geological Survey data release, https://doi.org/10.5066/F7CC0XTR.
Summary
These geotiffs represent the raster GIS outputs of Linear Deconvolution (Linear Spectral Unmixing) analysis of ASTER image pixels covering various sand dune and sand sheet fields throughout the Western United States and Alaska. This particular dune field (DS-1) represents the Algodones Dunes near the Salton Sea of Southern California. The accompanying zip file contains linear deconvolution-derived mineral fractional abundance maps for a two-component mixture model of Quartz and Potassic Feldspars, as well as RMS and residual errors. Each geotiff layer has an associated metadata file with further details.
Summary
These geotiffs represent the raster GIS outputs of Linear Deconvolution (Linear Spectral Unmixing) analysis of ASTER image pixels covering various sand dune and sand sheet fields throughout the Western United States and Alaska. This particular dune field (DS-1) represents the Algodones Dunes near the Salton Sea of Southern California. The accompanying zip file contains linear deconvolution-derived mineral fractional abundance maps for a two-component mixture model of Quartz and Potassic Feldspars, as well as RMS and residual errors. Each geotiff layer has an associated metadata file with further details.
Click on title to download individual files attached to this item.
DS1-qtz-kspar.xml Original FGDC Metadata
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10.8 KB
application/fgdc+xml
DS1-qtz-kspar.zip
812.25 KB
application/zip
Related External Resources
Type: Related Primary Publication
Hubbard, B.E., Hooper, D.M., Solano, Federico, and Mars, J.C., 2018, Determining mineralogical variations of aeolian deposits using thermal infrared emissivity and linear deconvolution methods: Aeolian Research, v. 30, p. 54-96, https://doi.org/10.1016/j.aeolia.2017.12.001.
