Mako thermal infrared hyperspectral airborne emissivity image, field photographs, and ground-based spectra of the San Andreas fault and Thousand Palms Oasis in the Coachella Valley, California

Description

Contents: total of 64 files in three folders and one stand-alone file; one folder contains two files, another contains 54 files, and another contains seven files. The folder "airborneEmissivityImage" contains: The Mako thermal infrared hyperspectral airborne emissivity image, and its header file: 1) airborneEmissivityImage 2) airborneEmissivityImage.hdr The folder "fieldPhotographs" contains: 1) 27 .jpg image files of the field sites (overhead views of the one-square-meter frame) 2) 27 .jpg image files of the field sites (overview perspectives of the site locations) The folder "surfaceOutlinesShapefiles" contains the digitized boundary lines of the geomorphic surfaces studied, in a set of shape files: 1) seven surfaceOutlines files (.shp, .shx, .qpj, .prj, .dbf, .cpg, .kmz) The file "groundBasedSpectra.xlsx" contains all of the ground-based spectra for the 27 field sites, along with a summary. Thermal Infrared Hyperspectral Airborne Imagery Acquisition and Processing: We collected thermal infrared hyperspectral airborne imagery on 24 September 2015 (10:50 am PDT) using The Aerospace Corporation's Mako "whiskbroom" sensor. The imagery included here had one-meter spatial resolution from a flight at 1830 m above ground level (2070 m altitude), and 118 spectral bands with wavelengths 8.01-13.15 μm. The complete image scene (a rectangular area, 6.1 km along the flight path, and 4.7 km wide) covered portions of both the Mission Creek and Banning strands of the southern San Andreas fault. The flight path was roughly parallel to, and centered on the Mission Creek strand. The hyperspectral imagery was processed from at-sensor radiance to emissivity using Environment for Visualizing Images (ENVI) software, version 4.8 (Harris Geospatial Solutions, Broomfield, Colorado), in the following sequence: Mako thermal infrared hyperspectral airborne image data cubes delivered by the Aerospace Corporation in Level 2 (L2) files, which had undergone radiometric and wavelength calibration (Buckland et al., 2017; Witkosky et al., 2016), bad pixel replacement, and spectral smile removal; all 114 data cubes concatenated into a super cube for bulk processing; bands 1-10 (wavelengths 7.56-7.96 µm) removed because they were dominated by noise (remaining bands are 11-128, 118 total, with wavelengths 8.01-13.15 µm); in-scene atmospheric compensation (Young et al., 2002), setting the regression pixels to maximum hit, the fitting technique to normalized regression, and using for the noise equivalent spectral radiance (NESR), the median value of the super cube; minimum noise fraction (MNF) forward transformation (Green et al., 1988; Lee et al., 1990); discarded an MNF component that included a significant data artifact (an across-track gradation, perpendicular to the flight direction, was present near the edges of each individual data cube) in an MNF inverse transformation; temperature emissivity separation with the emissivity normalization method (Kealy and Hook, 1993); georeferenced using the geolocation files included with the L2 files. The shape files for the geomorphic surfaces are included so they can be superimposed onto the thermal infrared hyperspectral airborne image, for randomly or manually sampling emissivity spectra (for a given surface) from within the digitized boundaries. We modified the line work from the geomorphic surface boundaries in Blisniuk and Sharp (2014). Field Photographs and Ground-based Spectra From March until May 2017, we visited 27 sites on the geomorphic surfaces to take field photographs and measure ground-based spectra. At each site, a one-square-meter frame was placed on the ground to represent the airborne imagery pixel size. We took two photographs at each site: an overhead view photograph of the one-square-meter frame, and an overview perspective of the site location. We used an Agilent 4100 ExoScan™ portable Fourier Transform Infrared spectrometer (3-5 mm spot size, active source) to measure ground-based diffuse reflectance spectra (some in-situ, some later on collected samples) from exposed top sides of clasts, finer unconsolidated material (we refer to these as "sand" spectra, where "sand" does NOT imply a specific clast size), and vegetation. We converted the ground-based reflectance spectra to emissivity using Kirchhoff's law (Robitaille, 2009) to compare the shape and wavelength positions with the airborne spectra. All of the ground-based spectra (up to ten) and the average (arithmetic mean) were plotted for each site, to exemplify spectral mixtures of materials contained within the airborne image pixel size. The ground-based spectra are not quantitative. References Cited: Blisniuk, K., and Sharp, W.D., 2014, Estimating geologic slip rates on the southern San Andreas Fault, California: U-series and 10Be dating: U.S. Geological Survey Final Technical Report for USGS Award No. G13AP00031, 9 p. Buckland, K.N., Young, S.J., Keim, E.R., Johnson, B.R., Johnson, P.D., and Tratt, D.M, 2017, Tracking and quantification of gaseous chemical plumes from anthropogenic emission sources within the Los Angeles Basin: Remote Sensing of Environment, v. 201, p. 275- 296, https://doi.org/ 10.1016/j.rse.2017.09.012. Green, A.A., Berman, M., Switzer, P., and Craig, M.D., 1988, A transformation for ordering multispectral data in terms of image quality with implications for noise removal: IEEE Transactions on Geoscience and Remote Sensing, v. 26, no. 1, p. 65-74, https://doi.org/10.1109/36.3001. Kealy, P.S., and Hook, S.J., 1993, Separating temperature and emissivity in thermal infrared multispectral scanner data: implications for recovery of land surface temperatures: IEEE Transactions on Geoscience and Remote Sensing, v. 31, n. 6, p. 1155-1164, https://doi.org/10.1109/36.317447. Lee, J.B., Woodyatt, S., and Berman, M., 1990, Enhancement of high spectral resolution remote- sensing data by a noise-adjusted principal components transform: IEEE Transactions on Geoscience and Remote Sensing, v. 28, n. 3, p. 295-304. Robitaille, P. -M., 2009, Kirchhoff's law of thermal emission: 150 years: Progress in Physics, v. 4, p. 3-13. Witkosky, R.D., Adams, P., Akciz, S., Buckland, K., Harvey, J., Johnson, P., Lynch, D.K., Sousa, F., Stock, J., and Tratt, D., 2016, Geologic swath map of the Lavic Lake fault from airborne thermal hyperspectral imagery. Paper presented at 8th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Los Angeles, California, https://doi.org/10.1109/WHISPERS.2016.8071769. Young, S.J., Johnson, B.R., and Hackwell, J.A., 2002, An in-scene method for atmospheric compensation of thermal hyperspectral data: Journal of Geophysical Research, v. 107, no. D24, p. 4774-4793, https://doi.org/10.1029/2001JD001266.

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