Mako thermal infrared hyperspectral airborne imagery of the Lavic Lake fault: Imagery processed for supervised and unsupervised classifications

Description

Contents: two folders that contain a total of nine files; one folder contains seven files, and the other folder, two files. The folder "redFlakeSite" contains: The thermal infrared hyperspectral airborne image, and its header file, of the Red Flake site, processed to emissivity values: 1) emissivityImage 2) emissivityImage.hdr The lithologic contact boundary lines for the Red Flake site, in a set of shape files: 3) allRFlakeClasses.shp 4) allRFlakeClasses.shx 5) allRFlakeClasses.dbf 6) allRFlakeClasses.prj The thermal infrared laboratory spectra of lithologic sample chips from the Red Flake site, in a spreadsheet: 7) redFlakeSamplesLabSpectra.xlsx The folder "completeHyperspectralImageMNFcomponents" contains the complete thermal infrared hyperspectral airborne image, and its header file, of the Lavic Lake fault, processed to the first fifteen minimum noise fraction (MNF) components: 1) B21to128MnfForB1to15georef 2) B21to128MnfForB1to15georef.hdr For the supervised classifications at the Red flake site, the Red flake site emissivity image (with its header file) and the shape files for the lithologic classes (to randomly or manually choose representative endmember spectra) are included (laboratory spectra from lithologic sample chips are also included, but these were not used in the supervised classifications). The emissivity image was processed 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 files, which had undergone radiometric and wavelength calibration, bad pixel replacement, and spectral smile removal; bands 1-20 (wavelengths 7.56-8.40 µm) removed because they were dominated by noise (remaining bands are 21-128, wavelengths 8.45-13.15 µm); in-scene atmospheric compensation (Young et al., 2002); principal component analysis transformation; discarded components that included significant noise or data artifacts in a principal components inverse transformation; temperature emissivity separation with the emissivity normalization method (Kealy and Hook, 1993); georeferenced using the geolocation files included with the Level 2 files; georeferenced again, with more precision, using ground control points that were manually chosen from a National Agriculture Imagery Program (NAIP) satellite image; area outside of the Red flake site masked; image cropped to the areal extent of the Red flake site. The set of shape files that we used for the supervised classifications are digitized representative boundaries between the four distinct lithologic units (1. tuff and tuff breccia, 2. detritus or colluvium, 3. feldspar porphyry, and 4. microcrystalline lava) that we identified and mapped at the Red Flake site. Note that the four files in the set of shape files do not represent the four lithologic classes; all of the line work is encompassed in the complete shape file set, and all of the files are needed together for digitized plotting. The thermal infrared laboratory spectra were taken from the upward-facing weathered surfaces of the lithologic sample chips. Reflectance spectra were measured using the biconical reflectance method on a Thermo-Nicolet 6700 FTIR Spectrometer, with a Harrick Scientific "Praying Mantis" diffuse reflection accessory. All laboratory spectra were measured with a spot size of 1-2 mm, and each final spectrum was an average of 150 scans taken over 4-6 minutes. The laboratory spectra were converted to emissivity using Kirchhoff's law, emissivity=1-reflectance (Robitaille, 2009). For the unsupervised classification of the minimum noise fraction (MNF) components of the complete hyperspectral image swath, the MNF components image (with its header file) is included. The MNF image was processed 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 files, which had undergone radiometric and wavelength calibration, bad pixel replacement, and spectral smile removal; all 70 data cubes concatenated for bulk processing; bands 1-20 (wavelengths 7.56-8.40 µm) removed because they were dominated by noise ("B21to128" in file name means the remaining bands are 21-128, wavelengths 8.45-13.15 µm); in-scene atmospheric compensation (Young et al., 2002); MNF forward transformation (Green et al., 1988); discarded all MNF components beyond the first 15; georeferenced using the geolocation files included with the Level 2 files. References Cited: Green, A.A., Berman, M., Switzer, P., & 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, 26(1), 65-74. https://doi.org/10.1109/36.3001 Kealy, P.S., & Hook, S.J. (1993). Separating temperature and emissivity in thermal infrared multispectral scanner data: implications for recovering land surface temperatures. IEEE Transactions on Geoscience and Remote Sensing, 31(6), 1155-1164. https://doi.org/10.1109/36.317447 Robitaille, P.-M. (2009). Kirchhoff's law of thermal emission: 150 years. Progress in Physics, 4, 3-13. Young, S.J., Johnson, B.R., & Hackwell, J.A. (2002). An in-scene method for atmospheric compensation of thermal hyperspectral data. Journal of Geophysical Research, 107(D24), 4774-4793. https://doi.org/10.1029/2001JD001266

Files

Additional details