Abstract

WorldView 3 (WV3) standard 2A visible near-infrared (VNIR) and short-wave infrared (SWIR) data of Mountain Pass, California, were calibrated to reflectance and used to map rock types and minerals using true- and false-color composite images, band ratios, and logical operator algorithms. VNIR true-color composite (V5 = red, V3 = green, and V2 = blue) and band ratio imagery were useful for mapping Fe3+-rich Proterozoic gneiss, the Aztec Sandstone, and carbonatite rocks in the Sulphide Queen rare earth element (REE) mine pit but were unable to map neodymium-rich rocks due to low spectral resolution at VNIR wavelengths and overlap of REE spectral absorption features with those of Fe3+-bearing minerals.

SWIR false-color composite images, band ratio grayscale images, band ratio false-color composite images, and logical operator-derived mineral maps were able to map muscovite-rich Proterozoic granitic gneiss and Jurassic hydrothermally altered granites using the Al-O-H spectral absorption feature, chlorite-epidote–rich Proterozoic schist and Jurassic skarn deposits using the ferrous iron and Fe, Mg-O-H spectral absorption features, and calcite-rich Paleozoic limestone and dolomite-rich Paleozoic dolostone using the CO32 spectral absorption feature. In addition, logical operator algorithms were able to discriminate Fe-bearing muscovite from muscovite in the polymetallic (gold and silver) Morning Star mine, which supports previous studies that suggest Fe-bearing muscovite is closely associated with mineralization.

Introduction

WorldView 3 (WV3), launched in August 2014, is a commercial satellite owned and operated by DigitalGlobe, Inc. WorldView 3 is a multispectral sensor that measures reflected radiation in eight visible near-infrared (VNIR) bands between 390 and 1,050 nm and in eight short-wave infrared (SWIR) bands from 1,080 to 2,380 nm (Table 1; DigitalGlobe Inc. data sheet for WorldView 3, https://dg-cms-uploads-production.s3.amazonaws.com/uploads/document/file/95/DG2017_WorldView-3_DS.pdf). Because WV3 can point off nadir, spatial resolution varies from 1.24 m at nadir to 1.38 m at 20° off nadir for the VNIR bands and from 3.7 m at nadir to 4.10 m at 20° off nadir for the SWIR bands. In this study, the SWIR bands were resampled to 7.5-m spatial resolution due to United States government license restrictions on public release of SWIR high spatial resolution imagery. Although not used in this study, WV3 also has a panchromatic band spanning 450 to 800 nm with 0.31 m spatial resolution at nadir and twelve 30-m resolution atmospheric calibration (CAVIS) bands. For the purpose of clarity, Table 1 shows the band designations of each of the eight VNIR and eight SWIR bands referred to throughout this study.

Electronic and molecular spectral absorption features of minerals are well documented and have been used to map minerals using multispectral data (Hunt et al., 1972; Hunt, 1977; Rowan et al., 1977; Rowan and Mars, 2003; Rowan et al., 2003; Mars and Rowan, 2011). WorldView 3 VNIR and SWIR bands are positioned in order to resolve key spectral absorption features of minerals (Fig. 1). Sample spectra resampled to WV3 band passes indicate that WV3 VNIR bands are positioned to resolve electronic spectral absorption features of ferric and ferrous (Fe2+) minerals such as goethite, epidote, and chlorite and some rare earth elements (REEs), such as neodymium (Fig. 1; Clark et al., 1993). In the SWIR region, resampled WV3 spectra can resolve Al-O-H, FeMg-OH, and CO32 molecular spectral absorption features associated with muscovite, epidote and chlorite, and calcite and dolomite, respectively (Fig. 1).

In a previous study, WV3 SWIR data were used to map mineralogy at Cuprite Hills, Nevada (Kruse et al., 2015). In that study, sample spectra from the study area were used to calibrate the SWIR data to reflectance data, and various minerals such as alunite, muscovite, kaolinite, opal, and buddingtonite were mapped using a mixture-tuned match filter algorithm (Boardman and Kruse, 2011; Kruse et al., 2015). This study shows how VNIR and SWIR WV3 data from Mountain Pass, California, can be calibrated to reflectance data using atmospheric removal software, which allows for accurate reflectance calibration without using samples from the study area. In addition, this study shows how the VNIR and SWIR WV3 data can be used to compile true- and false-color composite images and mineral maps to aid in mapping lithology and mineralogy and to identify altered rocks that are associated with deposits of potential economic interest.

Geomorphology and Economic Geology of the Mountain Pass Area

The Mountain Pass study area is located approximately 70 km southwest of Las Vegas, Nevada, in southeastern California close to the Nevada state line (Fig. 2). The area mapped using WV3 data includes the entire SWIR coverage and overlapping VNIR coverage and includes parts of the Clark, Mescal, and Ivanpah mountain ranges with elevations that vary from approximately 1,200 to 1,850 m in the mountains to 950 to 1,200 m on large alluvial fans that extend into the lower elevations (Figs. 2, 3). Field observations indicated that sagebrush, Joshua trees, and various types of grasses and cactus cover approximately 10 to 30% of the surface (Fig. 4).

The northern and eastern parts of the study area are covered by Proterozoic chlorite schist and granite gneiss, migmatite, and minor amounts of syenite, shonkinite, and carbonatite, although field work also shows that chlorite schist is located in the southern part of the Proterozoic lithostratigraphic units (Fig. 5; Theodore et al., 2007). The southeastern part of the study area consists of Paleozoic limestone, dolostone, and sandstone. The Paleozoic rocks are intruded by Jurassic biotite-rich porphyritic granite and Cretaceous granodiorite and mafic igneous rocks. The east-central part of the study area is covered by the Jurassic Aztec Sandstone and Tertiary intermediate volcanic, volcaniclastic, and conglomeratic rocks as well as quartzite (Fig. 5; Theodore et al., 2007).

There are two major mineral economic deposits in the study area: the Sulphide Queen and Morning Star mines (Figs. 3, 5; Theodore et al., 2007). The Sulphide Queen mine, located in the northeastern part of the study area, contains the only carbonatite deposit in the United States that has been recently mined for the critical resource element neodymium. The Morning Star deposit, situated in Jurassic granite, is a polymetallic vein mined for Au and Ag that closed in 1991 (Theodore et al., 2007). Field investigations show that dozens of small prospects, which are located in the southeastern part of the study area, are primarily in skarns, situated at Paleozoic carbonate and Jurassic intrusive contacts (Fig. 3). The prospects were mined for W, Cu, Zn, and Au (Mineral Resources Data System, https://mrdata.usgs.gov/mrds).

Spectral Reflectance Calibration, Image Processing, and Data Validation Methods

The WV3 VNIR and SWIR data sets were acquired for the Mountain Pass, California, area on May 30, 2015. The VNIR and SWIR data do not cover all of the same areas, but the data sets do overlap (Fig. 2). In the area where VNIR and SWIR data overlapped, the SWIR data were spatially resampled to 1.2 m and combined with the VNIR data. A location accuracy of approximately ±4 m for the VNIR-SWIR data was determined using registered digital images in the field with enhanced location (±2 m) from a global positioning system (GPS) antenna.

WV3 absolute radiometric calibration factors, which are periodically updated, effective band width, gain, and offset values were applied to WV3 VNIR-SWIR standard 2A data sets using ENVI radiometric correction software to convert the data to top of atmosphere radiance data (https://dg-cms-uploads-production.s3.amazonaws.com/uploads/document/file/95/DG2017_WorldView-3_DS.pdf). The WV3 VNIR and SWIR radiance data were then converted to reflectance data using FLAASH atmospheric correction software. No water vapor data from DigitalGlobe for calibrating the WV3 data were available, so a water vapor value average for the WV3 VNIR-SWIR coverage was compiled from 1-km2 spatial resolution Moderate Resolution Imaging Spectrometer (MODIS) MOD_05 precipitable water vapor data, which were acquired 41 minutes after the WV3 scene acquisition (https://modis.gsfc.nasa.gov/data/dataprod/pdf/MOD_05.pdf). The MOD_05 water vapor value was used to adjust the FLAASH corrected reflectance data. FLAASH uses a water vapor model based on the location, elevation, and time of year to estimate levels of water vapor. However, incorrect estimates of the amount of water vapor can result in incorrect reflectance values, particularly for WV3 SWIR bands, as seen in tests varying water vapor levels when calibrating similar wavelength bands for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data (Mars and Rowan, 2010). The FLAASH water vapor model estimated the scene water vapor at 1.322 cm whereas the MODIS water vapor value for the scene area was 0.836 cm. The overestimation of water vapor resulted in slightly higher WV3 reflectance band values not only in the SWIR but also in the VNIR (Fig. 6).

Data validation of WV3 spectral data is based on visual comparisons of sample spectra taken from the study area to WV3 image spectra. Sample spectra were measured using an Analytical Spectral Devices (ASD) field spectrometer, which measures bidirectional reflectance from 350 to 2,500 nm. There are seven spectral field validation sites in the study area with typically 10 or more sample spectra collected at each site and averaged together. Each validation site covers the area of one WV3 SWIR pixel. When compared to WV3 resample averaged sample spectra, the FLAASH calibrated-MODIS water vapor modified image spectra from the same location are very similar (Figs. 7, granitic gneiss spectra, 8). Due to inaccessibility to the mine at the time of study, the sample carbonatite spectrum and WV3 resampled spectra in Figure 7 were measured from only one sample from the Sulphide Queen mine and thus show some spectral variation to an averaged image spectrum from the well-exposed, illuminated south pit surface.

WV3 band ratio and false-color composite images and map units from a SWIR mineral map were validated using a geologic map and a mineral map compiled from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data from previous studies of the Mountain Pass area (Figs. 912; Digital App.

; Rowan and Mars, 2003; Theodore et al., 2007). The AVIRIS data set used in the Rowan and Mars (2003) study consists of 224 spectral bands that span the 400- to 2,500-nm region with 17-m spatial resolution. A matched filtering algorithm was used to map mineral units. In addition, an averaged image spectrum for each WV3 SWIR mineral map unit was compiled and visually compared to WV3 resampled sample spectra taken from the field validation sites (Figs. 8, 12, 13).

Results: Spectral Reflectance Analysis

Color and false-color composite images

True- and false-color composite images can reveal important mineralogical information. The most useful band combination for the VNIR data in this study was the true-color composite consisting of WV3 bands V5 (red), V3 (green), and V2 (blue) (Fig. 9; Table 1). The three bands span the Fe3+ spectral absorption feature, and the color combination illustrates red- to orange-colored iron oxide-rich Proterozoic granitic gneiss and Aztec Sandstone (Figs. 1, 5, 9). The image also shows iron-stained rocks in the Sulphide Queen mine pit. The high spatial resolution 1.2-m image also clearly defines blue-green thinner-bedded schist and migmatite from thicker-bedded granitic gneiss of the Xg unit in the south-central part of the image (Fig. 9).

The most useful false-color WV3 SWIR composite band combination for the Mountain Pass area consists of S3 (red), S6 (green), and S8 (blue; Fig. 3). Short-wave infrared band 3 is affected by Fe2+ spectral absorption, SWIR band 6 is situated at the Al-O-H spectral absorption feature, and SWIR band 8 is located at the Fe, Mg-O-H, and CO32 spectral absorption features (Fig. 1). Thus, chlorite- and epidote-rich Proterozoic migmatite and schist and Mesozoic skarn deposits that have strong Fe2+ and Fe, Mg-O-H spectral absorption appear dark green, muscovite-rich Proterozoic granitic gneiss and hydrothermally altered Jurassic granite that have strong Al-O-H spectral absorption appear red, and calcite- and dolomite-rich Paleozoic limestone and dolostone that have strong CO32 spectral absorption appear yellow to light green (Fig. 3).

Lithologic mapping using band ratios

Band ratio images illustrate the spectral contrast of specific spectral absorption features of minerals (Rowan et al., 1977; Vincent, 1997; Rowan and Mars, 2003). Band ratios for ferrous and ferric iron, muscovite, epidote, chlorite, calcite, and dolomite were selected on the basis of the known geology, field work, and minerals that have diagnostic spectral absorption features as defined by WV3 spectral bands (Fig. 1).

Field investigations and true-color WV3 VNIR imagery showed that the central and northwestern parts of the Proterozoic granitic gneiss units mapped as Xg and Xg1 and the Aztec Sandstone were extensively iron stained (Fig. 9). Ferric iron minerals such as goethite and hematite have a set of broad spectral absorption features centered at approximately the 500 and 850- to 910-nm regions, which are defined by WV3 VNIR bands 2 through 4 and VNIR bands 6 and 7 and SWIR band 1, respectively (Fig. 1, goethite spectrum). Two band ratios were used to highlight ferric iron minerals: VNIR band 4/VNIR band 2 band ratio to map rocks with ferric iron absorption from hematite, goethite, and other minerals, and (VNIR band 7 + SWIR band 1)/VNIR band 8 (relative band depth [RBD] for VNIR band 8) to map the 850- to 910-nm goethite-rich Fe3+ rocks. The band ratios were applied to the overlapping WV3 VNIR and SWIR coverages (Figs. 2, 10A, B). The band ratio VNIR band 4/VNIR band 2 successfully mapped the granitic gneisses, the Aztec Sandstone, and rocks in the Sulphide Queen mine pit (Figs. 5, 10A). The band ratio (VNIR band 7 + SWIR band 1)/VNIR band 8 (RBD8) mapped goethite Fe3+ iron absorption in the granitic gneisses and parts of the Sulphide Queen mine pit (Figs. 5, 10B).

Although neodymium oxide has numerous narrow band spectral absorption features that span the WV3 VNIR bands (Fig. 1), sample spectra of a Nd-rich carbonatite rock from the mine show only three prominent, narrow spectral absorption features situated at 741, 799, and 865 nm (Fig. 7A, carbonatite spectrum). The carbonatite sample spectrum resampled using WV3 band passes shows that the Nd spectral absorption features form a broad, shallow absorption feature spanning WV3 VNIR bands 6, 7, and 8 (Fig. 7B, carbonatite spectrum). A WV3 resampled sample spectrum of Fe3+-rich granitic gneiss compared to the carbonatite WV3 resampled sample spectrum shows VNIR band 8 overlap of the Nd and Fe3+ iron absorption features (Fig. 7B, carbonatite and granitic gneiss spectra). Although a WV3 image spectrum taken from the south wall of the mine pit shows a shallow spectral absorption feature that spans WV3 VNIR bands 6, 7, and 8, the spectral feature may be partially due to Nd or Fe3+ absorption (Fig. 7C, carbonatite spectrum). In addition, because the WV3 VNIR spectral absorption features from the rocks in the mine are extremely shallow and span part of the Fe3+ spectral absorption feature, it was not possible to distinguish Nd-rich carbonatite rock from other Fe3+-rich rocks in the study area using VNIR band 6/VNIR band 7, VNIR band 8/VNIR band 7, or (VNIR band 6 + VNIR band 8)/VNIR band 7 band ratios.

Minerals such as epidote and chlorite have a prominent ferrous iron spectral absorption feature. The ferrous iron spectral absorption feature spans the 600- to 2,050-nm region and is defined by WV3 VNIR bands 3 through 8 and SWIR bands 1 through 5 (Figs. 1, 8, epidote and chlorite spectra). The Fe2+ band ratio SWIR band 5/SWIR band 1 was used to map ferrous iron-rich minerals (Fig. 10C). Field sample spectra and the geologic map for the study area indicate that the band ratio mapped chlorite-rich gneiss, migmatite, and schist in the south-central part of the Xg and epidote-chlorite–rich skarn deposits (Figs. 3, 5, 10C). Ferrous iron-rich rocks were also mapped using the Fe2+ band ratio at the Sulphide Queen mine (Fig. 10C).

Sample spectra from the Mountain Pass study area show that the Al-O-H spectral absorption feature ranges from 2,200 to 2,220 nm, is common in muscovite-rich rocks, and is defined by WV3 SWIR bands 5, 6, and 7 (Fig. 8, muscovite and Fe muscovite). A band ratio SWIR band 5/SWIR band 6 was used to map muscovite-rich rocks. Field sample spectra and the geologic map for the study area indicate that the band ratio mapped Proterozoic granitic gneiss and sericite-rich, hydrothermally altered Jurassic granite, including rocks at the Morning Star mine (Figs. 5, 10D).

Sample spectra from the Mountain Pass study area show that the calcite-dolomite SWIR spectral absorption features are defined by WV3 SWIR bands 5 through 8 (Fig. 8). Calcite and dolomite exhibit SWIR spectral absorption due to CO32 at 2,330 and 2,310 to 2,320 nm, respectively, which results in a lower WV3 SWIR band 8 reflectance for calcite when compared to dolomite SWIR band 8 reflectance (Figs. 1, 8). To map calcite and dolomite, a band ratio SWIR band 6/SWIR band 8 was used, which was verified by sample field spectra and the geologic map (Figs. 5, 8, 10E). The band ratio also mapped some epidote-chlorite–rich migmatite, schist, and gneiss because the Fe, Mg-O-H spectral absorption feature spans the 2,310- to 2,340-nm region (Figs. 1, 8, chlorite and epidote, 10E). Because of the stronger spectral absorption in WV3 SWIR band 8 for calcite as compared to dolomite, a band ratio of SWIR band 7/SWIR band 8 was also used to map the 2,330-nm calcite spectral absorption feature (Figs, 1, 8, 10F). Field spectra and the geologic map indicate that the SWIR band 7/SWIR band 8 ratio can discriminate limestone from dolomite but also map some chlorite and epidote gneiss and schist due to overlap with the Fe, Mg-O-H spectral absorption feature (Figs. 1, 5, 8, 10F).

Rocks are an assemblage of minerals, and band ratios used in red-green-blue combinations can produce images that highlight various rock types. In order to best resolve the geology of the study area, band ratios that identify muscovite-rich rocks (SWIR band 5/SWIR band 6 = red), ferrous iron-rich rocks (SWIR band 5/SWIR band 1 = green), and calcite-dolomite-rich (carbonate) rocks (SWIR band 6/SWIR band 8 = blue) were selected (Fig. 11). When compared with the geologic map, red to yellow hues in the false-color composite band ratio image depict muscovite-rich Proterozoic granitic gneiss and hydrothermally altered Jurassic and Cretaceous granites, green to light-blue hues illustrate epidote- and chlorite-rich Proterozoic schist and gneiss and Jurassic skarns, and the blue to dark-blue hues depict the Paleozoic carbonate rocks (Fig. 11). Dark-purple hues in the false-color composite band ratio image illustrate low-reflectance Mesozoic volcanic rocks.

SWIR mineral mapping using logical operator algorithms

Mineralogical mapping of specific mineral groups using logical operator algorithms (LOAs) was applied to the WV3 SWIR data set in order to identify important mineral groups associated with lithology and hydrothermally altered rocks (Table 2). The VNIR data were not used due to the limited spatial overlap of the data set with the SWIR coverage (Fig. 2). Regional hydrothermal alteration mineral maps have been produced using LOAs and ASTER data (Mars and Rowan, 2006; Mars, 2013, 2014; Berger et al., 2014). ASTER has six SWIR bands, of which five bands match WV3 SWIR band passes (Rowan and Mars, 2003). Thus, due to the similar SWIR spectral resolution of WV3 to ASTER, LOAs were used to map mineralogy in this study (Table 1; Rowan and Mars, 2003).

Logical operator algorithms use a series of band ratios to map spectral absorption features and reflectance limits of materials (Mars and Rowan, 2006). Each band ratio is displayed and contrast stretching is applied until only areas containing known target mineralogy and surrounding areas with the same or greater reflectance are highlighted. Spectra of highlighted areas are then compared to field spectra of known targets to determine if the band ratio is accurately mapping the correct mineral. The range of band ratio values of pixels highlighting the areas of known mineralogy is then examined to determine the band ratio value that is used as a threshold value in the LOAs (Table 2). Each mapped unit was validated by comparing WV3 image spectra and an averaged spectrum of each spectrally mapped unit to sample spectra collected in the field from the same areas (Figs. 8, 12, 13).

In order to use LOAs for the first time for a new sensor, target materials must be identified in the study area either by field work or through the use of other remote sensing data in order to determine band threshold values for each LOA. Regional mapping using ASTER data, however, have shown that LOAs with the same threshold values can be applied to other scenes, provided each scene has been calibrated to reflectance using the same method (Mars and Rowan, 2006; Mars, 2013, 2014; Berger et al., 2014).

Muscovite (KAl2[Si3Al]O10[OH]2) and Fe-bearing muscovite (KAl1.5[Mg,Fe]0.5[Al0.5Si3.5O10][OH]2) are important indicators of hydrothermally altered rocks, and Fe-bearing muscovite (phengitic mica) has been shown to be typically associated with mineralized deposits (Cibin et al., 2008; Yang et al., 2011; Graham et al., 2018). Muscovite and phengite contain Al-O-H spectral absorption features (Yang et al., 2011; Rowan and Mars, 2003). Sample spectra from the study area show that the muscovite Al-O-H spectral absorption feature is situated at 2,200 nm, and the Fe-bearing muscovite Al-O-H spectral absorption feature ranges from 2,210 to 2,220 nm (Fig. 8, muscovite and Fe-bearing muscovite). This spectral shift has been used to identify and map phengite-rich rocks at the Morning Star mine using AVIRIS and ASTER data in a previous study (Digital App.

; Rowan and Mars, 2003).

WV3 image spectra and sample spectra of Fe-bearing muscovite resampled to WV3 spectra show that WV3 SWIR bands 5, 6, and 7 span the Al-O-H spectral absorption features (Fig. 8, muscovite and Fe-bearing muscovite). Iron-bearing muscovite image spectra exhibit a slight decrease in WV3 SWIR band 7 reflectance values and a slight increase in WV3 SWIR band 5 reflectance values when compared to WV3 resampled muscovite sample spectra (Figs. 8, 13, Al muscovite and Fe-bearing muscovite). Muscovite and Fe-bearing muscovite LOAs use a SWIR band 5/SWIR band 6 and SWIR band 7/SWIR band 6 ratio to map the shoulders of the 2,200 and 2,210- to 2,220-nm Al-O-H spectral absorption features (Fig. 1; Table 2). A SWIR band 5/SWIR band 7 ratio is used to discriminate muscovite (lower band ratio values) from Fe-bearing muscovite (higher band ratio values; Figs. 8, 13, muscovite and Fe-bearing muscovite; Table 2). This method of using the shoulder reflectance data of the Al-O-H spectral absorption feature has been done in previous studies that used ASTER data (Cudahy et al., 2012). Mineral mapping results show that muscovite is found in Precambrian granitic gneiss in the east-central and northern parts of the study areas and in hydrothermal phyllic-altered Jurassic rocks in the southern part of the study area (Fig. 12). As seen in the Rowan and Mars (2003) AVIRIS mineral map, iron-bearing muscovite is located primarily at the Morning Star mine, which marks the location of gold and silver mineralized rocks in the southern part of the study area, suggesting that Fe-bearing muscovite is a key indicator of mineralization (Fig. 12; Digital App.

).

Epidote and chlorite are also important indicators of hydrothermally altered rocks in skarns and other types of hydrothermal deposits (Einaudi et al., 1981; Einaudi and Burt, 1982). The LOA that maps epidote-chlorite uses a SWIR band 5/SWIR band 2 ratio that maps the ferrous iron feature, a SWIR band 6/SWIR band 7 that maps the Fe, Mg-O-H feature, and a lower and upper threshold of WV3 SWIR band 4 (Figs. 1, 8, 13; Table 2). The upper threshold helps discriminate epidote-chlorite from carbonate rocks (limestone and dolostone) that typically have a higher SWIR band 4 reflectance (Figs. 1, 8, 13; Table 1). The lower SWIR band 4 threshold helps discriminate epidote-chlorite from low-reflectance rocks such as intermediate and mafic volcanic rocks. Short-wave infrared mineral mapping shows that epidote-chlorite–rich rocks are located primarily in the southern part of the Proterozoic schist and gneiss (Theodore et al., 2007). The epidote-chlorite LOA also maps skarns in the south-central and southwestern parts of the study area (Fig. 12). Both locations of epidote-chlorite–rich rocks match epidote mapped units using AVIRIS data in the Rowan and Mars (2003) study (Fig. 12; Digital App.

).

Limestone and dolostone constitute a large part of the southeastern study area (Fig. 5). Calcite and dolomite LOAs were compiled in order to map limestone and dolostone in the area (Table 2). Band ratio SWIR band 6/SWIR band 7 was used to map the CO32 spectral absorption feature for dolomite- and calcite-rich rocks. Because dolomite has a higher WV3 SWIR band 8 reflectance than limestone, band ratio SWIR band 7/SWIR band 8 was used to discriminate dolomite with lower band ratio values from calcite with higher band ratio values (Figs. 1, 8; Table 2). A band ratio of SWIR band 5/SWIR band 2 was also used to discriminate calcite and dolomite from epidote-chlorite due to the overlapping Fe, Mg-O-H, and CO32 spectral absorption features (Figs. 1, 8). Band ratio SWIR band 5/SWIR band 2 values for calcite and dolomite tend to be lower than the same ratio values for epidote-chlorite due to the ferrous iron absorption feature (Figs. 1, 8, 13). Logical operator algorithm mapping results show that the limestones and dolostones in the south-central and western parts of the study area on the geologic map and on the AVIRIS mineral map from the Rowan and Mars (2003) study match the calcite and dolomite units mapped using WV3 SWIR data (Figs. 5, 12; Digital App.

; Theodore et al., 2007).

Conclusions

WorldView 3 data have sufficient spectral resolution to resolve Fe3+, Fe2+, Fe/Mg-O-H, Al-O-H, and CO32 spectral absorption features (Fig 1). Using various band ratios and LOAs to map the spectral contrast of WV3 defined spectral absorption features, this study has successfully mapped goethite using the Fe3+ spectral absorption features, calcite and dolomite using the CO32 spectral absorption feature, epidote-chlorite using the Fe, Mg-O-H, and Fe2+ spectral absorption features, and muscovite and Fe-bearing muscovite using the Al-O-H spectral absorption feature (Figs. 1012). The WV3 mineral map units, band ratios, and the false-color composite band ratio maps have been used to successfully map muscovite-rich Proterozoic granitic gneiss in the central part of the study area, chlorite-epidote–rich Proterozoic migmatite, schist, and gneiss in the southern part of the study area, and Paleozoic limestone and dolostone in the southwestern part of the study area (Figs. 5, 1012). In particular, the WV3 false-color composite images and mineral map indicated that the Xg Proterozoic unit consists of chlorite-rich migmatite and schist in the southern part of the study area and muscovite-rich granitic gneiss in the central and northern parts of the study area (Figs. 3, 5, 11, 12). WorldView 3 data also were used to successfully map epidote-chlorite–rich skarn deposits and Fe-bearing muscovite-rich hydrothermally altered rocks of the Morning Star mine (Figs. 11, 12).

The ASTER instrument, launched in 1999, has global coverage and similar SWIR spectral resolution to WV3 (Rowan and Mars, 2003). There have been a number of regional mapping studies done using ASTER data to map basic mineralogy and hydrothermal alteration associated with economic deposits (Mars and Rowan, 2006; Mars, 2013, 2014). As compared to ASTER 30-m resolution mineral and band ratio maps of the Mountain Pass study area, the SWIR WV3 mineral maps not only have better spatial resolution at 7.5 m, but also have much better spatial coherence of mineralogical units and band ratio images, particularly when discriminating Fe-bearing muscovite from muscovite and calcite from dolomite (Rowan and Mars, 2003). This suggests that the WV3 SWIR instrument can be used to produce more detailed band ratio and mineralogical maps at the mine site scale. Thus, because the two instruments have similar spectral resolution, they can be used together in remote sensing studies, whereas WV3 data can be used on more detailed, site-specific mapping based on regional mapping compiled from ASTER data.

John Mars (Lyle) is a geologist at the U.S. Geological Survey in Reston, Virginia, where he has worked for the last 21 years. Lyle received his B.S. and M.S. degrees from the University of Alabama in 1983 and 1990, respectively, and a Ph.D. degree from the University of Kentucky in 1995. Lyle specializes in geologic remote sensing, mapping rocks and minerals using multispectral and hyperspectral data. His work is primarily used in mineral resource assessment, where he produces regional hydrothermal alteration maps of modern and ancient, volcanic and magmatic arcs from around the world.

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Supplementary data