Geology topics

Raymond F. Kokaly

Publications and source records attributed to Raymond F. Kokaly.

At least 37 records · Page 2Linked to original sources

USGS2022

Optimizing Landsat Next shortwave infrared bands for crop residue characterization

This study focused on optimizing the placement of shortwave infrared (SWIR) bands for pixel-level estimation of fractional crop residue cover ( f R ) for the upcoming Landsat Next mission. We applied an iterative wavelength shift approach to a database of crop residue field spectra collected in Beltsville, Maryland, USA (n = 916) and computed generalized two- and three-band spectral indices for all wavelength combinations between 2000 and 2350 nm, then used these indices to model field-measured f R . A subset of the full dataset with a Normalized Difference Vegetation Index (NDVI) < 0.3 threshold (n = 643) was generated to evaluate green vegetation impacts on f R estimation. For the two-band wavelength shift analyses applied to the NDVI < 0.3 dataset, a generalized normalized difference using 2226 nm and 2263 nm bands produced the top f R estimation performance ( R 2 = 0.8222; RMSE = 0.1296). These findings were similar to the established two-band Shortwave Infrared Normalized Difference Residue Index (SINDRI) ( R 2 = 0.8145; RMSE = 0.1324). Performance of the two-band generalized normalized difference and SINDRI decreased for the full-NDVI dataset ( R 2 = 0.5865 and 0.4144, respectively). For the three-band wavelength shift analyses applied to the NDVI < 0.3 dataset, a generalized ratio-based index with a 2031–2085–2216 nm band combination, closely matching established Cellulose Absorption Index (CAI) bands, was top performing ( R 2 = 0.8397; RMSE = 0.1231). Three-band indices with CAI-type wavelengths maintained top f R estimation performance for the full-NDVI dataset with a 2036–2111–2217 nm band combination ( R 2 = 0.7581; RMSE = 0.1548). The 2036–2111–2217 nm band combination was also top performing in f R estimation ( R 2 = 0.8690; RMSE = 0.0970) for an additional analysis assessing combined green vegetation cover and surface moisture effects. Our results indicate that a three-band configuration with band centers and wavelength tolerances of 2036 nm (±5 nm), 2097 nm (±14 nm), and 2214 (±11 nm) would optimize Landsat Next SWIR bands for f R estimation.

Remote Sensing↗

USGS2022

Microplastic particles in dust-on-snow, Upper Colorado River Basin, Colorado Rocky Mountains, 2013–16

Atmospheric dust deposited to snow cover (dust-on-snow) diminishes snow-surface albedo (SSA) to result in early onset and accelerated rate of melting, effects that challenge management of downstream water resources. During ongoing investigations to identify the light-energy absorbing dust particles most responsible for diminished SSA in the Upper Colorado River Basin of the Colorado Rocky Mountains, we found microplastic particles, which are defined as those less than 5 millimeters in any dimension. In each of the 38 samples that represented the last remaining dust layer during melt seasons of 2013–16, microplastics were identified by size, shape, and color, and their relative amounts were visually estimated using stereomicroscopy. Considering the remote, high-elevation settings of the sample sites, the microplastic particles must have been deposited from the atmosphere. The possible role of microplastics for diminishing SSA of snow cover in the Upper Colorado River Basin may be linked to the solar-energy absorptive properties of polymers and is the subject of ongoing investigation.

Colorado↗

USGS2022

Hyperspectral remote sensing of white mica: A review of imaging and point-based spectrometer studies for mineral resources, with spectrometer design considerations

Over the past ~30 years, hyperspectral remote sensing of chemical variations in white mica have proven to be useful for ore deposit studies in a range of deposit types. To better understand mineral deposits and to guide spectrometer design, this contribution reviews relevant papers from the fields of remote sensing, spectroscopy, and geology that have utilized spectral changes caused by chemical variation in white micas. This contribution reviews spectral studies conducted at the following types of mineral deposits: base metal sulfide, epithermal, porphyry, sedimentary rock hosted gold deposits, orogenic gold, iron oxide copper gold, and unconformity-related uranium. The structure, chemical composition, and spectral features of white micas, in this contribution defined as muscovite, paragonite, celadonite, phengite, illite, and sericite, are given. Reviewed laboratory spectral studies determined that shifts in the position of the white mica 2200 nm combination feature of 1 nm correspond to a change in Al oct content of approximately ±1.05%. Many of the reviewed spectral studies indicated that a shift in the position of the white mica 2200 nm combination feature of 1 nm was geologically significant. A sensitivity analysis of spectrometer characteristics; bandpass, sampling interval, and channel position, is conducted using spectra of 19 white micas with deep absorption features to determine minimum characteristics required to accurately measure a shift in the position of the white mica 2200 nm combination feature. It was determined that a sampling interval < 16.3 nm and bandpass <17.5 nm are needed to achieve a root mean square error (RMSE) of 2 nm, whereas a sampling interval < 8.8 nm and bandpass <9.8 nm are needed to achieve a RMSE of 1 nm. For comparison, commonly used imaging spectrometers HyMap, AVIRIS-Classic, SpecTIR®'s AisaFENIX 1K, and HySpex tm SWIR 384 have 2.1, 1.2, 0.96, and 0.95 nm RMSE in determining the position of the 2200 nm white mica combination feature, respectively. An additional sensitivity analysis is conducted to determine the effect of signal to noise ratio (SNR) on the RMSE of the position of the white mica 2200 nm combination feature, using spectra of 18 white micas with deep absorption features. For a spectrometer with sampling interval and bandpass of 1 nm, we estimate that RMSEs of 1 and 1.5 nm are achievable with spectra having a minimum SNR of approximately 246 and 64, respectively. For a spectrometer with sampling interval and bandpass of 5 nm, we estimate that RMSEs of 1 and 1.5 nm are attainable with spectra having a minimum SNR of approximately 431 and 84, respectively. When using a spectrometer with a sampling interval 8.8 nm and a bandpass of 9.8 nm, a RMSE of 1 is only achievable with convolved, noiseless reference spectra. For the 8.8_9.8 nm spectrometer, spectra with SNR of 250 and 100 result in RMSE of 1.1 and 1.3, respectively. Therefore, fine spectral resolution characteristics achieve RMSEs better than 1 nm for high SNR spectra while spectrometers with coarse spectral resolution have larger RMSE, perform well with noisy data, and are useful for white mica studies if RMSE of 1.1 to 1.5 nm is acceptable.

Remote Sensing of Environment↗

USGS2021

Evaluation of SWIR crop residue bands for the Landsat Next mission

This research reports the findings of a Landsat Next expert review panel that evaluated the use of narrow shortwave infrared (SWIR) reflectance bands to measure ligno-cellulose absorption features centered near 2100 and 2300 nm, with the objective of measuring and mapping non-photosynthetic vegetation (NPV), crop residue cover, and the adoption of conservation tillage practices within agricultural landscapes. Results could also apply to detection of NPV in pasture, grazing lands, and non-agricultural settings. Currently, there are no satellite data sources that provide narrowband or hyperspectral SWIR imagery at sufficient volume to map NPV at a regional scale. The Landsat Next mission, currently under design and expected to launch in the late 2020’s, provides the opportunity for achieving increased SWIR sampling and spectral resolution with the adoption of new sensor technology. This study employed hyperspectral data collected from 916 agricultural field locations with varying fractional NPV, fractional green vegetation, and surface moisture contents. These spectra were processed to generate narrow bands with centers at 2040, 2100, 2210, 2260, and 2230 nm, at various bandwidths, that were subsequently used to derive 13 NPV spectral indices from each spectrum. For crop residues with minimal green vegetation cover, two-band indices derived from 2210 and 2260 nm bands were top performers for measuring NPV (R 2 = 0.81, RMSE = 0.13) using bandwidths of 30 to 50 nm, and the addition of a third band at 2100 nm increased resistance to atmospheric correction residuals and improved mission continuity with Landsat 8 Operational Land Imager Band 7. For prediction of NPV over a full range of green vegetation cover, the Cellulose Absorption Index, derived from 2040, 2100, and 2210 nm bands, was top performer (R 2 = 0.77, RMSE = 0.17), but required a narrow (≤20 nm) bandwidth at 2040 nm to avoid interference from atmospheric carbon dioxide absorption. In comparison, broadband NPV indices utilizing Landsat 8 bands centered at 1610 and 2200 nm performed poorly in measuring fractional NPV (R 2 = 0.44), with significantly increased interference from green vegetation.

Remote Sensing↗

USGS2021

NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet , recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists.

Remote Sensing of Environment↗

USGS2021

Mineral Mapping of the Battle Mountain District, Nevada, USA, Using AVIRIS-Classic and SpecTIR Inc. AisaFENIX 1K Imaging Spectrometer Datasets

Imaging spectroscopy (hyperspectral imaging) has been used to successfully map minerals at the outcrop, deposit, district, and regional scale. This contribution presents spectral-based mineral maps of the Battle Mountain mining district, Nevada, USA, generated using multi-scale airborne imaging and ground-based point spectrometers. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and AisaFENIX 1K imaging spectrometer data were processed using Atmospheric and Topographic Correction (ATCOR-4) software with an empirical correction multiplier derived from field data. Data were used to generate spectral-based mineral maps with spatial resolutions of 13.5 and 1.8 m. A comparison of the various radiative transfer models used to convert radiance data to reflectance indicated that the ATCOR4 rugged model performed best for these datasets. These mineral maps were then used to spectrally characterize two potential porphyry mineral targets in the district.

Nevada↗

USGS2021

Multiscale hyperspectral imaging of hydrothermal alteration in Yellowstone National Park, USA

Imaging spectroscopy (hyperspectral imaging) data have mainly been used to map surface materials covering relatively small areas from airborne sensors over the past 20+ years. As part of the U.S. Geological Survey Integrated hyperspectral, geophysical and geochemical studies of Yellowstone National Park hydrothermal systems project, we have collected multiscale imaging spectrometer data including borehole core, field, and airborne data. These data give us the unique opportunity to map subsurface and surface alteration of shallow epithermal systems at scales ranging from microns to meters per pixel. Airborne Visible and Infrared Imaging Spectrometer (AVIRIS), Corescan HCI-3, HySpex VNIR-1800 and SWIR-384 imaging spectrometers, and a Riegl VZ-2000i terrestrial lidar system were used in this study. Maps utilizing spectral analysis of multiscale hyperspectral data indicate the presence of mineral assemblages consistent with epithermal deposits. Minerals such as alunite, hydrated silica, and kaolinite typically form in steam-heated cap systems and can be found in core and AVIRIS data. At depth, higher temperature fluids that are less acidic produce mixed-layer kaolinite with clay/muscovite and then montmorillonite and muscovite towards the bottom of the borehole. This transition can be seen in AVIRIS and field data at the Grand Canyon of the Yellowstone and in borehole Corescan data at Y-12 Norris-Ii R874. Instrument specifications, collection parameters, and setup for data acquisition will be discussed as well as mapping software and preliminary results.

Idaho, Montana, Wyoming↗

USGS2021

Imaging spectroscopy applied to mineral mapping over large areas: Impact of residual atmospheric artefacts in reflectance spectra on mineral identification and mapping

Around the world, imaging spectroscopy (hyperspectral imaging) has been successfully applied to mapping surface minerals over small areas. In this paper, we examine one challenge to reliably mapping surface mineral composition over large areas: the impacts of atmospheric artefacts in reflectance spectra on mineral identification. To examine these impacts, we use a set of Airborne Visible and Infrared Imaging Spectrometer-Classic (AVIRISc) data collected over ~30,000 sq. km of southern California (SoCal) in 2018. The AVIRISc2018 SoCal reflectance data showed residual atmospheric contamination in wavelength regions at the edges of strong water vapor absorption features: 1.44-1.5 µm, 1.75-1.8 µm, and 2.35-2.5 µm. These three regions of atmospheric residuals affect identification and discrimination of mineral absorption features, especially when mineral abundances are low and spectral features are weak. Of these three regions, improving atmospheric correction of imaging spectrometer data in the 2.35-2.5 µm region will result in the most enhancement in mineral identification, discrimination, and mapping. An additional region near 2.2 µm affects phyllosilicate mineral identification. The cause of the distorted reflectance in this region is still being investigated.

Conference Paper↗

USGS2020

Dust deposited on snow cover in the San Juan Mountains, Colorado, 2011-2016: Compositional variability bearing on snow-melt effects

Light-absorbing particles in atmospheric dust deposited on snow cover (dust-on-snow, DOS) diminish albedo and accelerate the timing and rate of snow melt. Identification of these particles and their effects are relevant to snow-radiation modeling and thus water-resource management. Laboratory-measured reflectance of DOS samples from the San Juan Mountains (USA) were compared with DOS mass loading, particle sizes, iron mineralogy, carbonaceous matter type and content, and chemical compositions. Samples were collected each spring for water years 2011-2016, when individual dust layers had merged into one (all layers merged) at the snow surface. Average reflectance values of the six samples were 0.2153 (sd, 0.0331) across the visible wavelength region (0.4-0.7 µm) and 0.3570 (sd, 0.0498) over the full-measurement range (0.4-2.50 µm). Reflectance values correlated inversely to concentrations of ferric oxide, organic carbon (1.4-10 wt. %), magnetite (0.05-0.13 wt. %), and silt (PM63-3.9; median grain sizes averaged 21.4 µm) but lacked correspondence to total iron and PM10 contents. Measurements of reflectance and Mössbauer spectra and magnetic properties indicated that microcrystalline hematite and nano-size goethite were primarily responsible for diminished visible reflectance. Positive correlations between organic carbon and metals attributed to fossil-fuel combustion, with observations from electron microscopy, indicated that some carbonaceous matter occurred as black carbon. Magnetite was a surrogate for related light-absorbing minerals, dark rock particles, and contaminants. Similar analyses of DOS from other areas would help evaluate the influences of varied dust sources, wind-storm patterns, and anthropogenic inputs on snow melt and water resources in and beyond the Colorado River basin.

Colorado↗

USGS2020

Quantifying uncertainty for remote spectroscopy of surface composition

Remote surface measurements by imaging spectrometers play an important role in planetary and Earth science. To make these measurements, investigators calibrate instrument data to absolute units, invert physical models to estimate atmospheric effects, and then determine surface properties from the spectral reflectance. This study quantifies the uncertainty in this process. Global missions demand predictive uncertainty models that can estimate future errors for varied environments and observing conditions. Here we validate uncertainty predictions with remote surface composition retrievals and in situ measurements in a field analogue of Earth and planetary exploration. We consider rover transects at Cuprite, Nevada, and remote observations by NASA's Next- Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG). We show that accounting for input uncertainties can benefit mineral detection methods such as constrained spectrum fitting. This suggests that operational uncertainty estimates could improve future NASA missions like the Earth Mineral dust source InvesTigation (EMIT) and the Lunar Trailblazer mission, as well as NASA's Decadal Surface Biology and Geology (SBG) Investigation.

Remote Sensing of Environment↗

USGS2019

Comparison of methods for modeling fractional cover using simulated satellite hyperspectral imager spectra

Remotely sensed data can be used to model the fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil in natural and agricultural ecosystems. NPV and soil cover are difficult to estimate accurately since absorption by lignin, cellulose, and other organic molecules cannot be resolved by broadband multispectral data. A new generation of satellite hyperspectral imagers will provide contiguous narrowband coverage, enabling new, more accurate, and potentially global fractional cover products. We used six field spectroscopy datasets collected in prior experiments from sites with partial crop, grass, shrub, and low-stature resprouting tree cover to simulate satellite hyperspectral data, including sensor noise and atmospheric correction artifacts. The combined dataset was used to compare hyperspectral index-based and spectroscopic methods for estimating GV, NPV, and soil fractional cover. GV fractional cover was estimated most accurately. NPV and soil fractions were more difficult to estimate, with spectroscopic methods like partial least squares (PLS) regression, spectral feature analysis (SFA), and multiple endmember spectral mixture analysis (MESMA) typically outperforming hyperspectral indices. Using an independent validation dataset, the lowest root mean squared error (RMSE) values were 0.115 for GV using either normalized difference vegetation index (NDVI) or SFA, 0.164 for NPV using PLS, and 0.126 for soil using PLS. PLS also had the lowest RMSE averaged across all three cover types. This work highlights the need for more extensive and diverse fine spatial scale measurements of fractional cover, to improve methodologies for estimating cover in preparation for future hyperspectral global monitoring missions.

Remote Sensing↗

USGS2019

Imaging spectroscopy for the detection, assessment and monitoring of natural and anthropogenic hazards

Natural and anthropogenic hazards have the potential to impact all aspects of society including its economy and the environment. Diagnostic data to inform decision-making are critical for hazard management whether for emergency response, routine monitoring or assessments of potential risks. Imaging spectroscopy (IS) has unique contributions to make via the ability to provide some key quantitative diagnostic information. In this paper, we examine a selection of key case histories representing the state of the art to gain an insight into the achievements and perspectives in the use of visible to shortwave infrared IS for the detection, assessment and monitoring of a selection of significant natural and anthropogenic hazards. The selected key case studies examined provide compelling evidence for the use of the IS technology and its ability to contribute diagnostic information currently unattainable from operational spaceborne Earth observation systems. User requirements for the applications were also evaluated. The evaluation showed that the projected launch of spaceborne IS sensors in the near-, mid and long term future, together with the increasing availability, quality and moderate cost of off the shelf sensors, the possibilities to couple unmanned autonomous systems with miniaturized sensors, should be able to meet these requirements. The challenges and opportunities for the scientific community in the future when such data become available will then be ensuring consistency between data from different sensors, developing techniques to efficiently handle, process, integrate and deliver the large volumes of data, and most importantly translating the data to information that meets specific needs of the user community in a form that can be digested/understood by them. The latter is especially important to transforming the technology from a scientific to an operational tool. Additionally, the information must be independently validated using current trusted practices and uncertainties quantified before IS derived measurement can be integrated into operational monitoring services.

Colorado↗

USGS2018

Characteristics of tropical tree species in hyperspectral and multispectral data

Remote sensing has been hailed as a promising technology to provide spatially explicit information on tree species distribution. Such information is of high value for ecologists and forest managers, particularly in tropical environments in which it is acquired by costly field inventories performed at the plot level (∼1 ha). Over the last decade, hyperspectral sensors, usually on board airborne platforms, have been successfully employed for tropical tree species classification. Most of the studies focused on the improvement of the classification accuracy, without examining the sources of variability in canopy reflectance that contribute to discriminate among species. Trees of different species usually feature distinct crown structural and chemical characteristics defined by size, shape, leaf density, branching, pigment and nonpigment biochemical constituents, among others. Understanding the role of these characteristics on tree species discrimination is important to improve and optimize the use of hyperspectral data. Here, we show how spectral characteristics of tree species, from highly diverse tropical forests, are related to their chemical and structural properties. These spectral characteristics were retrieved by spectral mixture and feature analysis, using narrow-band data acquired in the optical domain (450–2400 nm). Finally, we assessed how the spectral resolution affects the classification accuracy and spectral separability.

Book chapter↗

USGS2018

Application of imaging spectroscopy for mineral exploration in Alaska: A study over porphyry Cu deposits in the eastern Alaska Range

The U.S. Geological Survey tested the utility of imaging spectroscopy (also referred to as hyperspectral remote sensing) as an aid to regional mineral exploration efforts in remote parts of Alaska. Airborne imaging spectrometer data were collected in 2014 over unmined porphyry Cu deposits in the eastern Alaska Range using the HyMap™ sensor. Maps of the distributions of predominant minerals, made by matching reflectance signatures in the remotely sensed data to reference spectra in the shortwave infrared region, do not uniquely discriminate individual rock units. However, they do highlight hydrothermal alteration associated with porphyry deposits and prospects hosted mostly within the Nabesna pluton. In and around porphyry Cu deposits at Orange Hill and Bond Creek, unique spectral signatures are related to variations in chlorite and white mica abundance and their chemical composition. This is best revealed in the longer-wavelength 2,200-nm Al-OH absorption feature positions in pixels spectrally dominated by white mica proximal to porphyry deposits. Similar spectral signatures of chlorite and white mica wavelength positions were also recognized away from the porphyry deposits; follow-up sampling identified these satellite areas to also contain Cu-Mo-Au mineralized rock. Our study confirms that airborne imaging spectroscopy has application for regional mineral exploration in exposed mountainous terrain in Alaska.

Alaska↗

USGS2017

Oiling accelerates loss of salt marshes, southeastern Louisiana

The 2010 BP Deepwater Horizon (DWH) oil spill damaged thousands of km 2 of intertidal marsh along shorelines that had been experiencing elevated rates of erosion for decades. Yet, the contribution of marsh oiling to landscape-scale degradation and subsequent land loss has been difficult to quantify. Here, we applied advanced remote sensing techniques to map changes in marsh land cover and open water before and after oiling. We segmented the marsh shorelines into non-oiled and oiled reaches and calculated the land loss rates for each 10% increase in oil cover (e.g. 0% to >70%), to determine if land loss rates for each reach oiling category were significantly different before and after oiling. Finally, we calculated background land-loss rates to separate natural and oil-related erosion and land loss. Oiling caused significant increases in land losses, particularly along reaches of heavy oiling (>20% oil cover). For reaches with ≥20% oiling, land loss rates increased abruptly during the 2010–2013 period, and the loss rates during this period are significantly different from both the pre-oiling (p < 0.0001) and 2013–2016 post-oiling periods (p < 0.0001). The pre-oiling and 2013–2016 post-oiling periods exhibit no significant differences in land loss rates across oiled and non-oiled reaches (p = 0.557). We conclude that oiling increased land loss by more than 50%, but that land loss rates returned to background levels within 3–6 years after oiling, suggesting that oiling results in a large but temporary increase in land loss rates along the shoreline.

Louisiana↗

USGS2017

USGS Spectral Library Version 7

We have assembled a library of spectra measured with laboratory, field, and airborne spectrometers. The instruments used cover wavelengths from the ultraviolet to the far infrared (0.2 to 200 microns [μm]). Laboratory samples of specific minerals, plants, chemical compounds, and manmade materials were measured. In many cases, samples were purified, so that unique spectral features of a material can be related to its chemical structure. These spectro-chemical links are important for interpreting remotely sensed data collected in the field or from an aircraft or spacecraft. This library also contains physically constructed as well as mathematically computed mixtures. Four different spectrometer types were used to measure spectra in the library: (1) Beckman™ 5270 covering the spectral range 0.2 to 3 µm, (2) standard, high resolution (hi-res), and high-resolution Next Generation (hi-resNG) models of Analytical Spectral Devices (ASD) field portable spectrometers covering the range from 0.35 to 2.5 µm, (3) Nicolet™ Fourier Transform Infra-Red (FTIR) interferometer spectrometers covering the range from about 1.12 to 216 µm, and (4) the NASA Airborne Visible/Infra-Red Imaging Spectrometer AVIRIS, covering the range 0.37 to 2.5 µm. Measurements of rocks, soils, and natural mixtures of minerals were made in laboratory and field settings. Spectra of plant components and vegetation plots, comprising many plant types and species with varying backgrounds, are also in this library. Measurements by airborne spectrometers are included for forested vegetation plots, in which the trees are too tall for measurement by a field spectrometer. This report describes the instruments used, the organization of materials into chapters, metadata descriptions of spectra and samples, and possible artifacts in the spectral measurements. To facilitate greater application of the spectra, the library has also been convolved to selected spectrometer and imaging spectrometers sampling and bandpasses, and resampled to selected broadband multispectral sensors. The native file format of the library is the SPECtrum Processing Routines (SPECPR) data format. This report describes how to access freely available software to read the SPECPR format. To facilitate broader access to the library, we produced generic formats of the spectra and metadata in text files. The library is provided on digital media and online at https://speclab.cr.usgs.gov/spectral-lib.html . A long-term archive of these data are stored on the USGS ScienceBase data server ( https://dx.doi.org/10.5066/F7RR1WDJ ).

Data Series↗

USGS2017

Multiscale hyperspectral imaging of the Orange Hill Porphyry Copper Deposit, Alaska, USA, with laboratory-, field-, and aircraft-based imaging spectrometers

In the past decade, use of hyperspectral imaging (imaging spectroscopy) for mineral exploration and mining operations has been increasing at different spatial scales. In this paper, we focus on recent trends in applying imaging spectrometer data to: 1) airborne imaging of high latitude deposits, 2) field-based imaging of outcrops, and 3) laboratory-level imaging of geologic samples. Comparing mineral information derived from imaging spectrometer data acquired at these three scales in Alaska in areas of exposed porphyry Cu-Au-Mo deposits, Orange Hill and Bond Creek, we find notable consistency in identifications of spectrally predominant minerals, including white mica, chlorite, clays, and gypsum. Variations in the wavelength position of white mica 2200 nm Al-OH absorption seen at the airborne level are echoed by finerscale field and laboratory imaging, with wavelength positions spanning the 2199 to 2207 nm range. The longerwavelength micas associated with porphyry formation are more phengitic in composition, and thus distinct from mica in plutonic and volcanic arc rocks not affected by magmatic-hydrothermal fluids. The hillside imagery, collected on a cloudy day that would have precluded aircraft survey, gave comparable result to airborne and laboratory data, indicating field-based imaging spectroscopy can be a feasible alternative to airborne survey for accessible targets. Direct spectral observation of molybdenite in rocks collected from the Orange Hill deposit demonstratesthat additional important mineral information can be revealed with laboratory level imaging spectroscopy that is difficult to obtain in coarser scale data, commonly due to low areal extent of target minerals. The spatial association of the clinochlore + white mica and long wavelength white mica spectral classes to multi-element Cu-Mo-Au anomalies from geochemical analyses of rocks and sediments support a causative relationship with magmatic-hydrothermal alteration. Mineral maps from the airborne data were used to guide field sampling that found additional CuMo-Au mineralized areas, which were previously unknown or unreported. The results from this study provide support for utilization of imaging spectroscopy for assisting mineral exploration in other portions of the state of Alaska as well as other areas at high latitudes. Imaging spectroscopy has the potential to provide targeting information for follow-up sampling and investigations, potentially reducing subsequent exploration costs.

Alaska↗

USGS2016

Hyperspectral surveying for mineral resources in Alaska

Alaska is a major producer of base and precious metals and has a high potential for additional undiscovered mineral resources. However, discovery is hindered by Alaska’s vast size, remoteness, and rugged terrain. New methods are needed to overcome these obstacles in order to fully evaluate Alaska’s geology and mineral resource potential. Hyperspectral surveying is one method that can be used to rapidly acquire data about the distributions of surficial materials, including different types of bedrock and ground cover. In 2014, the U.S. Geological Survey began the Alaska Hyperspectral Project to assess the applicability of this method in Alaska. The primary study area is a remote part of the eastern Alaska Range where porphyry deposits are exposed. In collaboration with the Alaska Division of Geological and Geophysical Surveys, the University of Alaska Fairbanks, and the National Park Service, the U.S. Geological Survey is collecting and analyzing hyperspectral data with the goals of enhancing geologic mapping and developing methods to identify and characterize mineral deposits elsewhere in Alaska.

Alaska↗