Reflectance spectroscopy (0.3–2.5 µm) at various scales for bulk-rock identification

We discussed the possibilities and open questions concerning laboratory refl ectance spectroscopy, spectroscopic measurements in the fi eld, hyperspectral image data from spacecraft, and integration of multiscale data. Open questions included: (1) bulk-rock spectral complexity, which provides the geologic basis for every spectroscopic analysis; (2) criteria for laboratory and fi eld spectra classifi cation, as a tool for (3) end-member selection for image data classifi cation; (4) peculiar spectral characteristics of Mount Etna basalts; and (5) effects of remote-sensing data quality. The last three items emerged during a multiscale survey on the Mount Etna volcano. Our laboratory spectroscopic analyses, supported by specifi c petrographic analyses, showed the relationship between absorption-band frequency and spectrally active functional groups and the unexpected effects of bulk-rock composition on this relationship. We studied the muscovite Al-OH band in quartzite and micaschist and Fe 2+ band in pyroxene-bearing cumulates. Laboratory refl ectance spectra of rocks were classifi ed using the concept of spectrofacies. In the case of metamorphic rocks, the result was a tree-structure of rock spectral classes mainly based on the predominant vibrational processes. In the case of basalts, characterized by an overall similarity in their composition, the classes were determined on the basis of overall shape of the spectral curve and on electronic process intensities. Here, we report fi rst results of multiscale data integration for the Mount Etna volcano. Etna rocks consist of basalts, with very low albedo and variable degrees of alteration, and recent lava fl ows are characterized by overall low refl ectance in both ASTER (advanced spaceborne thermal emission and refl ection radiometer) and Hyperion color-composite images. We carried out Spectral Angle Mapper (SAM) classifi cation of Hyperion images, where individual fi eld spectra represented suitable end members for classifi cation of recent lava and pyroclastic deposits. We used fi eld spectra linear combinations to classify mixed pixels and to approximate the classifi cation of altered and oxidized effusive products. Only two laboratory spectral classes coincided with fi eld spectra classes; laboratory spectra were mainly used for spectral features attribution. The overall spectral shape of some of these spectra is still under study. Noise level in Hyperion data precluded the identifi cation of subtle diagnostic iron absorption bands

Detection and Classification of Non-Photosynthetic Vegetation from PRISMA Hyperspectral Data in Croplands

This study introduces a first assessment of the capabilities of PRISMA (PRecursore IperSpettrale della Missione Applicativa)—the new hyperspectral satellite sensor of the Italian Space Agency (ASI)—for Non-Photosynthetic Vegetation (NPV) monitoring, a topic which is becoming very relevant in the field of sustainable agriculture, being an indicator of crop residue (CR) presence in the field. Data-sets collected during the mission validation phase in croplands are used for mapping the NPV presence and for modelling the diagnostic absorption band of cellulose around 2.1 μm with an Exponential Gaussian Optimization approach, in the perspective of the prediction of the abundance of crop residues. Results proved that PRISMA data are suitable for these tasks, and call for further investigation to achieve quantitative estimates of specific biophysical variables, also in the framework of other hyperspectral missions

Characterization of fine metal particles derived from shredded WEEE using a hyperspectral image system: Preliminary results

Waste of electric and electronic equipment (WEEE) is the fastest-growing waste stream in Europe. The large amount of electric and electronic products introduced every year in the market makes WEEE disposal a relevant problem. On the other hand, the high abundance of key metals included in WEEE has increased the industrial interest in WEEE recycling. However, the high variability of materials used to produce electric and electronic equipment makes key metalsâ recovery a complex task: the separation process requires flexible systems, which are not currently implemented in recycling plants. In this context, hyperspectral sensors and imaging systems represent a suitable technology to improve WEEE recycling rates and the quality of the output products. This work introduces the preliminary tests using a hyperspectral system, integrated in an automatic WEEE recycling pilot plant, for the characterization of mixtures of fine particles derived from WEEE shredding. Several combinations of classification algorithms and techniques for signal enhancement of reflectance spectra were implemented and compared. The methodology introduced in this study has shown characterization accuracies greater than 95%

Accuracy in mineral identification: image spectral and spatial resolutions and mineral spectral properties

Problems related to airborne hyperspectral image data are reviewed and the requirements for data analysis applied to mineralogical (rocks and soils) interpretation are discussed. The variability of mineral spectral features, including absorption position, shape and depth is considered and interpreted as due to chemical composition, grain size effects and mineral association. It is also shown how this variability can be related to well defined geologic processes. The influence of sensor noise and diffuse atmospheric radiance in classification accuracy is also analyzed

The Challenge of Diagnosing Atheroembolic Renal Disease

Background— Atheroembolic renal disease (AERD) is caused by showers of cholesterol crystals released by eroded atherosclerotic plaques. Embolization may occur spontaneously or after angiographic/surgical procedures. We sought to determine clinical features and prognostic factors of AERD. Methods and Results— Incident cases of AERD were enrolled at multiple sites and followed up from diagnosis until dialysis and death. Diagnosis was based on clinical suspicion, confirmed by histology or ophthalmoscopy for all spontaneous forms and for most iatrogenic cases. Cox regression was used to model time to dialysis and death as a function of baseline characteristics, AERD presentation (acute/subacute versus chronic renal function decline), and extrarenal manifestations. Three hundred fifty-four subjects were followed up for an average of 2 years. They tended to be male (83%) and elderly (60% >70 years) and to have cardiovascular diseases (90%) and abnormal renal function at baseline (83%). AERD occurred spontaneously in 23.5% of the cases. During the study, 116 patients required dialysis, and 102 died. Baseline comorbidities, ie, reduced renal function, presence of diabetes, history of heart failure, acute/subacute presentation, and gastrointestinal tract involvement, were significant predictors of event occurrence. The risk of dialysis and death was 50% lower among those receiving statins. Conclusions— Clinical features of AERD are identifiable. These make diagnosis possible in most cases. Prognosis is influenced by disease type and severity

Hydrothermal Alteration of Ultramafic Rocks in Ladon Basin, Mars—Insights From CaSSIS, HiRISE, CRISM, and CTX

The evolution of the Ladon basin has been marked by intense geological activity and the discharge of huge volumes of water from the Martian highlands to the lowlands in the late Noachian and Hesperian. We explore the potential of the ExoMars Trace Gas Orbiter/Color and Stereo Surface Imaging System color image data set for geological interpretation and show that it is particularly effective for geologic mapping in combination with other data sets such as HiRISE, Context, and Compact Reconnaissance Imaging Spectrometer for Mars. The study area displays dark lobate flows of upper Hesperian to early Amazonian age, which were likely extruded from a regional extensional fault network. Spectral analysis suggests that these flows and the underlying rocks are ultramafic. Two distinct altered levels are observed below the lobate flows. The upper, yellow-orange level shows hundreds of structurally controlled narrow ridges reminiscent of ridges of listwanite, a suite of silicified, fracture-controlled silica-carbonate rocks derived from an ultramafic source and from serpentine. In addition to serpentinite, the detected mineral assemblages may include chlorite, carbonates, and talc. Kaolin minerals are detected in the lower, white level, which could have formed by groundwater alteration of plagioclase in the volcanic pile. Volcanism, tectonics, hydrothermal activity, and kaolinization are interpreted to be coeval, with hydrothermal activity and kaolinization controlled by the interactions between the aquifer and the hot, ultramafic lobate flows. Following our interpretations, East Ladon may host the first listwanite ridges described on Mars, involving a hydrothermal system rooted in a Hesperian aquifer and affecting ultramafic rocks from a magmatic source yet to be identified

Visible and near-infrared reflectance spectroscopy of pyroxene-bearing rocks: New constraints for understanding planetary surface compositions

Laboratory visible and near-infrared reflectance spectra of solid rock slabs, mineral separates and systematic mixtures were simultaneously investigated. We apply an empirical approach to evaluate spectra, in order to achieve qualitative and quantitative information. We use cumulates (mostly norites, leuconorites, melanorites and anorthosites) belonging to the Bjerkreim-Sokndal Layered Intrusion, a sequence of genetically related rocks with simple textures. Laboratory spectra are measured on slightly polished rock slabs in the 350- to 2500-nm interval and directional-hemispherical reflectance geometry. Composition is determined using traditional techniques other than reflectance spectroscopy. We find that: (1) band minima measured on rock spectra are strongly influenced by the concurrent effects due to modal abundance of the spectroscopically active mineral and mineral chemistry; (2) band depths can be used for semiquantitative analyses, limited to the set of rocks investigated; (3) the spectral parameters derived from powdered pyroxene are in agreement with previously published calibrations; (4) the mineral mixture systematics can be reasonably considered as linear, when pyroxene is mixed with neutral components; and (5) the empirical evaluation of solid rock surface spectra needs further insights to give a great improvement to planetary researches. In addition, genetic sequences of rocks should be investigated in detail to help the geological interpretation of planetary evolution. Therefore more laboratory and analytical studies are required in order to understand the influence of composition and petrographic textures on the spectral analysis

Informational Clustering of Hyperspectral Data

Hyperspectral remote sensing is recognized as a powerful tool for mineralogical mapping of exposed surfaces on Earth and planets, as well. It allows for more rigorous discrimination among materials than multispectral imaging. Nevertheless, the huge data volume that comes with single observations results in severe limitations to successful data exploitation. Many techniques of feature reduction that have been developed so far do not allow for the complete exploitation of the informational content of the hyper-dimensional space. The present investigation aims at providing a feature reduction technique that preserves the spectral information and improves the classification results. We accomplished the feature reduction of synthetic and real hypercubes through exponential Gaussian optimization (EGO) and compared the results of k-means, spectral angle mapper (SAM), support vector machines (SVMs), and CLUES clustering techniques. The results show that the k-means clustering of hyper-dimensional spaces is the most efficient technique, but it does not automatically retrieve the optimal number of clusters. The SAM and SVM techniques give discrete results in terms of data partitioning, although the process of endmembers’ selection is challenging and the definition of model parameters is not trivial. The combination of EGO modeling and CLUES algorithm allows for correctly estimating the number of clusters and deriving the accurate partitions when the cluster separability lies on two variables, at least. With real data, the CLUES clustering in the reduced space allows for higher overall performances than the more conventional techniques, although it underestimates the number of categories

Informational Clustering of Hyperspectral Data

Hyperspectral remote sensing is recognized as a powerful tool for mineralogical mapping of exposed surfaces on Earth and planets, as well. It allows for more rigorous discrimination among materials than multispectral imaging. Nevertheless, the huge data volume that comes with single observations results in severe limitations to successful data exploitation. Many techniques of feature reduction that have been developed so far do not allow for the complete exploitation of the informational content of the hyper-dimensional space. The present investigation aims at providing a feature reduction technique that preserves the spectral information and improves the classification results. We accomplished the feature reduction of synthetic and real hypercubes through exponential Gaussian optimization (EGO) and compared the results of k-means, spectral angle mapper (SAM), support vector machines (SVMs), and CLUES clustering techniques. The results show that the k-means clustering of hyper-dimensional spaces is the most efficient technique, but it does not automatically retrieve the optimal number of clusters. The SAM and SVM techniques give discrete results in terms of data partitioning, although the process of endmembers’ selection is challenging and the definition of model parameters is not trivial. The combination of EGO modeling and CLUES algorithm allows for correctly estimating the number of clusters and deriving the accurate partitions when the cluster separability lies on two variables, at least. With real data, the CLUES clustering in the reduced space allows for higher overall performances than the more conventional techniques, although it underestimates the number of categories