Unmixing the effects of vegetation in airborne hyperspectral mineral maps over the Rocklea Dome iron-rich palaeochannel system (Western Australia)

Haest, Maarten; Cudahy, Thomas; Rodger, Andrew; Laukamp, Carsten; Martens, Evelien; Caccetta, Mike

doi:10.1016/j.rse.2012.10.011

Cited by 29 publications

(13 citation statements)

References 19 publications

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“…The errors with which wavelength position estimated by each method could estimate the proportion of goethite in samples were determined by converting the estimated wavelength position to values representing the proportion of goethite. To do this, linear regressions of the proportion of goethite (derived from laboratory XRD analyses) on wavelength position (determined directly from ASD spectra) were done separately for each surface (1)(2)(3)(4). Coefficients from these regressions were then applied to the wavelength positions estimated by GP and MLR to derive the proportion of goethite.…”

Section: F Data Analyses 1) Data For Deriving the Mlr Coefficients Amentioning

confidence: 99%

Gaussian Processes for Estimating Wavelength Position of the Ferric Iron Crystal Field Feature at $\sim$900 nm From Hyperspectral Imagery Acquired in the Short-Wave Infrared (1002–1355 nm)

Murphy

Chlingaryan

Melkumyan

2015

IEEE Trans. Geosci. Remote Sensing

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Many economically important minerals have absorption features in the short-wave infrared (SWIR; 2000-2500 nm). Sensors which measure this part of the spectrum cannot detect the wavelength minimum of a feature at ∼900 nm (F 900 ), indicative of ferric iron mineralogy. A method based on Gaussian processes (GPs) was developed and compared with multiple linear regression (MLR) to estimate the wavelength position of F 900 from SWIR data (1002-1355 nm). SWIR data with different signal-to-noise ratios were acquired from crushed rock samples by a nonimaging spectrometer and an imaging spectrometer. GP estimates of wavelength position were converted to the proportion of goethite using coefficients from a regression of the proportion of goethite determined from X-ray diffraction (XRD) on wavelength position measured directly from spectra. GP-estimated wavelength positions were within the 2-nm and ∼4-nm root-mean-square error of measurements made directly from spectra for nonimaging and imaging spectrometer data, respectively. Proportions of goethite derived from these estimates were respectively within 4% and 6% of the values measured by XRD. MLR performed poorly compared to GPs when applied to data with no added noise and failed when applied to data with added noise or to imaging spectrometer data. These findings indicate that the wavelength position of F 900 -an indicator of ferric iron mineralogy-can be estimated from data acquired at SWIR wavelengths (1002-1355 nm). This opens up possibilities for using a single (SWIR) sensor to acquire information on ferric iron mineralogy (using F 900 ) and other minerals with diagnostic absorptions between 1000 and 2500 nm.

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Section: F Data Analyses 1) Data For Deriving the Mlr Coefficients Amentioning

confidence: 99%

Gaussian Processes for Estimating Wavelength Position of the Ferric Iron Crystal Field Feature at $\sim$900 nm From Hyperspectral Imagery Acquired in the Short-Wave Infrared (1002–1355 nm)

Murphy

Chlingaryan

Melkumyan

2015

IEEE Trans. Geosci. Remote Sensing

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“…[1][2][3][4] Typically, spectroscopic data from laboratory-or hand-held scanners are used for ground measurements to validate aerial or satellite remote sensing data or, for example, in the mining industry for geologic core logging. [5][6][7][8] However, in contrast to airborne or satellite platforms, in situ scanners still mostly use point measurements instead of imaging methods, which limits the information about spatial variability in a given sample.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral imaging spectroscopy: a promising method for the biogeochemical analysis of lake sediments

Butz

Grosjean

Fischer

et al. 2015

J. Appl. Remote Sens

102

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Abstract. We investigate the potential of hyperspectral imaging spectrometry for the analysis of fresh sediment cores. A sediment-core-scanning system equipped with a camera working in the visual to near-infrared range (400 to 1000 nm) is described and a general methodology for processing and calibrating spectral data from sediments is proposed. We present an application from organic sediments of Lake Jaczno, a freshwater lake with biochemical varves in northern Poland. The sedimentary pigment bacteriopheophytin a (BPhe a) is diagnostic for anoxia in lakes and, therefore, an important ecological indicator. Calibration of the spectral data (BPhe a absorption ∼800 to 900 nm) to absolute BPhe a concentrations, as measured by high-performance-liquid-chromatography, reveals that sedimentary BPhe a concentrations can be estimated from spectral data with a model uncertainty of ∼10%. Based on this calibration model, we use the hyperspectral data from the sediment core to produce high-resolution intensity maps and time series of relative BPhe a concentrations (∼10 to 20 data points per year, pixel resolution 70 × 70 μm 2 ). We conclude that hyperspectral imaging is a very cost-and time-efficient method for the analysis of lake sediments and provides insight into the spatiotemporal structures of biogeochemical species at a degree of detail that is not possible with wet chemical analyses. © 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)

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“…There have been many studies which have used hyperspectral imagery to map minerals using their unique spectral curves in the shortwave infrared between 2000-2500 nm (e.g., Ellis & Scott, 2004;Haest et al, 2013;Kruse, 1988;Lagacherie, Baret, Feret, Madeira Netto & Robbez-Masson, 2008;Murphy, Schneider & Monteiro, 2014;Viscarra Rossel & Chen, 2011). Reflectance over spectral regions around 1400 nm and 1900 nm also provides important information relating to mineral composition by detecting the presence of hydroxyl (OH) or bound water which has been adsorbed into the mineral structure or onto the surface of mineral grains (Bishop, Pieters & Edwards, 1994;Clark, 1995).…”

Section: Introductionmentioning

confidence: 99%

Evaluating simple proxy measures for estimating depth of the ~ 1900 nm water absorption feature from hyperspectral data acquired under natural illumination

Murphy

2015

Remote Sensing of Environment

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Direct measurement of depth of the~1900 nm feature (F 1900 ) caused by molecular water is not possible from hyperspectral data acquired under natural illumination. Proxy measures for depth were evaluated to estimate the true depth of F 1900 from such data. Suitable proxy measures were identified using data acquired by a highresolution spectrometer (under artificial and natural illumination) and by a hyperspectral imaging sensor (under artificial illumination), with different amounts of simulated atmospheric noise. The best performing proxy measures were used to estimate depth from hyperspectral imagery acquired in the field under natural illumination. Proxy measures comprised a ratio of reflectance, continuum-removed depth at a single wavelength and continuum-removed depth integrated over several wavelengths. For high resolution spectra, a ratio of reflectance at 2017 nm and 1967 nm (Ratio 2017(Ratio /1967 ) and the continuum-removed depth at 1967 nm (CRD 1967 ) were strongly correlated with depth R 2 = 0.98-0.99 and R 2 = 0.95-0.96, respectively. For hyperspectral imagery acquired in the laboratory, Ratio 2017/1967 and CRD 1967 were strongly correlated with depth across all levels of atmospheric noise (R 2 = 0.94-0.98 and 0.94-0.97, respectively). Proxy measures using integrated depth performed relatively poorly (R 2 = 0.65-0.86). In independent tests, depth of F 1900 was consistently overestimated by CRD 1967 but not by Ratio 2017/1967 . Validation of field imagery was done by comparing depth predicted by the proxy measures with depth measured from laboratory imagery of collocated samples from the field. Ratio 2017/1967 derived from a polynomial fit to the data between 1961 and 2134 nm (RatioP 2017(RatioP /1967 ) most closely approximated measured depth. Ratio 2017/1967 , derived from the original data either under or overestimated depth. Measures of continuumremoved depth generated from the original data (CRD 1967 ) and from a polynomial fit to the original data (CRDP 1967 ) overestimated the depth of F 1900 . This study showed that the depth of F 1900 can be predicted from data acquired under natural illumination however, the choice of proxy measure can have a significant impact upon quantitative estimates.

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Unmixing the effects of vegetation in airborne hyperspectral mineral maps over the Rocklea Dome iron-rich palaeochannel system (Western Australia)

Cited by 29 publications

References 19 publications

Gaussian Processes for Estimating Wavelength Position of the Ferric Iron Crystal Field Feature at $\sim$900 nm From Hyperspectral Imagery Acquired in the Short-Wave Infrared (1002–1355 nm)

Gaussian Processes for Estimating Wavelength Position of the Ferric Iron Crystal Field Feature at $\sim$900 nm From Hyperspectral Imagery Acquired in the Short-Wave Infrared (1002–1355 nm)

Hyperspectral imaging spectroscopy: a promising method for the biogeochemical analysis of lake sediments

Evaluating simple proxy measures for estimating depth of the ~ 1900 nm water absorption feature from hyperspectral data acquired under natural illumination

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