HYPERSPECTRAL REMOTE SENSING AS A TOOL FOR …5thearselsigis.vgt.vito.be/CD/Fullpapers/Gersman_final.pdf · hyperspectral remote sensing as a tool for geological exploration – examples

Proceedings 5th EARSeL Workshop on Imaging Spectroscopy. Bruges, Belgium, April 23-25 2007 1

HYPERSPECTRAL REMOTE SENSING AS A TOOL FOR GEOLOGICAL EXPLORATION – EXAMPLES FROM THE NORTHERN DANAKIL

DEPRESSION, ERITREA

Gersman Ronen1,2,5, Ben-Dor Eyal2, Beyth Michael3 Avigad Dov1, Abraha Michael4 and Kibreab Alem4

1. The Hebrew University, Institute of Earth Sciences, Givat Ram, Jerusalem, 91904, Israel. 2. Tel Aviv University, Department of Geography, Ramat Aviv, 69978, Israel. 3. Geological Survey of Israel, 30 Malkhei Israel St., Jerusalem, 95501 4. Eritrean Department of Mines, Asmara, Eritrea. 5. University of Southern California, Department of Earth Sciences, 3651 Trousdale Pkwy, Los-

Angeles, CA 90089-0740

ABSTRACT A single EO-1 Hyperion scene was used to identify and map hydrothermally-altered rocks and Pre-cambrian basement outcrops at and around the Alid volcanic dome, at the Danakil Depression, Eri-trea. Thematic maps of the area were constructed from the Hyperion dataset, which was carefully pre-processed to evaluate and to correct the interferences present in the data. Despite the problem-atic Hyperion data, lithological mapping was possible using narrow spectral bands of specific absorp-tions. A diagnostic spectral signature attributed to ammonium was detected in the laboratory spectral measurements of the hydrothermally altered rocks from the Alid Dome. This spectral signature was found as spectral absorption clues in the atmospherically-corrected cube at the known hydrother-mally altered sites. The existence of ammonium in hydrothermally-altered rocks within the Alid Dome has been confirmed by previous studies. Spectral information of endmembers found in the area (e.g., dolomite, basalt, schist, etc.) enables producing a surface mineral map that stands in good agreement with the known geology along the sensor overpass. These maps present a first hyper-spectral overview of the surface mineralogy in this interesting arid area and may be used as a base for future studies of remote areas such as presented by the Danakil.

INTRODUCTION Mounted on the NASA Earth Observing 1 (EO-1) platform, the Hyperion is the first high spectral resolution imaging spectrometer that routinely acquires data from orbit (1, 2). Previous studies con-cluded that the Hyperion has a low Signal to Noise Ratio (SNR) relative to airborne imaging spec-trometers such as AVIRIS or HyMAP (3 - 5). Furthermore, it was shown that the Hyperion sensor has severe problems arising from the line-curvature effect, which eventually prevent adequate at-mospherical rectification and accordingly- precise thematic evaluation (6, 7). Nevertheless, additional geoscience applications of the Hyperion data are needed for a better understanding of the advances and limitations of imaging spectroscopy as a routine research tool. The goal of the present study was to examine the feasibility of the Hyperion data, coupled with a comprehensive field survey and labo-ratory analyses, as a tool for sustainable use in locating natural resources. The northern Danakil Depression and the Alid volcanic dome are of scientific and economic interest (8 - 14). This area has never been studied by any hyperspectral remote sensing (HSR) and only rarely studied on the ground. Important ground targets in this work were hydrothermally-altered rocks within the Alid vol-canic dome for their significant economical potential. Previous studies of Alid’s hot springs and fu-maroles assessed its geothermal potential and epithermal gold potential and reported the existence of ammonium-sulphate minerals (8, 14). Ammonium-bearing minerals are associated with hydro-thermally altered rocks, hydrocarbons in oil shale, and coal-bearing formations (15), which are found to occur in several economically important geological environments (16, 17). Knowledge of the origin and mineralogical relations of ammonium minerals in known hydrothermal systems is critical for the proper interpretation of remote sensing data and for testing possible links to mineralization (15).


GEOLOGICAL SETTING The Danakil Depression is the terminus of an embryonic spreading axis - the northern part of the Afar triangle, which formed between the African plate on the west and the Danakil microplate on the east (Fig. 1). The Alid volcanic centre is located along the axis of the NNW-SSE trending Danakil depression. The Alid graben is limited on its western side by east-dipping normal faults which form the Red-Sea escarpment. Neoproterozoic basement rocks, related to the East African Orogen rise up to 2500 m on the western side (9, 14, 18 - 21). The eastern topographic and structural boundary is marked by a steep 300-m-high west-dipping normal fault escarpment. Basement rocks are locally exposed at the base of this scarp and are unconformably overlain by a post-Miocene sequence of intercalated sedimentary and volcanic deposits (11, 14).

The volcanic sequences in the Alid dome include basalts and pumiceous rhyolites (some with xeno-lites of basement rocks), intercalated with a sedimentary sequence. Outcrops of Neoproterozoic pe-litic biotite-kyanite-schist are exposed on the eastern side of the dome beneath the volcanic-sedimentary succession (8, 11, 14, 22). Much of the pyroclastics and lava flows within the Alid dome have been strongly altered by hydrothermal activity. (8, 14) reported kaolinite, gypsum, anhydrite, montmorillonite, illite and ammonium-sulphate minerals around hydrothermal fumaroles and hot springs within the Alid dome.

METHODS Field Work A field reconnaissance was carried out between 5-15/2/2005. Samples for laboratory studies in-cluded fresh and weathered sides of representative rocks. Samples of hydrothermally altered rocks were taken from two sites within the Alid caldera: Darere (14.877º/39.915º) and Illegedi (14.882º/39.926º). Samples of alluvial fans and playas for atmospheric calibration (23 - 26) were collected from measured homogenous areas of 60 m x 60 m, representing a square of 4 pixels (in an image of 30 m pixel size). Laboratory work Mineralogy and petrography: The bulk mineralogy of fine grained samples was studied using the X-ray diffraction (XRD) technique (e.g., 27). The samples included hydrothermally altered rocks and alluvial/aeolian deposits for the atmospheric calibration. Petrography of selected rocks was studied using an optical polarized microscope.


Figure 1: The study area. Regional setting is described in the upper box, modified after (11): Dark grey is mostly Precambrian baesment; Pale grey is mostly Cenozoiz volcanics and sediments; Straight lines represent major spreading axes; small frame represents the big box location. The Alid-area map is based on an ASTER scene and after (8), (14) and (22).

Reflectance spectral analysis: Spectral measurements were performed using the Analytical Spectral Devices Inc. (ASD) field spectrometer (FieldSpec pro FR) with a spectral range of 350 - 2500 nm, at the Remote-Sensing laboratory in Tel-Aviv University using a contact-probe and a built-in illumina-tion source (A 4.5W Halogen-Krypton lamp with AC to DC current converter). The reflectance spec-tra were calibrated against a Halon plate (28) and the stability of the spectrometer was examined against a standard sample (dolomite). The collected spectra were run through the Continuum Re-moval (CR) technique (29) in order to recognize and allocate diagnostic spectral features. Hyperion Data Processing Processing the Hyperion data was carried out using the "ENvironment for Visualizing Images" (30) software packages, version 4.0 and other accessory codes to re-process the data. Pre-processing steps included the removal of overlapping and inactive bands (1-7, 58-78 and 225-242, after 31),


and of the first and last samples of the scene. The radiometric calibration to convert raw DN to Wxm-

2xnm-1xSteradian-1 units was done after (32). This pre processing step determined cube dimensions of 196 spectral bands, 254 columns and 2500 lines.

SNR estimation: Signal to Noise Ratio (SNR) was estimated from the corrected radiometric image. Three homogenous targets were selected for this purpose and the SNR was estimated according to (5) and (33). The SNR values are 90:1 in the VIS range, 60:1 at SWIR-I ( ~1000 to ~1600 nm (ex-cluding the water vapour signal at ~1400 nm)) and about 35:1 in SWIR-II ( ~2000 to 2400 nm).

Atmospheric correction: A two-stage atmospheric correction was applied to the scene: Radiative-modelled atmospheric correction and a fine-tuning atmospheric correction using the Empirical-Line (EL) technique. Removal of vertical lines (destriping) and residual-noise reduction steps separated between the atmospheric correction stages.

Radiative-modelled atmospheric correction: The principal atmospheric correction was carried out by using the Atmospheric CORrection Now (ACORN) software that is based on licensed MODTRAN technology to determine the atmospheric parameters (5, 34). The TRW curvature function of the Hy-perion (1) was integrated in the ACORN correction.

Line curvature estimation and correction: Line curvature (smile effect) refers to an across-track shift from a centre wavelength, which is due to the change of dispersion angle with field position (6, 7). For the Hyperion VNIR bands, the shifts range between 2.6 and 3.5 nm whereas for the SWIR bands the shifts are less than 1 nm (6). The VNIR variations are about 30% with respect to the 10 nm FWHM of the Hyperion instrument (34) and cannot be ignored (3, 6). To estimate the line curvature effect, we applied the Minimum Noise Fraction transformation (35, 36) to the raw data (DN) image and to the radiometric corrected image. In both of the images a brightness gradient related to the smile effect (3, 6) was clearly evident in the VNIR range and was less significant in the SWIR range. The line curvature effect was partly corrected by the ACORN 5.0 radiative-model atmospheric cor-rection using the TRW parameters measured prior the launch (34). However, as the smile effect is a dynamic phenomenon and might change from one scene to another (Stanze K, personal communi-cation), the spectral calibration data may have changed from the provided TRW parameters. This leads to residual curvature effects remaining and calls for specific correction (see below).

Vertical stripe removal (de-striping): A de-striping procedure was applied to the data in order to over-come a spatial problem which further reduced the remaining curvature effect. The correction of the vertical stripes is based on adjusting the brightness of each image column (in all bands) based on a calculated offset relative to the average detector response of the scene (5, 37). The procedure was done according to (5) and (37). The de-striping process reduced the residual line-array effect to a negligible level and slightly improved the SNR, especially in the NIR-SWIR range.

Residual noise removal: The MNF procedure (30, 35, 36) was used to reduce the residual noise af-ter the de-striping process. We chose to work with the MNF transformation of a 196 bands cube (rather than separate cubes of the VNIR and the SWIR ranges). The inverse MNF procedure in-cluded the first nine eigenbands based on the asymptotic curve behaviour obtained in eigenvalues higher than nine.

Fine tuning atmospheric correction using the EL method: After the first treatment process, in which dead pixels were gently removed, curvature alignment was corrected, and the atmospheric attenua-tion was removed by ACORN, a fine tuning stage was applied to the data. This was done by gener-ating meticulous calibration alignment using ground targets and the Empirical-Line (EL) procedure (23, 24, 30, 38). Selection of the best EL result was done after comparison between the apparent reflectance of eight known ground targets which served as validation sites and their corresponding laboratory-measured spectra (Fig. 2).


Figure 2: Comparison between lab-measured spectra of different rock types, and their apparent re-flectance in the EL-corrected image. Dashed curves (Lab) are laboratory-measured spectra. Solid curves (Hyp) are reflectance spectra extracted from the Hyperion data. Water absorptions are re-moved.

Classification: After receiving a confident ground reflectance cube, we moved forward for the the-matic part. In this stage, we used two methods for end-member selection to classify the area. The first method followed the approach suggested by (5) and (38), namely, the Pixel Purity Index (PPI). The basic concept is to reduce the data to manageable levels by finding those pixels in the image that can explain every other pixel in terms of a mixing model without a-priori knowledge of the ground surface (38 - 42). The PPI and the n-dimensional visualizer tools enabled selecting the major end-members in the scene. In the second method, diagnostic absorption bands of defined ground targets were identified and mapped based on a-priori knowledge of the composition and the labora-tory-measured reflectance signature of these targets. The Spectral Angle Mapper (SAM) (36, 43) was used for classifying the image's pixels. The strategy was to perform a general mapping using the PPI, and then to conduct an intimate mapping procedure for specific targets using the previous knowledge.

Geometric correction: The Hyperion image was geo-referenced relative to a rectified ASTER scene, using a 1st degree polynomial of selected Ground Control Points (GCP) using the "Economic and Social Research Institute" (ESRI) ArcView software, version 9.0 (http://www.esri.com/).

RESULTS Mineralogical data XRD analysis: The modal compositions of the hydrothermally altered rock samples from Darere and Illegedi included quartz, K-feldspar, plagioclase, kaolinite and sulphates. The samples used for the EL atmospheric removal process included mainly quartz, K-feldspar and plagioclase, minor mica/illite and traces of chlorite and amphibole.


Laboratory reflectance measurements: The reflectance of samples from Darere exhibited strong ab-sorption features of kaolinite and ferric iron and minor signatures of alunite. Other signatures exhib-ited similarities to K-feldspar and opal/hydrous silica. Two samples from Illegedi were analyzed, showing in the first sample strong features of jarosite and gypsum, and opal features in the second sample, both confirmed by the XRD analysis. Continuum removal (CR) of the reflectance curves from 1500 nm to 1600 nm revealed an absorption feature at 1558 nm in four samples of hydrother-mally altered rocks from Alid. This feature is attributed to the existence of ammonium in silicate min-erals (16, 17, and 44). Absorption at 2254 nm (possibly due to Fe-OH vibration) was found to be the most diagnostic spectral feature of the pelitic schist. More characteristic features are the absorptions of the combination modes of Mg-OH at 2324 nm ± 10 nm and of the Al-OH at ~2200 nm in clay min-erals. Processing of the hyperspectral data Atmospheric corrections: After the radiative-based correction, which partially corrected the line array effect, residual atmospheric absorptions of O2 and CO2 were still apparent. The combination for the EL correction comprised fresh basalt and a bright aeolian deposit, which is dominated by quartz. Nevertheless, the existence of calcite in the aeolian deposit may have introduced a minor false ab-sorption feature at ~2300 nm throughout the entire image.

Unsupervised classification: End-members for classification were chosen using a PPI procedure with 15,000 iterations with a threshold value of 1.5. A total of 18411 pixels were chosen in this process. Seventeen regions of interest (ROI) were finally chosen, in sizes which range from 19 to 82 pixels (17100 m2 to 73800 m2). Several classification sessions successfully recognized the hydrothermally altered rocks and schist within the Alid dome, and identified kaolinite-rich areas in the southern part of the scene, which has a spectral signature similar to that of the altered rocks of the Alid dome (Figs. 3, 4).


Figure 3: Integrated thematic map using the Hyperion scene. The map units were chosen either from the targets identified by the Pixel Purity Index (PPI) procedure, or according to lab-measured reflec-tance curves of representative rock samples. The map is overlaid on a base image that used an RGB combination of bands 29-20-11 (true colours).

Supervised classification: After successfully applied to prominent absorption features (e.g. clay, car-bonates, etc.) a classification session was applied to the continuum removed (CR) image limited to the spectral range between 1507 nm (band 136) and 1598 nm (band 145). The goal of this session was to detect the slight absorption at 1558 nm observed in the laboratory spectral reflectance of the hydrothermally altered samples. Four different shapes of CR absorption features were chosen as end-members for the classification. The end-members included schist rocks, kaolinite-rich rocks (nei-ther was reached in the field trip), black-slate and hydrothermally-altered rocks in the Alid dome. The classification result of the entire scene shows that the “Alid-type” end-member appears both in the kaolinite-rich and in the black-slate units (Fig. 3); each of the end-member units is characterized by a different reflectance signature. The “Alid-type” end-member is spatially associated with hydrother-mally altered rocks within the Alid area (14, 22; Fig. 3, 4). Furthermore, rocks in the eastern escarp-


ment of the Alid graben are classified both as hydrothermally altered and as ammonium-bearing (Fig. 4).

Figure 4: Map of hydrothermally altered areas within the Alid dome and on the eastern fault escarp-ment. Ground targets are from Fig. 3, and the ammonium signal at 1558 nm is added (in purple). The classification of the surrounding volcanic rocks is not included. Vegetation cover appears in a green distinct from the classified pelitic schist. Classification accuracy assessment A visual assessment of the classification accuracy of the entire scene can be made from the map, integrating the major at-surface geological/lithological elements and the ground observations. This map consists of ground-targets which were identified by the PPI procedure, some of which were vis-ited in the field. Ground targets which were visited in the field but were not recognized by the PPI procedure are also included. The main end-member spectra found for this analysis are shown in Figure 2. Mapping with these end-members was done using the SAM procedure.

Schist in the southwest corner of the map (Fig. 3) is classified as "alluvial fan" on the map, probably because of highly weathered surface. Dolomite patches intercalated between schistose areas are well mapped. Yet, the northern border of the dolomite unit merges with an alluvial fan, which is domi-nated by dolomitic debris. The black slate is carefully classified towards south, but is also found in the western slope of Alid - most probably an erroneous classification. Patches within the black slate domain are shaded areas, mistakenly classified as basalt. The identification of a basaltic cover


around Alid, however, is correct. The topography has a major effect on the apparent reflectance (45 - 48); hence the Alid slopes are poorly classified. Yet, a basic distinction between acidic volcanic rocks (a mixture of rhyolite, ignimbrite and obsidian) and basaltic flows was obtained, as well as a distinc-tion between the aeolian and alluvial covers. The hydrothermal sites recognized by the classification procedure included Illegedi, Ghinda and Humbebet (Fig. 4), described by (14). Two sites, Abakri and Asa'ela (Fig. 4) were missed, but could be spectrally recognized. The site of Darere, which was vis-ited in the field trip, was not spectrally recognized, probably due to relatively dense vegetation that was present at the time of the overpass. A group of pixels northeast of Alid were classified as hydro-thermally altered rocks (Fig. 4). The classified pixels are concentrated in a group and are not aligned on a residual vertical stripe, nor limited to shaded areas. This pattern suggests that the classification of those pixels is probably correct.

The classification procedure identified outcrops of schist within Alid (Fig. 3, 4). The distribution of the schist outcrops on the map does not necessarily represent their exact location in the field, since this area is subject to strong topographic and shading effects. Yet, the existence of kyanite schist at Il-legedi is known from the field (14, 22). The identification of rhyolite along the western border of the map, from Alid towards northeast, is most probably erroneous.

Mapping of the hydrothermally altered rocks of Alid The mineralogical assemblage detected around fumaroles and hot springs resembles the advanced argillic alteration type (49, 50). The existence of ammonium-sulphate minerals in Alid (tschermigite- NH4Al(SO4)2*12H2O, mascagnite- (NH4)2SO4, and kokatite- (NH4)2Ca(SO4)2*H2O)) was reported by (8) and (14). Tschermigite and alum (KAl(SO4)2) are salts that are formed where ammonium and sulphate-bearing waters react with crustal rocks at sub-boiling temperatures (up to 66ºC in the ther-mal pools of Alid (14)) and oxidizing atmospheric conditions. Since the primary dissolved anion within the acidic thermal pools in Alid is sulphate (up to 1767 ppm in Illegedi) and the main dissolved cations are ammonium, calcium and potassium (11, 14), salts containing these constituents are likely to precipitate as the solutions evaporate (14). These conditions may be favourable for the incorpora-tion of ammonium ions into alunite (15, 51), aluminous clay minerals (16, 44, 52, 53) or K-feldspar (15 – 17, 52), depending on the redox conditions of the fluids (16). Incorporation of ammonium into minerals occurs when ammonium ions replace alkali cations (usually K+, which is close in radius) in the crystal structure. Potassic and sericitic alteration, and advanced argillic alteration (with alunite) will create K substitution sites. Conversely, kaolinitization, where K-bearing minerals are removed (which is the case in the kaolinite-rich rocks of Alid), and silification will tend to prevent high ammo-nium values (17, 54). Mapping of ammonium in previous studies utilized different absorption features of the N-H bond representing the vibrational combination ν1 + ν3 (16). (17) used the absorption fea-tures at 2120 nm to map buddingtonite, whereas (44) pointed out the 1558 nm as a significant am-monium feature. The fact that the important ammonium-related feature at 2120 nm is absent from our laboratory spectra should make us very cautious in calling upon ammonium to explain the 1558 nm feature. Nevertheless, we could not find another mechanism to explain the observed absorption (15, 17, 44). Trace amounts of ammonium in minerals might not be detected by the X-Ray Diffraction method: The XRD pattern of buddingtonite (NH4AlSi3O8), for example, can be overlapped by the K-feldspar pattern (15, 17). This may be the case in our K-feldspar rich samples. (55) pointed out that XRD analyses of ammonium-bearing illite did not always indicate the presence of structural NH4

+. In this case reflectance spectroscopy seems to be more sensitive to the existence of NH4

+ than X-Ray diffraction (after 15, 17, 55). (15) claimed that minimum concentration for spectral detection of am-monium from whole-rock samples is approximately 0.06 wt. % (NH4)2O, depending upon the sam-ple's lithology. It is suggested, therefore, that the absorption at 1558 nm is due to the existence of ammonium-bearing phyllosilicates, which were not detected by the XRD analysis.


Figure 5: (a). CR of the apparent reflectance image, in the range of 1500-1600 nm (shaded areas). Note the distinction between the four groups. (b). The apparent reflectance of the end-members from (a). The feature at 1558 nm is not visible.

The detection of the feature at 1558 nm in the laboratory spectra has led us to seek an equivalent feature in the Hyperion dataset. As the SNR around 1558 nm is relatively high and no overlapping with other chromophors exists (i.e., clay minerals), this band was further studied and emphasized four major lithologies (Fig. 5), all maybe containing ammonium-bearing minerals (15, 17, 52). The spatial association between the hydrothermally altered areas within Alid and the areas where the feature attributed to ammonium was detected (Fig. 4), and the identification of the 1558 nm feature in a probably hydrothermally altered zone (kaolinite-rich type, Fig. 3) support the possibility that the spectral absorption feature at 1558 nm may represent ammonium at the surface. The 1558 nm sig-nal could have been distinguished from areas which did not display this feature. It was not aligned on residual vertical stripes, nor restricted to shaded areas. It also did not originate from the reflectance curves used for the Empirical Line correction and it is not a CO2 or an O2 residual absorption. The laboratory spectral measurements and studies made by (8) and (14) on the same area led us to suggest that the Hyperion was able to detect the 1558 nm spectral feature around this specific Alid area.

SUMMARY AND CONCLUSIONS A careful processing of the Hyperion data followed well-known techniques and mapped the area according to previous knowledge. A ground truth reconnaissance was performed along with careful laboratory analyses of the samples. The unsupervised classification approach separated between major rock units, whereas the supervised classification detected specific minerals (e.g. iron oxides, clays, sulphates, amphiboles) and pinpointed the unique spectral feature at 1558 nm, arguably am-monium-related. This feature has a strong spatial association with kaolinite-rich, hydrothermally-altered rocks. Absorption features in the SWIR-1 are significant because they are positioned in a high SNR spectral range that enables ammonium-bearing minerals to be detected quickly and non-destructively by a variety of airborne and ground-based sensors (after 15, 56) and now, presumably, also by the spaceborne Hyperion sensor. The ability of the Hyperion to detect ammonium spectral signatures was never reported before. While the mapping of the ammonium-related feature is argu-able, the suggested mineralogical mapping results demonstrate that the extraction of information from hyperspectral data is yet to be completed, and points out the significance of the smaller, indica-tive absorption features for this purpose. The need and use of hyperspectral technology for mapping areas far from civilization and the potential success of such mapping are well demonstrated here.


ACKNOWLEDGMENTS Without the support of Mr. H. Godar, Israel Ambassador to Eritrea, this project could not have been accomplished. We thank A. Mushkin, S. Peeri and Y. Golani for helping with the Aster and Hyperion images, T. Medhin and the staff geologists of the Dept. of Mines, Asmara, for sharing with us their profound knowledge during the field trip, A. Sandler for the XRD analysis and A. Koltunov for helping with the processing of the Hyperion data. This work was funded by the U. S. Agency for International Development, Bureau for Economic Growth, Agriculture and Trade, Project No. C23-001 and Award No. Ta-MOU-03-C23-001.

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