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Multi- and Hyperspectral Geologic Remote Sensing: a Review International Journal of Applied Earth Observation and Geoinformation 14 (2012) 112–128 Contents lists available at SciVerse ScienceDirect International Journal of Applied Earth Observation and Geoinformation jo urnal homepage: www.elsevier.com/locate/jag Review Multi- and hyperspectral geologic remote sensing: A review ∗ Freek D. van der Meer , Harald M.A. van der Werff, Frank J.A. van Ruitenbeek, Chris A. Hecker, Wim H. Bakker, Marleen F. Noomen, Mark van der Meijde, E. John M. Carranza, J. Boudewijn de Smeth, Tsehaie Woldai University of Twente, Faculty of Geo-information Science and Earth Observation (ITC), Hengelosestraat 99, 7514 AE Enschede, The Netherlands a r t i c l e i n f o a b s t r a c t Article history: Geologists have used remote sensing data since the advent of the technology for regional mapping, struc- Received 24 December 2010 tural interpretation and to aid in prospecting for ores and hydrocarbons. This paper provides a review Accepted 10 August 2011 of multispectral and hyperspectral remote sensing data, products and applications in geology. During the early days of Landsat Multispectral scanner and Thematic Mapper, geologists developed band ratio Keywords: techniques and selective principal component analysis to produce iron oxide and hydroxyl images that Geologic remote sensing could be related to hydrothermal alteration. The advent of the Advanced Spaceborne Thermal Emission Landsat and Reflectance Radiometer (ASTER) with six channels in the shortwave infrared and five channels in the ASTER Hyperspectral thermal region allowed to produce qualitative surface mineral maps of clay minerals (kaolinite, illite), sulfate minerals (alunite), carbonate minerals (calcite, dolomite), iron oxides (hematite, goethite), and silica (quartz) which allowed to map alteration facies (propylitic, argillic etc.). The step toward quanti- tative and validated (subpixel) surface mineralogic mapping was made with the advent of high spectral resolution hyperspectral remote sensing. This led to a wealth of techniques to match image pixel spectra to library and field spectra and to unravel mixed pixel spectra to pure endmember spectra to derive sub- pixel surface compositional information. These products have found their way to the mining industry and are to a lesser extent taken up by the oil and gas sector. The main threat for geologic remote sensing lies in the lack of (satellite) data continuity. There is however a unique opportunity to develop standardized protocols leading to validated and reproducible products from satellite remote sensing for the geology community. By focusing on geologic mapping products such as mineral and lithologic maps, geochem- istry, P-T paths, fluid pathways etc. the geologic remote sensing community can bridge the gap with the geosciences community. Increasingly workflows should be multidisciplinary and remote sensing data should be integrated with field observations and subsurface geophysical data to monitor and understand geologic processes. © 2011 Elsevier B.V. All rights reserved. Contents 1. Introduction . 113 2. Multispectral: the Landsat era . 113 3. Multispectral: the ASTER era . 116 4. The hyperspectral ERA . 118 4.1. Background . 118 4.2. Sensors . 119 4.3. Processing. 119 4.4. Applications in geology: the VIS-SWIR range . 120 4.5. Applications in geology: the TIR range . 123 5. Discussion and conclusions . 123 5.1. Validation issues .... 123 5.2. Bridging the gap between Earth observation science and Earth science . 123 ∗ Corresponding author. Tel.: +31534874353; fax: +31534874336. E-mail address: [email protected] (F.D. van der Meer). 0303-2434/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.jag.2011.08.002 F.D. van der Meer et al. / International Journal of Applied Earth Observation and Geoinformation 14 (2012) 112–128 113 5.3. Multidisciplinary approaches . 124 5.4. Data continuity . 124 Acknowledgements . 124 References . 125 1. Introduction van der Meer, 2006b); hence, it serves as a good example to illus- trate various techniques and sensor data sets. The image data used Geological remote sensing has been ill-defined in the literature. are shown in Fig. 3. The name suggests that remote sensing data (this can be field- based, airborne or spaceborne geophysical measurements) are used 2. Multispectral: the Landsat era to study geology. Traditionally, geology deals with the composi- tion, structure, and history of the Earth. However, more and more Ratioing techniques and geologic image interpretation date back geology deals with processes that have formed the Earth and other to the early days of air photo geology and the early Landsat mul- planets indicating that geology becomes more multidisciplinary tispectral scanner (MSS) data sets where the VNIR bands were and more positioned on issues of societal relevance. There are sev- first deployed to derive iron oxide ratio images (Goetz and Rowan, eral text books on geologic remote sensing. However, these books 1981). The French Satellite Pour l’Observation de la Terre (SPOT) predominantly are introductory to remote sensing using exam- system was used by the geology community because of its (at ples in the field of geosciences. Floyd Sabins wrote probably one that time) unprecedented spatial resolution and stereo capability. of the most sold and cited textbooks on remote sensing (Sabins, Several studies show the use of SPOT data for lithologic mapping 1996). Although he is a remote sensing geologist, his book is writ- (Bilotti et al., 2000) and for (semi)automatic detection and delin- ten for the general remote sensing audience contrary to the book eation of faults (Kaya et al., 2004; Kavak, 2005; Kavak and Inan, of Steven Drury, another remote sensing geologist, who dedicated 2002). It has also been shown that subpixel measurement of surface a book to geologic image interpretation (Drury, 1987). Ravi Gupta, displacement along faults can be derived from SPOT data (Crippen professor from the Earth Sciences department of the University of et al., 1991, 1996). While other instruments such as IRS-1A (Bhan Roorkee (India) wrote a book on geologic remote sensing (Gupta, et al., 1991) were also used by the geologic remote sensing commu- 2003). In addition, there are a number of review articles on aspects nity, the uptake of remote sensing in geology accelerated with the of geologic remote sensing including remote sensing for mineral advent of the Landsat Thematic Mapper (TM) instrument. Landsat exploration (Sabins, 1999), applications of hyperspectral remote TM images have been used for many years by the geologic remote sensing in geology (Cloutis, 1996), and the use of remote sensing sensing community for mapping of lithology and delineation of and GIS in mineral resource mapping (Rajesh, 2004). Some of the lineaments specifically to map alteration mineralogy. In particular early founding papers were published by Gregg Vane and Alexander band ratioing techniques, decorrelation stretching and saturation Goetz when both were working at the NASA Jet Propulsion Labora- enhancement and principal component (PC) analysis (PCA) have tory (Vane and Goetz, 1993, 1988). Much of the geologic remote been popular techniques (Chavez et al., 1991; Yesou et al., 1993). sensing in the visible-near infrared (VNIR), shortwave infrared The Landsat TM band 7/band 5 ratio is generally used to separate (SWIR), mid infrared (MIR) and thermal infrared (TIR) part of the argillic from non-argillic materials, thus, reflecting the presence spectrum resulted from pioneering work of Hunt and Salisbury or absence of hydroxyl absorption bands. Fe-O versus non-Fe-O (Hunt, 1977; Salisbury et al., 1989; Cooper et al., 2002) who metic- mapping is done using the band 3/band 1 ratio. A qualitative inter- ulously measured mineral and rock spectra forming the basis for pretation scheme for a false color composite ratio image of R = 5/7, airborne and spaceborne instruments. Remote sensing geologists G = 3/1, B = 5 in red–green–blue is provided in Table 1. Instead of have actively contributed to the development of active sensor using ratio images, spectral gradients can be used where the gra- technology (predominantly SAR and InSAR) and passive sensor dient expressed as a fraction of the average radiance is given by technology (multispectral and hyperspectral remote sensing in the the intensity of two channels normalized to their average value as VNIR to SWIR and TIR parts of the spectrum). The latter is the sub- (Gillespie et al., 1987): ject of this review. The aim is to present an overview of nearly 30 years of science at the interface of geology and remote sensing. DNi − DNj This paper is written to take stock of the developments, review the DDN = 2 ij DN , (1) + DN − papers that made an impact, signal trends and present some of the i j i j shortcomings and future challenges. Throughout this paper, examples from the Rodalquilar gold min- where DDNij is the value of the normalized intensity for Land- ing area (Fig. 1) located in the Sierra del Cabo de Gata (Cabo de Gata sat channels i and j and i and j are their respective wavelengths. National Park) in the south-eastern corner of Spain (Rytuba et al., This normalization reduces the effect of albedo and topographic 1990) will be presented as illustrative products generated from var- effects. A qualitative interpretation scheme for a false color com- ious multi- and hyperspectral sensors using analytical techniques posite normalized (following equation (1)) ratio image of 5/7, 3/1, discussed in the text. The area consists of calc-alkaline volcanic 5 in red–green–blue is provided in Table 2. rocks (andesites and rhyolites) of late Tertiary age which have To optimally display Landsat (MSS and TM) data in three color been extensively altered to form an assemblage of metamorphic composite images, the optimum index factor (OIF) has for some minerals from high to low temperature as: silica, alunite, kaolin- time been used (Chavez et al., 1980). The OIF is calculated for all pos- ite, montmorillonite and chlorite.
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