Proceeding Paper
Kaoline Mapping Using ASTER Satellite Imagery: The Case
Study of Kefalos Peninsula, Kos Island †
Maria Kokkaliari * , Christos Kanellopoulos
and Ioannis Illiopoulos
Department of Geology, University of Patras, 26504 Patras, Greece; ckanellopoulos@gmail.com (C.K.);
ilios@upatras.gr (I.I.)
* Correspondence: kokkaliari_m@upnet.gr
† Presented at International Conference on Raw Materials and Circular Economy, Athens, Greece, 5–9 September
2021.
Abstract: The present work aims to map kaolin occurrences on the Kefalos peninsula, SW Kos Island,
Greece, through the elaboration of ASTER satellite imagery. The island of Kos is located on the
eastern edge of the South Aegean Active Volcanic Arc (SAAVA) and is characterised by its complex
geologic structure. During Plio-Pleistocene, the voluminous eruption of the Kos Plateau Tuff was
recorded on Kefalos; the largest quaternary eruption in the Mediterranean. Kaolin is the product
of hydrothermal alteration of the Pliocene volcanic rocks with rhyolitic composition. Our study
emphasises the usefulness of satellite imagery combined with the Mixture Tuned Matched Filtering
(MTMF) technique to detect occurrences of industrial minerals, kaolin-group minerals in this case,
either in terms of raw mineral exploitation or by mapping hydrothermal alteration.
Keywords: ASTER; Kos island; Kefalos; industrial minerals; hydrothermal alteration; kaolinisation;
MTMF algorithm
Citation: Kokkaliari, M.;
Kanellopoulos, C.; Illiopoulos, I.
Kaoline Mapping Using ASTER
Satellite Imagery: The Case Study of
Kefalos Peninsula, Kos Island. Mater.
Proc. 2021, 5, 76. https://doi.org/
10.3390/materproc2021005076
Academic Editor: Evangelos Tzamos
Published: 10 December 2021
Publisher’s Note: MDPI stays neutral
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
1. Introduction
The main objective of this article is to identify through remote sensing kaolin occurrences, which are products of the hydrothermal alteration of volcanic rocks, on the
Kefalos peninsula, NW Kos Island, Greece. In the literature, the contribution of satellite
imagery in cases of hydrothermal alteration has been extensively appraised [1–8] since
important information about cost-effective mineral occurrences in the earth’s surface are
often deduced. The spectral properties of target minerals can help us recognise and map
sites of interest with potential economic value. However, the South Aegean Active Volcanic
Arc (SAAVA) has not been thoroughly studied through remote sensing, even though its
complex geological structure has aroused the interest of many researchers [9–20].
Kaolin occurrences in Kefalos are related to the Pliocene volcanic rocks with rhyolitic
composition. Their mineralogical composition includes quartz, Na-plagioclase, alkalifeldspar, and amorphous volcanic glass, scharacterised by perlitic texture. The kaolinisation
process in volcanic rocks on the Kefalos peninsula has been studied [21–25] by means of
geochemical analysis, mineralogical study and spectroscopical methods.
In the present study, we used accessible free multispectral satellite Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and the spectral signatures of characteristic minerals from the USGS spectral library. Using laboratory spectra
of kaolinite, dickite and kaolinite/smectite as pure endmembers and the Mixture-Tuned
Matched Filtering Algorithm (MTMF), we were able to identify kaolin occurrences.
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
2. Geological Settings
The island of Kos (Figure 1) is located at the north-eastern edge of the SAAVA, where
the African oceanic crust is subducted under the 30 km thick Aegean micro-plate [26] at
4.0/).
Mater. Proc. 2021, 5, 76. https://doi.org/10.3390/materproc2021005076
https://www.mdpi.com/journal/materproc
Mater. Proc. 2021, 5, 76
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a rate of 1 cm/year [19,27,28]. Kos comprises mainly of volcanic rocks on a Paleozoic–
Mesozoic carboniferous sedimentary basement that was deformed during the Tertiary by
the Hellenide orogeny [29]. I-type peraluminous Miocene plutonite, with monzonitic to
monzo-granitic chemical composition, intruded at Dikeos Massif [15] in folded PermoCarboniferous sedimentary to very low-grade metamorphic rocks [20]. The intrusion
caused contact metamorphic aureole to the surrounding rocks, mapped by [9]. The petrographic and geochemical characteristics of the contact metamorphic rocks were examined by [30] suggesting the contribution of hydrothermal solutions. Phase equilibrium
experiments of metapelitic hornfelses estimate the depth of the intrusion to be 5–8 km approximately, and the maximum temperatures near the contact of the plutonite were about
800 ◦ C [9,12,20]. Furthermore, the presence of amphibole- and mica-bearing lamprophyric
rocks in Dikeos Massif suggest a mantle metasomatism environment [20].
Figure 1. Digitised geologic map of Kefalos Peninsula (modified after [31]) and satellite view of Kos island in the south-east
Aegean Sea, provided by Google Earth Pro.
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Pleistocene calc-alkaline volcanic activity on the Kefalos Peninsula produced the oldest
dated volcanic rock, which is now exposed as dacitic domes [17,19]. However, younger
than 3 Ma rhyolitic domes and pyroclastic flows also appear in the Kefalos area [10,16,17].
The largest quaternary eruption of pyroclastic products in the eastern Mediterranean
occurred at ~161 Ka, forming the voluminous Kos Plateau Tuff, evacuating more than
60 km3 of ash and pumice [13]. Six major stratigraphic KPT units were mapped and studied
thoroughly [32] and suggested that the pyroclastic flows travelled tens of kilometres across
the sea [14].
3. Methodology
3.1. Preprocessing Techniques
In the present study, an ASTER L1A multispectral satellite image was utilised, acquired
on 9 March 2008. ASTER consists of three separate instrument subsystems that include
14 bands in the VNIR, SWIR and TIR regions (Table 1) of the electromagnetic spectrum,
measuring the reflected and emitted electromagnetic radiation from the earth’s surface and
atmosphere [33]. Reflectance measurements in the VNIR till the SWIR wavelength region
provide diagnostic information that can be used to identify rocks and their constituent
minerals [33,34].
Table 1. Characteristics of ASTER satellite imagery [33,35].
Spectral Bands
VNIR
SWIR
TIR
Spectral Bandwidth (µm)
1
2
3B
3N
4
5
6
7
8
9
10
11
12
13
14
0.52–0.60
0.63–0.69
0.78–0.80
0.78–0.86
1.650–1.700
2.145–2.185
2.185–2.225
2.235–2.285
2.295–2.395
2.360–2.430
8.125–8.475
8.475–8.825
8.925–9.275
10.25–10.95
10.95–11.65
Spatial Resolution (m)
15
30
90
The pre-processing steps followed are ssummarised in Figure 2 and include georeferencing, radiometric calibration and atmospheric correction. The ASTER image was
georeferenced in the Greek Geodetic Reference System (EGSA87). From the ASTER spectral
data set, the 3N and TIR bands were excluded, and then, by applying layer stacking, the
VNIR and SWIR bands were merged into one dataset so that all bands obtained the same
spatial resolution (15 m). Furthermore, the ASTER image was spatially subsetted to facilitate the workflow and reduce the technical requirements of the analysis. The satellite data
were radiometrically calibrated, converting DN values to radiance. Finally, the atmospheric
correction was achieved using the Fast-Line-of-sight Atmospheric Analysis of the Spectral
Hypercubes (FLAASH) module [36] to eliminate atmospheric effects of water vapour and
aerosols and rescale raw radiance to reflectance values.
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Figure 2. Flowchart summarising the methodology used in this study.
The Normalised Difference Vegetation Index (NDVI) was calculated to identify and
exclude pixels related to vegetation, thus facilitating the classification technique. In order
to extract information about NDVI, we used the following formula [7,37]:
−
(NIR − RED)/(NIR + RED)
(1)
μ
μ
representing wavelength values in band 2 (0.661 µm) and 3 (0.807 µm) for red and nir
wavelength regions, respectively. The NDVI threshold value was set to 0.3 after carefully
evaluating the study area considering Google Earth imagery.
Finally, the geologic map of western Kos from [31] was digitised in ArcGIS, to outline
the geologic formations and georeferenced as well, in EGSA87.
3.2. Endmember Selection
Spectra from the USGS Digital Spectrum Library were collected as pure endmembers
to utilise the spectral analysing algorithm. Volcanic rocks in the study area have a more
acidic chemical composition, including minerals such as quartz and feldspars that are not
active in the near-infrared region of the electromagnetic spectrum. Thus, the presence
of minerals indicative of hydrothermal alteration exhibits diagnostic spectral absorption
features. ASTER satellite imagery has been thoroughly used to discriminate hydrothermal
alteration [2,4–8,38–44], considering its wide spectral coverage in the VNIR and SWIR
region.
Altered rhyolitic rocks comprise mostly kaolinite, dickite, and mixed-layer kaolinite/smectite [21]. Kaolinite content increases progressively from the unaltered rhyolite to
completely kaolinised rocks, whereas smectite and illite are present in small concentrations
only in a few samples. The spectral features of kaolinite, dickite and kaolinite/smectite, as
well as the resampled spectra to ASTER bandpasses, are displayed in Figure 3.
3.3. Mixture Tuned Matched Filtering (MTMF) Algorithm
Spectral unmixing techniques are used to quantify spectral information related to
pure endmembers. MTMF algorithm is an advanced spectral unmixing process, differing
from others in adjusting the identification of specific spectral targets without considering
unwanted background or unknown pixels. In our research, we used the resampled spectra
from the USGS spectral library as a reference for the spatial distribution of kaolin. This
method combines the strength of the matched filter (MF) method (no requirement to be
aware of all the endmembers) with physical constraints imposed by the mixing theory (the
signature at any given pixel is a linear combination of the individual components contained
in that pixel) [40,46]. It peforms partial unmixing only by finding the abundance of a single,
user-defined endmember, by maximising the response of the endmember of interest, and
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by minimising the response of the unknown composite background thus “matching” the
known signature [47,48].
Figure 3. (A) USGS spectral signatures of the selected minerals used in this study [45]; (B) the resampled spectra according
to the 9 VNIR and SWIR bands of the ASTER data.
The MTMF method includes an MNF transformation of the acquired reflection
data [49], the matched filtering process to estimate the endmember abundance, and finally,
a mixture tuning (MT) process to identify infeasible or false-positive pixels [50]. The output
of MTMF is a set of rule images given as MF and infeasibility scores for each pixel related
to each end member.
Since spectral targets are identified in the sedimentary background as expected (marly
limestones, marls, and limestones), we used post-classification techniques to re-classify
the data, excluding the unwanted identified pixels, focusing only on the volcanic rock
occurrences of the studied area.
4. Results and Discussion
The spectral characteristics of the selected minerals are illustrated in Figure 3. Since
smectite and illite are only present in minor quantities [21], kaolinite, dickite and kaolinite/smectite spectra were used as a reference.
According to the reference spectra used, dickite has the higher reflectance values,
followed by kaolinite and finally by kaolinite/smectite. Kaolinite is characterised mostly
by two intense doublet absorption features at 1.39 and 1.41 µm, as well as at 2.16 and 2.20
µm, respectively. The absorption features at 1.40 µm region is associated with the innersurface OH- groups, whereas the absorption feature at 2.20 µm is indicative of the presence
of Al-OH bearing minerals [34]. Similar spectral features are exhibited in the spectrum
of dickite, showing the main absorption features located at 1.38, 1.41, 2.17 and 2.20 µm.
The spectrum of kaolinite/smectite differ from the other two mineral spectra because it
does not exhibit doublet characteristics at 1.4 and 2.2 µm region, rather displaying one
single feature centred at 1.41 and 2.20 µm. Furthermore, the kaolinite/smectite spectrum
also exhibits an intense absorption feature at 1.91 µm, indicating the presence of molecular
water in the mineral structure [51]. The resampled spectra of kaolinite and dickite exhibit
the Al-OH diagnostic feature at the 2.2 µm region at band 6, displaying peaks at bands 4
and 7. An absorption feature at band 6 also characterises the kaolinite/smectite resampled
spectrum, whereas characteristic peaks are centred at bands 4, 5 and 7.
The radiometrically calibrated and atmospherically corrected ASTER image of the
Kefalos peninsula, as well as the MTMF results, are displayed
μ in Figure 4, revealing the
presence of kaolinite occurrences predominantly.μ The threshold value was determined
μ
based on
μ
µ + 1.5 ×σ
(2)
μ
μ
Τ
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where µ and σ represent the mean value and standard deviation of the relevant image.
Figure 4. (A) Radiometrically calibrated and atmospherically corrected ASTER image of Kefalos
peninsula; (B) Spatial distribution of kaolinite (green), dickite (red) and kaolinite/smectite (blue)
derived from MTMF algorithm related with volcanic rocks (in red areas). Areas in numbered white
boxes are displayed on the right, on a magnified scale, representing the small occurrence of dickite
and kaolinite/smectite compared to kaolinite in west Kefalos (1) and the perlite quarry (2).
Several studies have used this statistic approach, usually applying 1.5σ, 2σ, and
2.5σ as thresholds to determine the alteration level of an area [8], through many different
processing approaches (i.e., PCA: [8,43]; MF: [7,43]; SAM: [42]). The threshold values of
kaolinite, dickite, kaolinite/smectite are 0.031, 0.039 and 0.05, respectively.
In Greece, kaolin is mainly mined periodically from Milos Island and Drama, representing different conditions of origin. It also occurs at Lesbos, Kimolos, Thera and Kos
Islands, as well as Rhodope and Kilkis [52,53]. The presence of kaolinite on the Kefalos
Peninsula, as an alteration product of the Pliocene volcanic rocks, agrees with the literature.
Dickite is slightly exposed, mainly in the southwestern part of Kefalos (Figure 4B). Kaolinite, and to a lesser extent kaolinite/smectite and dickite, are exposed in the Pleistocene
rhyolitic dome in eastern Kefalos, comprising rocks with rhyolitic compositions and perlitic, spherulitic texture. Perlite is a hydrous volcanic rock associated with volcanic fields
and perlite quarries, mainly on Tertiary to Quaternary rhyolitic domes [54]. In this area,
perlite occurs peripherally of the domes (Figure 4B) and is exploited due to its industrial
value [52,53].
Even though there are some limitations using the library spectra as the samples
are measured under certain atmospheric conditions, with specific instrumental laboratory
equipment, and they may not match precisely the spectra acquired from the satellite images,
we were able to utilise the MTMF algorithm to estimate their total area of distribution
(Table 2).
Table 2. Spatial distribution of the selected mineral according to the MTMF algorithm in volcanic
occurrences.
Mineral
Number of Pixels Classified
Area in Km2
Kaolinite
Dickite
Kaolinite/Smectite
5879
762
3640
1.323
0.171
0.819
5. Conclusions
Usage of the VNIR-SWIR spectral dataset of ASTER imagery and application of the
MTMF algorithm can lead to the successful identification of kaolin occurrences in the
study area. The available literature helped to verify the results and study the outcrops of
the kaolinised volcanic rocks through a robust, cost-effective approach. Image processing
techniques applied to ASTER satellite data can retrieve information about the hydrothermal
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alteration products or even occurrences of industrial minerals, i.e., kaolinite, highlighting
prospects for further investigations.
Future research includes the lithological classification of the Kefalos peninsula using
image spectra and the contribution of field observations to retrieve information from
multispectral images, considering as well other satellite datasets. Furthermore, the utility
of laboratory analytical methods of selected samples is necessary to correlate the results
from macro- to micro-scale.
Author Contributions: Conceptualization, M.K. and I.I.; methodology, M.K. and I.I.; software, M.K.;
validation, M.K., I.I. and C.K.; formal analysis, M.K.; investigation, M.K., I.I. and C.K.; resources,
M.K., I.I. and C.K.; data curation, M.K.; writing—original draft preparation, M.K.; writing—review
and editing, I.I. and C.K.; visualization, M.K.; supervision, I.I.; project administration, I.I.; funding
acquisition, M.K. All authors have read and agreed to the published version of the manuscript.
Funding: This study is a part of the Ph.D. studies of the first author and is financially supported
by the “General Secretariat for Research and Technology (GSRT)” and the “Hellenic Foundation for
Research and Innovation (HFRI)”, at the University of Patras.
Conflicts of Interest: The authors declare no conflict of interest.
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