第35回日本情報地質学会講演会 講演要旨集 007-008頁 2024年

A Novel Combination of Multi-Sensor Remote Sensing and Geostatistics for
Advanced Potential Mapping of Geothermal Resources over Java Island,Indonesia
Tedi Atmapradhana *, Katsuaki Koike *, and Asep Saepuloh ***京都大学大学院工学研究科都市社会工学専攻 Department of Urban Management,
Graduate School of Engineering, Kyoto University, Katsura C1-2, Kyoto 615-8540, Japan.
**Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Jl. Ganesha 10,Indonesia.

Key words: Weighted Fuzzy logic, Fuzzy ordinary kriging, LANDSAT OLI image, ASTER
TIR image

1. Introduction
Spatial priority analyses based on multi-sensor
remotesensing
data
providecomprehensive
information of ground surface condition. Forgeothermal exploration, these analyses are crucial not
only to detect the new potential areas but also topredict the extension areas surrounding a geothermal
power plant, however it has a limitation in tropicalconditions, which have a high precipitation and densevegetation. To overcome this problem, this study
proposed a Weighted Fuzzy Logic method termed as
WFL to obtain spatial priority rank from multiple
variables simultaneously. This method is different
with classical logic theory in which data set have only
two-logic values: false (=0) and true (=1). The WFL isa decision technique to calculate spatial rank ofgeothermal potential (Saepuloh et al. 2013). The
membership functions can be derived from multiple
values of datasets. In this study, we used datasets fromLANDSAT Operational Land Manager (OLI), ASTER
thermal infrared (TIR), digital elevation model (DEM)
data, and geophysical data such as gravity data andaeromagnetic data. The main reason for this study isto assist and support the Indonesian government effort
to optimize the utilization of geothermal energy,
especially for electricity generation of 7,242 MW by2030.

2. Data and Methodology
2.1 Study area and data
This research focuses on Java Island as its study
area. It is geographically located between E105 O7’114 O37’ and S5 O48’-8 O37’ and has approximately
138,800 km 2 (Figure1). The island has the largest
installed power generation capacity in Indonesia withtotal capacity 10,572 MW or 14.5% of the power
generation in Indonesia (Sidik and Harmoko, 2022).
Two data types from different sensors were used inthis study: 14 scenes from visible near infrared (VNIRSWIR) of Landsat (OLI), 114-night scenes from (TIR)of ASTER, gravity data, aeromagnetic data, and DEM
data. The raw data of Landsat OLI and ASTER TIR
was corrected geometrically and atmospherically toradiance at sensor before converting to reflectance andthermal radiance data.

Figure 1. Satellite Imagery covering the study area.
In addition, the DEM data was used to detect thelinear feature density related to geological structure,
the gravity observation data was used to analyzing
bouguer anomaly, and aeromagnetic data was used todetect magnetic properties of the surrounding rocks
around the volcano mountain at study area.
2.2 Methodology
Because the optical sensor is affected strongly by theatmosphericcondition,aFast
Line-of-sight
Atmospheric Analysis of Spectral Hypercubes(FLAASH) was used to remove the effect of
atmospheric propagation (Kaufman et al., 1997). Afterthe atmospheric artifact was removed, the SpectralAngle Mapper (SAM) classification was used to detectthe distribution of clay mineral. The six spectra of clay
minerals from measurements with spectrophotometer
were used as referenced spectra data (Fig. 2). These
spectra were used as input for the SAM classification.
To assess vegetation condition, the VIGS (Vegetation
Index considering Greenness and Shortwave infrared)algorithm by (Hede et al., 2015) was applied. This
index is aimed at wide availability to generalmultispectral satellite imagery by integrating visible
green, red, near infrared, and shortwave infrared.
Furthermore, the land surface temperature (LST) map

8was obtained from the separation of temperature andemissivity of ASTER TIR data.

indicates a high likelihood of encountering geothermal
manifestations in the center and southern part of Java
Island. The similarities in faultline formations, lowmagnetism value, and high vegetation stress between
this region and the middle area further support theprospects of significant geothermal resources in theseareas.

4. Summary

Figure 2. Spectral reflectance of clay minerals.

3. Result and Discussion
Following (Saepuloh et al., 2012) to compute thespatial rank or degree of evidence ( μω ), the ten
memberships were transformed to fuzzy membershipby the following equation:𝑥 − 𝑚𝑖𝑛(1)𝜇(𝑥) = ()𝑚𝑎𝑥 − 𝑚𝑖𝑛Then, the ten memberships could be integratedusing WFL methods, as illustrated by the followingequation:
𝜇𝜔 =𝑚
(∑𝑚
𝑗=1(1 − ∏𝑖=1(1 − 𝜇𝑖 )) × 𝜔𝑗 )

𝛾(2)

(1−𝛾)
𝑚
(∑𝑚
𝑗=1 ∏𝑖=1 𝜋𝑖 𝜔𝑖 )where m denotes the number of datasets, μᵢrepresents the fuzzy membership function, ω ᵢ is theweighted function, and γ is a selected constant(=0.975).
By
integratingthesedatasets,comprehensive geothermal prospect zone maps weregenerated as shown in Figure 3.
Based on generated geothermal prospect map reveal
a distinct pattern of geothermal manifestations
predominantly clustered in mountainous regions withhigh vegetation stress and low magnetism. Although
the surface temperature was medium to high, themineral mapping confirmed that the existence of theclay minerals, silica sinter, and sulfur was supposed to
be an indication of alteration zones. Furthermore, acloser examination of the pattern of fault lines

Combining the fuzzy theory and geostatistics issuperior for estimating the rank of membership byinterpolating the unobserved point using surrounding
observed point and weight criterion. However, theapproach has limitations, including false positive due
to topographic effects, resolution issues, surface
orientation, and limited pixel sampling areas, as there
are few bare land areas.

References
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Figure 3. Geothermal prospectivity map through Weighted Fuzzy Logic and data integration.