High Technology Letters ISSN NO : 1006-6748
Remote sensing Application for Geothermal Potential Mapping, A Case study Kone geothermal prospect, Main Ethiopian rift valley T. Suryanarayana 1Talema Moged Reda 2 Zeleke Simachew Anteneh 3 C Laxmikanth 4 1Professor & Geology &Wollo University) College of Natural Sciences, Department of Geology, WolloUniversity, Dessie, Ethiopia. 2Lecture & Geology &Arba Minch University )College of Natural Sciences, Department of Geology, Arba Minch University, Ethiopia. 3Lecture & Geology &Wollo University )College of Natural Sciences, Department of Geology, Wollo University, Dessie, Ethiopia. 4 Professor & Physics & The University of Dodoma)Department of Physics, College of Natural and Mathematical Science, The University of Dodoma, Tanzania.
Abstract Ethiopia is among the few countries in Africa with a significant amount of geothermal resources. These resources are found scattered in the Ethiopian Rift valley and in the Afar Depression, which are both part of the Great East African Rift System. The Ethiopian rift extends from the Ethiopia-Kenya border to the Red Sea in NNE direction for over 1000km within Ethiopia, and covers an area of 150,000 Km2. The main objective of this study is to assess geothermal resource potential target area for further exploration in Kone utilizing rapid and cost-effective tools of GIS and Thermal Infrared Remote sensing approach. This paper demonstrates the effectiveness of GIS and satellite remote sensing infrared data for assessing geothermal resource potential in Kone area. Thermal Infrared (TIR) remote sensing data is used to map and quantify Land surface temperature anomalies associated with surface geothermal features such as, fumaroles, and steaming ground, which comes from the subsurface of the area reach the ground by conduction and convection. The identification of geothermal resources was successfully found utilize the thermal infrared remote sensing method provided the Land Surface Temperature of Landsat 8 published on January 2019-01-16 . The maximum value of LST for data of January sixteen 2019 was 38.72_ 42.59°C. T and minimum value was 25.85_32.22 °C.
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Key words : Geothermal Resources, Ethiopian Rift Valley, GIS and satellite remote sensing, TIR.
1. Introduction
1.1 Background of the study area
Access to energy is among the key elements for the economic and social developments of Ethiopia. The energy sector in Ethiopia can be generally categorized in to two major components:traditional (biomass) and modern (such as electricity and petroleum) (Kebede, 2012) . Domestic energy requirements in rural and urban areas are mostly met from wood, animal dung and agricultural residues. At the national level, it is estimated that biomass fuels meet 88 % of total energy consumed in the country. In urban areas access to petroleum fuels and electricity has enabled a significant proportion of the population there to employ these for cooking and other domestic energy requirements. Geothermal energy is one of the renewable energy that can be used as alternatives to increasingly scarce fossil energy, which is commonly used by people to support their life, is exhaustible (Siahaan et al., 2011). According to Siahaan et al., (2011), this clean energy is considered more secure because it does not require a big space for the purposes of exploration and exploitation.This energy occurs in regions of anomalously high crustal heat flow that may be relatedto the presence of young igneous bodies or hot rocks located deeper in the crust (Ronald,2005). This elevated geothermal heat is normally transferred to the surface by theconvection of ground waters that forms hydrothermal systems, surface waters circulate todepth where they are heated and rise to the surface via a subterranean „plumbing system ‟of closely spaced fractures or other zones of permeable rock. If rising hot waters reach thesurface then characteristic geothermal features such as hot springs, fumaroles, geysers,and mud pots may form.Ethiopia is among the few countries in Africa with a significant number of geothermalresources. These resources are found scattered in the Ethiopian Rift valley and in the AfarDepression, which are both part of the Great East African Rift System. The Ethiopian riftextends from the Ethiopia-Kenya border to the Red Sea in NNE direction for over 1000km within Ethiopia, and covers an area of 150,000 Km2. Ethiopia started a long term geothermal exploration undertaking in 1969. Over the years, a good inventory of the possible resource areas have been built up and a number of the more important sites have
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been explored (Teklemariam, and Beyene, 2005). Of these areas, about sixteen geothermal prospect areas are judged to have potential for high temperature steam suited to electricity generation. The identified “geothermal prospect areas” are widespread in the whole Ethiopian rift valley. The explored prospects in the country are at various stages of exploration and include: more advanced prospects where exploration drilling has been conducted (AlutoLangano and Tendaho), prospects where surface exploration is relatively at higher level (abaya, Corbetti, Tulu Moye and Fentale and Dofan), and prospects where surface exploration is at lower level and warrant further exploration in the future (Kone, Meteka, Teo, Danab ,L.Abe and Dallol) (Kebede, 2012). During the1980s, reconnaissance geological, geochemical and geophysical investigations had been conducted in Kone areas and revealed the existence of young volcanic features and active surface thermal manifestations. Surface exploration is at lower level and warrant detailed
surface investigation, to be followed by exploration drilling. Kone is located at 8.8 0
degree N and 39.69 0-degree E and covers an area of 250 square km at the northern end ofthe Ethiopian, near the junction with the Afar depression.The methodology used ArcGIS software to produce factor mapsof land surface temperature to produce suitable areas for geothermal potential. Landsat 8satellite data carries two-sensor payload: The Operational Land Imager (OLI) and the thermal infrared Sensor (TIRS), used to analysis land surface temperature suitable for geothermal potential area.
1.2 Objective
1.2.1 Main objective The main objective of this study is to assess geothermal resource potential target area for further exploration in Kone utilizing rapid and cost-effective tools of GIS and ThermalInfrared Remote sensing approach.
1.2.2 Specific objective
• To retrieve Land surface temperature from Landsat 8 thermal bands. • Interpretation and assessing of geothermal surface temperature anomalies • To map high thermal manifestation areas in Kone
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1.3 Significance of the study
Ethiopia has embarked on a multi-billion-dollar energy sector development program to become one of Africa's major exporters of electricity. Country Growth and Transformation Plan (GTP) has ambitious goal to develop its geothermal energy resources to boost electricity supply to all Ethiopians and increasingly become a regional power hub. Now a days, governmental, international investor and private organization have also shown an interest in investing in geothermal power in different site along the rift valley to meet its expanding power generation ambitions. The area along rift valley have been identified as potential geothermal power area, but Country has been utilizing less than 1 percent of its total thermal power potential (MoWE, 2011) . If significantly increasing developing thermal electricity across Ethiopia, it needs better means and approaches to find best targets for the first few deep wells.
2. General Geology of Kone geothermal prospect
2.1 Tectonic setting
The Main Ethiopian Rift (MER) is a roughly NE trending sector of the East African Rift system that includes a series of rift segments extending from the Afar Triple Junction at the Red Sea-Gulf of Aden intersection to the Kenya Rift (Figure 1) . The MER is characterized by active extensional tectonics accommodating the 6–7 mm/yr relative movement between the African and Somalian plates [e.g., Chu and Gordon, 1999; Fernandes et al., 2004] (Figure 1). Active extension in the area is manifested by topography [Hayward and Ebinger, 1996], Bouguer gravity anomalies [Makris and Ginzburg, 1987; Makris et al., 1991], volcanism [Mohr, 1967; Davidson and Rex, 1980; WoldeGabriel et al., 1990; Ebinger et al., 1993] and seismic activity [Gouin, 1979; Fairhead and Stuart, 1982; Asfaw, 1992; Kebede and Kulhanek, 1991; Jestin et al., 1994; Foster and Jackson, 1998; Ayele, 2000] . Active faulting and volcanic activity is mostly localized along a N-S to N20 trending fault system (Wonji Fault Belt) developed within the rift floor [e.g., Mohr, 1962, 1967, 1983, 1987; Gibson, 1969; Mohr and Wood, 1976; Kazmin, 1980]. The KVC is located in the northern MER, which is a complex of symmetric tectonicgraben that trend southwest from the Afar region of Ethiopia. The rift in this region may
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be divided into two geographic segments, based on its axial trend. The northern segment of the MER connects, geographically and tectonically, to the Afar depression ( Meyer et al., 1975 ). The southern segment of the Rift extends towards, but does not connect directly with, the Kenyan Gregory Rift ( Meyer et al., 1975 ). Volcanic plateaux bounding the rift (e.g. Di Paola, 1972 ) are thought to have developed above one or more mantle plumes ( Schilling et al., 1992; Marty et al., 1996; Ebinger and Sleep, 1998; George et al., 1998; Rogers et al., 2000; Ebinger and Casey, 2001; George and Rogers, 2002; Nyblade, 2002; Krienitz et al., 2009 ). According to Rooney et al. (2007) , the location of the KVC coincides with the part of the MER that has a lithosphere transitional between continental and oceanic settings.
2.2 Structural geology
Here are describing the volcanic structures found in the KVC. The description is divided into two sections, corresponding to two major compound collapse structures: Birenti Caldera and Kone Caldera.
Birenti Caldera was named by Cole (1968a,b, 1969) . Birenti Caldera is identified by several isolated segments of eroded caldera wall ranging from 2 to 4 km in length, and up to 140 m high. The highest and most extensive of these segments is Mount Birenti, after which the caldera is named. The nature of the escarpments suggests that Birenti Caldera consists of at least two nested collapse structures. Assuming a circular plan form, the maximum caldera diameter was 11 km, corresponding to an area of 95 km2. Kone caldera was lava massif is located within the northwestern interior of Birenti Caldera. It is cut by numerous fault escarpments (up to 200 m high), some of which trend north and others west. This massif is cut on its northeastern flanks and summit by the collapse escarpments of the Kone Caldera MCA.
2.3Volcanic activity
Volcanism in the Ethiopian Rift Valley and in the adjacent Afar Triangle assumes considerable importance in understanding the relationships between extensional tectonics, intra-continental rifting, and the development of new oceanic basins (e.g. Ebinger et al.,
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2008 ). Kone geothermal prospect is one of the largest volcanic centres in the northern Main Ethiopian Rift (MER), namely the Quaternary Kone Volcanic Complex (KVC), previously known as the Gariboldi Pass cauldrons ( Mohr, 1962a ) or the Gariboldi Volcanic Complex ( Cole, 1969 ). The target of this paper is to study geothermal energy potential based on KVC because of its size: it comprises two calderas of 8 and 11 km diameter, suggesting large magnitude eruptions capable of yielding important regional tephra marker horizons ( Pyle, 1999 ). The KVC is part of a tectono-magmatic segment identified by Kurz et al. (2007) that is likely to have played a significant role in the development of the northern part of the MER. Finally, the KVC has erupted in the historic period and represents a potentially hazardous volcano.
2.4 Geothermal activity
A Kone geothermal prospect is at reconnaissance level. During the 1980s, reconnaissance geological, geochemical and geophysical investigations had been conducted in these areas and revealed the existence of young volcanic features and active surface thermal manifestations. Meteka and Teo hold promise for the discovery of economically exploitable geothermal resources at high temperature and warrant detailed surface investigation, followed by exploratory drilling. Detail exploration studies will be conducted in the near future to test the presence of economically exploitable geothermal resources.
3. Material and Methods
3.1 The study area
The study area is part of the main Ethiopian rift valley and geographically located at 8.8 0
degree N and 39.69 0-degree E and covers an area of 250 square km at the northern end of the Ethiopian, near the junction with the Afar depression. It is about 160 km east of Addis Ababa and about 30 km southwest of Fentale volcano and it is part of Oromia regional state administrative boundary.
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Figure 1. Location of the study area
3.2 Data sets and source 3.2.1 Satellite image archives
Landsat imagery of 2019 (OLI) and (TIRS) with Path 168 and row 054 acquired from one source of free satellite imagery web:
3.2.2 Material and tools
Table 1. Material and tools No Name Version Specification Data analysis and map 1 ArcGIS 10.6.1 preparation 2 ERDAS 2014 Image processing 3 ENVI 4.8 Image processing
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3.3 Geology of study area
Figure 2. Geology of the study area: Source Rampey et al., 2010
3.4 Methods
The algorithm was created in ArcGIS 10.6.1, and it can only be used to process LANDSAT 8 data because of the data complexity. The LST of any Landsat 8 satellite image can be retrieved following the steps. In this study, the TIR band 10 was used to estimate brightness temperature and bands 4 and 5 were used for calculating the NDVI.The metadata of the satellite images used in the algorithm. The Land Surface
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Temperature can be estimated or calculated using the Landsat 8 thermal bands. It simply requires applying a set of equations through a raster image calculator (Arc Map ). To calculate the LST used the USGS formulas. The band combination and MODIS used to validate LST.
3. 4.1 Top of atmospheric spectral radiance
The first step of the algorithm is the input of Band 10. After inputting band 10, in the background, the tool uses formulas taken from the USGS web page for retrieving the top
of atmospheric (TOA) spectral radiance ( Lλ)
Where M L represents the band-specific multiplicative rescaling factor, Q cal is the Band 10
image, A L is the band-specific additive rescaling factor, and O i is the correction for Band 10 (Barsi et al., 2014). 3.4.2 Conversion of radiance to at sensor temperature After the digital numbers (DNs) are converted to reflection, the TIRS band data should be converted from spectral radiance to brightness temperature (BT) using the thermal constants provided in the metadata file. The following equation is used in the tool’s algorithm to convert reflectance to BT (USGS, 2013):
Where, K1and K2 stands for the band-specific thermal conversion constants from the metadata. For obtaining the results in Celsius, the radiant temperature is revised by adding the absolute zero (approx. -273.15 ∘C) (Weng et al, 2004).
Metadata of the satellite Symbols Values image K1 Thermal constant, Band 10 1321.08 K2 Thermal constant, Band 10 777.89 ML Rescaling factor, Band 10 0.000342 AL Rescaling factor, Band 10 0.1 Oi Correction, Band 10 0.29 Table 2. Meta data value
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3.4.3 NDVI method for Emissive correction
3. 4.3.1 Calculating of NDVI
Landsat visible and near-infrared bands were used for calculating the Normal Difference Vegetation Index (NDVI). The importance of estimating the NDVI is essential since the amount of vegetation present is an important factor and NDVI can be used to infer general vegetation condition (Weng et al., 2004). The calculation of the NDVI is important because, afterward, the proportion of the
vegetation (P V):
Where NIR represents the near-infrared band (Band 5) and R represents red band (band4).
3.4.3.2 Calculating the proportion of vegetation (PV)
PV is calculated according to (4). A method for calculating PV (4) Suggests using the
NDVI values for vegetation and soil (NDVI V = 0.5 and NDVI S= 0.2) to apply in global conditions (Sobrino et al., 2004):
However, since the NDVI values differ for every area, the value for vegetated surfaces, 0.5, may be too low. Global values from NDVI can be calculated from at-surface reflectivities, but it would not be possible to
establish global values in the case of an NDVI computed from TOA reflectivities, since NDVI V and
NDVI S will depend on the atmospheric conditions (Jimene-Munoz et al., 2009).
3. 4.3.3 Calculating land surface temperature emissivity
The land surface emissivity (LSE ( ɛ)) must be known in order to estimate LST, since the LSE is a proportionality factor that scales blackbody radiance (Planck’s law) to predict emitted radiance, and it is the efficiency of transmitting thermal energy across the surface into the atmosphere (Jimene-Munoz et al., 2006 ). The determination of the ground emissivity is calculated conditionally as suggested in (Sobrino et al., 2004) :
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Where ɛVand ɛs are the vegetation and soil emissivity’s, respectively, and C represents the surface roughness (C = 0 for homogenous and flat surfaces) taken as a constant value of 0.005 (Sobrino and Raissouni, 2000).
When the NDVI is less than 0, it is classified as water, and the emissivity value of 0.991 is assigned. ForNDVI values between 0 and 0.2, it is considered that the land is covered with soil, and the emissivity valueof 0.996 is assigned. Values between 0.2 and 0.5 are considered mixtures of soil and vegetation cover and(Barsi et al., 2014) is applied to retrieve the emissivity. In the last case, when the NDVI value is greaterthan 0.5, it is considered to be covered with vegetation, and thevalue of 0.973 is assigned.
3.4.4 Retrieving land surface temperature
The last step of retrieving the LST or the emissivity corrected land surface temperature TS computed asfollows (Stcthopoulou and Cartalis, 2007):
Where T S is the LST in Celsius (C 0, (2)), BT is at-sensor BT (C 0), λ is the wavelength of emitted radiance (for which the peak response and the average of the limiting wavelength (λ = 10.895) [15] will be used), ɛλis the emissivity calculated in (6), and
Where σ is the Boltzmann constant (1.38 × 10 -23 J/K), ℎ is
Planck’s constant (6.626 × 10 -34 J s), and c is the velocity of light (2.998 × 108 m/s) [9]. 4. Results 4.1 Top of atmospheric spectral radiance
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Figure 3. Top of atmospheric spectral Radiance value
4.2 Conversion of radiance to at sensor temperature
Figure 4. Temperature Brightness value
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4.3 NDVI
Figure 5. NDVI value
4.4 Vegetation Proportion
Figure 6 Vegetation proportion value
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4.5 Land Surface Emissivity
Figure 7 Land Surface Emissivity value
4.6Retrieving land surface temperature
Figure 8 Land Surface Temperature value
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The LST result at January 2019-01-16 was classified into some categories. Thecategories were very low, low, moderate, high and very high. The minimum temperature (25.85_32.22 °C) has identified as red color, for temperature claffication of 32.22 – 34.25 °C identificated as reddish color, temperature claffication of 34.25 – 36.50 °C identificated as green color, temperature claffication of 36.50– 38.72°C identificated as greenish color, and higher 38.72_ 42.59°C identificated as blue color. Finally, the distribution of classified temperature was achieved and used for predicting the potential of the geothermal energy as shown in Fig.11. Thus, it was verified that calculated temperature from thermal infrared image could be predicting the geothermal resources area and could be used as a reference on exploitation. The highest value of LST was 38.72_ 42.59°C. The highest LST value most probably indicating the potential of geothermal energy.
4.7 LST Validation
The Landsat8 LST validation model is MODIS Land Surface Temperature and Emissivity (MODIS/TERRA MOD11A2 Land Surface Temperature and Emissivity 8_Day L3 Global 1km version6) LST data. The data was acquired from one source of free satellite imagery web:
The MOD11A2 Version 6 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MOD11A2 is a simple average of all the corresponding MOD11A1 LST pixels collected within that 8-day period (https://earthexplorer.usgs.gov/).The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverage along with bands 31 and 32 emissivity from land cover types. The minimum
8_day average MOD11A2 LST was 36.73_37.99C 0 and the maximum 8_day average
MOD11A2 LST was 41.73_43.09C 0.The MOD11A2 LST result was in good agreement with Landsat 8 LST.
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Figure 9 MODIS Land Surface Temperatures
5. Conclusion
The identification of geothermal resources was successfully found utilize the thermal infrared remote sensing method provided the Land Surface Temperature of Landsat 8 published on January 2019-01-16 . The maximum value of LST for data of January sixteen 2019 was 38.72_ 42.59°C. T and minimum value was 25.85_32.22 °C. Thus it was concluded that the location of research areas in the Kone geothermal prospect was potentially for geothermal energy resource and this result can be used as a reference for further exploitation of geothermal.
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