Integrated remote sensing in determining areas of high geothermal potential in the exploration of the Amacan Geothermal Prospect, Philippines Jeffrey T. Bermido, Kevin G. Guillermo, Oliver A. Briola, Leonardo L. Morales, Releo D. Contemplacion, and Joeffrey A. Caranto Energy Development Corporation, One Corporate Center, Julia Vargas cor. Meralco Ave, Ortigas, Pasig City, Philippines [email protected], [email protected], [email protected], [email protected], [email protected], and [email protected] ABSTRACT Leonard Kniassef is one of the active volcanoes in the Philippines, located in the eastern portion of Mindanao Island. It is interpreted to host the high temperature Amacan geothermal system, known for its Quaternary volcanism set on a favorable structural environment. In order to identify priority areas for detailed exploration, this study integrated three remote sensing techniques with minor statistics application namely: lineament analysis, hydrothermal alteration, and thermal anomaly mapping. Digital elevation models were processed for the lineament analysis, where slope statistics were correlated with the underlying tectonics. Thermal Infrared Sensor Band 10 of Landsat 8 was also processed using a series of raster calculations to highlight areas with high surface temperatures. The Operational Land Imager (OLI) bands of Landsat 8 were also processed using composite and band ratio operations to highlight alteration zones and discriminate the type of alteration present. Combining the three remote sensing results, five priority areas are identified to have high geothermal potential and activity. These areas are recommended for more detailed geoscientific assessments. Keywords: remote sensing, geothermal exploration, lineament analysis, hydrothermal alteration, thermal mapping 1. INTRODUCTION Remote sensing has been applied widely in the earth sciences, including geohazard assessments and natural resource management. This study extends the application of remote sensing to geothermal exploration as one of the first steps in an overall work program. As a time- and cost- efficient technology, remote sensing has high impact on how geological surveys are planned, especially on identifying priority areas for detailed assessment. It would also help address the data gap in areas that are difficult to reach due to challenging terrains and lack of access paths. The study is focused on the Amacan Geothermal Prospect, centered on the Leonard Kniassef Volcano. Carbon dating of the youngest tephra deposits yielded an age of 1800 ka (Wood, 1980), which makes it one of the active volcanoes in the Philippines. The area is also generally active with the presence of the Philippine Trench offshore and the Philippine Fault inland. The sinistral Philippine Fault in Eastern Mindanao cuts through Holocene sandstones exposed in Mati, Davao Oriental signifying its active status (Yumul et al., 2008). The Philippine Fault bifurcates into two, namely the Eastern Mindanao Fault in the west and the Mati Fault in the east forming a spindle- shaped structure (PHIVOLCS, 2015). This mirrors the fault architecture of the Philippine Fault in Leyte Island (e.g. West Fault Line and Central Fault Line) where the Tongonan Geothermal Project and other geothermal prospects are all straddled. The Philippine Fault in Eastern Mindanao also forms a couple with the younging southward Philippine Trench (Quebral et al., 1996, Lallemand et al., 1998). The active status of this tectonic couple makes the area seismically active relative to the rest of the archipelago. This tectonism which translates to permeability is thereby favorable for mineralization and development of geothermal systems. 1 Figure 1: (Left) Regional geologic setting of the Amacan geothermal prospect in blue box. Modified from PHIVOLCS (2015). (Right) The prospect is hosted by the active Leonard volcano and bounded by two segments of the NW-SE trending Philippine Fault. Given this impressive regional volcano-tectonic setting, the study aims to refine and further add to this existing information on the local scale through the application of remote sensing. This will be used as reference during the conduct of the geothermal exploration survey. 2. MATERIALS AND METHODS 2.1 Slope Analysis Shuttle Radar Topography Mission (SRTM) dataset from Earth Explorer was used in the geomorphological and lineament analysis. The DEMs are processed to produce hillshade, slope aspect, and slope gradient maps. Hillshade processing calculates the illumination value of a cell given a particular azimuth and altitude of a hypothetical light source. Slopes facing the illumination will be highlighted, while shadows will be casted on other slopes. For this study, illumination angle azimuths used are 045°, 135°, 225°, and 315°. The Z factor of 0.00000912 for the 0-10° latitude of Eastern Mindanao was also used in the analysis. The elevation angle of the illumination source is set at 20°. Slope aspect reflects the direction into where the slopes are facing according to a color scheme displaying the 0-360° azimuth. Aspect maps are found to be suitable in defining abrupt changes in slope directions that may represent the presence of geologic structures. This may be indicated by the presence of valleys, ridges, and lineaments. Volcanic deposits can also be differentiated based on the variations in slope aspect. Using automated slope statistics from ArcGIS, the mean, maximum, and minimum value of slope orientation were also determined. Slope gradient processing, usually generated for geohazard analysis, calculates the slope inclination or steepness of a surface in either degree or percentage values. According to Braganza (2014), while steep-sided slopes and flat terrains can be deduced from hillshade map, the values and degree of flatness or steepness of the topography is more properly illustrated in the slope gradient map. Broad flat areas are typically represented by white to green shaded regions while steep sided slopes are in the orange to red. Slope statistics was also applied to determine the mean, maximum, and minimum slope values. 2 2.2 Land Surface Temperature The second dataset is the Landsat 8 package of Eastern Mindanao with acquisition dated August 2015. This is comprised of Operational Land Imager (OLI) bands covering Bands 1-7 and Thermal Infrared Sensor (TIRS) bands covering Bands 10-11. As recommended by the US Geological Survey (2016), Band 11 was not used due to its large uncertainty factor. A series of steps based on Avdan and Javanovksa (2016) were applied to convert the OLI Band 10 using raster calculations in ArcGIS. Using the raster calculation tool in ArcGIS, the first step in the process (Equation 1) was the calculation of the Top of Atmospheric Spectral Radiance using the radiance rescaling factors included in the metadata file downloaded. 퐿휆 = 푀퐿푄푐푎푙 + 퐴퐿 (1) Where Lλ is the Top of Atmosphere spectral radiance, ML stands for the band-specific multiplicative rescaling factors, Qcal is the quantized and calibrated standard product pixel values, AL is the band-specific additive rescaling factor from the metadata file. The value of Lλ was then used to determine the brightness temperature (BT) in Equation 2 using the thermal constants provided in the metadata file. BT is defined as the temperature measured- at satellite and not the temperature on ground. Note that the resulting brightness temperature is in Kelvin and has to be converted into Celsius using conversion formulas. 퐾2 B푇 = 퐾 (2) 퐼푛 ( 1+ 1) 퐿휆 Where BT is the at-satellite brightness temperature (K), Lλ is Top of Atmosphere Spectral Radiance, and K1 and K2 are band-specific thermal conversion constants from the metadata file. On a separate calculation, Bands 4 and 5 were used to determine the Normalized Difference Vegetative Index (NDVI). The NDVI is used to quantify the green leaf vegetation around the globe. 푁퐼푅 (퐵푎푛푑 5)− 푅 (푏푎푛푑 4) 푁퐷푉퐼 = (3) 푁퐼푅 (퐵푎푛푑 5)+ 푅 (퐵푎푛푑 4) Where NIR or near-infrared is represented by Band 5 and R or the red band is represented by Band 4. The value of NDVI was then used to calculate for the Proportion of Vegetation (PV). 2 푁퐷푉퐼−푁퐷푉퐼푚푖푛 푃푉 = ( ) (4) 푁퐷푉퐼푚푎푥−푁퐷푉퐼푚푎푥 Where NDVImin and NDVImax are the minimum and maximum values of the NDVI, respectively. The value of PV was then used to calculate for the Land Surface Emissivity (LSE). The LSE is defined as the proportionality factor that scales blackbody radiance or the efficiency of transmitting thermal energy across the surface into the atmosphere (Avdan & Jovanovska, 2016). A simplified version of the equation is presented in Equation 5. 퐿푆퐸 = 0.0004푃푉 + 0.986 (5) For the final calculation of the Land Surface Temperature (LST), the values of the Land Surface Emissivity, brightness temperature, and other scientific constants were used in the equation. 퐵푇 퐿푆푇 = 퐵푇 (6) 1+푤[( ) (ln(퐿푆퐸)] ] 푝 Where LST is the Land Surface Temperature, BT is the brightness temperature, and LSE is the land surface emissivity. W is the wavelength of emitted radiance at 11.5µm in the TIRS bands. The value of p was calculated using Equation 7 which is equal to 1.438 x 10-2 mK. p=h c/s (7) 3 Where h is the Planck’s constant equal to 6.626 x 10-34 J.s., c is the Boltzmann constant equal to 1.38 x 10-25 J/K, s is the speed of light in vacuum (3 x 10-8 m/s). 2.3 Hydrothermal Alteration Mapping The OLI bands were processed using ArcGIS Model Builder for the hydrothermal alteration mapping. Color composite processing was performed to show the spatial distribution of hydrothermal alteration relative to the background. Three additive colors were used to display multispectral bands (Mia, 2012) corresponding to the RGB values of a false composite image. The ratio applied in this study was 5:7:6. Band ratio operation, on the other hand, was employed to identify the type of alteration present. Abrams ratio (6/7:4/3:5/6) was applied in this study to distinguish areas with iron oxide and clay alteration.