Characterizing Surface Manifestation of Geothermal System Under Torrid Zone Using Synthetic Aperture Radar (SAR) Data

Abstract

The optical remote sensing sensors were facing problem to identify ground surface under Torrid Zone for last decades. The cloud and thick vegetation canopy always becomes an obstacle for remotely sensed sensor to reveal surface geology features. Therefore, the advantage of satellite data for geology exploration as well as hazard mitigation is less effective. Nowadays, remote sensing technology is approaching a new era, especially in the use of microwave sensors onboard space borne termed as Synthetic Aperture Radar (SAR). The SAR is an active remote sensing technology which operates regardless time and weather condition. The application of SAR sensor for geology and geothermal related study is growing rapidly. However, the use of backscattering intensity of SAR data for geothermal prospection is still limited. The main problem might be originated from the geometric distortion and/or limitation of waveband. Overcoming the problem, we present our achievements in the use of backscattering intensity of SAR data for detecting surface manifestation precisely. An automatic extraction of linear feature density from Synthetic Aperture Radar (lifedSAR) is demonstrated to estimate the fluid path related to faults and fractures at Mt. Tangkuban Parahu, West Java, Indonesia. In addition, a Polarimetric SAR data is used to detect the distribution of surface manifestation based on surface roughness. Combining both methods is superior to minimize the detection errors from environment noises. Following this approach, we could correlate the surface manifestation with Linear Features Density (LFD) and surface roughness. The alteration zones are located at medium to high LFD values and low to medium surface roughness, respectively. The Fluid Path Index (FPI) and field investigation confirmed the location of alteration zones in the SAR backscattering image. Therefore, detection accuracy using remotely sensed data under Torrid Zone could be improved as presented in this study.