ANALYZING SURFACE ROUGHNESS MODELS DERIVED BY SAR AND DEM DATA AT GEOTHERMAL FIELDS - Tahjudil Witra; Asep Saepuloh; Agung Budi Harto; Ketut Wikantika (Bulletin of Geology, 2017)
Surface roughness is a physical property which is used in many applications such as hydrological analyses, erosivity of rocks, and identification of geothermal surface manifestations. In this study, the surface roughness was calculated by a pin-meter. This tool is expected be able to measure the fragmental size at ground surface. However, there is a possibility that the tool still has some errors from the effect of topography undulation. In previous research, detrending method was used to minimise the topographical effect in the measured surface roughness. In this paper, we used Synthetic Aperture Radar (SAR) data from Sentinel-1A, and Digital Elevation Model (DEM) SRTM to evaluate the effectiveness of detrending method of pin-meter. Therefore, the measured surface roughness originated solely from fragmental materials. The selected research areas were Wayang Windu and Patuha geothermal field in Indonesia. Modelling the surface roughness by Sentinel-1A image was conducted by utilising backscattering coefficient and local incidence angle. While surface roughness model from DEM is formed by the Root mean square (RMS) for each grid with the optimum size 19×19 pixels. Both models were compared to pin-meter data which have been detrended. Then, the comparison was analyzed based on determination correlation value (R2). Surface roughness model derived by Sentinel-1A produced R2 about 0.1130 higher than DEM about 0.060. It might indicate that the surface roughness measured by the pin-meter following detrending process is free from the effect of topography undulation. Then, surface roughness model derived by Sentinel-1A data was used to identify surface manifestation. Analysis was performed based on pH measurement at field and scatter plot pattern. According to the selected model, the surface roughness at geothermal surface manifestation zones are inversely proportional to the soil pH.