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Integration of Thermal Infrared and Synthetic Aperture Radar Images to Identify Geothermal Steam Spots Under Thick Vegetation Cover

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Abstract

Identifying physical properties of geothermal features such as surface temperature and roughness is crucial for geothermal assessment, hydrology, and environmental studies. Surface temperature and roughness-related rock properties strongly influence the thermal infrared and back-scattering intensities, respectively, of synthetic aperture radar (SAR) images. This study aimed to identify geothermal steam spots based on the surface temperature and roughness of altered surfaces, mud pools, and hot springs in tropical conditions. The thermal emissivity separation method was adopted to calculate kinetic temperature using the thermal infrared (TIR) of the advanced spaceborne thermal emission and reflection radiometer (ASTER) data as well as ground temperature data. Surface temperatures calculated by the thermal emissivity separation method were corrected using measured ground temperatures to eliminate the effects of atmospheric absorption, thermal inertia of materials, and/or diurnal temperature in the images. In addition, the linear fitting method was applied to obtain the final surface roughness model based on the calculated determination coefficient (R2) between the initial surface roughness model derived by Sentinel-1A SAR and field data. The highest R2 between surface roughness values from Sentinel-1A SAR and field data were selected to calculate spatially the surface roughness of the Wayang Windu Geothermal Field, West Java, Indonesia. The satellite imagery and field measurements showed that surface temperature and roughness features correlate with each other, and high and low thermal features correlate with high and low surface roughness values, respectively. Accordingly, we integrated the TIR and SAR data to propose the thermal resistivity index (TRI) as an indicator of the hydrothermal fluid paths to the surface. Low TRI was encountered at geothermal features and intersections of subsurface faults, which indicated weak zones that radiate heat from the subsurface to the surface.

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References

  • Anderson, M., Gao, F., Knipper, K., Hain, C., Dulaney, W., Baldocchi, D., et al. (2018). Field-scale assessment of land and water use change over the California Delta using remote sensing. Remote Sensing, 10(6), 889.

    Google Scholar 

  • Berg, S. S., & Skar, T. (2005). Controls on damage zone asymmetry of a normal fault zone: Outcrop analyses of a segment of the Moab fault, SE Utah. Journal of Structural Geology, 27(10), 1803–1822.

    Google Scholar 

  • Bogie, I., Kusumah, Y. I., & Wisnandary, M. C. (2008). Overview of the Wayang Windu geothermal field, West Java, Indonesia. Geothermics, 37(3), 347–365.

    Google Scholar 

  • Brogi, A., Capezzuoli, E., Buracchi, E., & Branca, M. (2012). Tectonic control on travertine and calcareous tufa deposition in a low-temperature geothermal system (Sarteano, Central Italy). Journal of the Geological Society, 169(4), 461–476.

    Google Scholar 

  • Campbell, B. A., & Garvin, J. B. (1993). Lava flow topographic measurements for radar data interpretation. Geophysical Research Letters, 20(9), 831–834.

    Google Scholar 

  • Campbell, B. A., & Shepard, M. K. (1996). Lava flow surface roughness and depolarized radar scattering. Journal of Geophysical Research Planets, 101(E8), 18941–18951.

    Google Scholar 

  • Chalik, C. A., & Saepuloh, A. (2019). Detection of ground thermal anomaly under dense vegetation based on ASTER TIR images. In IOP conference series: Earth and environmental science (Vol. 254, p. 012001). IOP Publishing. https://doi.org/10.1088/1755-1315/254/1/012001.

  • Conolly, J., & Lake, M. (2006). Geographical information systems in archaeology. London: Cambridge University Press.

    Google Scholar 

  • Coolbaugh, M. F., Kratt, C., Fallacaro, A., Calvin, W. M., & Taranik, J. V. (2007). Detection of geothermal anomalies using advanced spaceborne thermal emission and reflection radiometer (ASTER) thermal infrared images at Bradys Hot Springs, Nevada, USA. Remote Sensing of Environment, 106(3), 350–359.

    Google Scholar 

  • Copernicus Sentinel data. (2015). Retrieved from ASF DAAC 16 September 2015, processed by ESA.

  • Diningrat, W., Sugiono, S., & Daud, Y. (2018). Fault-related fractures characteristic of Kijang Fault at Wayang Windu geothermal field. Presented at the Proceedings of the 7th ITB International Geothermal, Bandung: Study Program of Geothermal Engineering, Bandung Institute of Technology.

  • Duarte, R. M., Wozniak, E., Recondo, C., Cabo, C., Marquínez, J., & Fernández, S. (2008). Estimation of surface roughness and stone cover in burnt soils using SAR images. Catena, 74(3), 264–272.

    Google Scholar 

  • Elsaid, M., Aboelkhair, H., Dardier, A., & Hermas, E. (2014). Investigation of a relation between radiogenic heat production and kinetic surface temperature from multispectral ASTER-TIR data: a case study on Elmissikat-Eleridiya granites, Central Eastern Desert, Egypt. Arabian Journal of Geosciences, 7(11), 4615–4628.

    Google Scholar 

  • Evans, J. P., Forster, C. B., & Goddard, J. V. (1997). Permeability of fault-related rocks, and implications for hydraulic structure of fault zones. Journal of Structural Geology, 19(11), 1393–1404.

    Google Scholar 

  • Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., et al. (2007). The shuttle radar topography mission. Reviews of Geophysics, 45(2), 1–33.

    Google Scholar 

  • Floyd, F., & Sabins, J. R. (1987). Remote sensing principles and interpretation. Long Grove, IL: Waveland Press Inc.

    Google Scholar 

  • Fung, A. K., Chen, K. S., & Chen, K. S. (2010). Microwave scattering and emission models for users. Artech house.

  • Gillespie, A., Rokugawa, S., Matsunaga, T., Cothern, J. S., Hook, S., & Kahle, A. B. (1998). A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. IEEE Transactions on Geoscience and Remote Sensing, 36(4), 1113–1126.

    Google Scholar 

  • Hajnsek, I., Pottier, E., & Cloude, S. R. (2003). Inversion of surface parameters from polarimetric SAR. IEEE Transactions on Geoscience and Remote Sensing, 41(4), 727–744.

    Google Scholar 

  • Hilman, Z., Saepuloh, A., & Susanto, V. (2020). Application of Land Surface Temperature Derived from ASTER TIR to Identify Volcanic Gas Emission Around Bandung Basin. International Journal of Remote Sensing and Earth Sciences (IJReSES), 16(2), 75–82.

    Google Scholar 

  • Hulley, G. C., & Hook, S. J. (2010). Generating consistent land surface temperature and emissivity products between ASTER and MODIS data for earth science research. IEEE Transactions on Geoscience and Remote Sensing, 49(4), 1304–1315.

    Google Scholar 

  • Julien, Y., Sobrino, J. A., Mattar, C., Ruescas, A. B., Jimenez-Munoz, J. C., Soria, G., et al. (2011). Temporal analysis of normalized difference vegetation index (NDVI) and land surface temperature (LST) parameters to detect changes in the Iberian land cover between 1981 and 2001. International Journal of Remote Sensing, 32(7), 2057–2068.

    Google Scholar 

  • Kealy, P. S., & Hook, S. J. (1993). Separating temperature and emissivity in thermal infrared multispectral scanner data: Implications for recovering land surface temperatures. IEEE Transactions on Geoscience and Remote Sensing, 31(6), 1155–1164.

    Google Scholar 

  • Kim, Y.-S., Peacock, D. C., & Sanderson, D. J. (2004). Fault damage zones. Journal of Structural Geology, 26(3), 503–517.

    Google Scholar 

  • Kuenzer, C., & Dech, S. (2013). Thermal infrared remote sensing sensors, methods, applications (Vol. 17). Dordrecht: Springer.

    Google Scholar 

  • Lopes, A., Nezry, E., Touzi, R., & Laur, H. (1993). Structure detection and statistical adaptive speckle filtering in SAR images. International Journal of Remote Sensing, 14(9), 1735–1758.

    Google Scholar 

  • Matsunaga, T. (1994). A temperature-emissivity separation method using an empirical relationship between the mean, the maximum, and the minimum of the thermal infrared emissivity spectrum. Journal of the Remote Sensing Society of Japan, 14(3), 230–241.

    Google Scholar 

  • Mia, M. B., Bromley, C. J., & Fujimitsu, Y. (2012). Monitoring heat flux using Landsat TM/ETM+ thermal infrared data—a case study at Karapiti (‘Craters of the Moon’) thermal area, New Zealand. Journal of Volcanology and Geothermal Research, 235, 1–10.

    Google Scholar 

  • Mia, M. B., Nishijima, J., & Fujimitsu, Y. (2014). Exploration and monitoring geothermal activity using Landsat ETM+ images: A case study at Aso volcanic area in Japan. Journal of Volcanology and Geothermal Research, 275, 14–21.

    Google Scholar 

  • Milloy, S. F., Newson, J., & Sepulveda, F. (2014). Geothermal surface features at Geyser Valley, Wairakei, New Zealand. In Proceedings of the 39th annual Stanford geothermal workshop, Stanford University, California.

  • Miranda, N., & Meadows, P. J. (2015). Radiometric calibration of S-1 level-1 products generated by the S-1 IPF. Technical note (Tecnical Note ESA-EOPG-CSCOP-TN-0002). European Space Agency. Accessed 24 February 2018.

  • Nurtyawan, R., Saepuloh, A., Budiharto, A., & Wikantika, K. (2018). Modified Campbell-Shepard model to retrieve surface roughness in paddy fields using Radarsat-2 data and field measurements. International Journal of Tomography and Simulation, 31(1), 27–43.

    Google Scholar 

  • Qin, Z., & Karnieli, A. (1999). Progress in the remote sensing of land surface temperature and ground emissivity using NOAA-AVHRR data. International Journal of Remote Sensing, 20(12), 2367–2393. https://doi.org/10.1080/014311699212074.

    Article  Google Scholar 

  • Realmuto, V. J., & Worden, H. M. (2000). Impact of atmospheric water vapor on the thermal infrared remote sensing of volcanic sulfur dioxide emissions: A case study from the Pu’u ‘O’vent of Kilauea Volcano, Hawaii. Journal of Geophysical Research: Solid Earth, 105(B9), 21497–21507.

    Google Scholar 

  • Richards, J. A. (2009). Remote sensing with imaging radar (Vol. 1). Berlin: Springer.

    Google Scholar 

  • Saepuloh, A., Haeruddin, H., Heriawan, M. N., Kubo, T., Koike, K., & Malik, D. (2018). Application of lineament density extracted from dual orbit of Synthetic Aperture Radar (SAR) images to detecting fluids paths in the Wayang Windu geothermal field (West Java, Indonesia). Geothermics, 72, 145–155.

    Google Scholar 

  • Saepuloh, A., Koike, K., Heriawan, M. N., & Kubo, T. (2016). Quantifying surface roughness to detect geothermal manifestations from Polarimetric Synthetic Aperture Radar (PolSAR) data. In Proceedings of the 41th annual Stanford geothermal workshop, Stanford University, California (Vol. SGP-TR-209).

  • Saepuloh, A., Koike, K., & Omura, M. (2012). Applying Bayesian Decision Classification to Pi-SAR polarimetric data for detailed extraction of the geomorphologic and structural features of an active volcano. IEEE Geoscience and Remote Sensing Letters, 9(4), 554–558.

    Google Scholar 

  • Saepuloh, A., Koike, K., Urai, M., & Sumantyo, J. T. S. (2015). Identifying surface materials on an active volcano by deriving dielectric permittivity from polarimetric SAR data. IEEE Geoscience and Remote Sensing Letters, 12(8), 1620–1624.

    Google Scholar 

  • Saepuloh, A., Suryantini, S., Chris, H., & Hewson, R. (2017). Simulating ground thermal anomaly under conditions of dense vegetation based on laboratory and field measurements to support thermal infrared remote sensing techniques. In Proceedings the 5th Indonesia international geothermal convention & exhibition (IIGCE). Indonesian Geothermal Association.

  • Shepard, M. K., Campbell, B. A., Bulmer, M. H., Farr, T. G., Gaddis, L. R., & Plaut, J. J. (2001). The roughness of natural terrain: A planetary and remote sensing perspective. Journal of Geophysical Research Planets, 106(E12), 32777–32795.

    Google Scholar 

  • Shrestha, D. P., Saepuloh, A., & van der Meer, F. (2019). Land cover classification in the tropics, solving the problem of cloud covered areas using topographic parameters. International Journal of Applied Earth Observation and Geoinformation, 77, 84–93.

    Google Scholar 

  • Stelling, P., Shevenell, L., Hinz, N., Coolbaugh, M., Melosh, G., & Cumming, W. (2016). Geothermal systems in volcanic arcs: Volcanic characteristics and surface manifestations as indicators of geothermal potential and favorability worldwide. Journal of Volcanology and Geothermal Research, 324, 57–72.

    Google Scholar 

  • Urai, M., & Pieri, D. (2010). ASTER applications in volcanology. Land remote sensing and global environmental change (pp. 245–272). New York, NY: Springer.

    Google Scholar 

  • Vaughan, R. G., Keszthelyi, L. P., Davies, A. G., Schneider, D. J., Jaworowski, C., & Heasler, H. (2010). Exploring the limits of identifying sub-pixel thermal features using ASTER TIR data. Journal of Volcanology and Geothermal Research, 189(3), 225–237.

    Google Scholar 

  • Vaughan, R. G., Lowenstern, J. B., Keszthelyi, L. P., Jaworowski, C., & Heasler, H. (2012). Mapping temperature and radiant geothermal heat flux anomalies in the Yellowstone geothermal system using ASTER thermal infrared data. GRC Transactions, 36, 1403–1410.

    Google Scholar 

  • Wang, K., Jiang, Q., Yu, D., Yang, Q., Wang, L., Han, T., et al. (2019). Detecting daytime and nighttime land surface temperature anomalies using thermal infrared remote sensing in Dandong geothermal prospect. International Journal of Applied Earth Observation and Geoinformation, 80, 196–205.

    Google Scholar 

  • Weng, Q., Lu, D., & Schubring, J. (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89(4), 467–483.

    Google Scholar 

  • Witra, T., Saepuloh, A., Harto, A. B., & Wikantika, K. (2017). Analyzing surface roughness models derived by SAR and DEM data at geothermal fields. Bulletin of Geology, 1(2), 77–85.

    Google Scholar 

  • Yuan, F., & Bauer, M. E. (2007). Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sensing of Environment, 106(3), 375–386.

    Google Scholar 

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Acknowledgments

The authors wish to express their gratitude to the Beneficial and Advanced Geothermal Use System (BAGUS) Project within the framework of SATREPS for providing the field and laboratory equipment, RISTEK and LPPM ITB for the funding support of the field surveys, and Star Energy Geothermal, Ltd., for their great collaborative research assistance.

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Correspondence to Asep Saepuloh.

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Saepuloh, A., Saputro, R.H., Heriawan, M.N. et al. Integration of Thermal Infrared and Synthetic Aperture Radar Images to Identify Geothermal Steam Spots Under Thick Vegetation Cover. Nat Resour Res 30, 245–258 (2021). https://doi.org/10.1007/s11053-020-09754-9

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  • DOI: https://doi.org/10.1007/s11053-020-09754-9

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