Advertisement

Journal of the Geological Society of India

, Volume 92, Issue 6, pp 671–678 | Cite as

Satellite-Derived Regional Apparent Thermal Inertia and Gravity for Mapping Different Rock Types in Parts of Banswara, Rajasthan

  • Komal Rani (Pasricha)
  • Arindam Guha
  • Sanjit Kumar Pal
Article
  • 24 Downloads

Abstract

In this study, utility of satellite derived Apparent Thermal Inertia (ATI) and residual gravity data have been analyzed for delineating the major rock types of Aravalli Supergroup. Density is a common contributing factor responsible for ATI and gravity variations of rock types. In addition to density, thermal inertia is also controlled by specific heat and thermal conductivity. In the present study, MODIS (Moderate resolution imaging spectrometer) day and consecutive night time land surface temperature (LST), albedo data have been processed to derive ATI image for the study area. On the other hand, Bouguer gravity generated from EIGEN6C4 gravity model data is processed to derive the residual gravity over the study area. It has been observed that ATI values of different rock types are influenced by the variations in weathering pattern and forest cover developed over the rock in addition to the thermophysical parameters of rocks. Forest cover and agricultural activity are responsible for shifting the ATI values to higher side for any rocks due to evapotranspiration; which is responsible for reducing day and night land surface temperature difference. ATI pixels( collected from large rock pavements) of different rocks can however, be used to discriminate them from each other. Conjugate analysis of residual gravity and ATI has been proved useful to discriminate different rock types of spatially well distributed rock exposures. It is found that ATI and gravity values are well correlated for the spatially large surface exposures of rocks. In regional scale, low frequency changes of ATI broadly matches with the changes observed on the corresponding spatial profiles of residual gravity with some localized variations. This is due to the fact that shallow surface gravity anomaly is cumulative effect of vertical and horizontal variations of density in contrast to ATI which is more sensitive to near surface density, conductivity and specific heat variations of rocks. Moreover, high frequency variations imprinted on regional ATI spatial profiles are due to the cumulative effect of complex thermophysical properties of surface elements (weathering residuum, surface moisture variations) density variation and intrinsic thermophysical properties of rocks measured at regional scale. High frequency changes of ATI do not correspond well with residual gravity variation.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Chapin, D.A. (1996) The theory of the Bouguer gravity anomaly: A tutorial. The Leading Edge, v.15(5), pp.361–363CrossRefGoogle Scholar
  2. Cowan, D.R. (1993) Separation Filtering Applied to Aeromagnetic Data. Exploration Geophysics, v.24. pp.429–236.CrossRefGoogle Scholar
  3. Cracknell, A.P. and Xue, Y. (1996) Thermal inertia determination from space— a tutorial review. Internat. Jour. Remote Sens., v.17, pp.431–461.CrossRefGoogle Scholar
  4. Evans, P. and Crompton, W. (1946) Geological factors in gravity interpretation illustrated by evidence from India and Burma. Quart. Jour. Geol. Soc. London, v.102 (1-4), pp.211–249.CrossRefGoogle Scholar
  5. Förste, C., Bruinsma S.L., Abrikosov O., Lemoine J.M., Schaller, T., Götze H.J., Ebbing J., Marty J.C., Flechtner, F., Balmino. G. and Biancale, R. (2014) EIGEN-6C4 the latest combined global gravity field model including GOCE data up to degree and order 2190 of GFZ Potsdam and GRGS Toulouse. In: Presented at the 5th GOCE user workshop. Paris.Google Scholar
  6. Gillespie, A.R. and Kahle, A. B. (1977) Construction and interpretation of digital thermal inertia image. Photogramm. Engg. Rem. Sens., v.43, pp.983–1000.Google Scholar
  7. Gillespie, A., Rokugawa, S., Matsunaga, T., Cothern, J.S., Hook, S. and Kahle, A. B. (1998) A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. IEEE Trans. Geosci. Remote Sensing, v.36(4), pp.1113–1126.CrossRefGoogle Scholar
  8. GSI (2000): District resource map (Banswara).Google Scholar
  9. Gupta, S.N., Arora, Y.K., Mathur, R.K., Iqbaluddin Balmiki. P., Sahai, T.N. and Sharma, S. B. (1997) The Precambrian geology of the Aravalli region, southern Rajasthan and northeastern Gujarat. Mem. Geol. Surv. India, v.123, pp.262.Google Scholar
  10. Gupta, R.P. (2003). Remote Sensing Geology. Springer-Verlag. pp.655. Kahle, A.B. (1977) A simple thermal model of the Earth’s surface for geologic mapping by remote sensing. Jour. Geophy. Res., v.82 (11), pp.1673–1680.CrossRefGoogle Scholar
  11. Kahle, A.B., Gillespie, A.R. and Goetz, A.F. (1976) Thermal inertia imaging: A new geologic mapping tool. Geophys. Res. Lett., v.3(1), pp.26–28.CrossRefGoogle Scholar
  12. Kang, J., Jin, R., Li, X., Ma, C., Qin, J. and Zhang, Y. (2017) High spatiotemporal resolution mapping of soil moisture by integrating wireless sensor network observations and MODIS apparent thermal inertia in the Babao River Basin, China. Remote Sensing of Environ., v.191, pp. 232–245.CrossRefGoogle Scholar
  13. Kearey P., Brooks M. and Hill I. (2013) An introduction to geophysical exploration. John Wiley & Sons. pp.125–150.Google Scholar
  14. Kumar, U., Pal, S.K., Sahoo, S.D., Narayan, S., Saurav, Mondal S. and Gunguli, S.S. (2017) Lineament mapping over Sir Creek offshore and its surroundings using high resolution EGM2008 Gravity data: An integrated derivative approach. Jour. Geol. Soc. India, v.91(6), pp.671–678.CrossRefGoogle Scholar
  15. Lemoine, F.G., Kenyon, S.C., Factor, J.K., Trimmer, R.G., Pavlis, N.K., Chinn, D.S., Cox, C.M., Klosko, S.M., Luthcke, S.B., Torrence, M.H., Wang, Y.M., Williamson, R.G., Pavlis, E.C., Rapp, R.H. and Olson, T.R. (1998) The development of the joint NASA GSFC and the National Imagery and Mapping Agency (NIMA) geopotential model EGM96, NASA Tech. Publ. TP-1998-206861.Google Scholar
  16. Liang, S. (2001) An optimization algorithm for separating land surface temperature and emissivity from multispectral thermal infrared imagery. IEEE Trans. Geosci. Remote Sens., v.39(2), pp.264–274.CrossRefGoogle Scholar
  17. Majumdar, T. J. (2003) Regional thermal inertia mapping over the Indian subcontinent using INSAT-1D VHRR data and its possible geological applications. Internat. Jour. Rem. Sens., v.24, pp.2207–2220.CrossRefGoogle Scholar
  18. Majumdar, T.J., Swaminathan, V.L., Kamat, D.S., Mitra, D.S. and Varadarajan, K. (1983) Remote sensing and thermal inertia mapping and likely applications to geological exploration. In: Twelfth International Symposium on Remote Sens. of Environ. (Second Thematic Conference) Texas USA, pp.591–598.Google Scholar
  19. Mishra, D.C. (2011). Gravity and magnetic methods for geological studies. Hyderabad: BS Publications.Google Scholar
  20. Mishra, D.C. (2009) Gravity Anomalies, Geophysics and Geochemistry, Vol–III, edited by Jan Lastovicka, in Encyclopedia of Life Support Systems (EOLSS), Developed under the Auspices of the UNESCO, Eolss Publishers, Paris, France, [http://www.eolss.net]Google Scholar
  21. Mitra, D.S. and Majumdar, T.J. (2004) Thermal inertia mapping over the Brahmaputra basin, India using NOAA-AVHRR data and its possible geological applications. Internat. Jour. Rem. Sens. v.25(16), pp.3245–3260.CrossRefGoogle Scholar
  22. MODIS data (2017) (https://modis.gsfc.nasa.gov/data/dataprod): visited on 16.06.2017.
  23. Nasipuri, P., Majumdar, T.J. and Mitra, D.S. (2006) Study of high-resolution thermal inertia over western India oil fields using ASTER data. Acta Astronaut., v.58(5), pp.270–278.CrossRefGoogle Scholar
  24. Narayan, S., Sahoo, S.D., Pal, S.K., Kumar, U., Pathak, V.K., Majumdar, T.J. and Chouhan, A. (2016) Delineation of structural features over a part of the Bay of Bengal using total and balanced horizontal derivative techniques. Geocarto Int., v.32 (1), pp.1–16.Google Scholar
  25. Pal, S.K., Majumdar, T.J., Pathak, V.K., Narayan, S., Kumar, U. and Goswami, O.P. (2016) a Utilization of high-resolution EGM2008 gravity data for geological exploration over the Singhbhum-Orissa Craton, India. Geocarto Int., v.31(7), pp.783–802.CrossRefGoogle Scholar
  26. Pal, S.K., Narayan, S., Majumdar, T.J., Kumar, U. (2016) b Structural mapping over the 850E ridge and surroundings using EIGEN6C4 High Resolution Global Combined Gravity Field Model: an integrated approach. Mar. Geophys. Res., v.37, pp.159–184.CrossRefGoogle Scholar
  27. Pal, S.K. and Majumdar, T.J. (2015) Geological appraisal over the Singhbhum-Orissa Craton, India using GOCE, EIGEN6-C2 and in-situ gravity data. Internat. Jour. Appld. Earth Obs., v.35, pp.96–119.CrossRefGoogle Scholar
  28. Pohn, H. A., Offield, T.W. and Watson, K. (1974) Thermal inertia mapping from satellite-discrimination of geologic units in Oman. Jour. Res. USGS, v.2(2), pp.147–158.Google Scholar
  29. Pratt, D.A. and Ellyett, C.D. (1978) Image registration for thermal inertia mapping, and its potential use for mapping of soil moisture and geology in Australia. In International Symposium on Remote Sensing of Environment, 12 th, Manila, Philippines, pp.1207–1217.Google Scholar
  30. Pratt, D.A. and Ellyett, C.D. (1979). The thermal inertia approach to mapping soil moisture and geology. Remote Sens. Environ., v.8, pp.151–158.CrossRefGoogle Scholar
  31. Price, J.C. (1977) Thermal inertia mapping: A new view of the Earth. Jour. Geophys. Res., v.82(18) pp.2582–2590.CrossRefGoogle Scholar
  32. Price, J.C. (1985) On the Analysis of Thermal Infrared Imagery: The Limited Utility of Apparent Thermal Inertia. Remote Sens. Environ. v.18, pp.59–73.CrossRefGoogle Scholar
  33. Scheidt, S., Ramsey, M. and Lancaster, N. (2010) Determining soil moisture and sediment availability at White Sands Dune Field, New Mexico, from apparent thermal inertia data. Jour. Geophys. Res.: Earth Surface, v.115 (F2), pp.1–23.CrossRefGoogle Scholar
  34. Short, N.M. and Stuart, Jr. L.M. (1982) The Heat Capacity Mapping Mission (HCMM) Anthology (NASA SP-565).US Government Printing Office, Washington, DC.tutorial review. Internat. Jour. Remote Sens., v.17, pp.431–461.Google Scholar
  35. Salomon, J.G., Schaaf, C.B., Strahler, A.H., Gao, F. and Jin, Y. (2006) Validation of the MODIS bidirectional reflectance distribution function and albedo retrievals using combined observations from the aqua and terra platforms. IEEE Trans. Geosci. Remote Sensing, v.44(6), pp.1555–1565.CrossRefGoogle Scholar
  36. Telford, W.M., Geldart, L.P. and Sheriff, R.E. (1990) Applied Geophysics. Vol. 1, Cambridge University Press. pp.6–52.CrossRefGoogle Scholar
  37. Tontini, F.C., de Ronde, C.E., Scott, B.J., Soengkono, S., Stagpoole, V., Timm, C. and Tivey, M. (2016) Interpretation of gravity and magnetic anomalies at Lake Rotomahana: geological and hydrothermal implications. Jour. Volcanol. Geotherm. Res., v.314, pp.84–94.CrossRefGoogle Scholar
  38. Vaish, J. and Pal, S.K. (2015) Geological mapping of Jharia Coalfield, India using GRACE EGM2008 gravity data: a vertical derivative approach. Geocarto Int., v.30(4), pp.388–401.CrossRefGoogle Scholar
  39. Van Doninck, J., Peters, J.J., De Baets, B.De., Clercq, E.M., Ducheyne, E. and Verhoest, N.E. (2011) The potential of multitemporal Aqua and Terra MODIS apparent thermal inertia as a soil moisture indicator. Internat. Jour. Appld. Earth Obs., v.13(6), pp.934–941.CrossRefGoogle Scholar
  40. Watson, K. (1973) Periodic heating of a layer over semi-infinite solid. Jour. Geophys. Res. v.78, pp.5904–5910.CrossRefGoogle Scholar
  41. Watson, K. (1975) Geologic applications of thermal infrared images. Proc. IEEE, v.63, pp.128–137.CrossRefGoogle Scholar
  42. Williamson, S.N., Hik, D.S., Gamon, J.A., Kavanaugh, J.L. and Flowers, G.E. (2014) Estimating temperature fields from MODIS land surface temperature and air temperature observations in a sub-arctic alpine environment. Remote Sensing, v.6(2), pp.946–963.CrossRefGoogle Scholar
  43. Wolfe, R.E., Nishihama, M., Fleig, A.J., Kuyper, J.A., Roy, D.P., Storey, J.C. and Patt, F.S. (2002) Achieving sub-pixel geolocation accuracy in support of MODIS land science. Remote Sensing Environ., v.83, pp.31–49.CrossRefGoogle Scholar
  44. Xiao, F. and Wang, Z. (2017). Geological interpretation of Bouguer gravity and aeromagnetic data from the Gobi-desert covered area, Eastern Tianshan, China: Implications for porphyry Cu-Mo polymetallic deposits exploration. Ore Geology Rev., v.80, pp.1042–1055CrossRefGoogle Scholar
  45. Xue, Y. and Cracknell, A.P. (1995) Advanced thermal inertia modeling. Remote Sensing, v.16(3), pp.431–446.CrossRefGoogle Scholar

Copyright information

© Geological Society of India 2018

Authors and Affiliations

  • Komal Rani (Pasricha)
    • 1
  • Arindam Guha
    • 1
  • Sanjit Kumar Pal
    • 2
  1. 1.Geosciences Group, National Remote Sensing CentreIndian Space Research Organization, BalanagarHyderabadIndia
  2. 2.Department of Applied GeophysicsIndian Institute of Technology (ISM)DhanbadIndia

Personalised recommendations