Integrating Remote Sensing Data with Other Geodata (GIS Approach)

Chapter

Abstract

The purpose of integrated multidisciplinary investigations is to study a system or phenomenon using several approaches and as many attributes as possible or required, in order to obtain a more comprehensive and clearer picture. The growth in computing and data-processing capabilities, coupled with advances in geographic information system (GIS) technology and its integration with geostatistics, has played a very important role in developing integrated geo-exploration approach. Here only raster GIS is discussed. Besides remote sensing data, various types of geophysical data, geochemical data, topographic data and thematic data (vegetation, soil, groundwater etc.) can be integrated in and collectively analysed. Various GIS tools and classification approaches can be adopted.

References

  1. Aronoff S (1989) Geographic information systems: a management perception. WDL Publ, Ottawa, p 294Google Scholar
  2. Barringer AR (1976) Airborne geophysical and miscellaneous systems. In: Lintz J Jr, Simonett DS (eds) Remote sensing of environment, Addison-Wesley, Reading, pp 291–321Google Scholar
  3. Batchelor GB (1974) Practical approach to pattern classification. Plenum, LondonGoogle Scholar
  4. Bonham-Carter GF (1994) Geographic information systems for geoscientists: modeling with GIS. Pergamon Press, Ontario, Canada, p 398Google Scholar
  5. Bonham-Carter GF, Agterberg FP, Wright DF (1988) Integration of geological datasets for gold exploration in Nova Scotia. Photogramm Eng Remote Sens 54:1585–1592Google Scholar
  6. Brainard J, Lovett A, Parfitt J (1996) Assessing hazardous waste transport risks using a GIS. Int J Geog Inform Sys 10:831–849CrossRefGoogle Scholar
  7. Bristow Q (1979) Gamma ray spectrometric methods in uranium exploration airbome instrumentation. In: Hood PJ (ed) Geophysics and geochemistry in the search for metallic areas. Geological Survey of Canada Economic Geology Report 31:135–146Google Scholar
  8. Campbell AN, Hollister VF, Dutta RV, Hart PE (1982) Recognition of a hidden mineral deposit by an artificial intelligence program. Science 217(4563):927–928CrossRefGoogle Scholar
  9. Carranza EJM (2008) Geochemical anomaly and mineral prospectivity mapping in GIS. Handbook of Exploration and Environmental Geochemistry vol. 11. Elsevier, Amsterdam, 351 pGoogle Scholar
  10. Catlow DR, Parsall RJ, Wyutt BK (1984) The integrated use of digital cartographic data and remotely sensed imagery. In Proceedings of integrated approaches in remote sensing, Guildford, UK ESA-SP-214, pp 41–66Google Scholar
  11. Chang K (2008) Introduction to geographical information systems. McGraw Hill, 450 ppGoogle Scholar
  12. Davis JC (1986) Statistics and Data analysis in geology, 3rd edn. Wiley, New York, p 646Google Scholar
  13. Duval JS (1983) Composite color images of aerial gamma-ray spectrometric data. Geophysics 48:722–735CrossRefGoogle Scholar
  14. Erdogan EH, Erpul G, Bayramin I (2007) Use of USLE/GIS methodology for predicting soil loss in a semiarid agricultural watershed. Environ Monit Assess 131:153–161CrossRefGoogle Scholar
  15. Fabbri AG (1984) Image processing of geological data. Van Nostrand Reinhold, New York 244 pGoogle Scholar
  16. Foody GM (1995) Land cover classification by an artificial neural network with ancillary information. Int J Geog Inform Sys 9:527–542CrossRefGoogle Scholar
  17. Franklin SE (1994) Discrimination of subalpine forest species and canopy density using digital CASI, SPOT PLA and Landsat TM data. Photogram Eng Remote Sens 60:1233–1241Google Scholar
  18. Gong P (1996) Integrated analysis of spatial data for multiple sources: using evidential reasoning and artificial neural network techniques for geological mapping. Photogramm Eng Remote Sens 62:513–523Google Scholar
  19. Goosens MA (1991) Integration of remote sensing data and ground data as an aid to exploration for granite related mineralization, Salamance province, W-Spain. Proceedings of 8th International Conference on Geologic Remote Sensing, Vol I. Environmental Research Institute of Michigan, Ann Arbor, Mich, pp 393–406Google Scholar
  20. Harding AE, Forrest MD (1989) Analysis of multiple geological data sets from English Lake District. IEEE Trans Geosci Remote Sens 27:732–739CrossRefGoogle Scholar
  21. Harig C, Simons FJ (2015) Accelerated West Antarctic ice mass loss continues to outpace East Antarctic gains. Earth Planet Sci Lett 415:134–141Google Scholar
  22. Heywood I, Cornelius S, Carver T (2006) An introduction to geographical information systems, 3rd edn. Pearson Education Ltd, UK, p 426Google Scholar
  23. Hutchinson CF (1982) Techniques for combining Landsat and ancillary data for digital classifieation improvement. Photogramm Eng Remote Sens 48:123–130Google Scholar
  24. Joria PE, Jorgenson JC (1996) Comparison of three methods for mapping Tundra with Landsat digital data. Photogram Eng Remote Sens 62:163–169Google Scholar
  25. Konecny G (2003) Geoinformation. Taylor and Francis, London, New York, p 248CrossRefGoogle Scholar
  26. Kothyari UC, Jain SK (1997) Sediment yield estimation using GIS. Hydrol Sci J 42(6):833–843CrossRefGoogle Scholar
  27. Kundu S, Saha AK, Sharma DC, Pant CC (2013) Remote sensing and GIS based landslide susceptibility assessment using binary logistic regression model: a case study in the Ganeshganga watershed. Himalayas. J Indian Soc Remote Sens 41(3):697–709CrossRefGoogle Scholar
  28. Longley PA, Goodchild MF, Maguire DJ, Rhind DW (eds) (1999) Geographical information systems. Wiley, NewYorkGoogle Scholar
  29. Maguire DJ, Goodchild MF, Rhind DW (eds) (1991) Geographic information systems—principles and applications. Longman, Harlow, EssexGoogle Scholar
  30. Miranda FP, McCafferty AE, Taranik JV (1994) Reconnaissance geologic mapping of a portion of the rain-forest-covered Guiana Shield, northwestern Brazil, using SIR-B and digital aeromagnetic data. Geophysics 59:733–743CrossRefGoogle Scholar
  31. Ortega GE (1986) Intrduction to the geology and metallogeny of the Almaden area, Castro-Iberian zone, Spain. In Proceedings of 2nd European workshop on remote sensing in mineral exploration, EEC, BrusselsGoogle Scholar
  32. Parasnis DS (1996) Principles of applied geophysics. Springer 456 pGoogle Scholar
  33. Peddle DR (1993) An empirical comparison of evidential reasoning, linear discriminant analysis, and maximum likelihood algorithms for land cover classification. Can J Remote Sens 19:31–44CrossRefGoogle Scholar
  34. Rebillard P, Evans P (1983) Analysis of coregistered Landsat, Seasat and SIR-A images of varied terrain types. Geophys Res Lett 10(4):277–280CrossRefGoogle Scholar
  35. Rodell M, Velicogna I, Famiglietti JS (2009) Satellite-based estimates of groundwater depletion in India. Nature 460:999–1002CrossRefGoogle Scholar
  36. Rowan LC, Bowers TL (1995) Analysis of linear features mapped in landsat thematic mapper and side-Iooking radar images of the Reno, Nevada-California 1° × 2° quadrangle: implications of mineral resource studies. Photogram Eng Remote Sens 61:749–759Google Scholar
  37. Singhal BBS, Gupta RP (2010) Applied hydrogeology of fractured rocks, 2nd edn. Springer, DordrechtCrossRefGoogle Scholar
  38. Skidmore A (ed) (2002) Environmental modelling with GIS and remote sensing. Taylor and Francis, London, p 251Google Scholar
  39. Star J, Estes J (1990) Geographic information systems: an introduction. Prentice Hall, Englewood Cliffs, New JerseyGoogle Scholar
  40. Strahler AH, Logan TL, Bryant NA (1978) Improving forest cover classification accuracy from Landsat by incorporating topographic information. Proceedings of 12th Symposium on Remote Sensing of Environment, vol II. Ann Arbor, MI, pp 927–942Google Scholar
  41. Strahler AH, Estes JE, Maynard PF, Mertz FC, Stow DA (1980) Incorporating collateral data in Landsat classification and modelling procedures. In Proceedings of 14th Symposium, Remote Sensing of Environment, vol II. Ann Arbor, Michigan, pp 1009–1026Google Scholar
  42. Strauss GK, Roger G, Lecolle M, Lopera E (1981) Geochemical and geological study ofthe volcano-sedimentary sulfide orebody of La Zarza-Huelva, Spain. Econ Geol 76:1975–2000CrossRefGoogle Scholar
  43. Volk P, Haydn R, Bodechtel J (1986) Integration of remote sensing and other geodata for ore exploration—a SW Iberian case study. In Proceedings of International Symposium on Remote Sensing Environment, 5th Thematic Conf, Remote Sensing for Exploration Geology, Reno, NevadaGoogle Scholar
  44. Voss KA et al (2013) Groundwater depletion in the Middle-East with GRACE with implications for transboundary water management in the Tigris-Euphrates-Western Iran region. Water Resour Res 49:904–914CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2018

Authors and Affiliations

  1. 1.Formerly Professor, Earth Resources Technology, Department of Earth SciencesIndian Institute of Technology RoorkeeRoorkeeIndia

Personalised recommendations