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Abuduwasiti Wulamu , PhD Department of Earth & Atmospheric Sciences, Saint Louis University

Detecting submarine springs in Florida's coastal zone using thermal remote sensing data Teaching GIS and Remote Sensing in the 21 st Centry. Abuduwasiti Wulamu , PhD Department of Earth & Atmospheric Sciences, Saint Louis University. Overview. Lab Description Objective Data

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Abuduwasiti Wulamu , PhD Department of Earth & Atmospheric Sciences, Saint Louis University

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  1. Detecting submarine springs in Florida's coastal zone using thermal remote sensing dataTeaching GIS and Remote Sensing in the 21st Centry AbuduwasitiWulamu, PhD Department of Earth & Atmospheric Sciences, Saint Louis University

  2. Overview • Lab Description • Objective • Data • Retrieval of Surface temperature • Signatures of submarine springs from thermal anomaly • Conclusion • Acknowledgements

  3. Course Settings • College Level 4XX • Geospatial Methods • Remote Sensing • Lab • Middle or end of semester • A class project or lab assignment

  4. Lab Description • Objective • Familiarize students with thermal remote sensing with a practical example • Stimulate creative thinking skills • Data • Landsat ETM+ • Census dataset • Field collections

  5. Study area Tallahassee, Florida

  6. How to get the data Geovis.USGS.GOV

  7. How to get the data

  8. How to get the data

  9. How to get the data

  10. How to get the data Unzip the downloaded data

  11. Why there are two thermal bands with Landsat ETM+? ???

  12. Retrieval of surface temperature Theoretical background

  13. Thermal radiation http://en.wikipedia.org/

  14. Workflow

  15. Radiometric Calibration DN  Radiance • DN  Radiance where the LMIN and LMAX are the spectral radiances for each band at digital numbers 1 and 255. DN is the pixel DN value, λ is the wavelength. One gets LMIN and LMAX values from the header file. http://landsathandbook.gsfc.nasa.gov/handbook/handbook_htmls/chapter11/chapter11.html

  16. Radiometric Calibration Radiance Brightness temperature • Planck’s function Where, C1=1.19104356×10-16 W m2; C2=1.43876869×10-2 m K

  17. Radiometric Calibration Radiance Brightness temperature Let K1 = C1/λ5 , and K2 = C2/λ, and satellite measured radiant intensity B λ (T) = Lλ

  18. Land Surface Temperature BTLST • λ is the wavelength of emitted radiance. λ = 11.5 μm (Markham and Barker, 1986) and ρ = h × c/σ = 14380 m K. Here, σ is Boltzmann constant (1.38 * 10−23 J/K), h is Planck’s constant (6.26 * 10−34 Js) and c is velocity of light (2.998 * 108 m/s). Artis and Carnahan, 1982. Survey of emissivity variability in thermography of urban areas, Rem. Sens. Environ.12 (1982), pp. 313–329.

  19. Land surface temperature • BT LST • Radiative Transfer – MODTRAN • Quasi-physical models • JIMÉNEZ-MUÑOZ, J.C., SOBRINO, J.A. 2003. A generalized single-channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research, 108, doi: 10.1029/2003JD003480 • QIN, Z., KARNIELI, A., BERLINER, P. 2001. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. International Journal of Remote Sensing, 22, pp.3719-3746. • Srivastava, Majumdarand Bhattacharya. (2009).Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data. Advances in Space Research, 431(10): 563-1574

  20. Retrieval of surface temperature Implementing using ENVI

  21. Implementing using ENVI Basic Tools  Band Math DN  Radiance ((12.650-3.200)/(255.0-1.0))*(B6-1.0)+3.200 Radiance  Brightness Temperature 1282.71D/(alog(666.09D/B6+1D))

  22. Implementing using ENVI File Open Image file 

  23. Implementing using ENVI Color compositing

  24. Radiometric calibration Radiometric calibration DN  at sensor temperature Basic Tools  Preprocessing  Calibration Utilities  Landsat Calibration

  25. ENVI Color Mapping Display Window Tools  Color Mapping  Envi Color Tables Select RainBow

  26. GIS Visualization ArcGIS classification ENVI Color Mapping

  27. Export to ArcGIS Save File As Save Image As Export to ArcMap

  28. Export Image to ArcMap Save Image as Save File As

  29. Save Image As Display Window Tools  Color Mapping  Envi Color Tables Select RainBow

  30. Export Image to ArcMap

  31. Visualization

  32. Visualization

  33. Visualization

  34. Visualization Classification in ArcMap

  35. Visualization From ENVI Color Mapping

  36. Validation

  37. Conclusion • Extensive field work, validation needed • Geologic controls, e.g., fractures, aquifers that channels groundwater to the oceans need to be indentified • Radar and Optical data fusion is helpful • As stated earlier, the objective of this lab is to teach students how to use thermal remote sensing, rather than presenting a “solid” scientific research

  38. Acknowledgements & References • Locational information for the spring vents that were verified on the Taylor County coast provided by Tom Greenhalgh from Florida Geological Survey • Artis and Carnahan, 1982. Survey of emissivity variability in thermography of urban areas, Rem. Sens. Environ. 12 (1982), pp. 313–329. • JIMÉNEZ-MUÑOZ, J.C., SOBRINO, J.A. 2003. A generalized single-channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research, 108, doi: 10.1029/2003JD003480 • QIN, Z., KARNIELI, A., BERLINER, P. 2001. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. International Journal of Remote Sensing, 22, pp.3719-3746. • Srivastava, Majumdarand Bhattacharya. (2009).Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data. Advances in Space Research, 431(10): 563-1574 • http://www.dep.state.fl.us/geology/programs/hydrogeology/springs/powerpoint/McClean.ppt • http://www.dep.state.fl.us/ • http://glovis.usgs.gov/

  39. Questions and Discussion

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