2010 cuttingedge workshop detecting submarine springs in florida's coastal zone using thermal...

2010 CuttingEdge Workshop

Detecting submarine springs in Florida's coastal zone using thermal remote sensing data

Teaching GIS and Remote Sensing in the 21st CentryAbuduwasiti Wulamu, PhD

Department of Earth & Atmospheric Sciences, Saint Louis University

Mapping submarine springs in Florida2 Aug 8 – 12, 2010© Abduwasit Ghulam

Overview

Lab Description Objective Data

Retrieval of Surface temperature Signatures of submarine springs from

thermal anomaly Conclusion Acknowledgements

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Course Settings

College Level 4XX Geospatial Methods Remote Sensing

Lab Middle or end of semester A class project or lab assignment

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Lab Description

Objective Familiarize students with thermal remote

sensing with a practical example Stimulate creative thinking skills

Data Landsat ETM+ Census dataset Field collections

Tallahassee, Florida

Study area

Geovis.USGS.GOV

How to get the data

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How to get the data

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How to get the data

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How to get the data

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How to get the data

Unzip the downloaded data

???

Why there are two thermal bands with Landsat ETM+?

Theoretical background

Retrieval of surface temperature

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Thermal radiation

http://en.wikipedia.org/

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Workflow

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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

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Radiometric Calibration

Radiance Brightness temperature

Planck’s function

Where, C1=1.19104356×10-16 W m2; C2=1.43876869×10-2 m K

Mapping submarine springs in Florida17 Aug 8 – 12, 2010© Abduwasit Ghulam

Radiometric Calibration

Radiance Brightness temperature

Let K1 = C1/λ5 , and K2 = C2/λ, and satellite measured radiant intensity B λ (T) = Lλ

Description K1 (W m-2 sr-1 µm-1)

K2 (Kelvin)

Landsat 7 – ETM+

666.09 1282.71

Landsat 5 – TM 607.76 1260.56

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Land Surface Temperature

BTLST 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.

Mapping submarine springs in Florida19 Aug 8 – 12, 2010© Abduwasit Ghulam

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, Majumdar and 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

Implementing using ENVI

Retrieval of surface temperature

Basic Tools Band MathDN Radiance

((12.650-3.200)/(255.0-1.0))*(B6-1.0)+3.200

Radiance Brightness Temperature1282.71D/(alog(666.09D/B6+1D))

Implementing using ENVI

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Implementing using ENVI

File Open Image file

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Implementing using ENVI

Color compositing

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Radiometric calibration

Radiometric calibration

DN at sensor temperature

Basic Tools Preprocessing Calibration Utilities Landsat Calibration

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ENVI Color Mapping

Display Window

Tools Color Mapping Envi Color Tables

Select RainBow

ArcGIS classification

ENVI Color Mapping

GIS Visualization

Save File AsSave Image AsExport to ArcMap

Export to ArcGIS

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Export Image to ArcMap

Save Image as Save File As

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Save Image As

Display Window

Tools Color Mapping Envi Color Tables

Select RainBow

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Export Image to ArcMap

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Visualization

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Visualization

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Visualization

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Visualization

Classification in ArcMap

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Visualization

From ENVI Color Mapping

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Validation

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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

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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, Majumdar and 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/

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Questions and Discussion