1

Surface geothermal

exploration

Dr. Sandra Schumacher

Leibniz Institute for Applied Geophysics, Hannover

WS 2014/15

Exploration

Remote Sensing

Geochemistry

Geophysics

Remote Sensing

Temperature

Minerals

Tectonics

Exploration

Remote Sensing

Geochemistry

Geophysics

Geochemistry Geothermometer

Isotopes

CO2

Exploration

Remote Sensing

Geochemistry

Geophysics

Geophysics

TEM Seismics

Magnetotellurics

Magnetics

Gravimetry

Exploration

Remote Sensing

Geochemistry

Geophysics

How to characterise a

geothermal reservoir

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Surface exploration report

• Geological map

• Tectonic map

• Geothermal map

• Resistivity maps at different depths

• Bouguer gravity map

• Magnetic map

• Map showing lateral distribution of seismicity

• Heat flow and soil temperature maps

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Aims of report

• Likely temperature of the reservoir fluids

• Likely heat sources

• Likely flow pattern of reservoir fluids

• Likely geological structure of the reservoir rocks

• Likely volume of abnormally hot rocks

• Likely total natural heat loss

• A conceptual model of the geothermal system

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Aim

• To collect enough information to prevent expensive failures, e.g.:

– Drilling boreholes without sufficient yield

current conditions

– Investing in a plant, which after a few years loses output rapidly

prognosis

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What do we need?

Information about:

• Temperatures

• Reservoir depth

• Permeability / Transmissivity

• Rock type / rock strenght

• Stress field

• Geochemistry

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Where to start?

• Temperatures are fixed, permeability/transmissivity can be engineered (to a certain extent)

Temperatures are the most important factor (for Enhanced Geothermal Systems)

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Volcanic system and its indicators

(van der Meer et al., 2014)

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Direct indicators

• Surface features

– Caldera structures

– Hot springs

– Steaming ground

– Fumaroles

– Faults, lineaments

• Mineral assemblage

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Indirect indicators

• Surface temperature variations

– Heat sources

– Heat flux

• Surface deformation

• Microseismicity

• Changes in vegetation

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Remote

sensing

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Remote sensing

• Uses electromagnetic (EM) radiation

• Wavelengths: 0.4 μm to 1 m

• Sensors:

– Airborne: planes, helicopters, balloons, etc.

– Space-bound: satellites, rockets, etc.

– Ground-based: hydraulic platforms and hand-held instruments (for ground truth)

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Basics

• Each object reflects, emits and absorbs EM radiation

• Using more than

one wavelenght

discrimination

possible

(Singhal & Gupta, 2010)

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Advantages

• Synoptic overview: regional features and trends

• Feasibility: also possible in remote areas

• Time saving: information about large area in short time

• Multidisciplinary applications: one measurement, many uses

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Limitations

• Low penetration depth: < 1 mm to several meters (in dry desert conditions)

• High cost of satellite data

– BUT: (e.g.: free data of Landsat TM and ETM)

• Expensive software

– BUT: free software (e.g.: ILWIS)

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Different wavelengths

(Singhal & Gupta, 2010)

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Techniques

• Active:

using radar (microwave)

• Passive: using

– Solar radiation (ultraviolet – visible – near-infrared)

– Earth-emitted radiation (3 – 20 μm region, called thermal infrared)

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Atmospheric interactions

• Raleigh scattering: haze and low-contrast pictures in UV-blue parts

• Absorption by e.g. H2O-vapour, CO2, O3, etc.: blocking of signals

• Region of less absorption: atmospheric windows

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Sensor systems

• Photographic systems

• Line scanning systems

• Digital cameras

• Imaging radar systems

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Photographic systems

• Good geometric accurancy

• High resolution

• Limited spectral range

• Colour infrared film (CIR) most important

• Standard: air-borne, vertical shots with overlap of 70 – 75 % for stereo viewing

• Scales: 1:20,000 – 1:50,000

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Line-Scanning Systems

• Give digital data on intensity

of ground radiance

• Radiance from each cell

collected, integrated by

system brightness

value/digital number per

pixel

• OM or CCD systems

(Singhal & Gupta, 2010)

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Opto-mechanical (OM) scanners

• Used air-borne or space-borne

• Visible to thermal infrared

• Moving plane mirror refelcts radiation onto filter and detector assembly

• Typical: MSS, TM and ETM+ on Landsats

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Charge-Coupled Device (CCD) scanners

• No moving parts

• Detectors: photoconductors

• Linear array of CCDs with > 1000 elements at focal plane of camera

• Array converts radiation into electrical signals

• One array per spectral band

• Satellite sensors e.g. SPOT-HRV, IRS-LISS

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Digital cameras

• Using CCDs or CMOSs instead of film

• Digital output, fast processing, higher sensitivity, better image radiometry, higher geometric fidelity, lower costs

• Limited usability from visible to near-IR

• Satellite sensors e.g. IKONOS,CARTOSAT

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Imaging Radar System

• Side-looking Airborne

Radar (SLAR)

• Radar transmits short

microwave pulses,

back-scatter from

ground recorded

• Night, fog, rain, snow

less problematic than

for photographic systems (Singhal & Gupta, 2010)

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Imaging Radar System

(© NASA)

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Synthetic aperture radar (SAR)

• Can be used by night (active system)

• Advanced data processing algorithms

higher spatial resolution

• Resolution: 5 - 30 m

• Serious geometric distortions due to oblique viewing

• Strong shadows and look-direction effects

• Satellites e.g. ENVISAT-1

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SAR

• One small antenna

with many pictures

instead of one large

with one picture

• Example: in 10 km

1 m resolution:

big antenna: 300 m

small antenna: 2 m (© Dantor)

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Radar return

• Backscattered signal

• Affected by:

– Radar wavelength

– EM beam polarization

– Local incidence angle

– Target surface roughness

– Complex dielectric constants

Signal interpretation not trivial!

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Satellite programs

• LANDSAT (OM)

• TERRA-ASTER

• SPOT (CCD)

• IRS (CCD)

• FUYO (CCD)

• DAICHI

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Resolution

(Singhal & Gupta, 2010)

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Interpretation principles

• > 1 parameter used for interpretation

• All parameters are interpreted together

(multispectra, stereo, etc.)

• Remote sensing data are indexed clearly

(location, scale, orientation, etc.)

• Ground truth is obtained

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Ground truth

• Rock/soil type

• Geological structures

• Soil moisture

• Vegetation type and density

• Land use

• Groundwater level

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Photo-interpretation elements

• Tone (relative brightness)

• Colour

• Texture

• Pattern (arrangement of e.g. vegetation)

• Shadow

• Shape

• Size

• Site/association

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Geotechnical elements

• Landform

• Drainage

• Soil

• Vegetation

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Panchromatic Sensors

• Broad-band

• Visible range (0.4 – 0.7 μm)

• Higher resolution than multispectral

• Image in shades of gray

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Multispectral data

• Total absorption: black colour

• Each channel separately: shades of gray

• Clouds appear bright in all channels

(Singhal & Gupta, 2010)

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False colour composite (FCC)

• Three channels are combined/overlain

• Standard:

– Green response in blue

– Red response in green

– NIR response in red

True colour FCC (Landsat 7 (Landsat 7 ETM + Bands 3,2,1) ETM + Bands 4,3,2)

(© NASA)

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Thermal IR data

• 3 – 25 µm, most important: 8 – 14 µm

• Thermal radiative properties of materials:

– Surface temperature

• Thermal inertia

– Emissivity

• Typically: a pre-dawn and a day pass

• Topography shows strongly at day but not night

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TIR

(Singhal & Gupta, 2010)

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TIR

• Detection of faults or folds by:

– Evaporative cooling

– Spatial differences in thermal properties

• Aerial: 2- 6 m; space: e.g. 90 m for ASTER

–

(Singhal & Gupta, 2010)

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SAR

• Shades of gray; higher backscatter brighter

• Strong radar return by metallic objects and corner reflections

• Little return by smooth surfaces

• Important for interpretation:

– Terrain ruggedness

– Orientation of object to look direction

– Soil moisture (dielectric constant)

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SAR

(Singhal & Gupta, 2010)

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SAR

• Minor details are suppressed regional

landform studies structural lineations

• Penetration depth depends on:

– Wavelength (the longer, the better)

– Moisture content (less is better)

• < 0.5 m for C-band

• < 2.0 m for L-band

(Singhal & Gupta, 2010)

(Courtesy: ESA)

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Groundwater indicators

• 1. order:

– Recharge zones

– Discharge zones

– Soil moisture and vegetation

• 2. order

– Rock/soil type

– Structures e.g. rock fractures

– Landform

– Drainage characteristic

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Image selection

• Small-scale images for regional setting of landforms and structures

• Large-scale images for locating actual borehole sites

• Using the right spectral bands

• Considering temporal conditions (rainfall,

snow cover, vegetation, soil moisture, etc.)

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Temporal variations

Post-monsoon Pre-monsoon

(Singhal & Gupta, 2010)

Widespread vegetation Landforms (valley fills, lineaments)

are clearer

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DEM accuracy

• Shuttle radar topographic mapping (SRTM): ~ 90 m, sometimes 30 m

• Digital photogrammetry (SPOT, ASTER, etc.): 15-40 m (ASTER), ~ 1-2 m (HR-Stereo systems: Cartosat, Quick-Bird, IKONOS)

• GoogleEarth: up to 1 m in flat areas

• LIDAR surveys: 10-30 cm vertical (problems due to vegetation)

(© McElhanney)

LIDAR

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Digital image processing

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Basics

• Used for:

– Image data correction

– Superimposing digital image data

– Enhancement

– Classification

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Processing sequence

• Image correction

• Registration

– Superimposing images, maps, etc. with geometric congruence

• Enhancement

– To make an image easier to interpret

• Visual interactive interpretation

• Output

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Possible errors to be corrected

• Radiometric errors and anomalies

– Stripping

– Bad line data

– Atmospheric scattering effects

• Geometric distortions

– Caused e.g. by Earth‘s rotation

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Enhancement I

• Contrast enhancement: rescaling gray levels

– Linear stretch: expansion to fill the complete range of display

– Histogram equalized stretch (ramp stretch): assigning new image values based on the frequency of their occurence very high image contrast

– Logarithmic stretch: useful for lower DN-range

– Exponential stretch: useful for upper DN-range

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Histogram equalized stretch

(© Phillip Capper)

(© Jarekt)

Unequaliz

ed

E

qualiz

ed

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Enhancement II

• Edge enhancement: Object borders get enhanced

– Sharper image

– Enhancing fractures, etc. overall or in a preferred direction

• Addition and subtraction: combine multi-image data pixel-wise

– Addition: high contrast, general study

– Subtraction: reduced contrast, change detection

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Enhancement III

• Ratio image: dividing pixel value in one band by pixel value in other band

– Smaller effects of illumination/topography

– Enhanced spectral information

– Very useful for vegetation density

• Colour enhancement:

– Pseudo-colour: enhancing differences in a single gray image

– RGB coding: used for set of 3 images

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Color enhancement

(NASA/JPL)

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Pseudo-colour

Seismic data

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Geothermally relevant

observations

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Possible observation themes

• Surface deformation

• Gaseous emissions

• Structural analysis

• Mineral mapping

• Surface temperature mapping

• Heat flux mapping

• Geobotany

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Indicators for geothermal activity

• Hot springs, fumaroles

• Siliceous sinter, travertine or tufa deposits

• Hydrothermally altered rocks

• Borate or sulfate crusts at playas

• Changes in vegetation:

more at fault-controlled springs, less near faults leaking high concentrations of gasses such as SO2, H2S or CO2

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Temperatures

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Systems

(Haselwimmer et al., 2011)

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Types of geothermal manifestions

• Spring-dominated

– Low energy (T < 90 °C)

• Vapour-dominated

– Medium energy (90 °C < T < 150 °C)

– High energy (T > 150 °C)

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Vapour-dominated Craters of the Moon, NZ

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Vapour-dominated Te Puia, NZ

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Thermal Infrared (TIR)

• Rapid mapping and quantifying

• Monitoring of trends

• Estimates of surface heat loss (input for models)

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TIR

• Satellite thermal sensors

– Resolution: 60 – 90 m per pixel

– Landsat or ASTER

• Airborne thermal imagery

– Broadband or multispectral

– Wavelengths: mid (3 – 5 µm), long (8 -14 µm)

– High-resolution: pixel < 5 m

• Ground-based

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SEBASS

• Spatially Enhanced Broadband Array Spectrograph System

• hyperspectral airborne TIR pushbroom sensor

• 128 channels at 2.5–5.2 μm and 7.5–13.5 μm

• ~ 1 m/pixel spatial resolution with a swath width of 128 m at 915 m above ground level (AGL)

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MAGI

• Mineral and Gas Identifier

• new airborne TIR sensor

• 32 channel between 7.8 and 12.0 μm

• spatial resolution of 2 m/pixel at an altitude of 3657 m AGL

• up to 2800 pixels in the cross track

• up to 5600 m swath width

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Aim: Black-body radiance

1

5 2

( )

1

c

B Tc

expT

• Bλ(T): spectral black-body radiance [W/m2/μm/sr] • c1: first radiation constant for spectral radiance = 1.191×10−16 (Wm2/sr) • c2: second radiation constant = 1.438×10−2 (m*K) • λ: wavelength (μm)

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Black-body radiance

(Wikipedia)

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Thermal Infrared

(Haselwimmer et al., 2011)

Winter 2011 Fall 2010

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Steamboat Springs

(Coolbaugh et al., 2007)

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Albedo

• reflection coefficient

• albedo = reflected radiation/ incident radiation

• wavelength-dependent

• trees: 0.08 - 0.18

• green grass: 0.25

• new concrete: 0.55

• fresh snow: 0.8 - 0.9

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Sinter terrace, Te Puia, NZ

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Bradys Hot Springs

(Coolbaugh et al., 2007)

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ASTER

• Advanced Spaceborne Thermal Emission and Reflection Radiometer

• Channels:

– 3 VNIR

– 6 SWIR

– 5 TIR

• TIR used for emissivity and surface temperature imagery

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Bradys Hot Springs

• ASTER data:

– Corrected for atmospheric absorption

– Preprocessed data:

• AST07: surface reflectance

• AST08: surface kinetic temperature:

radiance temperature converted to kinetic temperature

• AST07 useful for albedo corrections to AST08

• AST08 available for day and night images, AST07 not

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Kinetic temperature

P: pressure

V: volume

n: amount of gas (number of moles)

R: gas constant

T: temperature

N: Boltzmann constant

m: mass

v: velocity

22 1[ ]

3 2PV nRT N mv

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Land surface energy balance

Q*: net radiation

H: sensible heat flux (convection + conduction)

λE: latent heat flux (evaporation)

G0: soil heat flux

Integrating this equation over time can give ground surface temperatures

Modeled temperatures compared to measured temperatures anomalies!

*

00 Q H E G

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Things to correct for

• Emissivity

• Thermal inertia

• Albedo

• Topographic slope

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Bradys Hot Springs

• Day/night images of the same date

diurnal effects can be corrected

• Albedo correction via visible and infrared bands

• Topography correction via Digital Elevation Model (DEM)

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Emissivity

• Low emissivities reduce radiant temperature which is measured

surfaces appear cooler

• 5 thermal bands measured

wavelength-dependent variations

true kinetic temperatures

• Surface temperature measurements at two sites to check AST08

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Area image

(Coolbaugh et al., 2007)

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Thermal inertia

I: Thermal inertia

k: thermal conductivity

ρ: density

c: heat capacity

24-h mean temperatures needed to correct for thermal inertia

I k c

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Thermal inertia

(Coolbaugh et al., 2007)

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Thermal inertia

• Images at minimum and maximum temperatures

• Surface measurements used for calibration, weighting factors for measured temperatures at flyover times to get mean temperature (1. approach)

• Using weighting factors for images taken to minimize the variance of combined day/night image (2. approach)

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Albedo / topographic slope

Q*: net surface heat flux

FSn: absorbed solar flux

FAn: absorbed sky radiation

FGn: re-emitted ground radiation

Difficult to solve, with several assumptions (cloud free day, etc.), only slope matters

*

n n nQ FS FA FG

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Albedo / topographic slope

• Slope calculated from Digital Elevation Model (DEM)

• AST07 ≈ albedo for flat terrain and normal

atmosphere

• Image brightness

affected by slope

• Correction using

DEM (Wikipedia)

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Correction for albedo effects

(Coolbaugh et al., 2007)

VNIR

Night

Day

Final

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Correction for albedo / slope / inertia

(Coolbaugh et al., 2007)

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Correction for thermal inertia

(Coolbaugh et al., 2007)

Corrected

for albedo

+ slope

Corrected

for albedo

+ slope

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Final result

(Coolbaugh et al., 2007)

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Yellowstone

(Seielstad and Queen, 2009)

Elevation effects on temperature

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Elevation effects

• The higher the terrain, the lower the air and surface temperature; even more so at night

• ≈ -6.5 °C/km (environmental lapse rate)

• During day, big T-contrast between shaded and sunlit areas

• Correction for elevation after albedo and topographic slope effetcs removed

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Nighttime image

(Eneva &

Coolbaugh, 2009)

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Daytime image

(Eneva &

Coolbaugh, 2009)

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Nighttime temperature inversions

(Eneva &

Coolbaugh, 2009)

109

Literature

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Literature used (1)

• Coolbaugh, M.F., C. Kratt, A. Fallacaro, W.M. Calvin, J.V. Taranik; 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, 350-359, 2007

• Eneva, M., M. Coolbaugh; Importance of Elevation and Temperature Inversions for the Interpretation of Thermal Infrared Satellite Images Used in Geothermal Exploration; GRC Transactions, Vol. 33, 2009

• Glassley, W.E.; Geothermal Energy; CRC Press, 2010

• Haselwimmer, C., A. Prakash; Thermal Infrared Remote Sensing of Geothermal Systems, in: Kuenzer, C., Dech, S. (Eds.), Thermal Infrared Remote Sensing, vol. 17, Spinger, Dordrecht, 453–473, 2013

• Singhal, B.B.S., R.P. Gupta; Applied Hydrolgeology of Fractured Rocks; Springer, 2010

111

Literature used (2)

• Van der Meer, F., C. Heckera, F. van Ruitenbeek, H. van der Werff, C. de Wijkerslooth, C. Wechsler; Geologic remote sensing for geothermal exploration: A review; International Journal of Applied Earth Observation and Geoinformation, 33, 255–269, 2014

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