vorlesung clausthal fernerkundung pdf1
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Vorlesung Clausthal Fernerkundung Pdf1TRANSCRIPT
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Surface geothermal
exploration
Dr. Sandra Schumacher
Leibniz Institute for Applied Geophysics, Hannover
WS 2014/15
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Exploration
Remote Sensing
Geochemistry
Geophysics
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Remote Sensing
Temperature
Minerals
Tectonics
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Exploration
Remote Sensing
Geochemistry
Geophysics
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Geochemistry Geothermometer
Isotopes
CO2
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Exploration
Remote Sensing
Geochemistry
Geophysics
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Geophysics
TEM Seismics
Magnetotellurics
Magnetics
Gravimetry
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Exploration
Remote Sensing
Geochemistry
Geophysics
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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)
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(© 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)
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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)
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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
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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