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Remote Sensing contribution for environmental impact assessment of geothermal activity
in mt. Amiata area
Manzo Ciro PhD candidate in Applied Sciences and Technologies for Environment
28/08/2012 1
SUSTAINABLE MANAGEMENT AND PROMOTION OF TERRITORY -SMPT
24th august - 2nd September 2012
Agricultural Citadel -Agricultural College “ A. Ciuffelli “ Todi-IT
28th August 2012
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Outline presentation
Introduction Study Area What is the problem?
Analytical techniques What we measure What we obtain
Conclusion & questions
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Mt. Amiata
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Study Area
Siena
Roma
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Geological setting The Amiata area was uplifted during the Pliocene as a consequence of pluton emplacement in an extensional setting. Neogenic magmatism (Dini et al. 2005) was intruded in superior crust at 6-7 km below sea-level. It has been estimated that this intrusive body has a diameter about 40 km. In the Middle Pleistocene, there was the volcanic activity of Mt. Amiata ended 200.000 years ago.(Batini et al. 1986; Gianelli et al. 1988; Marinelli et al. 1983; Acocella 2000). Now there are only geothermal phenomena in the area
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Geothermy
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The temperature under the ground increase going closer to the core
The gradient isn’t the same all around the world depending on the location (in volcanic regions and along tectonic plate is usually high) and change in function of the deep in example solid the Crust T.G. is much higher than in mantle, (25 - 30 °K/km)
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How a geothermal field works geological setting a relatively high heat flow
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Main Risks Water is needed to substitute vapor
extracted Subsidence process linked to activity
No good sealing of well can cause release in the groundwater of contaminant
Release of H2S and Hg emissions (need of AMIS)
Land is occupied by infrastructure
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How a geothermal field works geological setting a relatively high heat flow
28/08/2012 7
Main Risks Water is needed to substitute vapor
extracted Subsidence process linked to activity
No good sealing of well can cause release in the groundwater of contaminant
Release of H2S and Hg emissions (need of AMIS)
Land is occupied by infrastructure
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Amiata’s geothermal field 2 geothermal reservoirs: - one more superficial, located in the cataclastic horizon corresponding to the Late Triassic evaporites and the overlying Jurassic carbonatic formations; P=20 bar T= 130-190 °C -Deeper one, in fractured metamorphic rocks at depths ranging from 2000 to 4500 m; P= 200-250 bars T=300-360°
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Increasing plan of geothermal exploitation concerns local population • Water table decreasing and potential lack for antropic use?
• Pollution Risk because of power plant emissions (CO2, H2S, Hg, CH4. In addition N, H, ammonia, boric acid, rare gases and traces of volatile elements )?
What is the problem?
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Remote sensing
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RS can provide lots of information from land cover pattern to environmental condition, and helped us to assess if there were process on going in the Mt. Amiata area
Remote sensing allowed us to study •Land cover changes from 1954 to 2007 •Subsidence process •Assessment of vegetation indexes (NDVI) •Spectral response of targets sensitive to pollution
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Multitemporal land cover analysis
Panchromatic orthophotos related to the years 1954 and 2007 have been utilized for the production of the land use database according to the CORINE Land Cover Nomenclature, 2 level.
Increase of natural vegetated area, due to agriculture reduction
Year 1954 Year 2007
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Multitemporal land cover analysis
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Mt. Amiata
Abbadia San Salvatore
Piancastagnaio
Geothermal Power Plant
North
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Multitemporal land cover analysis
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1954
Land cover changes from 1954 to 2007
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Multitemporal land cover analysis
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2007
Land cover changes from 1954 to 2007
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Multitemporal land cover analysis
In this area there are interesting land cover changes, in particular the developing of forest and shrub demonstrate the reducing of agriculture activity.
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Land cover changes
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Land cover changes from 1954 to 2007
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TCC 321 FCC 754
Spectral study
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Visible
Infrared
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Spectral signature is a graph that shows the surface reflectance capacity at different light irradiation wavelength
It’s typical for every kind of material
“Spectrum signature” What we measure
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VIS NIR MIR
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Vegetation spectrum feature
Chlorophyll absorption
peaks
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Vegetation spectrum feature
Normalized Difference Vegetation Index (NDVI) is a spectral index that assesses if the target observed contains live green vegetation or not.
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Landsat and vegetation
Landsat bands
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Spectral Index by Landsat
Landsat TM of 03-08-1984
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Landsat ETM+ of 12-07-2002
FCC 453 – to assess different type of vegetation and agriculture Red= Band 4 Near Infrared (0.76 to 0.90 microns) Green = Band 5 Mid Infrared (1.55 to 1.75 microns) Blu = Band 3 Visible Red (0.63 to 0.69 microns)
Multitemporal satellite Landsat imageries have been utilized for the calculation of the NDVI Index (Roose at al., 1974) with the aim of highlight vegetation status and health and to verify possible anomalies nearby geothermal stations
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Vegetation spectrum feature
Place where NDVI is higher will be brighter 28/08/2012 22
Chestnut tree
Healty Stressed
NDVIst < NDVIh
Ref
lect
ance
(%
)
Wavelength (nm)
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NDVI 1984
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NDVI 1984 NDVI 2002
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NDVI Change Value
Increase
Decrease
In both high
In both down
Potential interpretation
Combining 2 NDVI
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In buffer of 500 m from Power plant NDVI is lower then in other area
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Spectral Response of vegetation matrix analysis
Analytical Tecnique
Support geochemical and ecotox icological analysis proof
in geothermic activity area
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Spectrum analysis in situ
Instrument used is FieldSpec Pro FR a truly portable field
spectroradiometer ranging from 350 nm to 2500 nm wavelength
100 soil samples
150 vegetation samples
142 lichen samples
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Lichen
Soil and plant
Geothermal power plant
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Vegetation spectrum feature • Rubus ulmifolius Schott (common noum Bramble); • Robinia pseudoacacia (common noum Acacia); •Castanea sativa (common noum Chestnut); •Ficus carica (common noum Fig); •Lotus corniculatus (common noum Broom); •Juglans regia (common noum Walnut); •Quercus cerris (common noum Oak); •Olea europaea (common noum Olive); •Quercus ilex (common noum Holm oak).
Red-edge is sensitive to phenological state variation of vegeteble (Gates, 1965)
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Vegetation spectrum feature • Rubus ulmifolius Schott (common noum Bramble); • Robinia pseudoacacia (common noum Acacia); •Castanea sativa (common noum Chestnut); •Ficus carica (common noum Fig); •Lotus corniculatus (common noum Broom); •Juglans regia (common noum Walnut); •Quercus cerris (common noum Oak); •Olea europaea (common noum Olive); •Quercus ilex (common noum Holm oak).
Red-edge is sensitive to phenological state variation of vegeteble (Gates, 1965)
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Spectrum analysis in situ
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• Sampling sites near the geothermal station
• Sampling sites far from the geot. station
Far from power plant
Near power plant
Walnut tree
Wavelength (nm)
Ref
lect
ance
(%
)
Fig
Wavelength (nm)
Ref
lect
ance
(%
)
Broom
Wavelength (nm)
Ref
lect
ance
(%
)
Chestnut tree
Wavelength (nm)
Ref
lect
ance
(%
)
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Fig
Chestnut tree
Far from power plant Near power plant
Wavelength (nm)
Wavelength (nm) 28/08/2012 32
Broom
Wavelength (nm)
Ref
lect
ance
(%
)
Ref
lect
ance
(%
) R
efle
ctan
ce (
%)
Walnut tree
Wavelength (nm)
Ref
lect
ance
(%
)
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Derivative reflectance curve analysis
Wavelength (nm)
Wavelength (nm)
Der
ivat
ive
Der
ivat
ive
Uncont. Bramble Cont. Bramble
Uncont. Acacia Cont. Acacia
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Technique adopted : ‐ Derivatives ratio 723/700 (Smith et al., 2004)
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Technique adopted : ‐ Derivatives ratio 723/700 (Smith et al., 2004)
Derivative reflectance curve analysis
Wavelength (nm)
Wavelength (nm)
Der
ivat
ive
Der
ivat
ive
Vaget. index Smith Far from
Geothermal plants
2,88
Near to Geothermal
plants
2,83
Uncont. Bramble Cont. Bramble
Uncont. Acacia Cont. Acacia
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Spectrum Signature analysis in situ Lichens as biomarker
Lichens Analysis Dataset divided in homogeneous group to define better common characteristic and spatial variability
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Ecophysiological parameter: • integrity cell membrane
(conducibility) • cholorophyll degradation • carotenoid amount
Geothermic power plant emission impact on environmental system
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cond
ucib
ility
H2S effect on the Lichens
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cond
ucib
ility
H2S effect on the Lichens
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y= -0,001x+0,345
R2=0,036
y= -0,001x+0,334
R2=0,579
30
Smith
Rat
io
723/
700
nm
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Static Buffer for chemical and spectral analysis comparison
- Linear Correlation Coefficient (Davis JC, 2002)
Spectrum Signature analysis in situ
R 2.8.0 free statistical analysis software
Spectral Analyses Chemical Analyses
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Correlation Smith spectral index Cond -0,81
Chla 0,88
H2S -0,89
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Elliptical Buffer Analysis We choosed conducibility as analysis
reference parameter, because it defines the leaf cells fitness
By geostatistic analysis it was possible identify an elliptical buffer with
major axis of 4000m and orientation N20
Autocorrelation range
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Enel power plant
M. Amita complex
Conducibility
Geostatistical approach
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Standard lichen unpolluted identified for Piancastagnaio dataset (FC5)
Lichen mean in elliptical buffer
Elliptical Buffer Analysis
Wavelength (nm)
Wavelength (nm)
First Derivative Spectra Red-Edge zone
41
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Elliptical buffer with better spectral analysis method Defined by correlation coefficient
Chla-H2S -0,79
0,65
Enel power plant
Enel power plant
Smith Ratio 723/700 nm
City Power Plant
Well Monophase W. Biphase Well
M. Amita complex
M. Amita complex
28/08/2012 42 Chla-d723/700
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Enel power plant
M. Amita complex
Sb
Enel power plant
M. Amita complex
S
Enel power plant
M. Amita complex
B
Enel power plant
M. Amita complex
As
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To assess the subsidence process
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Radar data processing: - D-INSAR
- Permanent Scatterers
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Analysis of potential subsidence phenomena: the presence of subsidence in the area, caused either by the exploitation and the reinlet of fluids in the geothermal reservoir or natural volcanic causes (i.e. volcanic spreading), has been studied by means of differential SAR interferometry using ERS (1 and 2) and Envisat imageries.
Differential Interferometry DInSAR
n.7 Ers1-2 from 10/ 05/ 1992 to 07/ 03/ 2000 Descendent path122 e frame 2745
n.7 Envisat from 17/ 06/ 2004 to 03/ 05/ 2007 Ascendent path 2444 and frame 855
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Radar Image
A particular kind of image… 28/08/2012 47
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Single Look Complex: Amplitude A:
E-M Field Intensity Backscatters
Phase φ Wave Time of flight satellite-target-satellite
Phase measure has an
ambiguity
Differential Interferometry DInSAR
σ ( 1 )
σ ( 2 )
σ ( 3 )
σ ( 4 )
R ( 4 )
R ( 3 )
R ( 2 )
R ( 1 ) P i x e l
σ ( 1 )
σ ( 2 )
σ ( 3 )
σ ( 4 )
R ( 4 )
R ( 3 )
R ( 2 )
R ( 1 ) P i x e l P i x e l
σ (1)
σ (2)
σ (3)
σ (4 ) Pixel
Coherent sum
σ (1)
σ (2)
σ (3)
σ (4 ) Pixel
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1st acquisition 2nd acquisition
Time To + T1
LOS Line of Sight
atmospheric Disturb
Reflection variation
Stable point
Time To
35 (or more) days
Interferometry
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2,8 cm
15027-19035 (13/01/2005 – 20/10/2005)
There are 2-pass or 3-pass technique We obtain an interferogram
Differential Radar Interferometry D-INSAR
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Differential Radar Interferometry D-INSAR
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Abbadia San Salvatore
Piancastagnaio
Castel del Piano
Coherence is a measure of correlation between the two images used to create interferogram. Because of high vegetation cover and topography the interferogram has coherence problems and so only few areas can be analysed. In particular City and industrial plant
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City
Geothermal Power Plant
Benchmark
Coherent Area
Levelling network
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Differential Radar Interferometry D-INSAR
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Time interval 18/04/1995 - 07/03/2000
Benchmark 20016 ¾(l/2)= 21mm
Interferogram Phase difference
Alt
imet
ric
vari
atio
n (m
m)
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Permanent Scatterers
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mm/y
T.r.e. srl, 2003
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Results shows ground altimetric variation of 1-2 mm/y and max values of 4-5 mm only for Piancastagnaio area.
Levelling Network 1992-2006
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Benchmark Power plant Geothermal Well
City
Bagnore’s benchmark
Piancastagnaio’s bench.
Lineament
Legend
Altimetric Variation (mm) Fault Lineament
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Conclusions
Land cover changes: reduction of agriculture areas (as generally occurred in Tuscany in the same epoch)
No generalized effects on vegetation from geothermal exploitation recognizable by means of satellite images Power plant activity may have an impact on local target near
Piancastagnaio (no contaminants emission reduction systems? – no adequate well sealing?). Plant stress may be also due to other sources (waste
mines areas, neighbor roads, other human activity, etc.)
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No risk for humans even for max value of H2S concentration Smell effect
28/08/2012 57 World Health Organization, 2003
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Topographic benchmarks experienced vertical displacement of ca. 1-2 mm/y From P.S. analysis local subsidence may be regarded to as
landsliding effect (rates up to 4-5 mm/y) Analysis of topographic leveling data suggest that some
leveling network is not adequate to highlight absolute vertical movements in the study area
Closed wells should be monitored by leveling or GPS or P.S. to control risk of steam eruption.
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Linear coregionalization model Lag = 400 m
Autocorrelation Range = 1600 m
Mean Std Error= 0,08
Variance std error = 0,5
H2S
Cond
Max der
723/700 nm
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10 km Tectonostratigraphic relationship (Batini et.al, 2003)
Geological Settings
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Tuscan metamorphic complex (sequence low metamorphic grademade of 2 groups: (a) Verrucano Triassic Group (b) Palaeozoico Group.
Brogi, 2007
Geological Settings
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