Remote Sensing contribution for environmental impact assessment of geothermal activity in mt. Amiata area

1. SUSTAINABLE MANAGEMENT AND
PROMOTION OF TERRITORY -SMPT
24th august - 2nd September 2012
Agricultural Citadel -Agricultural College “ A. Ciuffelli “ Todi-IT
Remote Sensing contribution for environmental
impact assessment of geothermal activity
in mt. Amiata area
28th August 2012
Manzo Ciro
PhD candidate in Applied Sciences and Technologies for Environment
manzo7@unisi.it
28/08/2012
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2. Outline presentation
Mt. Amiata

Introduction



Analytical techniques



Study Area
What is the problem?
What we measure
What we obtain
Conclusion & questions
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3. Study Area
Siena
Roma
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4. 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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5. Geothermy
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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6. How a geothermal field works


geological setting
a relatively high heat flow
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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7. How a geothermal field works


geological setting
a relatively high heat flow
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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8. 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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city
Power plant
Geothermal well
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9. What is the problem?
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 )?
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10. Remote sensing

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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11. 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.
Year 1954
Year 2007
Increase of natural vegetated area, due to agriculture reduction
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12. Multitemporal land cover analysis
Mt. Amiata
Abbadia San Salvatore
Piancastagnaio
North
Geothermal Power Plant
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13. Multitemporal land cover analysis
1954
Land cover changes from 1954 to 2007
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14. Multitemporal land cover analysis
2007
Land cover changes from 1954 to 2007
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15. 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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16. Land cover changes
Land cover changes from 1954 to 2007
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17. Spectral study
Visible
Infrared
FCC 321
TCC 754
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18. What we measure


“Spectrum signature”
Spectral signature is a
graph that shows the
surface
reflectance
capacity at different light
irradiation wavelength
It’s typical for every kind
of material
VIS
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NIR
MIR
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19. Vegetation spectrum feature
Chlorophyll
absorption
peaks
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20. Vegetation spectrum feature
Landsat and vegetation
Landsat
bands
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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21. Spectral Index by Landsat
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
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)
Landsat TM of 03-08-1984
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Landsat ETM+ of 12-07-2002
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22. Vegetation spectrum feature
Chestnut tree
Reflectance (%)
Healty
Stressed
Wavelength (nm)
NDVIst < NDVIh
Place where NDVI is higher will be brighter
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23. NDVI 1984
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24. NDVI 1984
NDVI 2002
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25. Combining
2 NDVI
NDVI Change
Value
Increase
Decrease
In both high
In both down
Potential
interpretation
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26. 
In buffer of 500 m from Power plant NDVI is lower then in other area
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27. Analytical Tecnique
Spectral Response of vegetation matrix
analysis
Support geochem ical and
ecotox icological analysis proof
in geotherm ic activity area
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28. Spectrum analysis in situ
100 soil samples
150 vegetation samples
142 lichen samples
Instrument used is FieldSpec
Pro FR a truly portable field
spectroradiometer ranging from
350 nm to 2500 nm wavelength
Lichen
Geothermal power plant
Soil and plant
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29. 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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30. 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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31. Spectrum analysis in situ
Far from power plant
Near power plant
Reflectance (%)
Reflectance (%)
Reflectance (%)
Reflectance (%)
Fig
Walnut tree
Chestnut tree
Broom
Wavelength (nm)
Wavelength (nm)
Wavelength (nm)
Wavelength (nm)
• Sampling sites near the geothermal station
• Sampling sites far from the geot. station
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32. Far from power plant
Near power plant
Walnut tree
Reflectance (%)
Reflectance (%)
Fig
Wavelength (nm)
Wavelength (nm)
Chestnut tree
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Reflectance (%)
Reflectance (%)
Broom
Wavelength (nm)
Wavelength (nm)
32

33. Technique adopted :
‐ Derivatives ratio
723/700 (Smith et al.,
2004)
Derivative
Derivative reflectance curve analysis
Uncont. Bramble
Cont. Bramble
Derivative
Wavelength (nm)
Uncont. Acacia
Cont. Acacia
Wavelength (nm)
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34. Technique adopted :
‐ Derivatives ratio
723/700 (Smith et al.,
2004)
Smith
Far from
Geothermal
plants
Cont. Bramble
2,88
Near to
Geothermal
plants
Uncont. Bramble
2,83
Wavelength (nm)
Derivative
Vaget. index
Derivative
Derivative reflectance curve analysis
Uncont. Acacia
Cont. Acacia
Wavelength (nm)
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35. 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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36. Ecophysiological parameter:
• integrity cell membrane
(conducibility)
• cholorophyll degradation
• carotenoid amount
Geothermic power plant emission
impact on environmental system
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37. conducibility
H2S effect on the Lichens
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38. H2S effect on the Lichens
y= -0,001x+0,345
R2=0,036
R2=0,579
Smith Ratio
723/700 nm
conducibility
y= -0,001x+0,334
30
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39. Spectrum Signature analysis in situ
Static Buffer for chemical and spectral analysis comparison
- Linear Correlation Coefficient
(Davis JC, 2002)
Correlation
Cond
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0,88
H2S
Chemical Analyses
-0,81
Chla
Spectral Analyses
Smith spectral index
-0,89
R 2.8.0
free statistical
analysis software
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40. Geostatistical approach
Conducibility
Enel power
plant
M. Amita
complex
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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41. Elliptical Buffer Analysis
Standard lichen
unpolluted identified for
Piancastagnaio dataset
(FC5)
Lichen mean in
elliptical buffer
Wavelength (nm)
First Derivative Spectra Red-Edge zone
Wavelength (nm)
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42. City
Power Plant
Smith Ratio
723/700 nm
Well
Monophase W.
Biphase Well
Enel power
plant
M. Amita
complex
Elliptical buffer with better spectral analysis method
Defined by correlation coefficient
Chla-H2S
Chla-d723/700
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-0,79
0,65
Enel power
plant
M. Amita
complex
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43. 28/08/2012
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44. As
B
Enel power
plant
Enel power
plant
M. Amita
complex
M. Amita
complex
Sb
S
Enel power
plant
M. Amita
complex
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Enel power
plant
M. Amita
complex
44

45. Radar data processing:
- D-INSAR
- Permanent Scatterers
To assess the subsidence process
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46. Differential Interferometry DInSAR
Analysis of potential subsidence phenomena: the presence of subsidence in
the area, caused either by the ex ploitation 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.
n.7 Ers1-2 from 10/ 05/ 1992 to 07/ 03/ 2000
Descendent path122 e fram e 2745
n.7 Envisat from 17/ 06/ 2004 to 03/ 05/ 2007
Ascendent path 2444 and fram e 855
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47. Radar Image
A particular kind of image…
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48. Differential Interferometry DInSAR
Single Look Complex:
 Amplitude A:



E-M Field Intensity
Backscatters
Phase φ

Wave Time of flight

satellite-target-satellite
Phase measure
has an
ambiguity
Coherent sum
R( 4 )
R(3)
R( 2)
σ
(4
)
Pixel
R(1)
σ (4 )
σ( 2 )
σ (2)
σ
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σ (3)
(1)
σ( 1 )
P ix e l
σ ( 3)
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49. Time To
Time To + T1
Interferometry
atmospheric
Disturb
LOS
Line of
Sight
Reflection
variation
1st acquisition
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35 (or more) days
Stable
point
2nd acquisition
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50. Differential Radar Interferometry D-INSAR
There are 2-pass or 3-pass technique
We obtain an interferogram
2,8 cm
15027-19035 (13/01/2005 – 20/10/2005)
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51. Differential Radar Interferometry D-INSAR
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
Castel del Piano
Abbadia San Salvatore
Piancastagnaio
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52. City
Geothermal Power Plant
Benchmark
Coherent Area
Levelling network
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53. Differential Radar Interferometry D-INSAR
Altimetric variation (mm)
Interferogram
Phase difference
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Time interval
18/04/1995 - 07/03/2000
Benchmark 20016
¾(l/2)= 21mm
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54. Permanent Scatterers
mm/y
T.r.e. srl, 2003
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55. Levelling Network 1992-2006
Results shows ground altimetric variation of 1-2 mm/y and max values
of 4-5 mm only for Piancastagnaio area.
Legend
Benchmark
Power plant
Geothermal Well
City
Bagnore’s benchmark
Piancastagnaio’s bench.
Lineament
Lineament
Fault
Altimetric Variation (mm)
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56. 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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57. 
No risk for humans
even for max value
of H2S
concentration

Smell effect
World Health Organization, 2003
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58. 


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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59. Thank you for the attention.
Any questions?
For more information:
manzo7@unisi.it
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60. Linear coregionalization model
723/700 nm
Lag = 400 m
Autocorrelation Range = 1600 m
Mean Std Error= 0,08
Variance std error = 0,5
Max der
H2S
Cond
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61. Geological Settings
10 km
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Tectonostratigraphic relationship
(Batini et.al, 2003)
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62. Geological Settings
Tuscan metamorphic complex (sequence low metamorphic grademade of 2
groups: (a) Verrucano Triassic Group (b) Palaeozoico Group.
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Brogi, 2007 62