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

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

manzo7@unisi.it

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SUSTAINABLE MANAGEMENT AND PROMOTION OF TERRITORY -SMPT

24th august - 2nd September 2012

Agricultural Citadel -Agricultural College “ A. Ciuffelli “ Todi-IT

28th August 2012

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

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)

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

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

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°

28/08/2012 city Power plant Geothermal well 8

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

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

Multitemporal land cover analysis

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1954

Land cover changes from 1954 to 2007

Multitemporal land cover analysis

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2007

Land cover changes from 1954 to 2007

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

TCC 321 FCC 754

Spectral study

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Visible

Infrared

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

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

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

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

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

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

(%

)

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

(%

)

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)

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

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

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

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

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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

To assess the subsidence process

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Radar data processing: - D-INSAR

- Permanent Scatterers

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

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

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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52

City

Geothermal Power Plant

Benchmark

Coherent Area

Levelling network

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)

Permanent Scatterers

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mm/y

T.r.e. srl, 2003

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

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.

Thank you for the attention.

Any questions?

For more information: manzo7@unisi.it

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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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