Rattanasuda (Paula) Cholathat, Xiaojing Li and Linlin Ge E-mail: [email protected]@student.unsw.edu.au Geodesy and Earth Observing.

Rattanasuda (Paula) Cholathat, Xiaojing Li and Linlin Ge

E-mail: [email protected]

Geodesy and Earth Observing System Group (GEOS)The School of Surveying and Spatial Information Systems, The University of New South Wales, Australia

Monitoring Natural Analog of Geologic Carbon Sequestration Using Multi-temporal Landsat TM images in Mammoth Mountain, CA, USA

Outline

1. Introduction

2. Mammoth Mountain

3. Methodology

4. Result

5. Conclusion and future research

I. Introduction

Climate change

Global warming

What is Carbon Capture and Storage (CCS)/ Geologic Carbon Sequestration (GCS)?

- Solution of CO2 reduction emission

Source: The Intergovernmental Panel on Climate Change (2005)

World-Wide projects

Active CO2 storage demonstration projects around the world (CO2CRC, 2010)

Proposed CO2 storage demonstration projects around the world (CO2CRC, 2010)

Why monitoring GCS?

1. To ensure and verify of geologic carbon

sequestration site

2. Safety and environmental concerns

3. To resolve any disputes arising from conflicts of use

of the subsurface and possible contamination of

underground resources

Monitoring Techniques Flux measurement

Isotope measurement Ground penetrating radar

Soil gas sampling Seismic

Source: Monitoring techniques (Benson, 2008)

II. Mammoth Mountain

Location: Inyo National Forest, CA, USA

Coordinates: 37°37′50.26″N , 119°1′57.45″W Elevation: 11,059 ft (3,371 m)

Natural CO2 and Environmental impact

Invisible CO2 Gas Killing Trees at Mammoth Mountain, California (USGS, 2004 )

- In 1989, there was a swarm of small earthquake in the central region of the Sierra Nevada, CA and a dormant volcano is woke up after 760,000 years of sleeping

- The swarm center was located beneath the mammoth mountain 2 km below the surface

- In early 1990’s, there was the first notice of dead and dying trees on the south side of Mammoth Mountain

- In1994, USGS surveyed for carbon dioxide gas around Mammoth mountain , and exceptionally high concentrations of gas were found in the soil beneath the trees

Source: USGS(2004)

Source: USGS(2004)

CO2 leakage models: Natural VS GCS

Natural leakage model (USGS, 2004)

CO2 storage leakage model ( Damen et al., 2005)

1. Large area coverage

2. Availability of data set: before and after event

Using multispectal imagery

Objective

To verify the usefulness of high altitude, low resolution Landsat TM image for monitoring natural CO2 leakages in order to apply in real sequestration site

III. Methodology- Data set: using Landsat 5 TM acquired on September of year

1988, 1992, 1993, 1994, 1995, 1996, 1999, 2003, 2004, 2005, 2008, 2010

- Normalized Difference Vegetation Index (NDVI) is vegetation index which is calculated from the visible and near-infrared light reflected by vegetation. A Landsat TM scene, NDVI is given by the following formula:

NDVI = (NIR — VIS)/ (NIR + VIS)

Where; NIR = near-infrared light (band 4)

VIS = visible light (band 3)

Data processing flowchart

IV. Result

According to USGS (2000), the spectrometer field results show that carbon-isotopic analyses of the annual growth rings in trees near the margins of the tree-kill areas imply that the gas-emission rate reached a peak in 1991, subsequently declined, and then have been relatively stable since about 1996

Spectrometer field results (USGS, 2000)

The SVM classification result shows the area of dead/dying tree was in similar location to the USGS result. However, the comparison of total coverage of classification result may not always provide the most efficient analysis of changes due to the difference in vegetation spectral

V. Conclusion

- NDVI change detection of high altitude, low resolution Landsat TM can provide the useful vegetation health information related to both areas of dead/dying trees and vegetation in not affected by CO2 leakage

- Particularly, we confirmed that the area of detected dead/drying vegetation at Mammoth Mountain agrees with the field survey result by USGS

- The continual monitoring using satellite data set for natural leaking analogue will benefit the monitoring of the actual CO2 sequestration site in a long term time scale

Further research

- Future research also might aim to use high resolution multispectral satellite image and hyperspectral satellite image to provide large area coverage into the actual CO2 sequestration site

- The combination of both imagery, optical and radar remote sensing should be concern for long term monitoring in order to ensure the safeguard of CO2 storage sequestration site

Acknowledgements

- This study is funded by the Australian Department of Resources, Energy and Tourism (RET) through the Australia-China Joint Coordination Group on Clean Coal Technology Research & Development Grants scheme under the project "Integrated radar and optical satellite remote sensing for safeguarding carbon capture and storage“

- USGS: field survey result and Landsat imagery

References[1] International Energy Agency (IEA) 2008, CO 2 capture and storage, viewed 18 June 2009, <

http://www.iea.org/Textbase/subjectqueries/cdcs.asp>.

[2] J.E Fessenden, P.H. Stauffer, and H.S. Viswanathan, Natural Analogs of Geologic CO2 Sequestration: Some General Implications for Engineered Sequestration, Natural Analog Research, New Mexico, pp. 1-23.

[3] M. Sorey, B. Evans, M. Kennedy, J. Rogie, and A. Cook, Magmatic gas emissions from Mammoth Mountain, Mono County, CA. Calif. Geol., pp.4-16, 1999

[4, 12] USGS 2000, Invisible CO2 gas killing trees at Mammoth Mountain, California, viewed 25 September 2010, <http://pubs.usgs.gov/fs/fs172-96/fs172-96.pdf>.

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[8] L. Paolini, F. Grings, J.A. Sobrino, J.C. Jime’Nezmun , and H. Karszenbaum, Radiometric correction effects in Landsat multi-date/multi-sensor change detection studies, International Journal of Remote Sensing, pp.685-704, 2006.

[9] S.H. Lee, C.M. Kim, and D.K. Rho, Ecological Change Detection of Burnt Forest Area Using Multi-Temporal Landsat TM Data, pp. 1–8, 2001.

[10] J.R Jensen, Introductory digital image processing: a remote sensing perspective, Pearson Prentice Hall; USA, pp.478-479, 2005.

[11] T. Costachioiu, and M. Datcu, Land cover dynamics classification using multi-temporal spectral Indies from satellite image time series, IEEE, pp. 157-160, 2010.

Thank you for attention

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