Comparative assessment of the performance of airborne and spaceborne spectral data for monitoring surface CO2 leakages

Rajesh Govindan, Anna Korre*, Sevket Durucan, Claire E. Imrie

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Recent research has demonstrated the potential of high resolution airborne optical remote sensing technology for monitoring CO2 storage sites. This research shows that lower resolution spaceborne datasets can also be used. Unlike airborne data, which are normally acquired on-demand, satellite data are acquired periodically with relatively good spatial and temporal resolutions, and a much wider coverage. The detection performance for CO2 leakage related anomalies is shown to vary with the spatial and spectral resolutions of the data. This was established using an airborne hyperspectral dataset acquired over Latera, Italy, which is a natural analogue site for CO2 leakage. The data cube was systematically degraded using a Gaussian Point Spread Function (PSF), processed using an unsupervised geostatistical and probabilistic methodology and the detection performances were compared using the Area Under the Receiver Operating Characteristic curve (AUROC). The implications are positive in terms of the use of satellite imagery to monitor leakages. The use of spectral data from spaceborne imaging instruments such as the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), flying on Terra satellite as part of NASA's Earth Observing System (EOS), was also investigated for the Latera site. The results obtained using ASTER data and those replicating the resolutions of spaceborne images (synthetic data) from degradation experiments were found to be promising.

Original languageEnglish
Pages (from-to)3421-3427
Number of pages7
JournalEnergy Procedia
Volume4
DOIs
Publication statusPublished - 2011
Externally publishedYes

Keywords

  • Leakage detection
  • Receiver operating characteristics
  • Remote sensing
  • Self-organising maps

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