A geostatistical and probabilistic spectral image processing methodology for monitoring potential CO2 leakages on the surface

Remote sensing has demonstrated success in various environmental applications over the past three decades. This is largely attributed to its ability for good areal coverage and continued development in sensor technologies. Carbon dioxide Capture and Storage (CCS) is an emerging climate change mitigation technology where monitoring is vital for its sustainability. This research investigates the use of spectral remote sensing imagery in detecting potential CO₂ occurrences at the surface, should a leakage occur from subsurface reservoirs where CO₂ is stored. Currently, there are no known leakages of CO₂ at industrial storage sites, therefore, this research was carried out at the Latera natural analogue site in Italy, in order to develop the methodology described. This paper describes the use of a popular probabilistic information fusion theory, referred to as the Dempster–Shafer theory of evidence, to analyse outlier pixels (anomalies). Outlier pixels are first determined using a new geostatistical image filtering methodology based on Intrinsic Random Function (IRF), Independent Component Analysis (ICA), and the industry standard parametric Reed–Xiaoli (RX) anomaly detection. Information fusion of detected outlier pixels and indirect surface effects of CO₂ leakage over time, such as stressed vegetation or mineral alterations, assigns a confidence measure per outlier pixel in order to identify potential leakage points. After visual validation using direct field measurements, it was demonstrated that the proposed methodology is able to detect majority of the seepage points at Latera, and holds promise as a new unsupervised CO₂ monitoring methodology.     

Blends of Poly (Butylene Adipate-Co-Terephthalate) and Thermoplastic Whey Protein Isolate: A Compatibilization Study

Journal of Polymers and the Environment - This work assesses the influence of the plasticizer polyethylene glycol (PEG) on the compatibilization of poly (butylene adipate-co-terephthalate) (PBAT)...     

Urbanization and the Loss of Resource Lands in the Chesapeake Bay Watershed

We made use of land cover maps, and land use change associated with urbanization, to provide estimates of the loss of natural resource lands (forest, agriculture, and wetland areas) across the 168,000 km² Chesapeake Bay watershed. We conducted extensive accuracy assessments of the satellite-derived maps, most of which were produced by us using widely available multitemporal Landsat imagery. The change in urbanization was derived from impervious surface area maps (the built environment) for 1990 and 2000, from which we estimated the loss of resource lands that occurred during this decade. Within the watershed, we observed a 61% increase in developed land (from 5,177 to 8,363 km²). Most of this new development (64%) occurred on agricultural and grasslands, whereas 33% occurred on forested land. Some smaller municipalities lost as much as 17% of their forest lands and 36% of their agricultural lands to development, although in the outlying counties losses ranged from 0% to 1.4% for forests and 0% to 2.6% for agriculture. Fast-growing urban areas surrounded by forested land experienced the most loss of forest to impervious surfaces. These estimates could be used for the monitoring of the impacts of development across the Chesapeake Bay watershed, and the approach has utility for other regions nationwide. In turn, the results and the approach can help jurisdictions set goals for resource land protection and acquisition that are consistent with regional restoration goals.