Sources and Sinks of Carbon Dioxide from Remote Sensing: A Spatial Analysis

• Activity Number: 24
• Type: Topic Contributed
• Date/Time: Sunday, August 9, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Sandy Burden* and Noel Cressie
• Companies: University of Wollongong and University of Wollongong
• Keywords: Bayesian hierarchical model ; Confidence regions ; Remote sensing ; Prediction ; Spatio-temporal flux data

Abstract

Carbon-dioxide sources and sinks are an important feature of the global carbon cycle, but their location and persistence are not presently well understood. Spatial analysis of remotely sensed carbon-dioxide-flux data can be used for predicting regions of positive flux (sources) and regions of negative flux (sinks) at given times. We use spatial empirical hierarchical models with an appropriate loss function to define an optimal spatial predictor, from which we obtain confidence regions for the sources and sinks. The methodology is demonstrated on simulated spatio-temporal flux data, and its dependence on the choice of spatial predictor at various time points is assessed.