Can automated image analysis techniques drive a revolution in our understanding of climate change?
COLLABORATORS: David Reynolds (CGES), Paul Halloran (Geography), Bryan Black (University of Arizona)
IDSAI Research Fellow: George De Ath
Description: Growth rings formed in trees, clam shells, corals and fish provide unique records of the environmental conditions at the time the ring was formed. Given the great age that these species can reach, these growth ring records can provide long-term perspectives on modern climate variability. These records are ultimately used to help improve the accuracy of climate models used for predicting future climate change. However, the process of developing these records is painfully slow as each ring is manually measured and validated using statistical techniques. Whilst advances have been made in the software used to perform these analyses, significant potential remains for optimising the methodology and improving our understanding of uncertainties within these data. This project seeks to explore the potential of using automated image analysis techniques and Bayesian statistics to automate the analysis of growth rings formed in trees and clams. It is hoped that these advanced techniques will speed up the development of new long-term environmental records whilst preserving the statistical rigour with which these records are built. These new and extended records will help further our understanding of how the oceans and atmosphere change through time and ultimately improve the accuracy of future climate forecasts.
