Automatic Volcanic Ash Detection from MODIS Observations using a Back-Propagation Neural Network
Gray, Tami Michelle
Due to the climate effects and aviation threats of volcanic eruptions, it is important to accurately locate ash in the atmosphere. This study aims to explore the accuracy and reliability of training a neural network to identify cases of ash using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Satellite images were obtained for the following eruptions: Kasatochi, Aleutian Islands, 2008; Okmok, Aleutian Islands, 2008; Grímsvötn, northeastern Iceland, 2011; Chaitén, southern Chile, 2008; Puyehue-Cordón Caulle, central Chile, 2011; Sangeang Api, Indonesia, 2014; and Kelut, Indonesia, 2014. The Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) was used to obtain ash concentrations for the same archived eruptions. A back-propagation neural network was then trained using brightness temperature differences as inputs obtained via the following band combinations: 12-11-μm, 11-8.6-μm, 11-7.3-μm, and 11-μm. Using the ash concentrations determined via HYSPLIT, a flag was created to differentiate between ash (1) and no ash (0) and used as output. When neural network output was compared to the test dataset, 93% of pixels containing ash were correctly identified and 7% were missed. Nearly 100% of pixels containing SO2-rich ash were correctly identified.