Automatic volcanic ash detection from MODIS observations using a back-propagation neural network

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 Spe...

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Published in:Atmospheric Measurement Techniques
Main Authors: Gray, T. M., Bennartz, R.
Format: Text
Language:English
Published: 2018
Subjects:
Online Access:https://doi.org/10.5194/amt-8-5089-2015
https://amt.copernicus.org/articles/8/5089/2015/
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spelling ftcopernicus:oai:publications.copernicus.org:amt30908 2023-05-15T16:51:29+02:00 Automatic volcanic ash detection from MODIS observations using a back-propagation neural network Gray, T. M. Bennartz, R. 2018-01-15 application/pdf https://doi.org/10.5194/amt-8-5089-2015 https://amt.copernicus.org/articles/8/5089/2015/ eng eng doi:10.5194/amt-8-5089-2015 https://amt.copernicus.org/articles/8/5089/2015/ eISSN: 1867-8548 Text 2018 ftcopernicus https://doi.org/10.5194/amt-8-5089-2015 2020-07-20T16:24:20Z 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 (HYSPLIT) model was used to obtain ash concentrations for the same archived eruptions. Two back-propagation neural networks were then trained using brightness temperature differences as inputs obtained via the following band combinations: 12–11, 11–8.6, 11–7.3, and 11 μm. Using the ash concentrations determined via HYSPLIT, flags were created to differentiate between ash (1) and no ash (0) and SO 2 -rich ash (1) and no SO 2 -rich ash (0) and used as output. When neural network output was compared to the test data set, 93 % of pixels containing ash were correctly identified and 7 % were missed. Nearly 100 % of pixels containing SO 2 -rich ash were correctly identified. The optimal thresholds, determined using Heidke skill scores, for ash retrieval and SO 2 -rich ash retrieval were 0.48 and 0.47, respectively. The networks show significantly less accuracy in the presence of high water vapor, liquid water, ice, or dust concentrations. Significant errors are also observed at the edge of the MODIS swath. Text Iceland Aleutian Islands Copernicus Publications: E-Journals Atmospheric Measurement Techniques 8 12 5089 5097
institution Open Polar
collection Copernicus Publications: E-Journals
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language English
description 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 (HYSPLIT) model was used to obtain ash concentrations for the same archived eruptions. Two back-propagation neural networks were then trained using brightness temperature differences as inputs obtained via the following band combinations: 12–11, 11–8.6, 11–7.3, and 11 μm. Using the ash concentrations determined via HYSPLIT, flags were created to differentiate between ash (1) and no ash (0) and SO 2 -rich ash (1) and no SO 2 -rich ash (0) and used as output. When neural network output was compared to the test data set, 93 % of pixels containing ash were correctly identified and 7 % were missed. Nearly 100 % of pixels containing SO 2 -rich ash were correctly identified. The optimal thresholds, determined using Heidke skill scores, for ash retrieval and SO 2 -rich ash retrieval were 0.48 and 0.47, respectively. The networks show significantly less accuracy in the presence of high water vapor, liquid water, ice, or dust concentrations. Significant errors are also observed at the edge of the MODIS swath.
format Text
author Gray, T. M.
Bennartz, R.
spellingShingle Gray, T. M.
Bennartz, R.
Automatic volcanic ash detection from MODIS observations using a back-propagation neural network
author_facet Gray, T. M.
Bennartz, R.
author_sort Gray, T. M.
title Automatic volcanic ash detection from MODIS observations using a back-propagation neural network
title_short Automatic volcanic ash detection from MODIS observations using a back-propagation neural network
title_full Automatic volcanic ash detection from MODIS observations using a back-propagation neural network
title_fullStr Automatic volcanic ash detection from MODIS observations using a back-propagation neural network
title_full_unstemmed Automatic volcanic ash detection from MODIS observations using a back-propagation neural network
title_sort automatic volcanic ash detection from modis observations using a back-propagation neural network
publishDate 2018
url https://doi.org/10.5194/amt-8-5089-2015
https://amt.copernicus.org/articles/8/5089/2015/
genre Iceland
Aleutian Islands
genre_facet Iceland
Aleutian Islands
op_source eISSN: 1867-8548
op_relation doi:10.5194/amt-8-5089-2015
https://amt.copernicus.org/articles/8/5089/2015/
op_doi https://doi.org/10.5194/amt-8-5089-2015
container_title Atmospheric Measurement Techniques
container_volume 8
container_issue 12
container_start_page 5089
op_container_end_page 5097
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