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

Abstract

Abstract. 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 SO2-rich ash (1) and no SO2-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 SO2-rich ash were correctly identified. The optimal thresholds, determined using Heidke skill scores, for ash retrieval and SO2-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.

The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 2. Validation

“…In the future it would be desirable to further analyze the retrieval performance also with respect to mineral dust. As volcanic ash and mineral dust share similar optical properties due to the common high silica content, it is likely that VACOS might misclassify dust as volcanic ash [7,41,42]. In this case, it would be interesting to see if VACOS could be used to retrieve dust cloud properties as well.…”

Identification of the Airspace Affected by the Presence of Volcanic Ash by Processing Satellite Images, Case Study: Popocatepétl Volcano Area

“…A total of 242 MODIS images were analyzed from 2000 to 2014, from which the BTD technique for identifying volcanic ash clouds was applied. We found that the detection of volcanic ash by the BTD technique can be affected by the presence of dust particles suspended in the atmosphere and the presence of extensive ice content in meteorological clouds (Gray and Bennartz, 2015). To classify the areas where volcanic ash was positively identified in the MODIS images, a filtering technique was applied using thresholding, and subsequently, the contour of the cloud was defined employing morphological operations.…”

The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development

“…One of the major advantages of ANNs is that, once they are trained, they are fast in application compared to other methods using time-consuming radiative transfer calculations during the retrieval. Gray and Bennartz [62] trained two ANNs for the detection of volcanic ash and SO 2 -rich ash, respectively, using Moderate Resolution Imaging Spectroradiometer (MODIS, e.g., [63]) measurements. The training data were composed of MODIS images of different volcanic eruptions with the target classification performed based on Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) simulations of the volcanic emissions.…”

“…To cover the variability of volcanic ash clouds, the extensive set of volcanic ash refractive indices and optical properties from Piontek et al [23] is used together with a wide range of possible ash cloud top heights, geometrical thicknesses and mass concentrations. This constitutes a major advantage compared to the previously mentioned ANN-based volcanic ash retrievals, which were trained on satellite images of only one [64,66], two [65,68] or seven [62] volcanic eruptions or used only a single volcanic ash type [71]. Methodologically, we train four separated ANNs for the classification and the retrieval of the optical depth at 10.8 µm (τ 10.8 ), ash cloud top height (z top ) and effective particle radius (r eff ) of the volcanic ash clouds.…”