Analysis of the Pyroclastic Flow Deposits of Mount Sinabung and Merapi Using Landsat Imagery and the Artificial Neural Networks Approach

Abstract

Volcanic eruptions cause pyroclastic flows, which can destroy plantations and settlements. We used image data from Landsat 7 Bands 7, 4 and 2 and Landsat 8 Bands 7, 5 and 3 to observe and analyze the distribution of pyroclastic flow deposits for two volcanos, Mount Sinabung and Merapi, over a period of 10 years (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017). The satellite data are used in conjunction with an artificial neural network method to produce maps of pyroclastic precipitation for Landsat 7 and 8, then we calculated the pyroclastic precipitation area using an artificial neural network method after dividing the images into four classes based on color. Red, green, blue and yellow were used to indicate pyroclastic deposits, vegetation and forest, water and cloud, and farmland, respectively. The area affected by a volcanic eruption was deduced from the neural network processing, including calculating the area of pyroclastic deposits. The main differences between the pyroclastic flow deposits of Mount Sinabung and Mount Merapi are: the sediment deposits of the pyroclastic flows of Mount Sinabung tend to widen, whereas those of Merapi elongated; the direction of pyroclastic flow differed; and the area affected by an eruption was greater for Mount Merapi than Mount Sinabung because the VEI (Volcanic Explosivity Index) during the last 10 years of Mount Merapi was larger than Mount Sinabung.

Land Use Planning for Disaster-Prone Areas in Southern Region of Mount Merapi

“…Since 1960, Mount Merapi has erupted more than eight times, and its eruption cycle ranges between 4 to 6 years. The pyroclastic flow caused by the eruption in 2006 had reached the Gendol River up to 7 km (Iguchi, Ishihara, Surono, & Hendrasto, 2011;Kadavi, Lee, & Lee, 2017).…”

Editorial for Special Issue: “Application of Artificial Neural Networks in Geoinformatics”

“…Hwang, J., Chae, S., Kim, D., Jung, H. [7] used X-band Kompsat-5 images to detect ships. Additionally, Piscini, A., Romaniello, V., Bignami, C., Stramondo, S. [8] proposed a damage assessment method based on SAR and Sentinel-2 images, and Kadavi, P., Lee, W., Lee, C. [9] analyzed pyroclastic flow deposits using Landsat images. Kwon, S., Jung, H., Baek, W., Kim, D. [10] classified the vertical structures of forests using aerial orthophoto and Lidar images.…”

Landsat images and artificial intelligence techniques used to map volcanic ashfall and pyroclastic material following the eruption of Mount Agung, Indonesia

“…Land cover (LC) mapping is a commonly used monitoring strategy that can incorporate predictive flow models or risk zonation, which can be particularly useful for developing countries, where the damage caused by volcano eruptions to growing populations is continually increasing. Kadavi (2017) reported that remote sensing imagery could help to monitor the spread of atmospheric eruptive fumes, pyroclastic deposits, incandescent lava, lahar distribution, and dome deformation. Thus, the use of remote sensing technologies can define a new paradigm for volcanology activity observations (Pieri and Abrams, 2004) that is advantageous in all phases of volcanic eruptions disaster management (Van Westen, 2000).…”