Tracing the evolution of 2010 Merapi volcanic deposits (Indonesia) based on object-oriented classification and analysis of multi-temporal, very high resolution images

Thouret, Jean‐Claude; Kassouk, Zeineb; Gupta, Avijit; Liew, Soo Chin; Solikhin, Akhmad

doi:10.1016/j.rse.2015.09.028

Cited by 18 publications

(18 citation statements)

References 62 publications

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“…Except close to Kaliadem, overflowing of the concentrated part was relatively limited. The overflowing, observed at numerous places along the Gendol valley (Figure c, south of y = 9160 km), which forms overbank lobes on the interfluves and relatively short channelized flows (<1 km) in neighboring drainage basins [ Charbonnier et al , ; Thouret et al , ], was formed later, during the retrogressive collapse of the old summit (between 0:13 and 01:57, phase 5 of Komorowski et al []). The flow deposits of the 00:02–00:13 phase are composed of massive blocks, lapilli, and ash of fresh, glassy, dense, dark‐grey andesite.…”

Section: The 2010 Eruption Of Merapi Volcanomentioning

confidence: 99%

Simulation of block‐and‐ash flows and ash‐cloud surges of the 2010 eruption of Merapi volcano with a two‐layer model

et al. 2017

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A new depth‐averaged model has been developed for the simulation of both concentrated and dilute pyroclastic currents and their interactions. The capability of the model to reproduce a real event is tested for the first time with two well‐studied eruptive phases of the 2010 eruption of Merapi volcano (Indonesia). We show that the model is able to reproduce quite accurately the dynamics of the currents and the characteristics of the deposits: thickness, extent, volume, and trajectory. The model needs to be tested on other well‐studied eruptions and the equations could be refined, but this new approach is a promising tool for the understanding of pyroclastic currents and for a better prediction of volcanic hazards.

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Section: The 2010 Eruption Of Merapi Volcanomentioning

confidence: 99%

Simulation of block‐and‐ash flows and ash‐cloud surges of the 2010 eruption of Merapi volcano with a two‐layer model

et al. 2017

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“…However, the object-based image segmentation can serve as an interesting tool to separate lava flows of contrasting spectral and textural characteristics and such potential has been demonstrated in previous studies of mapping volcanic deposits including lava flows (Kassouk et al, 2014;Thouret et al, 2015). In order to illustrate its applicability, image segmentation is also tested here on Fernandina Island, which is an active shield volcano of Ecuador's Galápagos Islands in the Pacific Ocean ( Figure 7) with a summit elevation of 1476 meters and multiple eruptions recorded in the last century (Smithsonian Institution, 2013).…”

Section: Discussionmentioning

confidence: 99%

“…Thanks to the homogenous mixing, these challenges might be solved by using the geographical object-based image analysis (GEOBIA) approach (Blaschke et al, 2014). Object-based classification has been applied for mapping volcanic deposits from SPOT imagery in recent studies focusing on lahars (Kassouk et al, 2014;Thouret et al, 2015), yet the potential of GEOBIA for automated mapping of lava flow fields has so far not been specifically investigated.…”

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confidence: 99%

Testing random forest classification for identifying lava flows and mapping age groups on a single Landsat 8 image

Solana

Canters

et al. 2017

Journal of Volcanology and Geothermal Research

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Mapping lava flows using satellite images is an important application of remote sensing in volcanology. Several volcanoes have been mapped through remote sensing using a wide range of data, from optical to thermal infrared and radar images, using techniques such as manual mapping, supervised/unsupervised classification, and elevation subtraction. So far, spectralbased mapping applications mainly focus on the use of traditional pixel-based classifiers, without much investigation into the added value of object-based approaches and into advantages of using machine learning algorithms. In this study, Nyamuragira, characterized by a series of more than 20 overlapping lava flows erupted over the last century, was used as a case study. The random forest classifier was tested to map lava flows based on pixels and objects. Image classification was conducted for the 20 individual flows and for 8 groups of flows of similar age using Landsat 8 imagery and a DEM of the volcano, both at 30-meter spatial resolution. Results show that object-based classification produces maps with continuous and homogeneous lava surfaces, in agreement with the physical characteristics of lava flows, while lava flows mapped through the pixel-based classification are heterogeneous and fragmented including much "salt and pepper 2 noise". In terms of accuracy, both pixel-based and object-based classification performs well but the former results in higher accuracies than the latter except for mapping lava flow age groups without using topographic features. It is concluded that despite spectral similarity, lava flows of contrasting age can be well discriminated and mapped by means of image classification. The classification approach demonstrated in this study only requires easily accessible image data and can be applied to other volcanoes as well if there is sufficient information to calibrate the mapping.

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“…Note: each scene has a different scale to improve visibility of the areas of elevated temperature due to the wide range in surface temperatures. Thouret et al, 2010;Thouret et al, 20155-Nov-2010 15.5 6.53 Surono et al, 2012Solikhin et al, 2015Unzen 1990-19955.5 Miyabuchi, 1999 (Herd et al, 2005). The…”

Section: Baf Deposit Distributionsmentioning

confidence: 99%

Parametric analysis of lava dome-collapse events and pyroclastic deposits at Shiveluch volcano, Kamchatka, using visible and infrared satellite data

Krippner

Belousov

Belousova

et al. 2018

Journal of Volcanology and Geothermal Research

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Tracing the evolution of 2010 Merapi volcanic deposits (Indonesia) based on object-oriented classification and analysis of multi-temporal, very high resolution images

Cited by 18 publications

References 62 publications

Simulation of block‐and‐ash flows and ash‐cloud surges of the 2010 eruption of Merapi volcano with a two‐layer model

Simulation of block‐and‐ash flows and ash‐cloud surges of the 2010 eruption of Merapi volcano with a two‐layer model

Testing random forest classification for identifying lava flows and mapping age groups on a single Landsat 8 image

Parametric analysis of lava dome-collapse events and pyroclastic deposits at Shiveluch volcano, Kamchatka, using visible and infrared satellite data

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