Landslide detection using COSMO-SkyMed images: a case study of a landslide event on Kii Peninsula, Japan

Konishi, Tomohisa; Suga, Yuki

doi:10.1080/22797254.2017.1418185

Cited by 49 publications

(40 citation statements)

References 36 publications

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“…Landslides can also be mapped by the backscattered energy difference using SAR imagery before and after a landslide (Yamada et al, 2013;Konishi and Suga, 2018) or with polarimetry-based approaches (Rodriguez et al, 2002;Czuchlewski et al, 2003;Shimada et al, 2014;Shibayama et al, 2015;Plank et al, 2016). Several polarimetric decomposition methods have been developed for classifying land cover based on scattering mechanisms Pottier, 1996, 1997;Freeman and Durden, 1998).…”

Section: Landslide Mappingmentioning

confidence: 99%

The State of Remote Sensing Capabilities of Cascading Hazards Over High Mountain Asia

et al. 2019

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Cascading hazard processes refer to a primary trigger such as heavy rainfall, seismic activity, or snow melt, followed by a chain or web of consequences that can cause subsequent hazards influenced by a complex array of preconditions and vulnerabilities. These interact in multiple ways and can have tremendous impacts on populations proximate to or downstream of these initial triggers. High Mountain Asia (HMA) is extremely vulnerable to cascading hazard processes given the tectonic, geomorphologic, and climatic setting of the region, particularly as it relates to glacial lakes. Given the limitations of in situ surveys in steep and often inaccessible terrain, remote sensing data are a valuable resource for better understanding and quantifying these processes. The present work provides a survey of cascading hazard processes impacting HMA and how these can be characterized using remote sensing sources. We discuss how remote sensing products can be used to address these process chains, citing several examples of cascading hazard scenarios across HMA. This work also provides a perspective on the current gaps and challenges, community needs, and view forward toward improved characterization of evolving hazards and risk across HMA.

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Section: Landslide Mappingmentioning

confidence: 99%

The State of Remote Sensing Capabilities of Cascading Hazards Over High Mountain Asia

et al. 2019

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“…SAR has the capability of obtaining both amplitude and phase backscattering echoes from targets. The SAR products, such as SAR amplitude and coherence, can be complementary to each other for landslide mapping in highly vegetated regions [32]. Figure 6 shows both descending and ascending SAR coherence and intensity images and the corresponding landslide features on these images.…”

Section: Sar Coherence and Intensity Differencementioning

confidence: 99%

Earthquake-Induced Landslide Mapping for the 2018 Hokkaido Eastern Iburi Earthquake Using PALSAR-2 Data

et al. 2019

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Timely information about landslides during or immediately after an event is an invaluable source for emergency response and management. Using an active sensor, synthetic aperture radar (SAR) can capture images of the earth’s surface regardless of weather conditions and may provide a solution to the problem of mapping landslides when clouds obstruct optical imaging. The 2018 Hokkaido Eastern Iburi earthquake (Mw 6.6) and its aftershocks not only caused major damage with severe loss of life and property but also induced many landslides across the area. To gain a better understanding of the landslides induced by this earthquake, we proposed a method of landslide mapping using pre- and post-event Advanced Land Observation Satellite 2 Phased Array L-band Synthetic Aperture Radar 2 (ALOS-2 PALSAR-2) images acquired from both descending and ascending orbits. Moreover, the accuracy of the classification results was verified by comparisons with high-resolution optical images, and ground truth data (provided by GSI, Japan). The detected landslides show a good match with the reference optical images by visual comparison. The quantitative comparison results showed that a combination of the descending and ascending intensity-based landslide classification had the best accuracy with an overall accuracy and kappa coefficient of 80.1% and 0.45, respectively.

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“…The Precision and Recall analysis are a well-established technique to measure information retrieval effectiveness (Van Rijsbergen, 1977) and it is commonly used for assessing KBS and prediction approaches (Powers, 2011). Its use in postearthquake data retrieval assessment is testified by applications in the fields of social media for emergency management (Sakaki et al, 2010;Caragea et al, 2011;Avvenuti et al, 2013;Wang et al, 2018), for retrieving damage conditions of reinforced concrete buildings (Zhu et al, 2011;German et al, 2012), and for detection of landslide conditions (Keyport et al, 2018;Konishi and Suga, 2018).…”

Section: Validation Of Log-ideahmentioning

confidence: 99%

Use of the Knowledge-Based System LOG-IDEAH to Assess Failure Modes of Masonry Buildings, Damaged by L'Aquila Earthquake in 2009

Novelli

D’Ayala

2019

Front. Built Environ.

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This article, first, discusses the decision-making process, typically used by trained engineers to assess failure modes of masonry buildings, and then, presents the rule-based model, required to build a knowledge-based system for post-earthquake damage assessment. The acquisition of the engineering knowledge and implementation of the rule-based model lead to the developments of the knowledge-based system LOG-IDEAH (Logic trees for Identification of Damage due to Earthquakes for Architectural Heritage), a web-based tool, which assesses failure modes of masonry buildings by interpreting both crack pattern and damage severity, recorded on site by visual inspection. Assuming that failure modes detected by trained engineers for a sample of buildings are the correct ones, these are used to validate the predictions made by LOG-IDEAH. Prediction robustness of the proposed system is carried out by computing Precision and Recall measures for failure modes, predicted for a set of buildings selected in the city center of L'Aquila (Italy), damaged by an earthquake in 2009. To provide an independent meaning of verification for LOG-IDEAH, random generations of outputs are created to obtain baselines of failure modes for the same case study. For the baseline output to be compatible and consistent with the observations on site, failure modes are randomly generated with the same probability of occurrence as observed for the building samples inspected in the city center of L'Aquila. The comparison between Precision and Recall measures, calculated on the output, provided by LOG-IDEAH and predicted by random generations, underlines that the proposed knowledge-based system has a high ability to predict failure modes of masonry buildings, and has the potential to support surveyors in post-earthquake assessments.

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Landslide detection using COSMO-SkyMed images: a case study of a landslide event on Kii Peninsula, Japan

Cited by 49 publications

References 36 publications

The State of Remote Sensing Capabilities of Cascading Hazards Over High Mountain Asia

The State of Remote Sensing Capabilities of Cascading Hazards Over High Mountain Asia

Earthquake-Induced Landslide Mapping for the 2018 Hokkaido Eastern Iburi Earthquake Using PALSAR-2 Data

Use of the Knowledge-Based System LOG-IDEAH to Assess Failure Modes of Masonry Buildings, Damaged by L'Aquila Earthquake in 2009

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