New Insight into Post-seismic Landslide Evolution Processes in the Tropics

Tanyaş, Hakan; Kirschbaum, Dalia; Görüm, Tolga; Westen, C.J. van; Lombardo, Luigi

doi:10.3389/feart.2021.700546

Cited by 14 publications

(11 citation statements)

References 44 publications

(88 reference statements)

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“…Various factors including seismicity or precipitation itself could trigger catastrophic landslides. Yet, this may seem a more likely scenario for a region exposed to intense seismic external forces rather than precipitation because, overall, even relatively low-intensity ground shaking may be more destructive than intense precipitation at triggering landslides [68]. In this context, it is not surprising that the relict landslides are mostly distributed closer to the North Anatolian Fault zone, whereas the active landslides concentrate far from it.…”

Section: Controls and Fate Of Active Landslidesmentioning

confidence: 99%

Distinct Susceptibility Patterns of Active and Relict Landslides Reveal Distinct Triggers: A Case in Northwestern Turkey

et al. 2022

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To understand the factors that make certain areas especially prone to landslides, statistical approaches are typically used. The interpretation of statistical results in areas characterised by complex geological and geomorphological patterns can be challenging, and this makes the understanding of the causes of landslides more difficult. In some cases, landslide inventories report information on the state of activity of landslides, adding a temporal dimension that can be beneficial in the analysis. Here, we used an inventory covering a portion of Northwestern Turkey to demonstrate that active and relict landslides (that is, landslides that occurred in the past and are now stabilised) could be related to different triggers. To do so, we built two landslide susceptibility models and observed that the spatial patterns of susceptibility were completely distinct. We found that these patterns were correlated with specific controlling factors, suggesting that active landslides are regulated by current rainfalls while relict landslides may represent a signature of past earthquakes on the landscape. The importance of this result resides in that we obtained it with a purely data-driven approach, and this was possible because the active/relict landslide classification in the inventory was accurate.

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Section: Controls and Fate Of Active Landslidesmentioning

confidence: 99%

Distinct Susceptibility Patterns of Active and Relict Landslides Reveal Distinct Triggers: A Case in Northwestern Turkey

et al. 2022

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“…Covariates Because of the complexity of the nature and evolution of landslides, there is no standard guideline on which causative factors should be included in the landslide susceptibility assessment model. Based on the literature review of other earthquake cases (e.g., Nowicki et al, 2014;Tanyas et al, 2021a;Fan et al, 2021), the same area (Fan et al, 2018b;Tian et al, 2019;Yi et al, 2019;Luo et al, 2021), and data availability and reliability for the study area, we considered 14 conditioning factors to explain the spatial distribution of pre-, co-, and postseismic landslides. (Dis2seisfaults).…”

Section: Covariates and Multi-collinearity Analysesmentioning

confidence: 99%

“…For instance, Fan et al (2021) exploited a multi-temporal inventory (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) spanning the epicentral region of the Wenchuan earthquake and a random forest model to model the landslide susceptibility. By applying the binary logistic regression model and analyzing the time series of landslides related to four earthquakes in Indonesia, Tanyas et al (2021a) and Tanyas et al (2021b) studied the coupled influence of earthquakes and rainfall to predict landslide occurrences. These studies are pioneer attempts on the spatial-temporal variation and dynamic susceptibility/hazard of post-seismic landslides in an earthquake-affected area.…”

Section: Introductionmentioning

confidence: 99%

Multi-Temporal Landslide Inventory-Based Statistical Susceptibility Modeling Associated With the 2017 Mw 6.5 Jiuzhaigou Earthquake, Sichuan, China

Luo

Pei

Zhong

et al. 2022

Front. Environ. Sci.

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The 2017 Mw 6.5 Jiuzhaigou earthquake (Sichuan, China) is the first strong ground motion that struck the famous world heritage site, causing widespread landslides and severe rock mass damage effects and landscapes undergoing rapid evolution in the Jiuzhaigou National Geopark. However, the understanding of the variability of pre- and post-earthquake landslide susceptibility and landslide conditioning factor effects over time remains limited. This study aims to carry out multi-temporal statistical landslide susceptibility modeling at the slope-unit level related to this event. To achieve this, we initially used a set of remote sensing imageries in GIS to obtain systematic landslide inventories across the pre-, co-, and post-seismic periods. Based on three landslide inventory datasets, we developed three statistical models by incorporating 14 landslide conditioning (seismic, topographic, and geologic) factors into a binary logistic regression (BLR) model. Finally, we utilized the area under the receiver operating characteristic (AUC) (QA) curve to assess each model’s calibration and validation performance. The results show that the BLR model has good prediction applicability for both normal and seismic landslides in the study area with outstanding to excellent predictive accuracy for Mod1 (pre-seismic, AUC = 0.801), Mod2 (co-seismic, AUC = 0.942), and Mod3 (post-seismic, AUC = 0.880) periods. There are variations in both the importance of landslide conditioning factors and susceptibility maps through time, and the number of slope units with a mean probability over 0.8 from only one (pre-seismic) increased to 21 (post-seismic). The dynamic susceptibility maps are of great significance for identifying potentially unstable slopes and providing references for hazard and risk assessment, which could provide new insights into geo-environmental protection and regional landslide evaluation in scenery spots, even for those world heritage sites in the tectonic active mountainous region. Moreover, more frequent or extended observation periods could contribute a further understanding of the post-seismic landslide developments in the Jiuzhaigou area.

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“…Based on the assessment of rainfall thresholds obtained from evaluating rainfall circumstances that have caused landslides to occur, empirical approaches for determining the temporal probability of landslides are used (Jaiswal & van Westen, 2009). MTL inventories can also help to study the development of coseismic landslides in the years after the earthquake and as the basis for consideration probabilistic earthquake-induced landslide hazard models (Guzzetti, Reichenbach, Cardinali, Galli, & Ardizzone, 2005;Fan et al (2017Fan et al ( , 2019Fan et al ( , 2021, Tang et al (2016), andTanyaş et al (2021).…”

Section: Introductionmentioning

confidence: 99%

Mapping landslides through a temporal lens: An insight towards multi-temporal landslide mapping using the U-Net deep learning model

Bhuyan¹,

Meena²,

Nava³

et al. 2022

Preprint

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Repeated temporal mapping of landslides is essential for investigating changes in landslide movements, legacy effects of the landslide triggering events, and susceptibility changes in the area. However, in order to perform such investigations, multi-temporal (MT) inventories of landslides are required. The traditional approach of visual interpretation from cloud-free optical remote sensing imageries is time consuming and expensive. Recent endeavours exploring Convolutional Neural Networks and deep learning models have made rapid and accurate mapping of landslides feasible but have not been applied for multi-temporal landslide mapping in the Himalayas, yet. Earlier models used a standard supervised learning approach, with a small landslide inventory over a limited area used for training , which is then utilized to predict landslides in nearby areas. We propose a new strategy, using geographically separate training samples to design a standard approach which can be utilized to create multi-temporal landslide inventories. RapidEye images of 5-metres spatial resolution are used to generate MT landslide inventories in the study area of Rasuwa district, Nepal. We test the effectiveness of the model by training with only 55 landslides and predicting for a different area. Then, using the weights attained from this first training phase, we use transfer learning to map landslides over a time period between 2013 and 2019 in the Rasuwa district. We also adopt data augmentation techniques to add more training samples, leading to higher overall accuracies ranging from 58% in 2015 to 80% in 2017. We also perform a spatial comparison between the manual (observed) and predicted inventories to evaluate the differences between landslide densities and overall landslide statistics of landslide area distribution. The benefit of a transfer learning-based model training is that it circumvents the need for generating annual inventories for training a deep learning. A single event based inventory is enough to generate landslide inventories over a number of years, at least until landslide preparatory conditions do not change significantly. This application can enable automated workflows to generate MT landslide inventories of particular areas as the basis for landslide evolution and movement change analysis.

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New Insight into Post-seismic Landslide Evolution Processes in the Tropics

Cited by 14 publications

References 44 publications

Distinct Susceptibility Patterns of Active and Relict Landslides Reveal Distinct Triggers: A Case in Northwestern Turkey

Distinct Susceptibility Patterns of Active and Relict Landslides Reveal Distinct Triggers: A Case in Northwestern Turkey

Multi-Temporal Landslide Inventory-Based Statistical Susceptibility Modeling Associated With the 2017 Mw 6.5 Jiuzhaigou Earthquake, Sichuan, China

Mapping landslides through a temporal lens: An insight towards multi-temporal landslide mapping using the U-Net deep learning model

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