Integrating spatial, temporal, and size probabilities for the annual landslide hazard maps in the Shihmen watershed, Taiwan

Chen, Su‐Chin

doi:10.5194/nhess-13-2353-2013

Cited by 19 publications

(3 citation statements)

References 33 publications

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“…Most of these landslides cannot be related to major triggering events, such as intense rainstorms or strong earthquakes. Thus, an improvement of the spatiotemporal understanding of landslide processes in this region requires a systematic assessment of landslide events in the form of multi-temporal landslide inventories forming the basis for objective and spatially differentiated analyses of landslide hazard and risk [11,[16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Automated Spatiotemporal Landslide Mapping over Large Areas Using RapidEye Time Series Data

et al. 2014

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Abstract:In the past, different approaches for automated landslide identification based on multispectral satellite remote sensing were developed to focus on the analysis of the spatial distribution of landslide occurrences related to distinct triggering events. However, many regions, including southern Kyrgyzstan, experience ongoing process activity requiring continual multi-temporal analysis. For this purpose, an automated object-oriented landslide mapping approach has been developed based on RapidEye time series data complemented by relief information. The approach builds on analyzing temporal NDVI-trajectories for the separation between landslide-related surface changes and other land cover changes. To accommodate the variety of landslide phenomena occurring in the 7500 km 2 study area, a combination of pixel-based multiple thresholds and object-oriented analysis has been implemented including the discrimination of uncertainty-related landslide likelihood classes. Applying the approach to the whole study area for the time period between 2009 and 2013 has resulted in the multi-temporal identification of 471 landslide objects. A quantitative accuracy assessment for two independent validation sites has revealed overall high mapping accuracy (Quality Percentage: 80%), proving the suitability of the developed approach for efficient spatiotemporal landslide mapping over large areas, representing an important prerequisite for objective landslide hazard and risk assessment at the regional scale. OPEN ACCESSRemote Sens. 2014, 6 8027

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Section: Introductionmentioning

confidence: 99%

Automated Spatiotemporal Landslide Mapping over Large Areas Using RapidEye Time Series Data

et al. 2014

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“…Apart from their potential to be replicated elsewhere, this set of factors was also selected for their suitability to capture both intrinsic (topography, geology and hydrology) and extrinsic (climate and land cover changes) [39], [40] aspects, being similar to those used in previous related studies [41]- [43]. Other variables such as slope aspect and wind speed were not considered for this case study.…”

Section: B Description Of the Factors Contributing To Rockfall Hazardmentioning

confidence: 99%

Spatial Statistical Modeling of Rockfall Hazard in a Mountainous Road in Cantabria (Spain)

Jato-Espino

Roldán-Valcarce

Collazos-Arias³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Rockfall events are one of the most frequent types of mass wasting in mountainous areas, causing service and traffic disruption, as well as infrastructure and human damage. Hence, having accurate tools to model these hazards becomes crucial to prevent fatalities, especially in a context of Climate Change whereby the effects of these phenomena might be exacerbated. Under this premise, this research concerned the development of a framework for assessing rockfall hazard in mountainous areas. First, a set of factors expected to favour rockfalls were processed and aggregated using spatial analysis tools, yielding a series of hazard maps with which to fit observed data through statistical modelling. The validation process was undertaken with the support of a database containing the number of rocks removed from a mountainous road section located in Cantabria, northern Spain. The results achieved, which demonstrated the accuracy of the proposed approach to reproduce rockfall hazard using frequency data, highlighted the primary role played by factors such as slope, runoff threshold and precipitation to explain the occurrence of these events. The effects of Climate Change were considerably influenced by the fluctuations in the projections of precipitation, which limited the variations in the spatial distribution and magnitude of rockfall hazard.

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“…The approaches based on exceedance probability can be further subdivided into two types, where the first type employs a landslide inventory induced by a single rainfall event and rainfall data for that event to analyze the return period of the landslide event [8,23,24], and the second type employs a long-term landslide inventory to calculate the exceedance probability for the occurrence of landslides. Concerning the latter type, the Poisson probability model [17,[25][26][27][28][29], binomial probability model, and empirical model [20] are commonly used to analyze the recurrence probability of landslide events. As a consequence, when a research area has a long-term landslide inventory, the Poisson probability model can be employed to estimate the temporal probability of landslides under the assumption that the frequency of future landslides occurring is the same as in the past.…”

Section: Introductionmentioning

confidence: 99%

A Landslide Probability Model Based on a Long-Term Landslide Inventory and Rainfall Factors

Yeh

2020

Water

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The prediction and advanced warning of landslide hazards in large-scale areas must deal with a large amount of uncertainty, therefore a growing number of studies are using stochastic models to analyze the probability of landslide occurrences. In this study, we used a modified Thiessen’s polygon method to divide the research area into several rain gauge control areas, and divided the control areas into slope units reflecting the topographic characteristics to enhance the spatial resolution of a landslide probability model. We used a 2000–2015 long-term landslide inventory, daily rainfall, and effective accumulated rainfall to estimate the rainfall threshold that can trigger landslides. We then employed a Poisson probability model and historical rainfall data from 1987 to 2016 to calculate the exceedance probability that rainfall events will exceed the threshold value. We calculated the number of landslides occurring from the events when rainfall exceeds the threshold value in the slope units to estimate the probability that a landslide will occur in this situation. Lastly, we employed the concept of conditional probability by multiplying this probability with the exceedance probability of rainfall events exceeding the threshold value, which yielded the probability that a landslide will occur in each slope unit for one year. The results indicated the slope units with high probability that at least one rainfall event will exceed the threshold value at the same time that one landslide will occur within any one year are largely located in the southwestern part of the Taipei Water Source Domain, and the highest probability is 0.26. These slope units are located in parts of the study area with relatively weak lithology, high elevations, and steep slopes. Compared with probability models based solely on landslide inventories, our proposed landslide probability model, combined with a long-term landslide inventory and rainfall factors, can avoid problems resulting from an incomplete landslide inventory, and can also be used to estimate landslide occurrence probability based on future potential changes in rainfall.

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Integrating spatial, temporal, and size probabilities for the annual landslide hazard maps in the Shihmen watershed, Taiwan

Cited by 19 publications

References 33 publications

Automated Spatiotemporal Landslide Mapping over Large Areas Using RapidEye Time Series Data

Automated Spatiotemporal Landslide Mapping over Large Areas Using RapidEye Time Series Data

Spatial Statistical Modeling of Rockfall Hazard in a Mountainous Road in Cantabria (Spain)

A Landslide Probability Model Based on a Long-Term Landslide Inventory and Rainfall Factors

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