Analysis of landslide inventories for accurate prediction of debris-flow source areas

Blahůt, Jan; Westen, C.J. van; Sterlacchini, Simone

doi:10.1016/j.geomorph.2010.02.017

Cited by 155 publications

(111 citation statements)

References 21 publications

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“…The Accountability evaluates the relative landslide concentration on the predisposing factor classes with the landslide density higher than the regional average (Blahut et al, 2010), through the following equation:…”

Section: Terrain Mapping Units and Landslide Predisposing Factorsmentioning

confidence: 99%

“…The Reliability considers the density of landslides in predisposing factor classes which are most relevant to their occurrence (Blahut et al, 2010), according to the following equation:…”

Section: Terrain Mapping Units and Landslide Predisposing Factorsmentioning

confidence: 99%

“…According to Blahut et al (2010), the Accountability index explains how classes of predisposing map that are relevant for the analysis correlate with landslides, while the Reliability index indicates the average landslide density in classes of predisposing factors more prone to landslide occurrence. Terrain mapping units were classified according to stability conditions, following the approach proposed by Guzzetti et al (2005).…”

Section: Terrain Mapping Units and Landslide Predisposing Factorsmentioning

confidence: 99%

See 2 more Smart Citations

Susceptibility assessment to different types of landslides in the coastal cliffs of Lourinhã (Central Portugal)

Epifânio

Zêzere

Neves

2014

Journal of Sea Research

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The coastal zone of Lourinhã (located in Central Portugal) is characterized by beach-cliff systems, where beaches are narrow and cliffs have notorious slope instability. These cliffs evolve by different types of landslides, which are one of the main sources of natural hazard and risk in this coastal region. In this work, aerial photo interpretation and a systematic field survey were performed in order to obtain an inventory of landslides of the following types: rotational slides, translational slides and debris flows. The entire coast was then split into 261 terrain mapping units. For each unit, landslide predisposing factors were derived and classified: cliff elevation, slope angle (maximum, mean and standard deviation), potential solar radiation, slope curvature (profile and plan), lithological units and geologic structure. The predictive susceptibility models were computed for each type of landslide using a bi-variate statistical method -the Information Value Method. The degree of fit and the predictive capacity of the models were assessed using the Effectiveness Ratio, the standard Receiver Operator Characteristic curves and the respective Area Under Curve. Results show that each landslide typology occurs in particular terrain conditions. Individual susceptibility models evidence better predictive capacity than susceptibility model for total landslides.

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Section: Terrain Mapping Units and Landslide Predisposing Factorsmentioning

confidence: 99%

“…The Reliability considers the density of landslides in predisposing factor classes which are most relevant to their occurrence (Blahut et al, 2010), according to the following equation:…”

Section: Terrain Mapping Units and Landslide Predisposing Factorsmentioning

confidence: 99%

Section: Terrain Mapping Units and Landslide Predisposing Factorsmentioning

confidence: 99%

See 1 more Smart Citation

Susceptibility assessment to different types of landslides in the coastal cliffs of Lourinhã (Central Portugal)

Epifânio

Zêzere

Neves

2014

Journal of Sea Research

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“…Su representación cartográfica y el análisis se llevaron a cabo e incorporaron en los SIG (Peark et al, 2005; Washington State Department of Natural Resources, 2006;Kalderon-Asael et al, 2008;Hervás y Bobrowsky, 2009;Guzzetti et al, 2009;Blahut et al, 2010;Wenkey et al, 2011). Por su parte, la evaluación de la susceptibilidad a deslizamientos y su zonificación se ha abordado de manera heurística, estadística o determinística (Dai et al, 2002;Clerici et al, 2002;Zhou et al, 2003;Metternicht et al, 2005;Demoulin y Chung, 2007;Castellanos-Abella y Van Westen, 2008).…”

Section: Antecedentesunclassified

Inventario multitemporal, análisis de susceptibilidad y estimación de volumen de deslizamientos en el flanco SW del volcán Pico de Orizaba, Puebla-Veracruz

Paulín

Bursik

Pouget

et al. 2014

BSGM

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Inventario multitemporal, análisis de susceptibilidad y estimación de volumen de deslizamientos 343 ResumenEste artículo provee un panorama general de la investigación en curso del Instituto de Geografía de la UNAM, que busca crear una metodología integral para el desarrollo de cartografía de inventarios multitemporales, de susceptibilidad y estimación de volúmenes de deslizamientos mediante el uso de Sistemas de Información Geográfica (SIG). La metodología se establece para terrenos volcáni-cos, donde deslizamientos superficiales y flujos de escombros son frecuentes a lo largo de las laderas y los sistemas fluviales de los volcanes. La cuenca del Río Chiquito-Barranca del Muerto en el flanco suroeste del volcán Pico de Orizaba se ha seleccionado como caso de estudio para caracterizar las áreas que son propensas a la inestabilidad de las laderas en terrenos volcánicos. La ocurrencia de deslizamientos se determinó a través de un inventario de deslizamientos creado a partir de fotos aéreas multitemporales y trabajo de campo. En este estudio se modela (simula) la susceptibilidad a deslizamientos usando un modelo cartográfico-hidrológico (Stability Index MAPping (SINMAP)) y un modelo estadístico (Regresión Logística Múltiple (RLM)). Ambos modelos están integrados dentro de la aplicación LOGISNET (desarrollado mediante el uso de macros (AML) del sistema de información geográfica (SIG) ArcInfo Workstation. Su implementación mostró que ambos modelos poseen un grado moderado de concordancia con el inventario de deslizamientos, prefiriéndose la RLM para futuros análisis por su flexibilidad de incluir otras variables. Para la estimación de volúmenes de materiales aportados por los deslizamientos a los cauces, se seleccionaron los deslizamientos superficiales. El área y volumen de deslizamientos superficiales individuales fueron obtenidos en campo y a través de la base de datos del inventario de deslizamientos, para establecer una relación empírica: área-volumen. La relación se utilizó para el uso de una ley de potencias con la que se estimó el volumen total de deslizamientos de tierra en el área de estudio. Los resultados son importantes para comprender la evolución a largo plazo del sistema fluvial en el flanco suroeste del volcán Pico de Orizaba, y apoyar el desarrollo de una metodología útil para la elaboración de inventarios, el análisis de la susceptibilidad y estimación de volúmenes de deslizamientos en terrenos volcánicos.

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“…The application of statistically based models for landslide susceptibility modelling is common practice (Chung and Fabbri, 1999;Brenning, 2005;Guzzetti et al, 2006;Frattini et al, 2010;Blahut et al, 2010;Sterlacchini et al, 2011;Goetz et al, 2011). Statistical models are typically applied to spatially predict areas susceptible to future landslides.…”

Section: Introductionmentioning

confidence: 99%

Forest harvesting is associated with increased landslide activity during an extreme rainstorm on Vancouver Island, Canada

Goetz

Guthrie

Brenning

2015

Nat. Hazards Earth Syst. Sci.

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Abstract. Safe operations of forest practices in mountainous regions require effective development planning to mitigate hazards posed by landslides. British Columbia, Canada, has for the past 2 decades implemented landslide risk management policies aimed at reducing the impacts of the forestry industry on landslides. Consequently, it is required that timber harvesting sites be evaluated for their potential or existing impacts on terrain stability. Statistical landslide susceptibility modelling can enhance this evaluation by geographically highlighting potential hazardous areas. In addition, these statistical models can also improve our understanding of regional landslide controlling factors. The purpose of this research was to explore the regional effects of forest harvesting activities, topography, precipitation and geology on landslides initiated during an extreme rainfall event in November 2006 on Vancouver Island, British Columbia. These effects were analyzed with a nonparametric statistical method, the generalized additive model (GAM). Although topography was the strongest predictor of landslide initiation, low density forest interpreted as regrowth areas and proximity to forest service roads were jointly associated with a 6-to 9-fold increase in the odds of landslide initiation, while accounting for other environmental confounders. This result highlights the importance of continuing proper landslide risk management to control the effects of forest practices on landslide initiation.

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Analysis of landslide inventories for accurate prediction of debris-flow source areas

Cited by 155 publications

References 21 publications

Susceptibility assessment to different types of landslides in the coastal cliffs of Lourinhã (Central Portugal)

Susceptibility assessment to different types of landslides in the coastal cliffs of Lourinhã (Central Portugal)

Inventario multitemporal, análisis de susceptibilidad y estimación de volumen de deslizamientos en el flanco SW del volcán Pico de Orizaba, Puebla-Veracruz

Forest harvesting is associated with increased landslide activity during an extreme rainstorm on Vancouver Island, Canada

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