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Fausto Guzzetti - One of the best experts on this subject based on the ideXlab platform.
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a predictive model of societal landslide risk in italy
Earth-Science Reviews, 2019Co-Authors: Mauro Rossi, Fausto Guzzetti, Paola Salvati, Marco Donnini, Elisabetta Napolitano, Cinzia BianchiAbstract:Abstract We propose a novel approach to evaluate the spatial and the temporal distribution of societal landslide risk from historical, sparse, point information on fatal Landslides and their direct human consequences. We test the approach using a record of 5571 fatalities caused by 1017 Landslides at 958 sites across Italy, in the 155-year period 1861–2015. Adopting a Zipf distribution, we model societal landslide risk for the whole of Italy, and for seven physiographic and 20 administrative subdivisions of Italy. Results confirm that the Zipf distribution is adequate to describe the frequency (and the probability) of fatal Landslides, and show that societal landslide risk varies in Italy depending on the largest magnitude landslide F, the number of fatal events E, and the scaling exponent of the Zipf distribution s, which controls the relative proportion of low vs. large magnitude Landslides. To model societal landslide risk, we then test different grid spacings, g and circular kernel sizes, r finally adopting g = 10 km and r = 55 km. Using such geometrical constraints, we prepare maps of the variables F, E and s, revealing the complexity of landslide risk in Italy, which cannot be described properly with a single metric. For each grid cell, we assign the {F, E, s} variables to the red, green and blue bands of a composite image to obtain a single view of landslide risk to the population of Italy. Next, we prepare risk scenarios for Landslides of increasing magnitudes, which we validate checking the anticipated return period of the fatal events against information on 130 fatal Landslides between 1000 and 1860, and eleven fatal Landslides between January 2016 and August 2018. Despite incompleteness in the old part of the record for the low magnitude Landslides, and the short length and limited number of events in the recent period 2016–2018, the anticipated return periods are in good agreement with the occurrence of fatal Landslides in both validation periods. Despite the known difficulty in modelling sparse datasets, the approach provided a coherent and realistic representation of societal landslide risk in Italy. Our results give new insight on the spatial and temporal variations of societal landslide risk in Italy. We expect this to contribute to improve existing zonings of landslide risk in Italy; to foster the efficacy of national and regional landslide early warning systems; and to design and implement better landslide communication, mitigation and adaptation strategies. Our approach is general and not constrained to the information on fatal Landslides available for Italy. We therefore expect the approach to be used to model societal landslide risk in other geographical areas for which adequate information is available, and to model the fatal consequences of other hazards.
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Landslides in a changing climate
Earth-Science Reviews, 2016Co-Authors: Stefano Luigi Gariano, Fausto GuzzettiAbstract:Abstract Warming of the Earth climate system is unequivocal. That climate changes affect the stability of natural and engineered slopes and have consequences on Landslides, is also undisputable. Less clear is the type, extent, magnitude and direction of the changes in the stability conditions, and on the location, abundance, activity and frequency of Landslides in response to the projected climate changes. Climate and Landslides act at only partially overlapping spatial and temporal scales, complicating the evaluation of the climate impacts on Landslides. We review the literature on landslide-climate studies, and find a bias in their geographical distribution, with large parts of the world not investigated. We recommend to fill the gap with new studies in Asia, South America, and Africa. We examine advantages and limits of the approaches adopted to evaluate the effects of climate variations on Landslides, including prospective modelling and retrospective methods that use landslide and climate records. We consider changes in temperature, precipitation, wind and weather systems, and their direct and indirect effects on the stability of single slopes, and we use a probabilistic landslide hazard model to appraise regional landslide changes. Our review indicates that the modelling results of landslide-climate studies depend more on the emission scenarios, the Global Circulation Models, and the methods to downscale the climate variables, than on the description of the variables controlling slope processes. We advocate for constructing ensembles of projections based on a range of emissions scenarios, and to use carefully results from worst-case scenarios that may over/under-estimate landslide hazards and risk. We further advocate that uncertainties in the landslide projections must be quantified and communicated to decision makers and the public. We perform a preliminary global assessment of the future landslide impact, and we present a global map of the projected impact of climate change on landslide activity and abundance. Where global warming is expected to increase the frequency and intensity of severe rainfall events, a primary trigger of rapid-moving Landslides that cause many landslide fatalities, we predict an increase in the number of people exposed to landslide risk. Finally, we give recommendations for landslide adaptation and risk reduction strategies in the framework of a warming climate.
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an algorithm for the objective reconstruction of rainfall events responsible for Landslides
Landslides, 2015Co-Authors: Massimo Melillo, Stefano Luigi Gariano, M T Brunetti, Silvia Peruccacci, Fausto GuzzettiAbstract:In Italy, rainfall-induced Landslides are recurrent phenomena that cause societal and economic damage. Thus, assessing the rainfall conditions responsible for Landslides is important and may contribute to reducing risk. The prediction of rainfall-induced Landslides relies primarily on empirical rainfall thresholds. However, the thresholds are affected by uncertainties that limit their use in operational warning systems. A source of uncertainty lies in the characterization of the rainfall events responsible for Landslides. Objective criteria for the definition of rainfall events are lacking. To overcome the problem, we propose an algorithm that reconstructs the rainfall events, identifies the rainfall conditions that have resulted in Landslides, and measures the duration and the cumulated rainfall for the events. The algorithm is independent from the local settings and uses a reduced set of parameters to account for different physical settings and operational conditions. We tested the algorithm in Sicily, Italy, with rainfall and landslide information between January 2002 and December 2012. The rainfall conditions responsible for Landslides identified by the algorithm were compared against results obtained manually. The algorithm was proven capable of accurately reconstructing most (87.7 %) of the rainfall events. For each landslide, the algorithm identified a variable number of rainfall conditions responsible for the failures, which are equally likely triggers of the landslide. This opens the possibility of evaluating the uncertainty introduced by different criteria to determine the rainfall events responsible for Landslides. Use of the algorithm shall contribute to reducing the uncertainty in the definition of landslide-triggering rainfall events, to compiling large catalogues of rainfall events with Landslides and to determining reliable rainfall thresholds for possible landslide occurrence.
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bayesian framework for mapping and classifying shallow Landslides exploiting remote sensing and topographic data
Geomorphology, 2013Co-Authors: Alessandro Cesare Mondini, Mauro Rossi, Ivan Marchesini, Kangtsung Chang, G Pasquariello, Fausto GuzzettiAbstract:Abstract We propose a semi-automatic approach to detect, map and classify rainfall-induced shallow Landslides. The approach combines the classification of a post-event multispectral satellite image with information on the morphometric signature of Landslides in a Bayesian framework. We apply the approach in two steps. First, we detect and map the rainfall-induced Landslides separating the stable ground from the failed areas. Next, we classify internally the Landslides separating the source from the run out areas. We obtain the prior probability from the Mahalanobis discriminant function used to classify the satellite image, and the likelihood from the frequency distribution of terrain slope and cross section convexity in the pre-existing shallow Landslides. We tested the approach in southern Taiwan, in a catchment where Typhoon Morakot caused abundant Landslides in August 2009. Using the semi-automatic approach, we obtained a detailed event landslide inventory map that we compared to an inventory obtained through the visual interpretation of post-event ortho-photographs taken a few days after the landslide triggering rainfall event. Quantitative comparison in a Geographical Information System revealed a degree of matching between the two event inventories exceeding 90%. The approach is general and flexible, and can be used with different satellite imagery and topographic data. Best suited in landscapes where shallow Landslides leave distinct radiometric and topographic signatures, the approach is expected to facilitate the production of event landslide inventory maps with positive consequences for geomorphological investigations, landslide hazard and risk modeling, and for post event recovery efforts.
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seasonal landslide mapping and estimation of landslide mobilization rates using aerial and satellite images
Geomorphology, 2011Co-Authors: Federica Fiorucci, Mauro Rossi, Francesca Ardizzone, M. Cardinali, Alessandro Cesare Mondini, R Carla, Leonardo Santurri, Fausto GuzzettiAbstract:Abstract We tested the possibility of using digital, color aerial ortho-photographs and monoscopic, panchromatic satellite images of comparable spatial and radiometric resolution, to map recent Landslides in Italy and to update existing measures of landslide mobilization. In a 90-km 2 area in Umbria, central Apennines, rainfall resulted in abundant Landslides in the period from September 2004 to June 2005. Analysis of the rainfall record determined the approximate dates of landslide occurrence and revealed that the slope failures occurred in response to moderately wet rainfall periods. The slope failures occurred primarily in cultivated terrain and left subtle morphological and land cover signatures, making the recognition and mapping of the individual Landslides problematic. Despite the difficulty with the identification of the Landslides without the use of stereoscopic visualization, visual analysis of the aerial and satellite images allowed mapping 457 new Landslides, ranging in area 3.0 × 10 1 A L 4 m 2 , for a total landslide area A LT = 6.92 × 10 5 m 2 . To identify the Landslides, the investigators adopted the interpretation criteria commonly used to identify and map Landslides on aerial photography. The result confirms that monoscopic, very high resolution images taken by airborne and satellite sensors can be used to prepare landslide maps even where slope failures are difficult to detect, provided the imagery has sufficient geometric and radiometric resolutions. The different dates of the aerial (March 2005) and the satellite (June–July 2005) images allowed the temporal segmentation of the landslide information, and studying the statistics of landslide area and volume for different periods. Compared to pre-existing information on the abundance and size of the Landslides in the area, the inventory obtained by studying the aerial and satellite images proved more complete. The new mapping showed 145% more Landslides and 85% more landslide area than a pre-existing reconnaissance inventory. As a result of the improved mapping, the rate of landslide mobilization for the 2004–2005 landslide season was determined to be φ L = 27.1 mm year − 1 , 30% higher than a previous estimate for the same period. This seasonal rate of landslide mobilization is significantly larger than the long-term regional erosion rate in the central Apennines. The accelerated rate is attributed to agricultural practices that favor slope instability.
David R Montgomery - One of the best experts on this subject based on the ideXlab platform.
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holocene history of deep seated landsliding in the north fork stillaguamish river valley from surface roughness analysis radiocarbon dating and numerical landscape evolution modeling
Journal of Geophysical Research, 2017Co-Authors: Adam M Booth, Sean Richard Lahusen, Alison R Duvall, David R MontgomeryAbstract:Documenting spatial and temporal patterns of past landsliding is a challenging step in quantifying the effect of Landslides on landscape evolution. While landslide inventories can map spatial distributions, lack of dateable material, landslide reactivations, or time, access, and cost constraints generally limit dating large numbers of Landslides to analyze temporal patterns. Here, we quantify the record of the Holocene history of deep-seated landsliding along a 25 km stretch of the North Fork Stillaguamish River valley, Washington State, USA, including the 2014 Oso landslide, which killed 43 people. We estimate the ages of more than 200 deep-seated Landslides in glacial sediment by defining an empirical relationship between landslide deposit age from radiocarbon dating and landslide deposit surface roughness. We show that roughness systematically decreases with age as a function of topographic wavelength, consistent with models of disturbance-driven soil transport. The age-roughness model predicts a peak in landslide frequency ~1,000 ybp, with very few landslide deposits older than 7,000 ybp or younger than 100 ybp, likely reflecting a combination of preservation bias and a complex history of changing climate, base level, and seismic shaking in the study area. Most recent Landslides have occurred where channels actively interact with the toes of hillslopes composed of glacial sediments, suggesting that lateral channel migration is a primary control on the location of large deep-seated Landslides in the valley.
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surface roughness dating of long runout Landslides near oso washington usa reveals persistent postglacial hillslope instability
Geology, 2016Co-Authors: Sean Richard Lahusen, Adam M Booth, Alison R Duvall, David R MontgomeryAbstract:Establishing regional landslide chronologies is necessary to advance from hazard recognition to risk assessment, and to understand the evolution of landslide-prone terrain. Despite recent advances in landslide mapping due to the availability of high-resolution lidar imagery, estimating the timing of slope failures remains a challenge. Here we present a new integrated approach to dating Landslides on a regional scale by augmenting quantitative surface roughness analysis with radiocarbon dating and numerical landscape modeling. We calibrate a roughness-age curve, which we use to date 25 deep-seated Landslides in glacial sediment surrounding the catastrophic A.D. 2014 Oso landslide along the North Fork Stillaguamish River in Washington State (USA). Lidar bare-earth images show a high density of long-runout Landslides in this region. Using our roughness-age curve, we estimate an average Holocene landslide frequency of 1 every 140–500 yr, and show that the 2014 Oso landslide was the latest event in an active history of slope failures throughout the Holocene. With each landslide, substantial sediment is delivered to the North Fork Stillaguamish River, driving shifts in the active channel that ultimately affect the pattern of Landslides across the valley. The high frequency of Landslides in this area, where river incision and isostatic uplift rates have dropped dramatically since peaking soon after ice retreated from the region, shows that landscapes inundated by glacial sediment do not require dramatic changes in base level to remain highly unstable for tens of thousands of years.
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surface roughness dating of long runout Landslides near oso washington usa reveals persistent postglacial hillslope instability
Geology, 2016Co-Authors: Sean Richard Lahusen, Adam M Booth, Alison R Duvall, David R MontgomeryAbstract:Establishing regional landslide chronologies is necessary to advance from hazard recognition to risk assessment, and to understand the evolution of landslide-prone terrain. Despite recent advances in landslide mapping due to the availability of high-resolution lidar imagery, estimating the timing of slope failures remains a challenge. Here we present a new integrated approach to dating Landslides on a regional scale by augmenting quantitative surface roughness analysis with radiocarbon dating and numerical landscape modeling. We calibrate a roughness-age curve, which we use to date 25 deep-seated Landslides in glacial sediment surrounding the catastrophic A.D. 2014 Oso landslide along the North Fork Stillaguamish River in Washington State (USA). Lidar bare-earth images show a high density of long-runout Landslides in this region. Using our roughness-age curve, we estimate an average Holocene landslide frequency of 1 every 140–500 yr, and show that the 2014 Oso landslide was the latest event in an active history of slope failures throughout the Holocene. With each landslide, substantial sediment is delivered to the North Fork Stillaguamish River, driving shifts in the active channel that ultimately affect the pattern of Landslides across the valley. The high frequency of Landslides in this area, where river incision and isostatic uplift rates have dropped dramatically since peaking soon after ice retreated from the region, shows that landscapes inundated by glacial sediment do not require dramatic changes in base level to remain highly unstable for tens of thousands of years.
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landslide erosion controlled by hillslope material
Nature Geoscience, 2010Co-Authors: Isaac J Larsen, David R Montgomery, Oliver KorupAbstract:Landslide erosion is a primary control of landscape relief. A wide-ranging analysis of landslide geometry shows that soil-based Landslides are generally less voluminous than Landslides that involve the failure of bedrock, and provides refined metrics for estimating the volume of a landslide from the area of the failure
Sean Richard Lahusen - One of the best experts on this subject based on the ideXlab platform.
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holocene history of deep seated landsliding in the north fork stillaguamish river valley from surface roughness analysis radiocarbon dating and numerical landscape evolution modeling
Journal of Geophysical Research, 2017Co-Authors: Adam M Booth, Sean Richard Lahusen, Alison R Duvall, David R MontgomeryAbstract:Documenting spatial and temporal patterns of past landsliding is a challenging step in quantifying the effect of Landslides on landscape evolution. While landslide inventories can map spatial distributions, lack of dateable material, landslide reactivations, or time, access, and cost constraints generally limit dating large numbers of Landslides to analyze temporal patterns. Here, we quantify the record of the Holocene history of deep-seated landsliding along a 25 km stretch of the North Fork Stillaguamish River valley, Washington State, USA, including the 2014 Oso landslide, which killed 43 people. We estimate the ages of more than 200 deep-seated Landslides in glacial sediment by defining an empirical relationship between landslide deposit age from radiocarbon dating and landslide deposit surface roughness. We show that roughness systematically decreases with age as a function of topographic wavelength, consistent with models of disturbance-driven soil transport. The age-roughness model predicts a peak in landslide frequency ~1,000 ybp, with very few landslide deposits older than 7,000 ybp or younger than 100 ybp, likely reflecting a combination of preservation bias and a complex history of changing climate, base level, and seismic shaking in the study area. Most recent Landslides have occurred where channels actively interact with the toes of hillslopes composed of glacial sediments, suggesting that lateral channel migration is a primary control on the location of large deep-seated Landslides in the valley.
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surface roughness dating of long runout Landslides near oso washington usa reveals persistent postglacial hillslope instability
Geology, 2016Co-Authors: Sean Richard Lahusen, Adam M Booth, Alison R Duvall, David R MontgomeryAbstract:Establishing regional landslide chronologies is necessary to advance from hazard recognition to risk assessment, and to understand the evolution of landslide-prone terrain. Despite recent advances in landslide mapping due to the availability of high-resolution lidar imagery, estimating the timing of slope failures remains a challenge. Here we present a new integrated approach to dating Landslides on a regional scale by augmenting quantitative surface roughness analysis with radiocarbon dating and numerical landscape modeling. We calibrate a roughness-age curve, which we use to date 25 deep-seated Landslides in glacial sediment surrounding the catastrophic A.D. 2014 Oso landslide along the North Fork Stillaguamish River in Washington State (USA). Lidar bare-earth images show a high density of long-runout Landslides in this region. Using our roughness-age curve, we estimate an average Holocene landslide frequency of 1 every 140–500 yr, and show that the 2014 Oso landslide was the latest event in an active history of slope failures throughout the Holocene. With each landslide, substantial sediment is delivered to the North Fork Stillaguamish River, driving shifts in the active channel that ultimately affect the pattern of Landslides across the valley. The high frequency of Landslides in this area, where river incision and isostatic uplift rates have dropped dramatically since peaking soon after ice retreated from the region, shows that landscapes inundated by glacial sediment do not require dramatic changes in base level to remain highly unstable for tens of thousands of years.
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surface roughness dating of long runout Landslides near oso washington usa reveals persistent postglacial hillslope instability
Geology, 2016Co-Authors: Sean Richard Lahusen, Adam M Booth, Alison R Duvall, David R MontgomeryAbstract:Establishing regional landslide chronologies is necessary to advance from hazard recognition to risk assessment, and to understand the evolution of landslide-prone terrain. Despite recent advances in landslide mapping due to the availability of high-resolution lidar imagery, estimating the timing of slope failures remains a challenge. Here we present a new integrated approach to dating Landslides on a regional scale by augmenting quantitative surface roughness analysis with radiocarbon dating and numerical landscape modeling. We calibrate a roughness-age curve, which we use to date 25 deep-seated Landslides in glacial sediment surrounding the catastrophic A.D. 2014 Oso landslide along the North Fork Stillaguamish River in Washington State (USA). Lidar bare-earth images show a high density of long-runout Landslides in this region. Using our roughness-age curve, we estimate an average Holocene landslide frequency of 1 every 140–500 yr, and show that the 2014 Oso landslide was the latest event in an active history of slope failures throughout the Holocene. With each landslide, substantial sediment is delivered to the North Fork Stillaguamish River, driving shifts in the active channel that ultimately affect the pattern of Landslides across the valley. The high frequency of Landslides in this area, where river incision and isostatic uplift rates have dropped dramatically since peaking soon after ice retreated from the region, shows that landscapes inundated by glacial sediment do not require dramatic changes in base level to remain highly unstable for tens of thousands of years.
Adam M Booth - One of the best experts on this subject based on the ideXlab platform.
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holocene history of deep seated landsliding in the north fork stillaguamish river valley from surface roughness analysis radiocarbon dating and numerical landscape evolution modeling
Journal of Geophysical Research, 2017Co-Authors: Adam M Booth, Sean Richard Lahusen, Alison R Duvall, David R MontgomeryAbstract:Documenting spatial and temporal patterns of past landsliding is a challenging step in quantifying the effect of Landslides on landscape evolution. While landslide inventories can map spatial distributions, lack of dateable material, landslide reactivations, or time, access, and cost constraints generally limit dating large numbers of Landslides to analyze temporal patterns. Here, we quantify the record of the Holocene history of deep-seated landsliding along a 25 km stretch of the North Fork Stillaguamish River valley, Washington State, USA, including the 2014 Oso landslide, which killed 43 people. We estimate the ages of more than 200 deep-seated Landslides in glacial sediment by defining an empirical relationship between landslide deposit age from radiocarbon dating and landslide deposit surface roughness. We show that roughness systematically decreases with age as a function of topographic wavelength, consistent with models of disturbance-driven soil transport. The age-roughness model predicts a peak in landslide frequency ~1,000 ybp, with very few landslide deposits older than 7,000 ybp or younger than 100 ybp, likely reflecting a combination of preservation bias and a complex history of changing climate, base level, and seismic shaking in the study area. Most recent Landslides have occurred where channels actively interact with the toes of hillslopes composed of glacial sediments, suggesting that lateral channel migration is a primary control on the location of large deep-seated Landslides in the valley.
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surface roughness dating of long runout Landslides near oso washington usa reveals persistent postglacial hillslope instability
Geology, 2016Co-Authors: Sean Richard Lahusen, Adam M Booth, Alison R Duvall, David R MontgomeryAbstract:Establishing regional landslide chronologies is necessary to advance from hazard recognition to risk assessment, and to understand the evolution of landslide-prone terrain. Despite recent advances in landslide mapping due to the availability of high-resolution lidar imagery, estimating the timing of slope failures remains a challenge. Here we present a new integrated approach to dating Landslides on a regional scale by augmenting quantitative surface roughness analysis with radiocarbon dating and numerical landscape modeling. We calibrate a roughness-age curve, which we use to date 25 deep-seated Landslides in glacial sediment surrounding the catastrophic A.D. 2014 Oso landslide along the North Fork Stillaguamish River in Washington State (USA). Lidar bare-earth images show a high density of long-runout Landslides in this region. Using our roughness-age curve, we estimate an average Holocene landslide frequency of 1 every 140–500 yr, and show that the 2014 Oso landslide was the latest event in an active history of slope failures throughout the Holocene. With each landslide, substantial sediment is delivered to the North Fork Stillaguamish River, driving shifts in the active channel that ultimately affect the pattern of Landslides across the valley. The high frequency of Landslides in this area, where river incision and isostatic uplift rates have dropped dramatically since peaking soon after ice retreated from the region, shows that landscapes inundated by glacial sediment do not require dramatic changes in base level to remain highly unstable for tens of thousands of years.
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surface roughness dating of long runout Landslides near oso washington usa reveals persistent postglacial hillslope instability
Geology, 2016Co-Authors: Sean Richard Lahusen, Adam M Booth, Alison R Duvall, David R MontgomeryAbstract:Establishing regional landslide chronologies is necessary to advance from hazard recognition to risk assessment, and to understand the evolution of landslide-prone terrain. Despite recent advances in landslide mapping due to the availability of high-resolution lidar imagery, estimating the timing of slope failures remains a challenge. Here we present a new integrated approach to dating Landslides on a regional scale by augmenting quantitative surface roughness analysis with radiocarbon dating and numerical landscape modeling. We calibrate a roughness-age curve, which we use to date 25 deep-seated Landslides in glacial sediment surrounding the catastrophic A.D. 2014 Oso landslide along the North Fork Stillaguamish River in Washington State (USA). Lidar bare-earth images show a high density of long-runout Landslides in this region. Using our roughness-age curve, we estimate an average Holocene landslide frequency of 1 every 140–500 yr, and show that the 2014 Oso landslide was the latest event in an active history of slope failures throughout the Holocene. With each landslide, substantial sediment is delivered to the North Fork Stillaguamish River, driving shifts in the active channel that ultimately affect the pattern of Landslides across the valley. The high frequency of Landslides in this area, where river incision and isostatic uplift rates have dropped dramatically since peaking soon after ice retreated from the region, shows that landscapes inundated by glacial sediment do not require dramatic changes in base level to remain highly unstable for tens of thousands of years.
Jean Poesen - One of the best experts on this subject based on the ideXlab platform.
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impact of Landslides on soil characteristics implications for estimating their age
Catena, 2017Co-Authors: E Van Den Eynde, Stefaan Dondeyne, Moses Isabirye, Jozef Deckers, Jean PoesenAbstract:The slopes of Mount Elgon, a complex volcano at the border between Uganda and Kenya, are frequently affected by Landslides with disastrous effects on the livelihood of its population. Since local people greatly depend on the land for crop production, this paper examines if and how fast physico-chemical characteristics in landslide scars recover. A chronosequence of 18 Landslides covering a period of 103 years was sampled in order to explore differences between topsoil inside and outside landslide scars. For each landslide, two topsoil samples were taken within the landslide and two in nearby undisturbed soils to compare their physico-chemical characteristics. Samples inside the Landslides were located at the transition zone between the depletion and accumulation zone, which is situated at the contact line between the plan concave and plan convex section of the landslide. No differences were found for available phosphorus, Ca2+, Mg2+ content or for the fine earth texture. Recent Landslides had however lower content of soil organic carbon (OC) and K+, and higher content of rock fragments and Na+. than the adjacent soils. Soil OC content increased significantly with age and reached levels of the corresponding undisturbed soils after ca. 60 years. Older Landslides had even higher OC contents than soils adjacent to the landslide. Hence landslide scars act as local carbon sink. We suggest that the occurrence of rock fragments in the topsoil is a useful indicator for mapping past Landslides. Moreover, the difference in soil OC content between landslide scars and adjacent soil could be used for estimating the age of Landslides in data-poor regions.
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object oriented identification of forested Landslides with derivatives of single pulse lidar data
Geomorphology, 2012Co-Authors: Miet Van Den Eeckhaut, Jean Poesen, N Kerle, Javier HervasAbstract:In contrast to the many studies that use expert-based analysis of LiDAR derivatives for landslide mapping in forested terrain, only few studies have attempted to develop (semi-)automatic methods for extracting Landslides from LiDAR derivatives. While all these studies are pixel-based, it has not yet been tested whether object-oriented analysis (OOA) could be an alternative. This study investigates the potential of OOA using only single-pulse LiDAR derivatives, such as slope gradient, roughness and curvature to map Landslides. More specifically, the focus is on both LiDAR data segmentation and classification of slow-moving Landslides in densely vegetated areas, where spectral data do not allow accurate landslide identification. A multistage procedure has been developed and tested in the Flemish Ardennes (Belgium). The procedure consists of (1) image binarization and multiresolution segmentation, (2) classification of landslide parts (main scarps and landslide body segments) and non-landslide features (i.e. earth banks and cropland fields) with supervised support vector machines at the appropriate scale, (3) delineation of landslide flanks, (4) growing of a landslide body starting from its main scarp, and (5) final cleaning of the inventory map. The results obtained show that OOA using LiDAR derivatives allows recognition and characterization of profound morphologic properties of forested deep-seated Landslides on soil-covered hillslopes, because more than 90% of the main scarps and 70% of the landslide bodies of an expert-based inventory were accurately identified with OOA. For mountainous areas with bedrock, on the other hand, creation of a transferable model is expected to be more difficult
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modelling landslide hazard soil redistribution and sediment yield of Landslides on the ugandan footslopes of mount elgon
The Second World Landslide Forum. Putting Science into Practice FAO Rome 3-9 October 201 Abstract Book, 2011Co-Authors: Lieven Claessens, Jean Poesen, Anke Knapen, M G Kitutu, Jozef DeckersAbstract:In this study, the LAPSUS-LS landslide model, together with a digital terrain analysis of topographic attributes, is used as a spatially explicit tool to simulate recent shallow Landslides in Manjiya County on the Ugandan slopes of Mount Elgon. Manjiya County is a densely populated mountainous area where Landslides have been reported since the beginning of the twentieth century. To better understand the causal factors of landsliding, 81 recent Landslides have been mapped and investigated. Through statistical analysis it was shown that steep concave slopes, high rainfall, soil properties and layering as well as human interference were the main factors responsible for Landslides in the study area. LAPSUS-LS is used to construct a landslide hazard map, and to confirm or reject the main factors for landsliding in the area. The model is specifically designed for the analysis of shallow landslide hazard by combining a steady state hydrologic model with a deterministic infinite slope stability model. In addition, soil redistribution algorithms can be applied, whereby erosion and sedimentation by landsliding can be visualized and quantified by applying a threshold critical rainfall scenario. The model is tested in the Manjiya study area for its ability to delineate zones that are prone to shallow landsliding in general and to group the recent Landslides into a specific landslide hazard category. The digital terrain analysis confirms most of the causal topographic factors for shallow landsliding in the study area. In general, shallow Landslides occur at a relatively large distance from the water divide, on the transition between steep concave and more gentle convex slope positions, which points to concentration of (sub)surface flow as the main hydrological triggering mechanism. In addition, LAPSUS-LS is capable to group the recent shallow Landslides in a specific landslide hazard class (critical rainfall values of 0.03– 0.05 m day −1 ). By constructing a landslide hazard map and simulating future landslide scenarios with the model, slopes in Manjiya County can be identified as inherently unstable and volumes of soil redistribution can yield four times higher than currently observed. More than half of this quantity can end up in the stream network, possibly damming rivers and causing major damage to infrastructure or siltation and pollution of streams. The combination of a high population density, land shortage and a high vulnerability to Landslides will likely continue to create a major sustainability problem. © 2007 Elsevier B.V. All rights reserved.
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comparison of two landslide susceptibility assessments in the champagne ardenne region france
Geomorphology, 2010Co-Authors: A Marre, M Van Den Eeckhaut, Jean PoesenAbstract:Abstract The vineyards of the Montagne de Reims are mostly planted on steep south-oriented cuesta fronts receiving a maximum of sun radiation. Due to the location of the vineyards on steep hillslopes, the viticultural activity is threatened by slope failures. This study attempts to better understand the spatial patterns of landslide susceptibility in the Champagne–Ardenne region by comparing a heuristic (qualitative) and a statistical (quantitative) model in a 1120 km² study area. The heuristic landslide susceptibility model was adopted from the Bureau de Recherches Geologiques et Minieres, the GEGEAA – Reims University and the Comite Interprofessionnel du Vin de Champagne. In this model, expert knowledge of the region was used to assign weights to all slope classes and lithologies present in the area, but the final susceptibility map was never evaluated with the location of mapped Landslides. For the statistical landslide susceptibility assessment, logistic regression was applied to a dataset of 291 ‘old’ (Holocene) Landslides. The robustness of the logistic regression model was evaluated and ROC curves were used for model calibration and validation. With regard to the variables assumed to be important environmental factors controlling Landslides, the two models are in agreement. They both indicate that present and future Landslides are mainly controlled by slope gradient and lithology. However, the comparison of the two landslide susceptibility maps through (1) an evaluation with the location of mapped ‘old’ Landslides and through (2) a temporal validation with spatial data of ‘recent’ (1960–1999; n = 48) and ‘very recent’ (2000–2008; n = 46) Landslides showed a better prediction capacity for the statistical model produced in this study compared to the heuristic model. In total, the statistically-derived landslide susceptibility map succeeded in correctly classifying 81.0% of the ‘old’ and 91.6% of the ‘recent’ and ‘very recent’ Landslides. On the susceptibility map derived from the heuristic model, on the other hand, only 54.6% of the ‘old’ and 64.0% of the ‘recent’ and ‘very recent’ Landslides were correctly classified as unstable. Hence, the landslide susceptibility map obtained from logistic regression is a better tool for regional landslide susceptibility analysis in the study area of the Montagne de Reims. The accurate classification of zones with very high and high susceptibility allows delineating zones where viticulturists should be informed and where implementation of precaution measures is needed to secure slope stability.
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use of lidar derived images for mapping old Landslides under forest
Earth Surface Processes and Landforms, 2007Co-Authors: M Van Den Eeckhaut, Jean Poesen, Gert Verstraeten, Veerle Vanacker, J Moeyersons, Jan Nyssen, L P H Van Beek, L VandekerckhoveAbstract:Large, deep-seated Landslides are common features in the Flemish Ardennes (Belgium). As most of these old (>100 years) Landslides are located under forest in this hilly region, aerial photograph interpretation is not an appropriate landslide mapping method. This study tested the potential of LIDAR (Light Detection and Ranging) images for mapping old Landslides under forest. Landslide inventory maps were created for a 125 km2 area by applying the expert knowledge of seven geomorphologists to LIDAR-derived hillshade, slope and contour line maps in a GIS environment. Each of the seven LIDAR-based landslide inventories was compared (i) with the other six, (ii) with a detailed field survey-based inventory, and (iii) with a comparable study in which topographic data were extracted from a topographical map. The combination of the percentage of field Landslides indicated by an expert and the percentage of positional discrepancies (expressed in terms of positional mismatch) were used to evaluate the quality of the LIDAR-based inventory maps. High-quality LIDAR-derived landslide inventory maps contain more than 70 per cent of the Landslides mapped during the field survey, and have positional discrepancies smaller than 70 per cent when compared with the field survey-based inventory map. Four experts and the combination map of all experts satisfied these criteria. Together the seven experts indicated all Landslides mapped in the field. Importantly, LIDAR enabled the experts to find ten new Landslides and to correct the boundaries of eleven (of the 77) Landslides mapped during the field survey. Hence, this study showed that large-scale LIDAR-derived maps analysed by experienced geomorphologists can significantly improve field survey-based inventories of Landslides with a subdued morphology in hilly regions. Copyright © 2006 John Wiley & Sons, Ltd.
