The application of predictive modeling techniques to landslides induced by earthquakes: the case study of the 26 September 1997 Umbria–Marche earthquake (Italy)

Carro, Monica; Amicis, M De; Luzi, Lucia; Marzorati, S.

doi:10.1016/s0013-7952(02)00277-6

Cited by 87 publications

(42 citation statements)

References 22 publications

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“…This valley is characterized by high rock fall risk because of its rough morphology, local seismic activity, and heterogeneous rock mass fracturing. Rock falls abound near the Triponzo village; in September and October 1997, a seismic sequence affected the investigated area (Amato et al 1998;Carro et al 2003;Marzorati et al 2002) and many rock fall events seriously damaged the main roads (S.R. 209, S.R.…”

Section: Investigated Areamentioning

confidence: 99%

Terrestrial laser scanner and geomechanical surveys for the rapid evaluation of rock fall susceptibility scenarios

et al. 2012

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The primary objective of this paper is to present a semiautomatic procedure that, integrated with traditional methods, can be useful for a rapid definition of rock fall susceptibility scenarios with the purpose of civil protection. Due to its morphology (steep slopes and narrow valleys), regional seismicity, and rock mass characteristics, the Nera Valley (Valnerina, Umbria Region, Italy) is characterized by high rock fall risk. With the aim of covering a wide range of features and investigating the main advantages and drawbacks of the proposed approach, data collection (terrestrial laser scanning (TLS) and geomechanical surveys) was carried out at three different slopes. Detailed threedimensional (3D) models were created to reconstruct the shape and volume of the most unstable blocks, to define the position of the main rock fall source areas, and to precisely distinguish the outcropping materials and the position of the elements at risk for reliable runout analyses. The proposed approach can be useful in supporting proper maintenance and land management programs both in ordinary and in emergency circumstances.

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Section: Investigated Areamentioning

confidence: 99%

Terrestrial laser scanner and geomechanical surveys for the rapid evaluation of rock fall susceptibility scenarios

et al. 2012

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“…The earthquake caused widespread damage west of and in the capital city of Port-au-Prince, and killed more than 230 000 people (Bilham, 2010;Bellerive, 2010;Calais et al, 2010;Hough et al, 2010;Koehler and Mann, 2011). The earthquake also triggered extensive landslides, some of Carro et al (2003) Hyōgo-ken-Nanbu, Japan 17 January 1995 6.9 Points 674 700 Fukuoka et al (1997) Northridge, California 17 January 1994 6.7 Polygons 11 000 23.8 10 000 Jibson (1995, 1996); Jibson and Harp (1994) Loma Prieta, California 17 October 1989 6.9 Points 1046 2000 Keefer (2000) Ecuador 5 March 1987 7.0 2500 Tibaldi et al (1995) Borah Peak, Idaho 28 October 1983 6.9 Points Several hundreds 4200 Keefer et al (1985) Murchison, New Zealand 17 June 1929 7.7 Polygons > 7400 200 5000 Pearce and O'Loughlin (1985); Adams (1980) which caused damages such as blocked roads, dammed rivers and streams, and threatened infrastructures in many parts of Haiti (Eberhard et al, 2010;Stumpf and Kerle, 2011;Xu et al, 2012). Co-seismic landslide inventory compiling is essential for associated co-seismic landslides studies, and spatial distribution statistical analysis of those landslides is important in understanding which areas are most susceptible to landslides in future earthquakes.…”

Section: Introductionmentioning

confidence: 99%

Landslides triggered by the 12 January 2010 Port-au-Prince, Haiti, <i>M</i><sub>w</sub> = 7.0 earthquake: visual interpretation, inventory compiling, and spatial distribution statistical analysis

Ôta

2014

Nat. Hazards Earth Syst. Sci.

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Abstract. The 12 January 2010 Port-au-Prince, Haiti, earthquake (M w = 7.0) triggered tens of thousands of landslides. The purpose of this study is to investigate the correlations of the occurrence of landslides and the thicknesses of their erosion with topographic, geologic, and seismic parameters. A total of 30 828 landslides triggered by the earthquake covered a total area of 15.736 km 2 , distributed in an area more than 3000 km 2 , and the volume of landslide accumulation materials is estimated to be about 29 700 000 m 3 . These landslides are of various types, mostly belonging to shallow disrupted landslides and rock falls, but also include coherent deep-seated landslides and rock slides. These landslides were delineated using pre-and post-earthquake high-resolution satellite images. Spatial distribution maps and contour maps of landslide number density, landslide area percentage, and landslide erosion thickness were constructed in order to analyze the spatial distribution patterns of co-seismic landslides. Statistics of size distribution and morphometric parameters of co-seismic landslides were carried out and were compared with other earthquake events in the world. Four proxies of co-seismic landslide abundance, including landslides centroid number density (LCND), landslide top number density (LTND), landslide area percentage (LAP), and landslide erosion thickness (LET) were used to correlate coseismic landslides with various environmental parameters. These parameters include elevation, slope angle, slope aspect, slope curvature, topographic position, distance from drainages, lithology, distance from the epicenter, distance from the Enriquillo-Plantain Garden fault, distance along the fault, and peak ground acceleration (PGA). A comparison of these impact parameters on co-seismic landslides shows that slope angle is the strongest impact parameter on co-seismic landslide occurrence. Our co-seismic landslide inventory is much more detailed than other inventories in several previous publications. Therefore, we carried out comparisons of inventories of landslides triggered by the Haiti earthquake with other published results and proposed possible reasons for any differences. We suggest that the empirical functions between earthquake magnitude and co-seismic landslides need to be updated on the basis of the abundant and more complete coseismic landslide inventories recently available.

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“…On 21 September 1999, the Chi-Chi earthquake with ML 7.3, which is the most serious natural catastrophe in Taiwan of the past century, shocked central Taiwan and significantly changed the geographical features of the area. Earthquake-induced landslides represent one of the most hazardous impacts after severe seismic events [1][2][3][4][5][6][7][8][9]. Thousands of landslide spots induced by the Chi-Chi earthquake produced a large amount of soil and stones that could turn into debris flow and threaten the residents and agricultural activities [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Long-Term Vegetation Recovery Estimation for Mt. Jou-Jou Using Multi-Date SPOT 1, 2, and 4 Images

2017

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Vegetation recovery monitoring is critical for assessing denudation areas after landslides have occurred. A long-term and broad area investigation using remote sensing techniques is an efficient and cost-effective approach incorporating the consideration of radiometric correction and seasonality variations across multi-date satellite images. This paper investigates long-term vegetation recovery using 14 SPOT satellite images spanning from 1999 to 2011 over the landslide area of Mt. Jou-Jou in central Taiwan, which was caused by the Chi-Chi earthquake in 1999. The vegetation status was evaluated by the Normalized Difference Vegetation Index (NDVI) with radiometric correction between multi-date images based on pseudoinvariant features, and subsequently a vegetation recovery rate (VRR) model was empirically established after seasonality adjustment was performed on the multi-date NDVI images. An increasing tendency of the vegetation recovery in the landslide area of Mt. Jou-Jou appeared based on the NDVI value rising to 0.367 in March 2011 from −0.044 right after the catastrophic earthquake. The vegetation recovery rate with seasonality adjustment approached 81.5% for the total area and 81.3% for the landslide area through 12 years succession. The seasonality adjustment also enhanced the VRR model with a determination coefficient that increased from 0.883 to 0.916 for the landslide area and from 0.584 to 0.915 for the total area, highlighting the necessity of seasonality adjustment in multi-date vegetation observations using satellite images. Furthermore, the association between precipitation and NDVI was discussed, and the inverse relationship with the reoccurrence of high-intensity short-duration rainfall and yearly heavy rainfall was observed, in agreement with the on-site investigation.

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The application of predictive modeling techniques to landslides induced by earthquakes: the case study of the 26 September 1997 Umbria–Marche earthquake (Italy)

Cited by 87 publications

References 22 publications

Terrestrial laser scanner and geomechanical surveys for the rapid evaluation of rock fall susceptibility scenarios

Terrestrial laser scanner and geomechanical surveys for the rapid evaluation of rock fall susceptibility scenarios

Landslides triggered by the 12 January 2010 Port-au-Prince, Haiti, <i>M</i><sub>w</sub> = 7.0 earthquake: visual interpretation, inventory compiling, and spatial distribution statistical analysis

A Long-Term Vegetation Recovery Estimation for Mt. Jou-Jou Using Multi-Date SPOT 1, 2, and 4 Images

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The application of predictive modeling techniques to landslides induced by earthquakes: the case study of the 26 September 1997 Umbria–Marche earthquake (Italy)

Cited by 87 publications

References 22 publications

Terrestrial laser scanner and geomechanical surveys for the rapid evaluation of rock fall susceptibility scenarios

Terrestrial laser scanner and geomechanical surveys for the rapid evaluation of rock fall susceptibility scenarios

Landslides triggered by the 12 January 2010 Port-au-Prince, Haiti, &lt;i&gt;M&lt;/i&gt;&lt;sub&gt;w&lt;/sub&gt; = 7.0 earthquake: visual interpretation, inventory compiling, and spatial distribution statistical analysis

A Long-Term Vegetation Recovery Estimation for Mt. Jou-Jou Using Multi-Date SPOT 1, 2, and 4 Images

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Landslides triggered by the 12 January 2010 Port-au-Prince, Haiti, <i>M</i><sub>w</sub> = 7.0 earthquake: visual interpretation, inventory compiling, and spatial distribution statistical analysis