Landslide overview map of the conterminous United States

Radbruch-Hall, D. H.; Colton, R.B.; Davies, William E.; Lucchitta, Ivo; Skipp, Betty; Varnes, David J.

doi:10.3133/pp1183

Cited by 50 publications

(20 citation statements)

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“…Brabb and Pampeyan 1972;Antonini et al 1993;Duman et al 2005) to states or nations (e.g. Delaunay 1981;Radbruch-Hall et al 1982;Brabb and Harrod 1989;Cardinali et al 1990;Trigila et al 2010); (ii) as a preliminary step toward landslide susceptibility, hazard and risk assessment (e.g. Cardinali et al 2002Cardinali et al , 2006Guzzetti et al 2005Guzzetti et al , 2006aVan Westen et al 2006Bălteanu et al 2010); (iii) to investigate the distribution, types and patterns of landslides in relation to morphological and geological characteristics (e.g.…”

Section: Introductionmentioning

confidence: 99%

The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: geomorphological features and landslide distribution

et al. 2017

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In this paper, the updating of the landslide inventory of Tuscany region is presented. To achieve this goal, satellite SAR data processed with persistent scatter interferometry (PSI) technique have been used. The updating leads to a consistent reduction of unclassified landslides and to an increasing of active landslides. After the updating, we explored the characteristics of the new inventory, analysing landslide distribution and geomorphological features. Several maps have been elaborated, as sliding index or landslide density map; we also propose a density-area map to highlight areas with different landslide densities and sizes. A frequency-area analysis has been performed, highlighting a classical negative power-law distribution. We also explored landslide frequency for lithology, soil use and several morphological attributes (elevation, slope gradient, slope curvature), considering both all landslides and classified landslide types (flows, falls and slides).

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Section: Introductionmentioning

confidence: 99%

The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: geomorphological features and landslide distribution

et al. 2017

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“…Brabb and Pampeyan, 1972;Antonini et al, 1993;Duman et al, 2005), and even entire states or nations (Delaunay, 1981;Radbruch-Hall et al, 1982;Brabb et al, 1989;Reichenbach et al, 1998;Trigila et al, 2010). LIMs are prepared using a variety of techniques and methods , including: (i) analysis of archive and historical information (Reichenbach et al, 1998;Salvati et al, 2003), (ii) visual interpretation of stereoscopic aerial photographs (aerial photo interpretation; API) Cardinali, 1989, 1990;Brunsden, 1993;Antonini et al, 2002b;Guzzetti et al, 2002Guzzetti et al, , 2012Santangelo et al, 2010Santangelo et al, , 2013, (iii) visual analysis of lidar-derived images (Ardizzone et al, 2007;Van den Eeckhaut et al, 2007;Haneberg et al, 2009;Guzzetti et al, 2012;Razak et al, 2011Razak et al, , 2013, (iv) visual inspection of monoscopic (Marcelino et al, 2009;Gao and Maroa, 2010;Fiorucci et al, 2011;Giordan et al, 2013) and stereoscopic (Fiorucci et al, 2011;Ardizzone et al, 2013;Murillo-García et al, 2015) satellite images, and (v) image-processing techniques applied to lidar elevation data (Martha et al, 2010;Lu et al, 2011;Stumpf and Kerle, 2011;Van den Eeckhaut et al, 2012) and satellite imagery (Rosin and Hervás, 2005;Borghuis et al, 2007;…”

Section: Introductionmentioning

confidence: 99%

An approach to reduce mapping errors in the production of landslide inventory maps

Santangelo

Marchesini

Bucci

et al. 2015

Nat. Hazards Earth Syst. Sci.

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Abstract. Landslide inventory maps (LIMs) show where landslides have occurred in an area, and provide information useful to different types of landslide studies, including susceptibility and hazard modelling and validation, risk assessment, erosion analyses, and to evaluate relationships between landslides and geological settings. Despite recent technological advancements, visual interpretation of aerial photographs (API) remains the most common method to prepare LIMs. In this work, we present a new semi-automatic procedure that makes use of GIS technology for the digitization of landslide data obtained through API. To test the procedure, and to compare it to a consolidated landslide mapping method, we prepared two LIMs starting from the same set of landslide API data, which were digitized (a) manually adopting a consolidated visual transfer method, and (b) adopting our new semi-automatic procedure. Results indicate that the new semi-automatic procedure (a) increases the interpreter's overall efficiency by a factor of 2, (b) reduces significantly the subjectivity introduced by the visual (manual) transfer of the landslide information to the digital database, resulting in more accurate LIMs. With the new procedure, the landslide positional error decreases with increasing landslide size, following a power-law. We expect that our work will help adopt standards for transferring landslide information from the aerial photographs to a digital landslide map, contributing to the production of accurate landslide maps.

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“…Small-scale inventories (<1:200 000) can be compiled through inquires to public organizations and private consultants, by searching chronicles, journals, technical and scientific reports (Radbruch-Hall et al, 1982) or by interviewing landslide experts ('archive' inventories, e.g. Reichenbach et al, 1998).…”

Section: Inventory Mapsmentioning

confidence: 99%

Landslide inventories and their statistical properties

Malamud

Turcotte

Guzzetti

et al. 2004

Earth Surf Processes Landf

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1,208

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Landslides are generally associated with a trigger, such as an earthquake, a rapid snowmelt or a large storm. The landslide event can include a single landslide or many thousands. The frequency-area (or volume) distribution of a landslide event quantifies the number of landslides that occur at different sizes. We examine three well-documented landslide events, from Italy, Guatemala and the USA, each with a different triggering mechanism, and find that the landslide areas for all three are well approximated by the same three-parameter inverse-gamma distribution. For small landslide areas this distribution has an exponential 'roll-over' and for medium and large landslide areas decays as a power-law with exponent −2·40. One implication of this landslide distribution is that the mean area of landslides in the distribution is independent of the size of the event. We also introduce a landslide-event magnitude scale m L = log(N LT ), with N LT the total number of landslides associated with a trigger. If a landslide-event inventory is incomplete (i.e. smaller landslides are not included), the partial inventory can be compared with our landslide probability distribution, and the corresponding landslide-event magnitude inferred. This technique can be applied to inventories of historical landslides, inferring the total number of landslides that occurred over geologic time, and how many of these have been erased by erosion, vegetation, and human activity. We have also considered three rockfall-dominated inventories, and find that the frequency-size distributions differ substantially from those associated with other landslide types. We suggest that our proposed frequency-size distribution for landslides (excluding rockfalls) will be useful in quantifying the severity of landslide events and the contribution of landslides to erosion.

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Landslide overview map of the conterminous United States

Cited by 50 publications

References 0 publications

The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: geomorphological features and landslide distribution

The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: geomorphological features and landslide distribution

An approach to reduce mapping errors in the production of landslide inventory maps

Landslide inventories and their statistical properties

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