Comparative analysis of surface roughness algorithms for the identification of active landslides

Berti, Matteo; Corsini, Alessandro; Daehne, Alexander

doi:10.1016/j.geomorph.2012.10.022

Cited by 85 publications

(52 citation statements)

References 31 publications

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“…Surface roughness (Grohmann et al, 2010;Hani et al, 2011;Berti et al, 2013) and hypsometric integral were used to classify landscapes between different erosional (and thus evolutionary) stages. The surface roughness (SR) represents the ratio between the area of the analyzed topographic surface and the area of a flat surface with a constant elevation and the same geographic extent (Grohmann, 2004;Grohmann et al, 2007Grohmann et al, , 2011.…”

Section: Morphometric Mapsmentioning

confidence: 99%

Geomorpho-tectonic evolution of the Jamaican restraining bend

et al. 2015

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Section: Morphometric Mapsmentioning

confidence: 99%

Geomorpho-tectonic evolution of the Jamaican restraining bend

et al. 2015

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“…A wide range of methods have been developed for the calculation of surface roughness, such as area ratio, vector dispersion, or different measurements of variability of elevation (e.g., Hobson 1972;Day 1979;Kreslavsky and Head 2000;Grohmann 2004;McKean and Roering 2004;Grohmann et al 2007;Berti et al 2013). In a comparative assessment of six methods used to calculate surface roughness using DEMs (area ratio, vector dispersion, standard deviation of residual topography, standard deviation of elevation, standard deviation of slope, and standard deviation of profile curvature), Grohmann et al (2011) concluded that standard deviation of slope and standard deviation of profile curvature and vector dispersion provided good results, depicting most terrain features, while standard deviation of elevation and standard deviation of residual topography have an intermediate performance, and area ratio fails to distinguish between landforms in areas of low relief.…”

Section: History Of Investigationmentioning

confidence: 99%

Sinuous Rille

Komatsu¹,

Hargitai²

2015

Encyclopedia of Planetary Landforms

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“…The topographic imprint of these geomorphologic processes is given by the slide/flow of the material downslope, and it is highly recognizable (Pike, 1988;Berti et al, 2013;Rossi et al, 2013;Marchesini et al, 2014), especially in high-resolution digital elevation models (DEMs) (Schulz, 2004;McKean and Roering, 2004;Van den Eeckhaut et al, 2005;Glenn et al, 2006;Schulz, 2007;Kasai et al, 2009;Jaboyedoff et al, 2012;Berti at al., 2013). In the last decades, big improvements have been made in landslide delineation (Cararra and Merenda, 1976;Brunsden, 1993;Keaton and DeGraff, 1996;Ardizzone et al, 2002Ardizzone et al, , 2007Guzzetti et al, 2012) and landslide inventories' analysis (Malamud et al, 2004;Guzzetti et al, 2012;.…”

Section: Introductionmentioning

confidence: 99%

Automatic landslide length and width estimation based on the geometric processing of the bounding box and the geomorphometric analysis of DEMs

Niculiţă

2016

Nat. Hazards Earth Syst. Sci.

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Abstract. The morphology of landslides is influenced by the slide/flow of the material downslope. Usually, the distance of the movement of the material is greater than the width of the displaced material (especially for flows, but also the majority of slides); the resulting landslides have a greater length than width. In some specific geomorphologic environments (monoclinic regions, with cuesta landforms type) or as is the case for some types of landslides (translational slides, bank failures, complex landslides), for the majority of landslides, the distance of the movement of the displaced material can be smaller than its width; thus the landslides have a smaller length than width. When working with landslide inventories containing both types of landslides presented above, the analysis of the length and width of the landslides computed using usual geographic information system techniques (like bounding boxes) can be flawed. To overcome this flaw, I present an algorithm which uses both the geometry of the landslide polygon minimum oriented bounding box and a digital elevation model of the landslide topography for identifying the long vs. wide landslides. I tested the proposed algorithm for a landslide inventory which covers 131.1 km 2 of the Moldavian Plateau, eastern Romania. This inventory contains 1327 landslides, of which 518 were manually classified as long and 809 as wide. In a first step, the difference in elevation of the length and width of the minimum oriented bounding box is used to separate long landslides from wide landslides (long landslides having the greatest elevation difference along the length of the bounding box). In a second step, the long landslides are checked as to whether their length is greater than the length of flow downslope (estimated with a flow-routing algorithm), in which case the landslide is classified as wide.By using this approach, the area under the Receiver Operating Characteristic curve value for the classification of the long vs. wide landslides is 87.8 %. An intensive review of the misclassified cases and the challenges of the proposed algorithm is made, and discussions are included about the prospects of improving the approach with further steps, to reduce the number of misclassifications.

show abstract

Comparative analysis of surface roughness algorithms for the identification of active landslides

Cited by 85 publications

References 31 publications

Geomorpho-tectonic evolution of the Jamaican restraining bend

Geomorpho-tectonic evolution of the Jamaican restraining bend

Sinuous Rille

Automatic landslide length and width estimation based on the geometric processing of the bounding box and the geomorphometric analysis of DEMs

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