2019
DOI: 10.1016/j.enggeo.2019.105237
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Accounting for covariate distributions in slope-unit-based landslide susceptibility models. A case study in the alpine environment

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Cited by 71 publications
(47 citation statements)
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References 85 publications
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“…The most common choice corresponds to a regular squared lattice or grid (e.g., Cama et al 2016;Hussin et al 2016;Reichenbach et al 2018). However, this mapping unit is sensible to mapping errors (Steger et al 2016) and the assignment of the instability status is inherently uncertain as it is often subjectively chosen between the centroid of the landslide body (e.g., Hussin et al 2016;Castro Camilo et al 2017) or the highest point along the landslide polygon (e.g., Amato et al 2019;Lombardo et al 2014).…”
Section: Mapping Unitmentioning
confidence: 99%
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“…The most common choice corresponds to a regular squared lattice or grid (e.g., Cama et al 2016;Hussin et al 2016;Reichenbach et al 2018). However, this mapping unit is sensible to mapping errors (Steger et al 2016) and the assignment of the instability status is inherently uncertain as it is often subjectively chosen between the centroid of the landslide body (e.g., Hussin et al 2016;Castro Camilo et al 2017) or the highest point along the landslide polygon (e.g., Amato et al 2019;Lombardo et al 2014).…”
Section: Mapping Unitmentioning
confidence: 99%
“…In fact, at the SU level or catchment or any large mapping unit, one needs to approximate the distribution of properties that vary at small spatial scales (e.g., Dreyfus et al 2013). For a continuous factor such as Slope or any other terrain derivative, this comes relatively easy by computing some descriptive statistics such as the mean and standard deviation (same as we did here) or a much finer description into quantiles (e.g., Amato et al 2019). However, for a geological map or a bedding measurement, representing these two properties at the SU level is more complex.…”
Section: Reference Model Interpretationmentioning
confidence: 99%
“…Traditional heuristic, general statistical, and machine learning methods used for LSM tasks are mostly pixel-based without considering surrounding pixels. A few other methods considering slopes as a single unit instead of a single pixel usually lead to a mapping result less smooth than that by pixel-based methods [76][77][78] because it is not always possible to divide different slope units. CNN models make it possible to mine the spatial structure information from surrounding pixels by use of convolution or pooling.…”
Section: Pixel Itself or Surrounding Pixels For Lsm Taskmentioning
confidence: 99%
“…Slope steepness (Slope) (Zevenbergen and Thorne, 1987) 8. Slope Unit area (SU area) (e.g., Amato et al, 2019) 9. Topographic Position Index (TPI) (Guisan et al, 1999) 10.…”
Section: Covariatesmentioning
confidence: 99%