A sapping erosion susceptibility model for the southern Cantabrian Range, North Spain

Fernández, Susana; Marquínez, J.; Menéndez-Duarte, Rosana

doi:10.1016/j.geomorph.2007.05.013

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…Although models developed for gully erosion commonly deal with the estimation of erosion rates (Knisel, 1980;Merkel et al, 1988;Knisel, 1993;Flanagan and Nearing, 1995;Sidorchuk, 1999), only a few authors focused on gully erosion susceptibility (Meyers and Martínez-Casasnovas, 1999;Bou Kheir et al, 2007;Conoscenti et al, 2008;Fernández et al, 2008;Gómez Gutiérrez et al, 2009;Krishna Bahadur, 2009;Pike et al, 2009;Conforti et al, 2011). Conversely, it is possible to find many studies in literature for landslide susceptibility and hazard mapping.…”

Section: Introductionmentioning

confidence: 99%

Comparison of GIS-based gullying susceptibility mapping using bivariate and multivariate statistics: Northern Calabria, South Italy

2011

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Section: Introductionmentioning

confidence: 99%

Comparison of GIS-based gullying susceptibility mapping using bivariate and multivariate statistics: Northern Calabria, South Italy

2011

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“…The static CFs have been identified to trigger soil erosion and applied by many researchers in susceptibility studies. For instance, drainage density [8,[24][25][26], lineament density [8,27,28], LS-factor [9,[29][30][31][32][33], K-factor [34][35][36], and NDVI [25,28,37] were implemented in those studies. However, some dynamic CFs such as R-factor, LST and SMI are rarely considered despite their roles in the soil erosion process.…”

Section: Erosion Cfs In Susceptibility Assessmentmentioning

confidence: 99%

Multivariate Logistic Regression Model for Soil Erosion Susceptibility Assessment under Static and Dynamic Causative Factors

Sholagberu¹,

Mustafa²,

Yusof³

et al. 2019

Pol. J. Environ. Stud.

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Soil erosion is a devastating land degradation process that needs to be spatially analyzed for identification of critical zones for sustainable management. Geospatial prediction through susceptibility analysis assesses the occurrence of soil erosion under a set of causative factors (CFs). Previous studies have considered majorly static CFs for susceptibility analysis, but neglect dynamic CFs. Thus, this study presents an evaluation of erosion susceptibility under the influence of both non-redundant static and dynamic CFs using multivariate logistic regression (MLR), remote sensing and geographic information system. The CFs considered include drainage density, lineament density, length-slope and soil erodibility as static CFs, and land surface temperature, soil moisture index, vegetation index and rainfall erosivity representing the dynamic CFs. These were parameterized to establish geospatial relationships with the occurrence of erosion. The results showed that length-slope had the highest positive impact on the occurrence of erosion, followed by lineament density. During the MLR classification process, predicted accuracies for the eroded and non-eroded locations were 89.1% and 83.6% respectively, with an overall prediction accuracy of 86.6%. The model's performance was satisfactory, with 81.9% accuracy when validated using the area-under-curve method. The output map of this study will assist decision makers in sustainable watershed management to alleviate soil erosion.

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“…En estas regiones semiá-ridas la actividad de las cárcavas está limitada en el tiempo a la aparición de eventos de lluvia erosiva que ocurren unas pocas veces al año. En menor medida y de modo más reciente aparecen trabajos sobre el desarrollo y evolución de las cárcavas en zonas subhúmedas y húmedas peninsulares, como las que tienen lugar sobre las formaciones margosas y arenosas del flysch eoceno del Pirineo oscense (Nadal-Romero et al, 2008), sobre los regolitos de la Cordillera Cantábrica (Menéndez-Duarte et al, 2007), los conglomerados poco consolidados del sector septentrional de la Cuenca Terciaria del Duero (Fernández et al, 2008) o sobre las margas de la Depresión Terciaria del Penedés (Martínez-Casasnovas et al, 2003).…”

Section: Introductionunclassified

Escorrentía y pérdida de suelo en cárcavas : modelización en un agrosistema del Prepirineo oscense

López‐Vicente

Navas

2013

CIG

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A sapping erosion susceptibility model for the southern Cantabrian Range, North Spain

Cited by 4 publications

References 25 publications

Comparison of GIS-based gullying susceptibility mapping using bivariate and multivariate statistics: Northern Calabria, South Italy

Comparison of GIS-based gullying susceptibility mapping using bivariate and multivariate statistics: Northern Calabria, South Italy

Multivariate Logistic Regression Model for Soil Erosion Susceptibility Assessment under Static and Dynamic Causative Factors

Escorrentía y pérdida de suelo en cárcavas : modelización en un agrosistema del Prepirineo oscense

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