Interrelations Between Soil Erosion Conditioning Factors in Basins of Ecuador: Contributions to the Spatial Model Construction

Land degradation caused by soil erosion remains an important global issue due to its adverse consequences on food security and environment. Geospatial prediction of erosion through susceptibility analysis is very crucial to sustainable watershed management. Previous susceptibility studies devoid of some crucial conditioning factors (CFs) termed dynamic CFs whose impacts on the accuracy have not been investigated. Thus, this study evaluates erosion susceptibility under the influence of both non-redundant static and dynamic CFs using support vector machine (SVM), remote sensing and GIS. The CFs considered include drainage density, lineament density, length-slope and soil erodibility as non-redundant static factors, and land surface temperature, soil moisture index, vegetation index and rainfall erosivity as the dynamic factors. The study implements four kernel tricks of SVM with sequential minimal optimization algorithm as a classifier for soil erosion susceptibility modeling. Using area under the curve (AUC) and Cohen’s kappa index (k) as the validation criteria, the results showed that polynomial function had the highest performance followed by linear and radial basis function. However, sigmoid SVM underperformed having the lowest AUC and k values coupled with higher classification errors. The CFs’ weights were implemented for the development of soil erosion susceptibility map. The map would assist planners and decision makers in optimal land-use planning, prevention of soil erosion and its related hazards leading to sustainable watershed management.

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Characterization of Susceptible Landslide Zones by an Accumulated Index

Landslides - Investigation and Monitoring ◽

10.5772/intechopen.89828 ◽

2020 ◽

Author(s):

Rocío N. Ramos-Bernal ◽

René Vázquez-Jiménez ◽

Sulpicio Sánchez Tizapa ◽

Roberto Arroyo Matus

Keyword(s):

Spatial Model ◽

Central Zone ◽

Landslide Inventory ◽

Individual Contribution ◽

Conditioning Factors ◽

Jackknife Test ◽

Landslide Inventory Map ◽

Inventory Map ◽

The Individual

In order to characterize the landslide susceptibility in the central zone of Guerrero State in Mexico, a spatial model has been designed and implemented, which automatically generates cartography. Conditioning factors as geomorphological, geological, and anthropic variables were considered, and as a detonating factor, the effect of the accumulated rain. The use of an inventory map of landslides that occurred in the past (IL) was also necessary, which was produced by an unsupervised detection method. Before the design of the model, an analysis of the contribution of each factor, related to the landslide inventory map, was performed by the Jackknife test. The designed model consists of a susceptibility index (SI) calculated pixel by pixel by the accumulation of the individual contribution of each factor, and the final index allows the susceptibility cartography to slide in the study area. The evaluation of the obtained map was performed by applying an analysis of the frequency ratio (FR) graphic, and an analysis of the receiver operating characteristic (ROC) curve was developed. Studies like this can help different safeguarding institutions, locating the areas where there is a greater vulnerability according to the considered factors, and integrating disaster attention management or prevention plans.

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A Spatial Model for Soil–Atmosphere Interaction: Model Construction and Linear Stability Analysis

Journal of Hydrometeorology ◽

10.1175/1525-7541(2000)001<0061:asmfsa>2.0.co;2 ◽

2000 ◽

Vol 1 (1) ◽

pp. 61-74 ◽

Cited By ~ 11

Author(s):

Amilcare Porporato ◽

Paolo D’Odorico ◽

Luca Ridolfi ◽

Ignacio Rodriguez-Iturbe

Keyword(s):

Stability Analysis ◽

Linear Stability ◽

Linear Stability Analysis ◽

Spatial Model ◽

Interaction Model ◽

Model Construction ◽

Soil Atmosphere ◽

Atmosphere Interaction

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Is Overgrazing Really Influencing Soil Erosion?

Water ◽

10.3390/w10081077 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1077 ◽

Cited By ~ 10

Author(s):

Ionut Nicu

Keyword(s):

Soil Erosion ◽

Success Rate ◽

Slope Angle ◽

Area Under The Curve ◽

Temporal Analysis ◽

Drainage Density ◽

Information Value ◽

Topographic Wetness Index ◽

Conditioning Factors ◽

Multi Temporal

Soil erosion is a serious problem spread over a variety of climatic areas around the world. The main purpose of this paper is to produce gully erosion susceptibility maps using different statistical models, such as frequency ratio (FR) and information value (IV), in a catchment from the northeastern part of Romania, covering a surface of 550 km2. In order to do so, a total number of 677 gullies were identified and randomly divided into training (80%) and validation (20%) datasets. In total, 10 conditioning factors were used to assess the gully susceptibility index (GSI); namely, elevation, precipitations, slope angle, curvature, lithology, drainage density, topographic wetness index, landforms, aspect, and distance from rivers. As a novelty, overgrazing was added as a conditioning factor. The final GSI maps were classified into four susceptibility classes: low, medium, high, and very high. In order to evaluate the two models prediction rate, the AUC (area under the curve) method was used. It has been observed that adding overgrazing as a contributing factor in calculating GSI does not considerably change the final output. Better predictability (0.87) and success rate (0.89) curves were obtained with the IV method, which proved to be more robust, unlike FR method, with 0.79 value for both predictability and success rate curves. When using sheepfolds, the value decreases by 0.01 in the case of the FR method, and by 0.02 in the case of the success rate curve for the IV method. However, this does not prove the fact that overgrazing is not influencing or accelerating soil erosion. A multi-temporal analysis of soil erosion is needed; this represents a future working hypothesis.

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