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Definition of the upslope and downslope component of the index of connectivity (from Crema et al., 2015; modified after Borselli et al., 2008). A: contributing area to the reference element; W i : weighting factor of the ith pixel; W : average weighting factor of the contributing area; S i : slope of the ith pixel; S: average slope of the contributing area.
Source publication
Surface texture analysis applied to High Resolution Digital Terrain Models (HRDTMs) is a promising approach for extracting useful fine-scale morphological information. Surface roughness, considered here as a synonym of surface texture, can have a discriminant role in the detection of different geomorphic processes and factors. Very often, the local...
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Context 1
... (2008), is a distributed GIS-based index mainly focused on the influence of topogra- phy on sediment connectivity. The connectivity aims to rep- resent the linkage between different parts of the catchment (i.e., hillslopes and features of interest such as catchment out- lets, main channel networks or a given cross section along the channel). IC ( Fig. 3) is defined by the logarithm of the ratio between an upslope (D up , units in m) and a downslope (D dn , units in m) component expressing, respectively, the potential for downward routing of the sediment-produced upslope and the sediment flux path length to the nearest target or sink. A weighting factor (W ) appears in both components ...
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... is encountered in areas associated with agricul- ture (Fig. 13); these areas are characterized as extended zones with lower R flow than R iso , with very small absolute differ- ences (less than 0.01 m) and considerable relative differences (more than 30 %). Considering the accuracy of the DTM, these small differences could be considered non-significant; however, comparing the shaded relief and the ...
Context 3
... between R flow and R iso . Terraces scarps and the road network are the main contributor of features (purple) with a higher R flow than R iso . Areas with erosional processes located along the flanks of the main valleys are particularly highlighted by features (light green) with lower R flow than R iso . For the highlighted area in (b) see Fig. 13. an index itself that can act as a new feature to be used in machine-learning approaches for automatic mapping (Bue and Stepinski, 2006;Cracknell and Reading, 2014;Macmillan et al., 2003) or for other predictive models, such as land- slide susceptibility models ( Booth et al., 2009;Jaboyedoff et al., ...
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... evaluate the impact of R flow on the connectivity index, we performed two runs of SedInConnect ( Crema et al., 2015), a stand-alone tool de- veloped to calculate the sediment connectivity index, using two different weighting factors (W flow and W iso ), one derived from R flow and one from R iso according to Eq. (5). Subse- quently, we derived two measures of the DC that we respec- tively name DC flow , the degree of connectivity derived using the weighting from R flow , and DC iso , the degree of connectiv- Figure 13. Detailed view of differences and relative differences between R flow and R iso in a highly anthropized environment. ...
Similar publications
Surface texture analysis applied to high-resolution digital terrain models
(HRDTMs) is a promising approach for extracting useful fine-scale
morphological information. Surface roughness, considered here as a synonym
of surface texture, can have a discriminant role in the detection of
different geomorphic processes and factors. Very often, the local...
Citations
... The TWI combines the contribution to runoff from a local area drained and the pending of it, and it is commonly used to quantify topographic control on hydrological processes and is defined as [6][7][8][9][10][18][19][20] (Equation (12): ...
... and it is commonly used to quantify topographic control on hydrological processes and is defined as 412 [6][7][8][9][10][18][19][20] (Equation (12): ...
... Flow accumulation is calculated as the upslope contributing (catchment) area using the D8 model or multiple flow direction approach [17][18][19]. The result is an accumulated flow raster for each cell, determined by the accumulation of the weight of all of the cells that flow into each cell of the descending slope. ...
On 3 June 2018, a strong eruption of the Fuego volcano in Guatemala produced a dense cloud of 10-km-high volcanic ash and destructive pyroclastic flows that caused nearly 200 deaths and huge economic losses in the region. Subsequently, due to heavy rains, destructive secondary lahars were produced, which were not plotted on the hazard maps using the LAHAR Z software. In this work we propose to complement the mapping of this type of lahars using remote-sensing (Differential Interferometry, DINSAR) in Sentinel images 1A and 2A, to locate areas of deformation of the relief on the flanks of the volcano, areas that are possibly origin of these lahars. To determine the trajectory of the lahars, parameters and morphological indices were analyzed with the software System for Automated Geoscientific Analysis (SAGA). The parameters and morphological indices used were the accumulation of flow (FCC), the topographic wetness index (TWI), the length-magnitude factor of the slope (LS). Finally, a slope stability analysis was performed using the Shallow Landslide Susceptibility software (SHALSTAB) based on the Mohr-Coulomb theory and its parameters: internal soil saturation degree and effective precipitation, parameters required to destabilize a hillside. In this case, the application of this complementary methodology provided a more accurate response of the areas destroyed by primary and secondary lahars in the vicinity of the volcano.
