Gully erosion susceptibility assessment by means of GIS-based logistic regression: A case of Sicily (Italy)

Abstract

This research aims at characterizing susceptibility conditions to gully erosion by means of GIS and multivariate statistical analysis. The study area is a 9.5 km 2 river catchment in central-northern Sicily, where agriculture activities are limited by intense erosion. By means of field surveys and interpretation of aerial images, we prepared a digital map of the spatial distribution of 260 gullies in the study area. In addition, from available thematic maps, a 5 m cell size digital elevation model and field checks, we derived 27 environmental attributes that describe the variability of lithology, land use, topography and road position. These attributes were selected for their potential influence on erosion processes, while the dependent variable was given by presence or absence of gullies within two different types of mapping units: 5 m grid cells and slope units (average size = 2.66 ha). The functional relationships between gully occurrence and the controlling factors were obtained from forward stepwise logistic regression to calculate the probability to host a gully for each mapping unit. In order to train and test the predictive models, three calibration and three validation subsets, of both grid cells and slope units, were randomly selected. Results of validation, based on ROC (receiving operating characteristic) curves, attest for acceptable to excellent accuracies of the models, showing better predictive skill and more stable performance of the susceptibility model based on grid cells.

UAS photogrammetry and object-based image analysis (GEOBIA): erosion monitoring at the Kazár badland, Hungary

“…The NE Hungarian badlands are unique landforms in the Carpathian Basin; therefore, it is quite topical to reveal the recent degradation processes there. High resolution digital elevation models are the essential elements for the majority of the calculations and models referring to watershed analysis (Czigány et al 2013), valley development (Telbisz et al 2012), soil erosion and badland morphology (Conoscenti et al 2008(Conoscenti et al , 2014Lopez Saez et al 2011) that could be combined with several other factors as well (i.e. lithology, land use) for delineating the erosion susceptible areas for instance.…”

Forest Fire Severity Assessment Using ALS Data in a Mediterranean Environment

“…It enables the creation of reliable predictive models from explanatory variables (either continuous or discrete). This technique, already used successfully to evaluate several phenomena-e.g., landslide hazard, water erosion, sinkhole susceptibility, human-caused ignition, etc.-evaluates the probability of an event occurrence (P) by estimating the possibility that a case will be classified into one of two mutually exclusive categories as opposed to the other category of the dependent dichotomous variable [54]. In this research, we used logistic regression to establish a functional relationship between the presence or not of fire impact within a plot in a binary-coded manner (1 = high burn severity; 0 = absence or low burn severity) and the set of the independent variables derived from ALS data.…”

“…The Hosmer and Lemeshow test is similar to a chi-square test, and it indicates the extent to which the model provides better fit than a null model with no predictors, i.e., how well the model fits the data, as in log-linear modelling. The significance was assessed individually for each independent variable incorporated in the model by means of the Wald test [50,54]. …”

Landslide Fissure Inference Assessment by ANFIS and Logistic Regression Using UAS-Based Photogrammetry

“…Despite their lower resolutions compared to UAS imagery, satellite and aerial images as data sources have also been studied for fissure extraction by various researchers [23,[34][35][36][37][38]. Youssef et al [34] demonstrated the use of high-resolution satellite images using QuickBird imagery, acquired on 2 June 2007 (0.61 m spatial resolution), for detailed mapping of recent developments and slope instability hazard zones.…”

“…Shruthi et al [37] also investigated the use of object-oriented image analysis to extract erosion features, using a combination of topographic, spectral, shape (geometric) and contextual information obtained from satellite imagery. Conoscenti et al [38] presented a characterization of susceptibility conditions using DEM and aerial images for gully erosion by means of GIS and multivariate statistical analysis. Table 1 summarizes scales, precisions, and data sources of some mentioned scientific analyses.…”