Search results for "gully"

Measuring, modelling and managing gully erosion at large scales: A state of the art

2018

Soil erosion is generally recognized as the dominant process of land degradation. The formation and expansion of gullies is often a highly significant process of soil erosion. However, our ability to assess and simulate gully erosion and its impacts remains very limited. This is especially so at regional to continental scales. As a result, gullying is often overlooked in policies and land and catchment management strategies. Nevertheless, significant progress has been made over the past decades. Based on a review of >590 scientific articles and policy documents, we provide a state-of-the-art on our ability to monitor, model and manage gully erosion at regional to continental scales. In this…

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

2014

article i nfo Article history: 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 ac- tivities are limited by intense erosion. By means of field surveys and interpretation of aerial images, we prepared a digitalmap of thespatial distribution of 260 gulliesinthestudy area.Inaddition,fromavailable thematicmaps, 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 f…

Rilievo di un ephemeral gully nell’area sperimentale di Sparacia mediante una tecnica fotografica

2016

Nella memoria sono riportati i risultati dell’applicazione di una tecnica imagebased per il monitoraggio di un ephemeral gully formatosi nel gennaio 2015 nell’area sperimentale di Sparacia. Nel gully, lungo 54 m, sono state individuate 24 sezioni trasversali che sono state rilevate in campo mediante l’uso di un profilometro. L’indagine ha inoltre previsto la realizzazione di un modello tridimensionale del terreno (DTM) ottenuto con l’impiego di un numero elevato di fotografie della stessa scena acquisite da differenti punti di vista (Tecnica “Structure-From Motion” SFM e “Multi-View-Stereo MVS). Dal DTM tridimensionale (3D) e dal modello 2.5D sono stati estratti i profili delle sezioni tras…

Measuring rill erosion at plot scale by a drone-based technology

2015

The traditional direct method (i.e. metric ruler and rillmeter) of monitoring rill erosion at plot scale is time consuming and invasive since it modifies the surface of the rilled area. Measuring rill features using a drone-based technology is considered a non-invasive method allowing a fast field relief. In the experimental Sparacia area a survey by a quadricopter Microdones md4-200 was carried out and this relief allowed the generation of a Digital Elevation Model (DEM), with a mesh size of 1 cm and a resolution elevation equal to 2 mm, for three plots (L, G and C) affected by rill erosion. At first for the experimental L plot, which is 44 m long, the rill features were surveyed by a “man…

Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion

2019

Assessing the performance of GIS- based machine learning models withdifferent accuracy measures for determining susceptibility togully erosionYounes Garosia, Mohsen Sheklabadia,⁎, Christian Conoscentib, Hamid Reza Pourghasemic,d, Kristof Van Ooste,faFaculty of Agriculture, Department of Soil Science, Bu Ali Sina University, Ahmadi Roshan Avenue, 6517838695 Hamedan, IranbDepartment of Earth and Sea Sciences (DISTEM), University of Palermo, Via Archirafi22, 90123 Palermo, ItalycCollege of Marine Sciences and Engineering, Nanjing Normal University, Nanjing, 210023, ChinadDepartment of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, IraneA- Fo…

Corrigendum to “Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gu…

2020

Evaluation of multi-hazard map produced using MaxEnt machine learning technique.

2021

Abstract Natural hazards are diverse and uneven in time and space, therefore, understanding its complexity is key to save human lives and conserve natural ecosystems. Reducing the outputs obtained after each modelling analysis is key to present the results for stakeholders, land managers and policymakers. So, the main goal of this survey was to present a method to synthesize three natural hazards in one multi-hazard map and its evaluation for hazard management and land use planning. To test this methodology, we took as study area the Gorganrood Watershed, located in the Golestan Province (Iran). First, an inventory map of three different types of hazards including flood, landslides, and gul…

Similitudine nelle caratteristiche morfologiche dei rill e degli ephemeral gully

2008

Gully erosion susceptibility mapping using GIS-based multi-criteria decision analysis techniques

2019

Abstract This research introduces a scientific methodology for gully erosion susceptibility mapping (GESM) that employs geography information system (GIS)-based multi-criteria decision analysis. The model was tested in Semnan Province, Iran, which has an arid and semi-arid climate with high susceptibility to gully erosion. The technique for order of preference by similarity to ideal solution (TOPSIS) and the analytic hierarchy process (AHP) multi-criteria decision-making (MCDM) models were integrated. The important aspect of this research is that it did not require gully erosion inventory maps for GESM. Therefore, the proposed methodology could be useful in areas with missing or incomplete …

A methodological comparison of head-cut based gully erosion susceptibility models

2020

Abstract A GIS-based hybrid approach for gully erosion susceptibility mapping (GESM) in the Biarjamand watershed in Iran is presented. A database comprised of 15 geo-environmental factors (GEFs) was compiled and used to predict the spatial distribution of 358 gully locations; 70% (251) of which were extracted for training and 30% (107) for validation. A Dempster-Shafer (DS) statistical model was employed to map susceptibility. Next, the results of four kernels (binary logistic, reg logistic, binary logitraw, and reg linear) of a boosted regression tree (BRT) model were combined to increase the efficiency and accuracy of the mapping. Area under receiver operating characteristics (AUROC), tru…