A century of gully erosion research: Urgency, complexity and study approaches

Castillo, Carlos; Gómez, José A.

doi:10.1016/j.earscirev.2016.07.009

Cited by 279 publications

(224 citation statements)

References 217 publications

Supporting

Mentioning

207

Contrasting

Unclassified

Order By: Relevance

“…Another significant factor in triggering and acceleration of soil erosion processes is overgrazing [49]; there are many studies worldwide, from different geographic areas, highlighting the negative effects of overgrazing. One of the approaches is referring to the desertification [50,51], erosion of mountain pastures [52], degradation of forests [53], and so on.…”

Section: And If Better Validation Results Are Obtained In This Waymentioning

confidence: 99%

Is Overgrazing Really Influencing Soil Erosion?

Nicu

2018

Water

View full text Add to dashboard Cite

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 km 2 . 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.

show abstract

Section: And If Better Validation Results Are Obtained In This Waymentioning

confidence: 99%

Is Overgrazing Really Influencing Soil Erosion?

Nicu

2018

Water

View full text Add to dashboard Cite

show abstract

“…Gully erosion, the main type of soil erosion by water, is a major land degradation process that adversely affects land management and agriculture [6]. In the past century, an increasing number of researches focus on the gully erosion due to its ubiquity and severity [7]. Evaluation of gully-affected areas is the basis for control and monitoring of gully erosion; as such, detection of these areas has become a growing interest in gully erosion community [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Detection of Catchment-Scale Gully-Affected Areas Using Unmanned Aerial Vehicle (UAV) on the Chinese Loess Plateau

Liu

Ding

Tang

et al. 2016

IJGI

View full text Add to dashboard Cite

Abstract:The Chinese Loess Plateau suffers from serious gully erosion induced by natural and human causes. Gully-affected areas detection is the basic work in this region for gully erosion assessment and monitoring. For the first time, an unmanned aerial vehicle (UAV) was applied to extract gully features in this region. Two typical catchments in Changwu and Ansai were selected to represent loess tableland and loess hilly regions, respectively. A high-powered quadrocopter (md4-1000) equipped with a non-metric camera was used for image acquisition. InPho and MapMatrix were applied for semi-automatic workflow including aerial triangulation and model generation. Based on the stereo-imaging and the ground control points, the highly detailed digital elevation models (DEMs) and ortho-mosaics were generated. Subsequently, an object-based approach combined with the random forest classifier was designed to detect gully-affected areas. Two experiments were conducted to investigate the influences of segmentation strategy and feature selection. Results showed that vertical and horizontal root-mean-square errors were below 0.5 and 0.2 m, respectively, which were ideal for the Loess Plateau region. The overall extraction accuracy in Changwu and Ansai achieved was 84.62% and 86.46%, respectively, which indicated the potential of the proposed workflow for extracting gully features. This study demonstrated that UAV can bridge the gap between field measurement and satellite-based remote sensing, obtaining a balance in resolution and efficiency for catchment-scale gully erosion research.

show abstract

“…Gully erosion is a globally significant land degradation process that has detrimental environmental, ecological and economic impacts across a range of landscape systems [1][2][3][4][5][6][7]. Effective management of gully erosion requires high-resolution topographic information that can be used to accurately quantify the spatial distribution and density of gully systems, gully morphology and morphologic change, and the catchment characteristics that influence gully evolution [2,[8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Assessment of UAV and Ground-Based Structure from Motion with Multi-View Stereo Photogrammetry in a Gullied Savanna Catchment

Koci

Jarihani

Leon

et al. 2017

IJGI

View full text Add to dashboard Cite

Abstract:Structure from Motion with Multi-View Stereo photogrammetry (SfM-MVS) is increasingly used in geoscience investigations, but has not been thoroughly tested in gullied savanna systems. The aim of this study was to test the accuracy of topographic models derived from aerial (via Unmanned Aerial Vehicle, 'UAV') and ground-based (via handheld digital camera, 'ground') SfM-MVS in modelling hillslope gully systems in a dry-tropical savanna, and to assess the strengths and limitations of the approach at a hillslope scale and an individual gully scale. UAV surveys covered three separate hillslope gully systems (with areas of 0.412-0.715 km 2 ), while ground surveys assessed individual gullies within the broader systems (with areas of 350-750 m 2 ). SfM-MVS topographic models, including Digital Surface Models (DSM) and dense point clouds, were compared against RTK-GPS point data and a pre-existing airborne LiDAR Digital Elevation Model (DEM). Results indicate that UAV SfM-MVS can deliver topographic models with a resolution and accuracy suitable to define gully systems at a hillslope scale (e.g., approximately 0.1 m resolution with 0.4-1.2 m elevation error), while ground-based SfM-MVS is more capable of quantifying gully morphology (e.g., approximately 0.01 m resolution with 0.04-0.1 m elevation error). Despite difficulties in reconstructing vegetated surfaces, uncertainty as to optimal survey and processing designs, and high computational demands, this study has demonstrated great potential for SfM-MVS to be used as a cost-effective tool to aid in the mapping, modelling and management of hillslope gully systems at different scales, in savanna landscapes and elsewhere.

show abstract

A century of gully erosion research: Urgency, complexity and study approaches

Cited by 279 publications

References 217 publications

Is Overgrazing Really Influencing Soil Erosion?

Is Overgrazing Really Influencing Soil Erosion?

Detection of Catchment-Scale Gully-Affected Areas Using Unmanned Aerial Vehicle (UAV) on the Chinese Loess Plateau

Assessment of UAV and Ground-Based Structure from Motion with Multi-View Stereo Photogrammetry in a Gullied Savanna Catchment

Contact Info

Product

Resources

About