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18 pages, 8984 KiB  
Article
Factors Influencing Ephemeral Gullies at the Regional Scale: Formation and Density
by Lei Ma, Chunmei Wang, Yuan Zhong, Guowei Pang, Lei Wang, Yongqing Long, Qinke Yang and Bingzhe Tang
Land 2024, 13(4), 553; https://doi.org/10.3390/land13040553 - 20 Apr 2024
Viewed by 1050
Abstract
Ephemeral gully (EG) erosion is an important type of water erosion. Understanding the spatial distribution of EGs and other influencing factors at a regional scale is crucial for developing effective soil and water management strategies. Unfortunately, this area has not been sufficiently studied. [...] Read more.
Ephemeral gully (EG) erosion is an important type of water erosion. Understanding the spatial distribution of EGs and other influencing factors at a regional scale is crucial for developing effective soil and water management strategies. Unfortunately, this area has not been sufficiently studied. The present study visually interpreted the EGs based on Google Earth images in 137 small watersheds uniformly distributed in the Loess Plateau, compared them with measured results, and analyzed the factors influencing EG formation and density using GeoDetector. The results showed that visually interpreting EGs from Google Earth images was suitable for EG regional studies. Out of the 137 small watersheds, 33.6% had EG occurrence with an average density of 3.41 km/km2. Rainfall (R) and slope gradient (S) were the primary factors influencing the formation of EGs, while the area proportion of sloping farmland (APSF) and soil erodibility (K) were the main factors affecting EG density. The interaction of dual factors had a greater influence compared to single factors, with the interaction between S and Normalized Difference Vegetation Index (NDVI) having the greatest impact on EG formation and the interaction between K and NDVI on EG density. Although natural forces significantly influence whether EGs can form in a specific area, human activities greatly affect the density of the gullies that develop. This underscores the importance of proper land management in controlling gully erosion. These findings could provide theoretical support for EG prediction models and a scientific basis for soil and water loss control strategies at the regional scale. Full article
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Figure 1

Figure 1
<p>Schematic diagram of the study area. (<b>a</b>) study area; (<b>b</b>) small watershed unit; (<b>c</b>) rectangular unit.</p>
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<p>Example of ephemeral gully interpretation.</p>
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<p>Overall methodology flowchart.</p>
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<p>The frequency of the relative error of ephemeral gully length. (<b>a</b>) Relative error of single EG. (<b>b</b>) Pareto Chart of the Relative Error in EGs Length.</p>
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<p>Spatial distribution of ephemeral gully density.</p>
Full article ">Figure 6
<p>The formation of EGs is influenced by several main factors. (<b>a</b>) Spatial distribution of EGs with different rainfall. (<b>b</b>) The proportion of EG sample units within the sample units with different rainfall. (<b>c</b>) Spatial distribution of EGs with different slope gradient. (<b>d</b>) The proportion of EG sample units within the sample units with different slope gradient.</p>
Full article ">Figure 7
<p>Results of interaction detection of EGs formation driving factor.</p>
Full article ">Figure 8
<p>The density of EGs is influenced by several main factors. (<b>a</b>) Spatial distribution of EG density with different proportions of sloping farmland. (<b>b</b>) Relationship between the area proportion of slopping farmland and density of EGs. (<b>c</b>) Spatial distribution of EG density with different soil erodibility. (<b>d</b>) Relationship between soil erodibility and density of EGs.</p>
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<p>Results of interaction detection of EG density driving factor.</p>
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3 pages, 185 KiB  
Editorial
Soil Erosion Measurement Techniques and Field Experiments
by Vito Ferro and Alessio Nicosia
Water 2023, 15(15), 2846; https://doi.org/10.3390/w15152846 - 7 Aug 2023
Cited by 1 | Viewed by 3142
Abstract
Soil erosion is a process in which soil particles are first detached from the soil surface and then transported by erosive agents such as rainfall, overland flow and channelized flows in rills, ephemeral gullies and gullies [...] Full article
(This article belongs to the Special Issue Soil Erosion Measurement Techniques and Field Experiments)
28 pages, 135573 KiB  
Article
Nature-Based Solutions for Flood Mitigation and Soil Conservation in a Steep-Slope Olive-Orchard Catchment (Arquillos, SE Spain)
by Patricio Bohorquez, Francisco José Pérez-Latorre, Inmaculada González-Planet, Raquel Jiménez-Melero and Gema Parra
Appl. Sci. 2023, 13(5), 2882; https://doi.org/10.3390/app13052882 - 23 Feb 2023
Cited by 2 | Viewed by 2315
Abstract
The frequency and magnitude of flash floods in the olive orchards of southern Spain have increased because of climate change and unsustainable olive-growing techniques. Affected surfaces occupy >85% of the rural regions of the Upper Guadalquivir Basin. Dangerous geomorphic processes record [...] Read more.
The frequency and magnitude of flash floods in the olive orchards of southern Spain have increased because of climate change and unsustainable olive-growing techniques. Affected surfaces occupy >85% of the rural regions of the Upper Guadalquivir Basin. Dangerous geomorphic processes record the increase of runoff, soil loss and streamflow through time. We report on ripple/dune growth over a plane bed on overland flows, deep incision of ephemeral gullies in olive groves and rock-bed erosion in streams, showing an extraordinary sediment transport capacity of sub-daily pluvial floods. We develop a novel method to design optimal solutions for natural flood management and erosion risk mitigation. We adopt physical-based equations and build a whole-system model that accurately reproduces the named processes. The approach yields the optimal targeted locations of nature-based solutions (NbSs) for active flow-control by choosing the physical-model parameters that minimise the peak discharge and the erosion-prone area, maximising the soil infiltration capacity. The sub-metric spatial resolution used to resolve microtopographic features of terrains/NbS yields a computational mesh with millions of cells, requiring a Graphics Processing Unit (GPU) to run massive numerical simulations. Our study could contribute to developing principles and standards for agricultural-management initiatives using NbSs in Mediterranean olive and vineyard orchards. Full article
(This article belongs to the Special Issue Sediment Transport)
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Figure 1
<p>General characteristics of the Manillas basin in Arquillos (Jaén, Southeast Spain). (<b>a</b>) Catchment overview (perimeter highlighted with the black solid line) with three regions of interest: the urban areas (black), the man-made channel that should protect the town from inundation (thick line in brown), and olive trees (green dots). The drainage network is composed of rills, gullies, and streams. (<b>b</b>) Hypsometric map and drainage network. For completeness, main traffic roads are coloured in orange. Maps created from scratch with Matlab and GlobalMapper based on a Light Detection and Ranging (LiDAR) dataset acquired on May 2021 with 1.5 points per square meter (<a href="https://www.ign.es/" target="_blank">https://www.ign.es/</a>, accessed on 1 July 2022). Rills, gullies, and streams were obtained from the distributed hydrological simulations presented in this paper.</p>
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<p>(<b>a</b>) DEM, (<b>b</b>) orthophoto, and (<b>c</b>) DEM slope in a small area surrounding the Arquillos town. We built the DEM from scratch using filtered LiDAR data and in-situ measurements with Leica Zeno 20 Global Positioning System (GPS). (<b>d</b>) Zoom of the computational mesh for the artificial channel and the floodplains. Note the hybrid topology of the cells with rectangular elements in the channel and triangles otherwise.</p>
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<p>Maximum values of daily (<b>a</b>) and hourly (<b>b</b>) precipitation depth recorded 1950–2022 and 2010–2022, respectively. Data source: <a href="http://www.chguadalquivir.es/saih/" target="_blank">http://www.chguadalquivir.es/saih/</a>, accessed on 1 July 2022.</p>
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<p>(<b>a</b>) Regions defined in <a href="#sec2dot2dot3-applsci-13-02882" class="html-sec">Section 2.2.3</a> to assign the Manning roughness. (<b>b</b>) Orthophoto in the dashed rectangular area of panel a showing sandy clay loam on the surface outside the olive tree canopy projection and inside streams because of the herbicide uses. The blue and red polygons are possible retention basins based on an artificial wetland or a large irrigation pond. (<b>c</b>–<b>e</b>) Possible NbS using ground branches, gravels and natural full-cover crop.</p>
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<p>Map and histogram of the local values of the Curve Number (CN) for the simulation setups CN64 (<b>a</b>,<b>b</b>) and CN89 (<b>c</b>,<b>d</b>) in the Arquillos Basin, computed and drawn by the authors using the Methods detailed in <a href="#sec2dot2dot4-applsci-13-02882" class="html-sec">Section 2.2.4</a>.</p>
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<p>Map of the maximum values of the (<b>a</b>) flow depth <span class="html-italic">h</span> [m], (<b>b</b>) magnitude of the velocity vector <math display="inline"><semantics> <mrow> <mo>|</mo> <mi mathvariant="bold">u</mi> <mo>|</mo> </mrow> </semantics></math> [m·s<sup>−1</sup>], and (<b>c</b>) magnitude of the shear stress vector <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mi mathvariant="bold-italic">τ</mi> <mi>b</mi> </msub> <mrow> <mo>|</mo> </mrow> </mrow> </semantics></math> [N·m<sup>−2</sup>] at peak flow (i.e., <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> h) for the present-day soil use and management (simulation CN89-A).</p>
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<p>Examples of fluvial geomorphological features in the Manillas watershed that serve to verify the accuracy of the simulation CN89-A. (<b>a</b>) Gully network in the eastern headwater (red rectangle in <a href="#applsci-13-02882-f006" class="html-fig">Figure 6</a>c): the left column shows a zoom of the shear-stress map, an orthophoto and the slope direction map for the DEM; the photo in the right-hand-side depicts an upstream view of the existing gullies. (<b>b</b>) Confined single-thread channel eroded in a rocky bed due to high shear stresses. (<b>c</b>) Paleostage indicators of flood levels and bedforms developing in unconfined flow on a floodplain. Photographs b and c were taken in the locations marked with the red and yellow dots in <a href="#applsci-13-02882-f001" class="html-fig">Figure 1</a>a few meters downstream and upstream of the cross between the Manillas stream and the road, respectively.</p>
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<p>(<b>a</b>) Map of the simulated flow depth (<span class="html-italic">h</span> in meter), for the present-day scenario CN89-A at the time of peak flow (i.e., <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> h), in the urban area inundated downstream of the overtopped artificial channel. Flow from right to left. The region in panel a occupies the blue rectangle in the flow-depth map shown in <a href="#applsci-13-02882-f006" class="html-fig">Figure 6</a>a. The pictures on the right-hand side correspond to the inundations on (<b>b</b>) 15 August 1996 and (<b>c</b>) 8 March 2013 in the boxed area of panel a when the artificial channel was unable to protect the town from flooding.</p>
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<p>Hydrograph at the basin outlet for the fifteen physical scenarios.</p>
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<p>Histograms of the simulated (<b>a</b>) flow depth <span class="html-italic">h</span>, (<b>b</b>) magnitude of the velocity vector <math display="inline"><semantics> <mrow> <mo>|</mo> <mi mathvariant="bold-italic">u</mi> <mo>|</mo> </mrow> </semantics></math>, and (<b>c</b>) the skin friction <math display="inline"><semantics> <msub> <mi>τ</mi> <mrow> <mi>b</mi> <mi>s</mi> </mrow> </msub> </semantics></math> for the fifteen scenarios explained in <a href="#sec3dot3-applsci-13-02882" class="html-sec">Section 3.3</a>.</p>
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<p>Optimal targeted placements of specific NbS to provide the most benefits and maximise their effectiveness for flood management and soil conservation, as described in <a href="#sec3dot4-applsci-13-02882" class="html-sec">Section 3.4</a>. The photos illustrate the actions at each location, as indicated by the frame colour.</p>
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<p>(<b>a</b>) Simulated hydrographs in the Manillas stream, near the town, for the current state (black) and the possible future (blue) after implementing the combined NbS B and D. (<b>b</b>–<b>e</b>) Snapshots of the simulated flow depth (in meters) with NbS B and D at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>60</mn> </mrow> </semantics></math>, 100, 180, and 200 min corresponding, respectively, with the streamflows <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>4.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>17.6</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>39.2</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mn>39.1</mn> </mrow> </semantics></math> m<sup>3</sup>·s<sup>−1</sup> (squares in panel a). (<b>f</b>) Maximum infiltration rate (in mm·h<sup>−1</sup>) achieved at the end of the rain (i.e., <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>180</mn> </mrow> </semantics></math> min) for the future scenario with NbS B and D.</p>
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23 pages, 1017 KiB  
Review
Machine Learning Techniques for Gully Erosion Susceptibility Mapping: A Review
by Hamid Mohebzadeh, Asim Biswas, Ramesh Rudra and Prasad Daggupati
Geosciences 2022, 12(12), 429; https://doi.org/10.3390/geosciences12120429 - 22 Nov 2022
Cited by 10 | Viewed by 3004
Abstract
Gully erosion susceptibility mapping (GESM) through predicting the spatial distribution of areas prone to gully erosion is required to plan gully erosion control strategies relevant to soil conservation. Recently, machine learning (ML) models have received increasing attention for GESM due to their vast [...] Read more.
Gully erosion susceptibility mapping (GESM) through predicting the spatial distribution of areas prone to gully erosion is required to plan gully erosion control strategies relevant to soil conservation. Recently, machine learning (ML) models have received increasing attention for GESM due to their vast capabilities. In this context, this paper sought to review the modeling procedure of GESM using ML models, including the required datasets and model development and validation. The results showed that elevation, slope, plan curvature, rainfall and land use/cover were the most important factors for GESM. It is also concluded that although ML models predict the locations of zones prone to gullying reasonably well, performance ranking of such methods is difficult because they yield different results based on the quality of the training dataset, the structure of the models, and the performance indicators. Among the ML techniques, random forest (RF) and support vector machine (SVM) are the most widely used models for GESM, which show promising results. Overall, to improve the prediction performance of ML models, the use of data-mining techniques to improve the quality of the dataset and of an ensemble estimation approach is recommended. Furthermore, evaluation of ML models for the prediction of other types of gully erosion, such as rill–interill and ephemeral gully should be the subject of more studies in the future. The employment of a combination of topographic indices and ML models is recommended for the accurate extraction of gully trajectories that are the main input of some process-based models. Full article
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<p>Classification of ML techniques.</p>
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15 pages, 2602 KiB  
Article
Effects of Erosion Micro-Topographies on Plant Colonization on Weathered Gangue Dumps in Northeast China
by Dongli Wang, Jingting Qiao, Ye Zhang, Tong Wu, Jia Li, Dong Wang, Xiaoliang Zhao, Haiou Shen and Junliang Zou
Sustainability 2022, 14(14), 8468; https://doi.org/10.3390/su14148468 - 11 Jul 2022
Cited by 1 | Viewed by 1560
Abstract
Micro-topography has been proved to be beneficial for plant colonization in severe environments. There are numerous micro-topographies caused by erosion of gangue dumps in the Northeast China, which can make plant colonization difficult. To determine how these micro-topographies affect plant colonization, the environment [...] Read more.
Micro-topography has been proved to be beneficial for plant colonization in severe environments. There are numerous micro-topographies caused by erosion of gangue dumps in the Northeast China, which can make plant colonization difficult. To determine how these micro-topographies affect plant colonization, the environment conditions, regeneration characteristics, vegetation characteristics of different erosion micro-topographies, such as bare slope, rill, ephemeral gully and deposit body were studied, and their relationships analyzed. The results showed that the content of particles with a size < 2 mm in the deposit body and bare slope was 33.7% and 7.8% higher than that in the ephemeral gully, respectively (p < 0.05), while the content of particles with a size > 20 mm in the ephemeral gully was 2.24 times higher than that in the deposit body. Except for the substrate water content, the substrate temperature and the surface humidity and temperature of the ephemeral gully were significantly different from those of the deposit body (p < 0.05); the surface temperature was the highest (54.6 °C) while the surface humidity and the substrate water content were the lowest among the erosion micro-topographies. The vegetation coverage, the plant and seedling density of the deposit body were significantly higher than those of the ephemeral gully (p < 0.05), with differences of 5.26, 35.9 and 16.8 times, respectively. The vegetation characteristics (Vdc) were more affected by the regeneration characteristics (Rc) as well as surface humidity and temperature (Sht), while Rc was significantly affected by Sht, which was extremely significantly affected by the soil physical properties and substrate water and temperature (p < 0.01). Different plant species had different responses to the environmental conditions of the erosion micro-topographies. In conclusion, the deposit body and rill are likely to promote plant colonization, which is driven mainly by the seed supply and comfortable growing conditions. The ephemeral gully is not suited to plant colonization because of its unhealthy mechanical composition and strong runoff scouring, and because it is prone to drought, high temperature, and a lack of seeds. Full article
(This article belongs to the Section Environmental Sustainability and Applications)
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Figure 1
<p>The location of the study region and respective sample sites with vivid photos of different soil erosion micro-topographies.</p>
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<p>Physical and chemical properties of substrates under different erosion micro-habitats. B, bare slope; R, rill; E, ephemeral gully; D, deposit body. Different letters (e.g., a, b) above the error bar in each column indicate there are significant differences between erosion micro-habitats at <span class="html-italic">p</span> &lt; 0.05 (LSD). The box plots represent the median (middle solid line), the average (dashed line), 25% and 75% percentiles (the lower and upper boundaries of the boxes, respectively), and the 1.5 interquartile range (whiskers). This is applicable to the following figures and tables as well.</p>
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<p>The surface humidity, substrate water content, surface temperature and substrate temperature under different erosion micro-topographies. Different letters (e.g., a, b, c) above the error bar in each column indicate there are significant differences under erosion micro-topographies at <span class="html-italic">p</span> &lt; 0.05 (LSD). The meaning of the abbreviations can be found in <a href="#sustainability-14-08468-f002" class="html-fig">Figure 2</a>.</p>
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<p>The vegetation density and coverage of different species and total plants in different erosion micro-topographies. Different symbol colors represent plant species in different erosion micro-topographies, with black symbols for bare slope, red symbols for rill, blue symbols for ephemeral gully, green symbols for deposit body. Different letters (e.g., a, b) above each error bar in each column indicate there are significant differences between erosion micro-topographies at <span class="html-italic">p</span> &lt; 0.05 (LSD). The meaning of the abbreviations can be found in <a href="#sustainability-14-08468-f002" class="html-fig">Figure 2</a>.</p>
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<p>The density of soil seed bank and seedlings under different erosion micro-topographies. Different letters (e.g., a, b) on right of each row or above the error bar in each column indicate there are significant differences between erosion micro-topographies at <span class="html-italic">p</span> &lt; 0.05 (LSD). The meaning of the abbreviations can be found in <a href="#sustainability-14-08468-f002" class="html-fig">Figure 2</a>.</p>
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<p>The partial least squares path modeling (PLS-PM) for the effects of oil physical properties (<span class="html-italic">Spp</span>), soil chemical properties (<span class="html-italic">Scp</span>), substrate water content and temperature (<span class="html-italic">Swt</span>), surface humidity and temperature (<span class="html-italic">Sht</span>) and regeneration characteristics (<span class="html-italic">Rc</span>) on vegetation density and coverage (<span class="html-italic">Vdc</span>). The values on the arrows from the circle to the rectangle or circle represent the correlation between variables. Positive and negative effects are indicated by the red and blue lines, respectively. * indicates 0.01 ≤ <span class="html-italic">p</span> &lt; 0.05, which means significant correlation, ** and *** indicate 0.01 ≤ <span class="html-italic">p</span> &lt; 0.001 and <span class="html-italic">p</span>≤ 0.001 respectively, which mean extremely significant correlation.</p>
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<p>The relationship between the substrate properties of erosion micro-topographies and plant species. Association of the plant species(density) and the substrate properties was analyzed with Mantel tests. The color gradient indicates Pearson correlation coefficients among the site conditions. The dark color represents negative correlation, the light color represents positive correlation, the number in the circle represents the specific correlation coefficient, the asterisk represents the degree of correlation, * indicates 0.01 ≤ <span class="html-italic">p</span> &lt; 0.05, which means significant correlation, ** and *** indicate 0.01 ≤ <span class="html-italic">p</span> &lt; 0.001 and <span class="html-italic">p</span> ≤ 0.001 respectively, which mean extremely significant correlation.</p>
Full article ">
25 pages, 9984 KiB  
Review
Measurement of Water Soil Erosion at Sparacia Experimental Area (Southern Italy): A Summary of More than Twenty Years of Scientific Activity
by Vincenzo Pampalone, Francesco Giuseppe Carollo, Alessio Nicosia, Vincenzo Palmeri, Costanza Di Stefano, Vincenzo Bagarello and Vito Ferro
Water 2022, 14(12), 1881; https://doi.org/10.3390/w14121881 - 11 Jun 2022
Cited by 4 | Viewed by 2577
Abstract
The main purpose of this article is to give a general idea of the scientific activity that was carried out starting from the 2000s on the basis of the data collected in the plots installed at the Sparacia experimental station for soil erosion [...] Read more.
The main purpose of this article is to give a general idea of the scientific activity that was carried out starting from the 2000s on the basis of the data collected in the plots installed at the Sparacia experimental station for soil erosion measurement in Sicily, South Italy. The paper includes a presentation of the experimental site, a description of the methods and procedures for measuring soil erosion processes both available in the literature and applied at the Sparacia station (sediment sampling and water level reading in the storage tanks for total erosion measurements; profilometer, and Structure from Motion technique for rill erosion measurements), and the main results obtained in the monitoring period in the experimental site. The latter concern the effects of plot size and steepness on soil loss, the measurement variability, the frequency analysis of soil loss, the rill erosion characterization, and the comparison between rill and interrill erosion rates. Each of these topics is addressed with multi-temporal analyses performed with increasing size of the available database, which allowed to draw robust conclusions. Soil loss did not vary appreciably with plot length in contrast with the assumption made in the USLE/RUSLE. The variability of the measurements of soil loss, runoff volume, and sediment concentration at the event scale in replicated plots decreased as the mean measured value increased. The normalized event soil loss was distributed according to a two-component distribution. A power relationship between rill volumes and lengths was established. The measurements also confirmed the morphological similarity between the channels of the rills and ephemeral gullies described by a power dimensionless relationship. Rill erodibility of the sampled clay soil varied over time, maintaining relatively low values. Finally, rill erosion was dominant relative to interrill erosion, and a more efficient sediment transport system through the rill network occurred as plot steepness increased. Full article
(This article belongs to the Special Issue Soil Erosion Measurement Techniques and Field Experiments)
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Figure 1
<p>Plan and view of the experimental plots installed on the (<b>a</b>) 14.9% slope and (<b>b</b>) 22% and 26% slopes (modified from [<a href="#B1-water-14-01881" class="html-bibr">1</a>]).</p>
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<p>View of the (<b>a</b>) storage tanks and (<b>b</b>) concentration profile (modified from [<a href="#B6-water-14-01881" class="html-bibr">6</a>]).</p>
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<p>View of the sampler (modified from [<a href="#B24-water-14-01881" class="html-bibr">24</a>]).</p>
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<p>Relationship of total, <span class="html-italic">A<sub>e</sub></span>, rill, and interrill soil loss vs. plot length, λ, for the (<b>a</b>) 1 September 2005 event and (<b>b</b>) 28 June 2008 event (modified from [<a href="#B28-water-14-01881" class="html-bibr">28</a>]).</p>
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<p>Log of the ratio between soil losses, <span class="html-italic">A<sub>e</sub></span>, measured in the plots with length λ = 22 m and plots with λ = 11 m vs. the erosivity index, <span class="html-italic">R<sub>e</sub></span> (MJ mm ha<sup>−1</sup> h<sup>−1</sup>) (modified from [<a href="#B29-water-14-01881" class="html-bibr">29</a>]).</p>
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<p>Plot length, λ, effect on event (<b>a</b>) soil loss per unit area, <span class="html-italic">A<sub>e</sub></span>, (<b>b</b>) runoff coefficient, <span class="html-italic">Q<sub>Re</sub></span>, and (<b>c</b>) sediment concentration, <span class="html-italic">C<sub>e</sub></span>, for the plots with slope steepness <span class="html-italic">s</span> = 14.9% (modified from [<a href="#B30-water-14-01881" class="html-bibr">30</a>]).</p>
Full article ">Figure 7
<p>Plot steepness, <span class="html-italic">s</span>, effect on event (<b>a</b>) soil loss per unit area, <span class="html-italic">A<sub>e</sub></span>, (<b>b</b>) runoff coefficient, <span class="html-italic">Q<sub>Re</sub></span>, and (<b>c</b>) sediment concentration, <span class="html-italic">C<sub>e</sub></span>, for the 22 m long plots (modified from [<a href="#B30-water-14-01881" class="html-bibr">30</a>]).</p>
Full article ">Figure 8
<p>Relative differences in measurements of soil loss between replicated plots, <span class="html-italic">R<sub>diff</sub></span>, vs. the measured soil loss value, <span class="html-italic">M</span>, for all plots established at the Sparacia station and 90 and 95% occurrence intervals for the data (L = lower limit; U = upper limit) calculated according to [<a href="#B39-water-14-01881" class="html-bibr">39</a>] (modified from [<a href="#B40-water-14-01881" class="html-bibr">40</a>]).</p>
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<p>Relative differences in the measurement of soil loss between replicated plots, <span class="html-italic">R<sub>diff</sub></span>, vs. the measured soil loss value, <span class="html-italic">M</span>, for all plots established at the Sparacia and Masse stations (modified from [<a href="#B41-water-14-01881" class="html-bibr">41</a>]).</p>
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<p>Plot of the absolute difference in measurement of soil loss between replicated plots, |<span class="html-italic">P</span> − <span class="html-italic">M</span>|, vs. the measured value, <span class="html-italic">M</span>, and of Equation (2), the regression line associated with a frequency <span class="html-italic">F</span> = 0.87 and the data enveloping line (modified from [<a href="#B42-water-14-01881" class="html-bibr">42</a>]).</p>
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<p>Gumbel’s plot for the sequence of annual maximum soil losses (<span class="html-italic">x</span> = <span class="html-italic">A<sub>e</sub></span>/μ(<span class="html-italic">A<sub>e</sub></span>), <span class="html-italic">y</span> = −ln ln(1/<span class="html-italic">F</span>(<span class="html-italic">x</span>)) is the normalized Gumbel’s variable, α<sub>i</sub> and u<sub>i</sub> are the two parameters of Gumbel’s distribution, modified from [<a href="#B44-water-14-01881" class="html-bibr">44</a>]).</p>
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<p>View of the plot (<b>a</b>) before and (<b>b</b>) after rill network formation. Rill channels are numbered from 1 to 10 (modified from [<a href="#B61-water-14-01881" class="html-bibr">61</a>]).</p>
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<p>View of the P3 plot (22 m × 6 m, <span class="html-italic">s</span> = 26%) with Ground Control Points (GCPs) numbered from 1 to 6, contributing rills, non-contributing rills, and example of sediment deposition area (modified from [<a href="#B67-water-14-01881" class="html-bibr">67</a>]).</p>
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<p>Relationship between the total length, <span class="html-italic">L</span>, and the total volume, <span class="html-italic">V</span>, for rills, EGs, and gullies (modified from [<a href="#B1-water-14-01881" class="html-bibr">1</a>]).</p>
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<p>Relationship between the dimensionless groups <span class="html-italic">wH</span>/<span class="html-italic">L<sub>r,s</sub></span><sup>2</sup> and <span class="html-italic">V<sub>r,s</sub></span>/<span class="html-italic">L<sub>r,s</sub></span><sup>3</sup> (modified from [<a href="#B1-water-14-01881" class="html-bibr">1</a>]).</p>
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<p>Comparison between the <span class="html-italic">L</span>-<span class="html-italic">V</span> power equation and the <span class="html-italic">L</span>-<span class="html-italic">V</span> pairs detected for the plots L (44 m × 8 m), G (33 m × 8 m), and C (22 m × 8 m) using the automatic extraction of the rills (modified from [<a href="#B62-water-14-01881" class="html-bibr">62</a>]).</p>
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<p>Comparison between the dimensionless power equation and the (<span class="html-italic">wH</span>/<span class="html-italic">L<sub>r,s</sub></span><sup>2</sup>, <span class="html-italic">V<sub>r,s</sub></span>/<span class="html-italic">L<sub>r,s</sub></span><sup>3</sup>) pairs detected for the plots L (44 m × 8 m), G (33 m × 8 m), and C (22 m × 8 m) using the automatic extraction of the rills (modified from [<a href="#B62-water-14-01881" class="html-bibr">62</a>]).</p>
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<p>Effect of plot steepness <span class="html-italic">s</span> on drainage density <span class="html-italic">D<sub>k</sub></span> for contributing and non-contributing rills (modified from [<a href="#B67-water-14-01881" class="html-bibr">67</a>]).</p>
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<p>Comparison among the plot soil loss values measured by the different survey methods for the P3 (22 m × 6 m, <span class="html-italic">s</span> = 26%) and P2 (22 m × 6 m, <span class="html-italic">s</span> = 22%) plots (modified from [<a href="#B67-water-14-01881" class="html-bibr">67</a>]).</p>
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<p>Relationship between mean soil loss, μ(<span class="html-italic">A<sub>e</sub></span>), and plot area, <span class="html-italic">A</span>, for some selected events (modified from (<b>a</b>) [<a href="#B35-water-14-01881" class="html-bibr">35</a>], and modified from (<b>b</b>,<b>c</b>) [<a href="#B64-water-14-01881" class="html-bibr">64</a>]).</p>
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13 pages, 2871 KiB  
Article
A Field Investigation on Gully Erosion and Implications for Changes in Sediment Delivery Processes in Some Tributaries of the Upper Yellow River in China
by Hui Yang, Changxing Shi and Jiansheng Cao
ISPRS Int. J. Geo-Inf. 2022, 11(5), 288; https://doi.org/10.3390/ijgi11050288 - 28 Apr 2022
Cited by 4 | Viewed by 2145
Abstract
Erosion and sediment delivery have been undergoing considerable variations in many catchments worldwide owing to climate change and human interference. Monitoring on-site erosion and sediment deposition is crucial for understanding the processes and mechanisms of changes in sediment yield from the catchments. The [...] Read more.
Erosion and sediment delivery have been undergoing considerable variations in many catchments worldwide owing to climate change and human interference. Monitoring on-site erosion and sediment deposition is crucial for understanding the processes and mechanisms of changes in sediment yield from the catchments. The Ten Kongduis (kongdui is the transliteration of ephemeral creeks in Mongolian) are 10 tributaries of the upper Yellow River. Severe erosion in the upstream hills and gullies and huge aeolian sand input in the middle reaches had made the 10 tributaries one of the main sediment sources of the Yellow River, but the gauged sediment discharge of the tributaries has decreased obviously in recent years. In order to find out the mechanisms of changes in the sediment load of the tributaries, topographic surveys of four typical gullies in 3 of the 10 tributaries were made repeatedly in the field with the terrestrial laser scanning (TLS) technique. The results show that all the monitored gullies were silted with a mean net rate of 587–800 g/m2 from November 2014 to June 2015 and eroded by a mean net rate of 185–24,800 g/m2 from June to November 2015. The monitoring data suggest that the mechanism of interseasonal and interannual sediment storage and release existed in the processes of sediment delivery in the kongduis. The contrast of the low gauged sediment load of the kongduis in recent years against the high surveyed gully erosion indicates the reduction in their sediment delivery efficiency, which can be attributed to the diminution in hyperconcentrated flows caused mainly by the increase in vegetation coverage on slopes and partly by construction of sediment-trapping dams in gullies. Full article
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<p>Locations of the 10 kongduis and the plots surveyed (MBLC is a surveyed gully of the Maobula Kongdui named by us; XLGBC and XLGNC are two surveyed gullies of the Xiliugou Kongdui named by us; DLGC is a surveyed gully of the Dongliugou Kongdui named by us).</p>
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<p>Changes in surface elevation of gullies (DEMs of difference) between November 2014 and June 2015 (Refer to <a href="#ijgi-11-00288-f001" class="html-fig">Figure 1</a> for the abbreviations).</p>
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<p>Changes in surface elevation of gullies (DEMs of difference) between June and November 2015 (Refer to <a href="#ijgi-11-00288-f001" class="html-fig">Figure 1</a> for the abbreviations).</p>
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<p>Changes in sediment load of the Maobula and Xiliugou kongduis.</p>
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<p>Comparison of percentages of different daily rainfalls at Dongsheng station between two periods.</p>
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<p>Changes in NDVI in two kongduis.</p>
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18 pages, 6307 KiB  
Article
An Exploration of Loess Landform Development Based on Population Ecology Method
by Ling Yang, Xin Yang and Jiaming Na
ISPRS Int. J. Geo-Inf. 2022, 11(2), 104; https://doi.org/10.3390/ijgi11020104 - 2 Feb 2022
Cited by 3 | Viewed by 2233
Abstract
The study of gully characteristics is one of the most effective ways to explore the loess landform development in the Loess Plateau of China. However, current studies mostly focus on gullies’ overall characteristics and ignore the different composition of the whole gully system. [...] Read more.
The study of gully characteristics is one of the most effective ways to explore the loess landform development in the Loess Plateau of China. However, current studies mostly focus on gullies’ overall characteristics and ignore the different composition of the whole gully system. Therefore, a new perspective is provided in this paper for exploring loess landform development from the population characteristics of the gully system. Firstly, different types of gullies were extracted based on DEM and high-resolution images in three sample watersheds, including hillslope ephemeral gully, bank gully and different-level valley gully. Secondly, population characteristics from the amount, length, age structure and convergent relationship were calculated and analyzed by referring to the biological population in ecology. Finally, the development stages of loess landform in three watersheds were explored based on their population characteristics. The results showed that: (1) The population characteristics, including number density, length density, age structure and convergence, were obviously different in three sample watersheds. (2) The development differences of three watersheds were obtained by synthesizing all population characteristics: Linjiajian was the most developed and oldest watershed, followed by Yangjiaju and then Wangjiagou. (3) The comparison based on the existing soil erosion intensity map and predisposing factors proved that the findings of this paper were more reasonable than that of the traditional hypsometric integral. This research provides a new quantitative-based approach to explore the development degree of loess landform from the gully population, and is a beneficial attempt to combine geomorphology and ecology, further supplementing and improving the study of loess landform development. Full article
(This article belongs to the Special Issue Geomorphometry and Terrain Analysis)
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<p>Location and data of the three selected study areas. (<b>a</b>) digital elevation model of Linjiajian watershed; (<b>b</b>) digital elevation model of Yangjiaju watershed; (<b>c</b>) digital elevation model of Wangjiagou watershed.</p>
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<p>Workflow of the loess landform development research.</p>
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<p>Typical gully system in a loess hilly area of China.</p>
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<p>The schematic diagram of age structure.</p>
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<p>Results of gully system extraction in the study area for (<b>a</b>) Linjiajian, (<b>b</b>) Wangjiagou and (<b>c</b>) Yangjiaju.</p>
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<p>Results of gully system extraction in the study area for (<b>a</b>) Linjiajian, (<b>b</b>) Wangjiagou and (<b>c</b>) Yangjiaju.</p>
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<p>The number density of the loess gully system for the three watersheds.</p>
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<p>Length of the loess gully system for the three watersheds.</p>
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<p>Length Density of the loess gully system.</p>
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<p>The dominant gully type of the loess gully system for (<b>a</b>) Linjiajian, (<b>b</b>) Wangjiagou and (<b>c</b>) Yangjiaju.</p>
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<p>Age structure of the loess gully system for (<b>a</b>) Linjiajian, (<b>b</b>) Wangjiagou and (<b>c</b>) Yangjiaju.</p>
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<p>Hypsometric integral results of the loess gully system for (<b>a</b>) Linjiajian, (<b>b</b>) Wangjiagou and (<b>c</b>) Yangjiaju.</p>
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16 pages, 14479 KiB  
Article
The Suitability of Remote Sensing Images at Different Resolutions for Mapping of Gullies in the Black Soil Region, Northeast China
by Biwei Wang, Zengxiang Zhang, Xiao Wang, Xiaoli Zhao, Ling Yi and Shunguang Hu
Remote Sens. 2021, 13(12), 2367; https://doi.org/10.3390/rs13122367 - 17 Jun 2021
Cited by 7 | Viewed by 2145
Abstract
Remote sensing images with different spatial resolutions have different performance capabilities for gully extraction, so it is very important to study the suitability of different spatial resolutions for this purpose. In this study, part of the black soil area in Northeast China with [...] Read more.
Remote sensing images with different spatial resolutions have different performance capabilities for gully extraction, so it is very important to study the suitability of different spatial resolutions for this purpose. In this study, part of the black soil area in Northeast China with serious gully erosion was taken as the study area, and Google Earth images with seven spatial resolutions ranging from 0.51 to 32.64 m, commonly used in gully erosion research, were selected as data sources. Combined with auxiliary data, gullies were extracted by visual interpretation. The interpretation results of images of different spatial resolutions were analyzed qualitatively and quantitatively, and the interpretation suitability of images of different spatial resolutions for different types of gullies under different classification systems was emphatically explored. The results indicate that the image with a spatial resolution of 1.02 m has the best performance when not considering the types of gullies. However, the image with a spatial resolution of 2.04 m is the most cost-effective and, therefore, the most suitable for general research. When it is necessary to distinguish the type of gully, the image with a spatial resolution of 0.51 m can be adapted for all situations. However, research on ephemeral gullies is of little practical significance. Therefore, the image with a spatial resolution of 1.02 m is the most universally useful image, being cheaper and easier to obtain. When the spatial resolution is 2.04 m or lower, it is necessary to select the spatial resolution according to the gully type required for practical application. When the spatial resolution is 8.16 or lower, the interpretation of gullies becomes very difficult or even impossible. Full article
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Graphical abstract

Graphical abstract
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<p>Location of the study area. (<b>a</b>) China. (<b>b</b>) Soil and water conservation areas of the black soil region, Northeast China. (<b>c</b>) Baiquan County and points of the first investigation. (<b>d</b>) Study area and areas and gullies of the second investigation shown in GE image at 0.5 m spatial resolution.</p>
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<p>Gullies by visual interpretation. (<b>a</b>–<b>f</b>) Gullies visually interpreted using GE images with a resolution of 0.51, 1.02, 2.04, 4.08 and 8.16 m, respectively.</p>
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<p>Area of gullies. (<b>a</b>–<b>c</b>) Area of gullies in validation areas I, II and III, respectively.</p>
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<p>Number of gullies. (<b>a</b>–<b>c</b>) Number of gullies visually interpreted in validation areas I, II and III, respectively.</p>
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<p>Area of gullies. (<b>a</b>,<b>b</b>) Area of permanent gullies and modern incised valleys. (<b>c</b>,<b>d</b>) Area of active gullies and stable gullies. (<b>e</b>,<b>f</b>) Area of untreated gullies and treated gullies.</p>
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15 pages, 4106 KiB  
Article
Population Characteristics of Loess Gully System in the Loess Plateau of China
by Jiaming Na, Xin Yang, Guoan Tang, Weiqin Dang and Josef Strobl
Remote Sens. 2020, 12(16), 2639; https://doi.org/10.3390/rs12162639 - 15 Aug 2020
Cited by 19 | Viewed by 3790
Abstract
Gullies in the Loess Plateau of China vary in developmental stages and morphologic sizes. In this case study, in Linjialian watershed in the loess hilly region, we introduced some perspectives from population ecology to explore the population characteristics of the loess gully system. [...] Read more.
Gullies in the Loess Plateau of China vary in developmental stages and morphologic sizes. In this case study, in Linjialian watershed in the loess hilly region, we introduced some perspectives from population ecology to explore the population characteristics of the loess gully system. Different types of gullies were extracted based on the digital elevation model and imagery data. Population analysis was then carried out from three aspects, namely, quantity, structure, and distribution. Results showed that in terms of the quantity, hillslope ephemeral gullies (187 numbers/km2 in number density) and bank gullies (8.3 km/km2 in length density) are the most active gullies in this area with an exponential growth trend, and the hillslope ephemeral gully is the dominant type. Along with age structure analysis, the pyramid-shaped age structure indicated that the gully system is at its early or middle stages of development. The spatial distribution of hillslope ephemeral gullies has a clear aspect asymmetry pattern, and the bank gully distribution is symmetrical. A hierarchical structure (hillslope ephemeral gully–bank gully–valley gully in upslope–shoulder line–bottom area) in an elevation distribution is presented. These preliminary results are helpful for further understanding the organized, systematic development, and evolution of the gully system. Full article
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Graphical abstract
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<p>Typical gully system in loess hilly area of China.</p>
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<p>Study Area and Data of Linjiajian.</p>
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<p>Field survey. Bank gully heads were measured by GNSS and total station in the study area.</p>
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<p>Workflow of gully system extraction.</p>
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<p>Result of gully system extraction in the study area. Gully system includes hillslope ephemeral gully, bank gully, and multilevel valley gully. The valley gullies were classified in five levels by the Strahler classification rule (1963).</p>
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<p>Quantitative characteristics of the gully system.</p>
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<p>Expansive age pyramid of the gully system.</p>
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<p>Elevation difference of three types of gullies.</p>
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<p>Distribution of hillslope ephemeral and bank gullies with topographic aspect. (<b>a</b>) hillslope ephemeral gullies; (<b>b</b>) bank gullies.</p>
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17 pages, 4910 KiB  
Article
Spatial-Temporal Dynamics of the Ephemeral Gully Belt on the Plowed Slopes of River Basins in Natural and Anthropogenic Landscapes of the East of the Russian Plain
by Oleg Yermolayev, Evgeniya Platoncheva and Benedict Essuman-Quainoo
Geosciences 2020, 10(5), 167; https://doi.org/10.3390/geosciences10050167 - 6 May 2020
Cited by 9 | Viewed by 2564
Abstract
Erosion is the leading process of soil degradation on agricultural land. In the spectrum of erosion processes, the most unfavorable for soil degradation are the processes of linear (ephemeral and gully) erosion. An assessment of the dynamics of linear erosion in the intensive [...] Read more.
Erosion is the leading process of soil degradation on agricultural land. In the spectrum of erosion processes, the most unfavorable for soil degradation are the processes of linear (ephemeral and gully) erosion. An assessment of the dynamics of linear erosion in the intensive farming zone of the European part of Russia (EPR) is relevant due to the lack of generalized data on the development of this type of erosion in the post-Soviet period and also, due to the highest intensity of soil erosion in the ephemeral gully erosion. The development of information technologies and the availability of high-resolution and ultra-high-resolution satellite images make it possible to solve the problems of ephemeral gully erosion belts identification, and also makes it possible to trace the dynamics of development of stream erosion on arable lands over a period characterized by the greatest changes in the climate system and economic conditions in the post-Soviet period (1980s–2010s). The study was conducted on the eastern wing of the boreal ecotone of the Russian Plain within the southern border of these zones of mixed and broad-leaved forests, forest-steppe, and steppe landscapes using the basin approach. For the initial material, satellite images of medium (30 m) and high resolution (0.5–1.5 m) were used in the work. The study used methods of image interpretation such as remote sensing of the earth and geoinformation mapping. For 70 key areas (interfluve spaces of river basins), the study developed a method of geoinformation mapping of the ephemeral gully erosion belt dynamics on arable lands. In the same way, the research developed a system of quantitative indicators characterizing its development on arable slopes. The dynamics of ephemeral gully erosion was evaluated over three-time intervals: the 1980s, 2000s, and 2010s by determining the horizontal dissection (density) and density of ephemeral gully erosion. Over the past 30 years, in the direction from the south of the forest sub-zone to the forest-steppe and steppe landscapes, there was a sharp increase in the horizontal dissection and density of the ephemeral gully network: an average of 4.6 and 10 times, respectively. The ephemeral gully erosion belt advances toward the watershed because of the formation of new erosion in the upper parts of the ephemeral gully networks and its extension, while there is a noticeable reduction in the width of the erosion-weakly active belt-sheet and rill erosion. Full article
(This article belongs to the Special Issue Geography and Geoecology of Rivers and River Basins)
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<p>The allocation of erosion belts using interpretation aerial photographs. A is the detection of the lower boundary of the sheet and rill erosion at the tops of stream erosion; B is the boundary of the gully erosion belt changed by landslide processes (aerial photograph fragment; scale 1:17,000).</p>
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<p>(<b>a</b>) Gullies from the satellite image (scale 1:30,000) and during the field study at different development stages; (<b>b</b>) Gullies forms (1) on topographic map (scale 1:10,000), satellite image and during the field study (Samara river basin), ephemeral gullies forms (2) on satellite image.</p>
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<p>Satellite image interpretation (scale 1:40,000) of the ephemeral gully erosion belt at the interfluve (1 km southwest of the village of Pogromnoye, Orenburg Region). The black line shows the location of the upper boundary of the ephemeral gully erosion belt.</p>
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<p>The layout of the areas to study the dynamics of ephemeral gully erosion (1—key areas in the Mesh river basin, No. 1–7; 2—key areas in the Samara river basin, No. 8–22; 3—key areas of the Medveditsa basin, No. 23–32).</p>
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<p>The dynamics of the horizontal dissection of the ephemeral gully network (the Samara River basin, Orenburg Region) for the periods 1988–2000, 2000–2015 and in general from 1988 to 2015.</p>
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<p>The density dynamics of the ephemeral gully network (the Samara River basin, Orenburg Region) for the periods 1988–2000, 2000–2015 and in general from 1988 to 2015.</p>
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<p>The distribution of areas by exposure of slopes in the Samara river basin (scale 1:80,000).</p>
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<p>The Dynamics of the planar ephemeral gully network pattern for Landsat satellite images at different time (Samara River basin, area No. 8). Red—2015, green—2000, and blue—1988.</p>
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<p>The dynamics of the horizontal dissection of the ephemeral gully network (Medveditsa river basin) for the periods 1988–2000, 2000–2015 and from 1988 to 2015.</p>
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<p>The density dynamics of the ephemeral gully network (Medveditsa river basin) for the periods 1988–2000, 2000–2015 and from 1988 to 2015.</p>
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<p>The dynamic pattern of the horizontal dissection of the ephemeral gully network (the Mesha river basin) in the period from 1988 to 2015.</p>
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<p>The density dynamics of the ephemeral gully network (Mesha river basin) for the period 1988 to 2015.</p>
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17 pages, 4792 KiB  
Article
Towards an Assessment of the Ephemeral Gully Erosion Potential in Greece Using Google Earth
by Christos Karydas and Panos Panagos
Water 2020, 12(2), 603; https://doi.org/10.3390/w12020603 - 23 Feb 2020
Cited by 30 | Viewed by 5545
Abstract
Gully erosion may cause considerable soil losses and produce large volumes of sediment. The aim of this study was to perform a preliminary assessment on the presence of ephemeral gullies in Greece by sampling representative cultivated fields in 100 sites randomly distributed throughout [...] Read more.
Gully erosion may cause considerable soil losses and produce large volumes of sediment. The aim of this study was to perform a preliminary assessment on the presence of ephemeral gullies in Greece by sampling representative cultivated fields in 100 sites randomly distributed throughout the country. The almost 30-ha sampling surfaces were examined with visual interpretation of multi-temporal imagery from the online Google Earth for the period 2002–2019. In parallel, rill and sheet erosion signs, land uses, and presence of terraces and other anti-erosion features, were recorded within every sample. One hundred fifty-three ephemeral gullies were identified in total, inside 22 examined agricultural surfaces. The mean length of the gullies was 55.6 m, with an average slope degree of 9.7%. Vineyards showed the largest proportion of gullies followed by olive groves and arable land, while pastures exhibited limited presence of gullies. Spatial clusters of high gully severity were observed in the north and east of the country. In 77% of the surfaces with gullies, there were no terraces, although most of these surfaces were situated in slopes higher than 8%. It was the first time to use visual interpretation with Google Earth image time-series on a country scale producing a gully erosion inventory. Soil conservation practices such as contour farming and terraces could mitigate the risk of gully erosion in agricultural areas. Full article
(This article belongs to the Special Issue The Effect of Hydrology on Soil Erosion)
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<p>The random sampling scheme within the agricultural land of Greece.</p>
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<p>Examples of ephemeral gully detection. The sampled surfaces are denoted by white circles and gullies by cyan lines in the far views (geographic coordinates and image date in brackets) (<b>a</b>) sparse gullies formed towards a torrent in sloping olive plantation near Gerakini (40°18′56.41″ N/23°25′45.34″ E, 3 November 2016); (<b>b</b>) close view to case (<b>a</b>); (<b>c</b>) the longest identified gully near Almyros (39°10′55.07″ N/22°40′36.56″ E, 28 October 2013); (<b>d</b>) a close view to 0.6-m wide sections of the gully of case (<b>c</b>); (<b>e</b>) long parallel gullies in an arable field out of Sitia (35°11′42.82″ N/26°6′41.08″ E, 12 April 2013); (<b>f</b>) intensive rill and sheet erosion signs close to lake Doirani (41°8′26.07″ N/22°46′18.52″ E, 4 September 2013).</p>
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<p>Examples of ephemeral gully detection. The sampled surfaces are denoted by white circles and gullies by cyan lines in the far views (geographic coordinates and image date in brackets) (<b>a</b>) sparse gullies formed towards a torrent in sloping olive plantation near Gerakini (40°18′56.41″ N/23°25′45.34″ E, 3 November 2016); (<b>b</b>) close view to case (<b>a</b>); (<b>c</b>) the longest identified gully near Almyros (39°10′55.07″ N/22°40′36.56″ E, 28 October 2013); (<b>d</b>) a close view to 0.6-m wide sections of the gully of case (<b>c</b>); (<b>e</b>) long parallel gullies in an arable field out of Sitia (35°11′42.82″ N/26°6′41.08″ E, 12 April 2013); (<b>f</b>) intensive rill and sheet erosion signs close to lake Doirani (41°8′26.07″ N/22°46′18.52″ E, 4 September 2013).</p>
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<p>Geographic distribution of the total gully length per randomly sampled site (graduated symbol in five categories of magnitude).</p>
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<p>Relative proportion of gullies, rills, and sheet erosion in the randomly sampled surfaces; five categories of severity were considered for gullies, and three for rills and sheet erosion.</p>
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<p>Spatial independence of gully length visualized: (<b>a</b>) at the global scale using the Getis-Ord Gi* statistic (percentages indicate level of confidence); and (<b>b</b>) at the local scale using Anselin Local Moran’s I statistic.</p>
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<p>Share of the main agricultural land uses in Greece found to contain ephemeral gullies (CORINE coding in brackets).</p>
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<p>Number of samples found with and without ephemeral gullies (CORINE coding in brackets).</p>
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<p>Trend plots: negative trend between total gully length and number of gullies (<b>a</b>) and between gully length and slope degree (<b>b</b>); positive trend between number of gullies and slope degree (<b>c</b>) and sheet erosion grade and elevation (<b>d</b>).</p>
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<p>Trend plots: negative trend between total gully length and number of gullies (<b>a</b>) and between gully length and slope degree (<b>b</b>); positive trend between number of gullies and slope degree (<b>c</b>) and sheet erosion grade and elevation (<b>d</b>).</p>
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<p>Indicative cases of detected gullies overlaid on the drainage network (<b>a</b>) variety of gullies in a 4.5% slope vines - arable land complex, close to Thiva (38°17′8.45″ N/23°27′1.68″ E, 20 February 2014); (<b>b</b>) the longest detected gully in a 4.4% slope naked arable soil, close to Almyros (39°10′55.07″ N/22°40′36.56″ E, 28 October 2013); (<b>c</b>) short gullies in a 2% slope arable land, close to Agrinio (38°33′59.95″ N/21°23′35.14″ E, 9 September 2009); (<b>d</b>) moderate gullies in 11.4% slope olive groves, close to Gerakini (40°18′56.41″ N/23°25′45.34″ E, 3 November 2016); (<b>e</b>) highly dense gully pattern in a 29.2% slope olive-vines complex, close to Egio (38°33′59.95″ N/21°23′35.14″ E, 9 July 2009); (<b>f</b>) legend.</p>
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<p>Indicative cases of detected gullies overlaid on the drainage network (<b>a</b>) variety of gullies in a 4.5% slope vines - arable land complex, close to Thiva (38°17′8.45″ N/23°27′1.68″ E, 20 February 2014); (<b>b</b>) the longest detected gully in a 4.4% slope naked arable soil, close to Almyros (39°10′55.07″ N/22°40′36.56″ E, 28 October 2013); (<b>c</b>) short gullies in a 2% slope arable land, close to Agrinio (38°33′59.95″ N/21°23′35.14″ E, 9 September 2009); (<b>d</b>) moderate gullies in 11.4% slope olive groves, close to Gerakini (40°18′56.41″ N/23°25′45.34″ E, 3 November 2016); (<b>e</b>) highly dense gully pattern in a 29.2% slope olive-vines complex, close to Egio (38°33′59.95″ N/21°23′35.14″ E, 9 July 2009); (<b>f</b>) legend.</p>
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19 pages, 5348 KiB  
Article
Soil and Water Conservation in Rainfed Vineyards with Common Sainfoin and Spontaneous Vegetation under Different Ground Conditions
by Nahed Ben-Salem, Sara Álvarez and Manuel López-Vicente
Water 2018, 10(8), 1058; https://doi.org/10.3390/w10081058 - 9 Aug 2018
Cited by 44 | Viewed by 6296
Abstract
Soil erosion seriously affects vineyards. In this study, the influence of two vegetation covers on topsoil moisture and the effect of different physiographic conditions on runoff and sediment yields were evaluated in a rainfed vineyard formed by four fields (NE Spain) during 15 [...] Read more.
Soil erosion seriously affects vineyards. In this study, the influence of two vegetation covers on topsoil moisture and the effect of different physiographic conditions on runoff and sediment yields were evaluated in a rainfed vineyard formed by four fields (NE Spain) during 15 months. One field had spontaneous vegetation in the inter-row areas, and three fields had a cover crop of common sainfoin. Moisture conditions were dry and stable in the vineyards’ rows, wet and very variable in the inter-row areas and wet and very stable in the corridors. Topsoil moisture in the areas with common sainfoin was much higher than in the rows (62–70%), whereas this difference was lower with spontaneous vegetation (40%). Two runoff and sediment traps (STs) were installed in two ephemeral gullies, and 26 time-integrated surveys (TIS) were done. The mean runoff yields were 9.8 and 13.5 L TIS−1 in ST2 and ST3. Rainfall depth (12 mm) and erosivity (5.2 MJ mm ha−1 h−1) thresholds for runoff initiation were assessed. The mean turbidity was 333 (ST2) and 19 (ST3) g L−1. Changes in the canopy covers (grapevines and vegetation covers), topography and rainfall parameters explained the runoff and sediment dynamics. Full article
(This article belongs to the Special Issue Soil and Water Conservation in Agricultural and Forestry Systems)
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Graphical abstract

Graphical abstract
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<p>Location of the study area in Huesca Province, NE Spain (<b>a</b>); orthophoto and boundary of “Los Oncenos” sub-catchment and the location of the two sediment traps (<b>b</b>); drainage area (<b>c</b>) and slope gradient (<b>d</b>) of the two sediment traps; location of the two weather stations used in this study (<b>e</b>).</p>
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<p>Location of the topsoil water content (TSWC) measurement points in the four vineyards of “Los Oncenos” sub-catchment (<b>a</b>). Photos of the device to measure TSWC (<b>b</b>), of the sediment trap #2 with the upslope contributing area (<b>c</b>) (taken on 12 February 2018) and of the cover crop in the contributing area of the sediment trap #3 after the mowing pass in late May 2017 (<b>d</b>) (taken on 27 June 2017).</p>
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<p>Temporal changes in the canopy cover of the grapevines and the cover crop of common sainfoin over the twelve months of the year.</p>
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<p>Monthly values of rainfall depth (<span class="html-italic">R</span>), maximum intensity (<span class="html-italic">I</span><sub>30</sub>) and erosivity (<span class="html-italic">EI</span><sub>30</sub>) and the number of medium and high rainfall erosivity events (<span class="html-italic">n</span>) in the synthetic weather station (Syn-WS).</p>
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<p>Evolution of the monthly values of TSWC in the three vineyards’ compartments of the four fields (4VYs) and of the mean minimum and maximum relative differences (MRD).</p>
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17 pages, 3013 KiB  
Article
Modelling Ephemeral Gully Erosion from Unpaved Urban Roads: Equifinality and Implications for Scenario Analysis
by Napoleon Gudino-Elizondo, Trent W. Biggs, Ronald L. Bingner, Yongping Yuan, Eddy J. Langendoen, Kristine T. Taniguchi, Thomas Kretzschmar, Encarnacion V. Taguas and Douglas Liden
Geosciences 2018, 8(4), 137; https://doi.org/10.3390/geosciences8040137 - 17 Apr 2018
Cited by 21 | Viewed by 5002
Abstract
Modelling gully erosion in urban areas is challenging due to difficulties with equifinality and parameter identification, which complicates quantification 0of management impacts on runoff and sediment production. We calibrated a model (AnnAGNPS) of an ephemeral gully network that formed on unpaved roads following [...] Read more.
Modelling gully erosion in urban areas is challenging due to difficulties with equifinality and parameter identification, which complicates quantification 0of management impacts on runoff and sediment production. We calibrated a model (AnnAGNPS) of an ephemeral gully network that formed on unpaved roads following a storm event in an urban watershed (0.2 km2) in Tijuana, Mexico. Latin hypercube sampling was used to create 500 parameter ensembles. Modelled sediment load was most sensitive to the Soil Conservation Service (SCS) curve number, tillage depth (TD), and critical shear stress (τc). Twenty-one parameter ensembles gave acceptable error (behavioural models), though changes in parameters governing runoff generation (SCS curve number, Manning’s n) were compensated by changes in parameters describing soil properties (TD, τc), resulting in uncertainty in the optimal parameter values. The most suitable parameter combinations or “behavioural models” were used to evaluate uncertainty under management scenarios. Paving the roads increased runoff by 146–227%, increased peak discharge by 178–575%, and decreased sediment load by 90–94% depending on the ensemble. The method can be used in other watersheds to simulate runoff and gully erosion, to quantify the uncertainty of model-estimated impacts of management activities on runoff and erosion, and to suggest critical field measurements to reduce uncertainties in complex urban environments. Full article
(This article belongs to the Special Issue Soil Hydrology and Erosion)
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Figure 1
<p>(<b>a</b>) UAS-SfM-derived orthophoto for San Bernardo (SB), and the 9 study watersheds with their outlets; (<b>b</b>) Geographic location of the Los Laureles Canyon Watershed (LLCW), SB, and the Tijuana River Estuarine Reserve (TJE); (<b>c</b>) one example of land degradation caused by gully erosion in Tijuana, Mexico.</p>
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<p>Daily rainfall time series for the 2016 water year. The grey box represents the rainfall threshold (~25–35 mm) for gully formation observed in the study area.</p>
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<p>(<b>a</b>) Digitized gullies, watershed boundary, outlet, and locations of field measurements of gully depths; (<b>b</b>) An example of field measurement of gully depth.</p>
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<p>Relationship between observed and simulated Specific Soil Loss (SSL, the average depth of soil loss in the watershed in mm) from gully erosion in San Bernardo, Tijuana, Mexico, obtained from 21 behavioural models. The blue dots show the results from the default model parameters (<a href="#geosciences-08-00137-t001" class="html-table">Table 1</a>).</p>
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<p>τ<sub>c</sub> and head cut erodibility as measured by the jet-test (black dots) compared with other values from the literature (lines), and with the parameters from the behavioural models (open circles).</p>
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<p>Impacts on water and sediment load ratios between current conditions and paving-all-roads scenario using the 21 behavioural models.</p>
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5659 KiB  
Article
Deriving Ephemeral Gullies from VHR Image in Loess Hilly Areas through Directional Edge Detection
by Xin Yang, Wen Dai, Guoan Tang and Min Li
ISPRS Int. J. Geo-Inf. 2017, 6(11), 371; https://doi.org/10.3390/ijgi6110371 - 18 Nov 2017
Cited by 14 | Viewed by 5506
Abstract
Monitoring ephemeral gullies facilitates water planning and soil conservation. Artificial interpretation based on high spatial resolution images is the main method for monitoring ephemeral gullies in large areas; however, this method is time consuming. In this study, a semiautomatic method for extracting ephemeral [...] Read more.
Monitoring ephemeral gullies facilitates water planning and soil conservation. Artificial interpretation based on high spatial resolution images is the main method for monitoring ephemeral gullies in large areas; however, this method is time consuming. In this study, a semiautomatic method for extracting ephemeral gullies in loess hilly areas based on directional edge detection is proposed. First, the area where ephemeral gullies developed was extracted because the weak trace of ephemeral gullies in images can hardly be detected by most image detectors, which avoided the noise from other large gullies. Second, a Canny edge detector was employed to extract all edges in the image. Then, those edges along the direction where ephemeral gullies developed were searched and coded as candidate ephemeral gullies. Finally, the ephemeral gullies were identified through filtering of pseudo-gullies by setting the appropriate length threshold. Experiments in three loess hilly areas showed that accuracy ranged from 38.18% to 85.05%, completeness ranged from 82.35% to 92.86%, and quality ranged from 35.29% to 79.82%. The quality of the remote sensing images highly affected the results. The accuracy was significantly improved when the image was used with less grass and shrubs. The length threshold in directional searching also affected the accuracy. A small threshold resulted in additional noise and disconnected gullies, whereas a large threshold disregarded the short gullies. A reasonable threshold can be obtained through the index of quality. The threshold also exhibits a strong relationship with the average length of ephemeral gullies, and this relationship can help obtain the optimum threshold in the hilly area of the Northern Loess Plateau of China. Full article
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Figure 1
<p>Pictures of ephemeral gullies in Jingbian (test area, T1), Loess Plateau of China: (<b>a</b>) ephemeral gullies in south side slope; (<b>b</b>) ephemeral gullies in the west side slope.</p>
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<p>Location of the study areas and their images: (<b>a</b>) location of the study areas; (<b>b</b>) image of T1; (<b>c</b>) image of T3; and (<b>d</b>) image of T2.</p>
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<p>Ephemeral gullies and their location in a gully system in a high-resolution image.</p>
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<p>Workflow of ephemeral gully extraction.</p>
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<p>Comparison of results from different edge detection operators in T1 area: (<b>a</b>) image of the upslope area with ephemeral gully developed; (<b>b</b>) Roberts operator; (<b>c</b>) Sobel operator; (<b>d</b>) Prewitt operator; (<b>e</b>) Log operator; and (<b>f</b>) Canny operator.</p>
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<p>Ephemeral gully directions and their searching order.</p>
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<p>Directional searching rules of four typical directions.</p>
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<p>Comparison of the sensitivity thresholds of the Canny operator: (<b>a</b>) the self-adapt threshold; (<b>b</b>) the threshold of upper 0.01 and lower 0.004; (<b>c</b>) the threshold of upper 0.02 and lower 0.008; and (<b>d</b>) the threshold of upper 0.05 and lower 0.02.</p>
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<p>Results of different length thresholds: (<b>a</b>) original result of the Canny edge detection; and (<b>b</b>–<b>f</b>) are directional detection results with length threshold of 5, 10, 15, 20, and 25 m respectively.</p>
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<p>Extracted results of ephemeral gullies and their comparison with manual interpretation results : (<b>T1</b>) result in Jingbian area; (<b>T2</b>) result in Dingbian area; and (<b>T3</b>) result in Huanxian area.</p>
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<p>The relationship between the length threshold and the average gully length in three test areas.</p>
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