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Article

Morphological Characteristics and Development Rate of Gullies in Three Main Agro-Geomorphological Regions of Northeast China

by
Zhengyu Wang
1,
Mingchang Shi
1,*,
Mingming Guo
2,
Xingyi Zhang
2,
Xin Liu
2,3 and
Zhuoxin Chen
2,3
1
School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
2
State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(16), 2905; https://doi.org/10.3390/rs16162905
Submission received: 6 July 2024 / Revised: 31 July 2024 / Accepted: 7 August 2024 / Published: 8 August 2024
(This article belongs to the Special Issue Soil Erosion Estimation Based on Remote Sensing Data)
Browse Figures
Graphical abstract
"> Figure 1
<p>Location of study area. Note: Subfigure (<b>a</b>) is the HL study area, subfigure (<b>b</b>) is the ML study area and subfigure (<b>c</b>) is the YKS study area.</p> "> Figure 2
<p>Changes in gully morphology in two periods and construction of S = a·A<sup>−b</sup> model. Note: The orange line represents the gully in 2013.</p> "> Figure 3
<p>Flow chart of this study.</p> "> Figure 4
<p>The distribution of gully morphological parameters. Note: Red curves represent the cumulative percentage of gullies. Note: Subfigure (<b>a</b>,<b>f</b>,<b>k</b>) show the gully length in HL, ML and YKS, Subfigure (<b>b</b>,<b>g</b>,<b>l</b>) show the gully width in HL, ML and YKS, Subfigure (<b>c</b>,<b>h</b>,<b>m</b>) show the gully perimeter in HL, ML and YKS, Subfigure (<b>d</b>,<b>i</b>,<b>n</b>) show the gully area in HL, ML and YKS, Subfigure (<b>e</b>,<b>j</b>,<b>o</b>) show the SI in HL, ML and YKS.</p> "> Figure 5
<p>S-A model for the three study areas.</p> "> Figure 6
<p>Differential rates of gully erosion in the three study areas: rate of headcut retreat (<b>a</b>), rate of gully area erosion (<b>b</b>), and rate of gully bank expansion (<b>c</b>). Different lowercase letters represent significant (<span class="html-italic">p</span> &lt; 0.05) differences in gully erosion rates between regions.</p> "> Figure 7
<p>Land use change in HL, ML, and YKS study area from 2013 to 2023. Note: Subfigure (<b>a</b>–<b>c</b>) show the 2013 HL land use, 2023 HL land use and land use change in HL, Subfigure (<b>d</b>–<b>f</b>) show the 2013 ML land use, 2023 ML land use and land use change in ML, Subfigure (<b>g</b>–<b>i</b>) show the 2013 YKS land use, 2023 YKS land use and land use change in YKS.</p> "> Figure 8
<p>Differences in gully head retreat rate (<b>a</b>), gully expansion rate (<b>b</b>), and areal gully erosion rate (<b>c</b>) among different land uses and study areas. Note: Different capital letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05) in gully erosion rate among different study areas for same land use. Different small letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05) among different land uses in a given study area. DFL is dry farmland, GL is grassland, WL is woodland.</p> "> Figure 9
<p>Differences in gully head retreat rate (<b>a</b>), gully expansion rate (<b>b</b>) and areal gully erosion rate (<b>c</b>) among different slope classes and study areas. Note: Different capital letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05) in gully erosion rate among different study areas for same slope class, and different small letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05) among different slope classes in a given study area.</p> "> Figure 10
<p>Differences in gully head retreat rate (<b>a</b>), gully expansion rate (<b>b</b>) and areal gully erosion rate (<b>c</b>) among different slope aspects and study areas. Note: Different capital letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05) in gully erosion rate among different study areas for the same slope aspect, and different small letters represent significant differences (<span class="html-italic">p</span> &lt; 0.05) among different slope aspects in a given study area.</p> "> Figure 11
<p>Modeling of gully criticality in different study areas.</p> "> Figure A1
<p>Ten years (2013–2023) annual average temperature of three study areas (HL, ML, and YKS).</p> ">
Versions Notes

Abstract

:
Gully erosion poses a significant global concern due to its role in land degradation and soil erosion, particularly pronounced in Northeast China’s diverse agro-geomorphic regions. However, there is a lack of comprehensive studies on gully characteristics, development rates, and the topographic threshold of gully formation in these areas. To address this gap, we selected three different agro-geomorphic watersheds, named HL (Hailun), ML (Muling), and YKS (Yakeshi), with areas of 30.88 km2, 31.53 km2, and 21.98 km2, respectively. Utilizing high-resolution (2.1 m, 2 m) remote sensing imagery (ZY-3, GF-1), we analyzed morphological parameters (length, width, area, perimeter, etc.) and land use changes for all permanent gullies between 2013 and 2023. Approximately 30% of gullies were selected for detailed study of the upstream drainage area and gully head slopes to establish the topographic threshold for gully formation (S = a·A−b). In HL, ML, and YKS, average gully lengths were 526.22 m, 208.64 m, and 614.20 m, respectively, with corresponding widths of 13.28 m, 8.45 m, and 9.32 m. The gully number densities in the three areas were 3.14, 25.18, and 0.82/km2, respectively, with a gully density of 1.65, 5.25, and 0.50 km km−2, and 3%, 5%, and 1% of the land has disappeared due to gully erosion, respectively. YKS exhibited the highest gully head retreat rate at 17.50 m yr−1, significantly surpassing HL (12.24 m yr−1) and ML (7.11 m yr−1). Areal erosion rates were highest in HL (277.79 m2 yr−1) and lowest in YKS (105.22 m2 yr−1), with ML intermediate at 243.36 m2 yr−1. However, there was no significant difference in gully expansion rate (0.37–0.42 m yr−1) among the three areas (p > 0.05). Differences in gully development dynamics among the three regions were influenced by land use, slope, and topographic factors. The topographic threshold (S = a·A−b) for gully formation varied: HL emphasized drainage area (a = 0.052, b = 0.52), YKS highlighted soil resistance (a = 0.12, b = 0.36), and the parameters a and b of ML fell within the range between these of HL and YKS (a = 0.044, b = 0.27). This study has enriched the scope and database of global gully erosion research, providing a scientific basis for gully erosion prevention and control planning in Northeast China.

Graphical Abstract">

Graphical Abstract

1. Introduction

Soil erosion, a globally recognized form of land degradation, has garnered significant public concern [1,2,3,4,5]. Gully erosion, specifically, is widespread and contributes to land fragmentation, vegetation destruction, the disappearance of croplands, soil desiccation, and the deposition of sediment in rivers and reservoirs [3,4,6,7]. Despite efforts, current rates of gully erosion remain high, posing ongoing threats to ecological environments [8]. Therefore, understanding the patterns of gully evolution is crucial. Such knowledge serves as a foundation for developing effective strategies to mitigate gully development, thereby addressing soil loss and combating land degradation caused by gully erosion.
Vanmaercke et al. [4] concluded that global linear and areal gully retreat rates vary widely, ranging from 0.01 to 135 m yr−1 and 0.01 to 3628 m2 yr−1, respectively. Gully morphology and development rates exhibit significant global variability, influenced by natural conditions and human activities. From a review of the literature, factors affecting gully morphology and development can be categorized as follows: topography [9], soil properties [10,11,12], precipitation [13], lithology [14,15,16], land use [8,17,18,19,20,21,22,23], vegetation [24,25], and anthropogenic factors [26]. These factors typically interact rather than act independently, collectively shaping the process of gully erosion. For instance, Wang et al. [27] demonstrated that cultivated land is more susceptible to gully erosion compared to grassland and shrubland, with vegetation playing a critical role in mitigating water flow energy and erosion resistance. The influence of lithological differences, as detailed by Parkner et al. [15], underscores that varying soil compositions underpin differential erosion rates. Regional studies, such as Wang et al. [28] and Zucca et al. [16], highlight the heterogeneity in gully density patterns relative to slope and land cover type. Capra et al. [29] further emphasize that gully development rates are influenced by the interaction of factors like slope length and soil characteristics. In conclusion, understanding gully morphology and development requires considering regional nuances and the complex interplay of diverse environmental and anthropogenic factors. However, few current studies published in international journals primarily focus on gully erosion in cold regions where freeze–thaw and snowmelt are the critical factors affecting land surface processes, so more attention should be paid to gully erosion in cold regions worldwide.
The formation of gullies hinges on the critical interaction between runoff shear and soil critical shear strength, which is influenced by several key factors. Therefore, gully formation typically results from a complex interplay of these variables. Previous studies have often evaluated gully occurrence using a topographic criticality model (S = a·A−b), as established by Patton and Schumm [30] and Begin [31]. The parameters ‘a’ and ‘b’ of this model are notably influenced by vegetation, topography, soil characteristics, land use practices, and anthropogenic disturbances [3]. Recent research indicates significant variability in the values of these critical model parameters. For instance, Torri and Poesen [32] demonstrated substantial variations in the ‘a’ value, even among similar vegetation types, due to differences in vegetation cover and anthropogenic activities such as soil loosening and tillage. Moreover, variations in gravel content and rock surface coverage can also impact the parameter ‘a’ of the critical model. This indicates that the parameters “a” and “b” have a high sensitivity to anthropogenic factors. However, few studies concerned and compared the critical conditions for gully formation among different agro-geomorphological regions. Therefore, future studies should be focused on this aspect to enrich the dataset of thresholds of gully formation worldwide and further explore the response of parameters “a” and “b” to environmental and anthropogenic factors.
In northeastern China, the expansive Mollisols region covers 1,087,500 km2, representing one of the world’s four major Mollisol regions and contributing significantly to China’s grain output, amounting to 21% annually. However, unsustainable land reclamation practices and irrational land use have exacerbated soil erosion, particularly gully erosion [33]. Currently, the Mollisol region hosts 666,700 gullies with a gully density of 0.21 km/km2, impacting 403,000 hectares of arable land. Alarmingly, 90% of these gullies are still in their formative stages, rapidly developing and expanding. Predominantly found in rolling hills and low mountainous areas, which are densely populated agriculturally, these gullies also affect the region’s forested areas significantly. Moreover, the conversion of forest and grassland areas into cultivated land and agroecosystems in the northeastern black soil region continues unabated, exacerbating land degradation and fragmentation [2]. Regrettably, existing studies have not comprehensively documented the evolution of gullies influencing factors and the topographic criticality model across different agro-geomorphic zones within the Mollisol region. Therefore, understanding gully morphology, developmental dynamics, and influencing factors is crucial for developing effective gully evolution models and implementing mitigation strategies.
In our research within the Mollisols region of Northeast China, we embarked on a comprehensive investigation into gully erosion, focusing on its morphology, development rates, and the myriad factors across diverse agro-geomorphological zones. Our study aimed to achieve several specific objectives: (1) to clarify differences in gully morphology in different agro-geomorphological zones; (2) to determine the development rate of gullies in different zones; and (3) to establish a critical model of gully formation (S = a·A−b). Ultimately, the study aimed to deepen the understanding of gully evolution rules in different agro-geomorphological zones and to provide scientific evidence for the prevention and control of gully erosion.

2. Materials and Methods

2.1. Study Area

The Mollisol region in northeastern China is a major grain-producing area, but long-term reclamation has caused severe soil erosion, particularly gully erosion [2]. Gully erosion is mainly associated with agricultural production and reclamation. In this region, agricultural production is concentrated in the low mountain hilly area and the rolling hilly area, which are also the main areas where gully erosion occurs [28,34,35,36,37]. Additionally, the low mountain-hilly forested areas make up a significant portion of the black soil area, but due to increased food demand, local farmers have expanded land reclamation into these areas. This expansion has led to cultivated land encroaching upon forested land and grassland, particularly in the forested mountains of Northeast China [28]. This encroachment has become a new source of gully development. Hence, this study selected three agro-geomorphic types: rolling-hilly area, low mountain-hilly area, and mountain-hilly area in Hailun city (HL), Yakeshi city (YKS), and Muling city (ML), respectively, to investigate gully morphology, gully formation threshold, and gully erosion rate. The ArcGIS software 10.5 and 12.5 m DEM data were used to accurately define the area of HL, YKS, and ML at 30.88 km2, 21.98 km2, and 31.53 km2 (Figure 1). The detailed information, including soil, climate, etc., of the three study areas is presented in Table 1.

2.2. Remote Sensing Image Data and Interpretation of Gully and Land Use

High-precision remote sensing data in 2013 and 2023 were primarily sourced from historical images using Google Earth data and GF-1 (gaofen-1) and ZY-3 (ziyuan-3) satellite remote sensing. These images feature a panchromatic resolution of 2 m and 2.1 m. For small watershed-scale feature identification and gully interpretation [38], near 2 m resolution remote sensing images are preferred due to their economical cost and higher reliability. Initial raw data underwent preprocessing using ERDAS IMAGE 9.2 software. Preprocessing steps included geometric correction (geolocation, fine geometric correction, image alignment, ortho correction), image fusion, mosaicking, cropping, cloud and shadow removal, and atmospheric correction [39,40,41]. These processes resulted in multispectral images with precise geographic coordinates and resolutions finer than raw data with 2 m precision.
Using ArcGIS 10.5, we loaded a pre-processed remote sensing image and created surface vector elements matching its projection’s coordinate system. To ensure comprehensive coverage, the surface vector’s boundary exceeded that of the study area. Employing the editing tool within the cropping tool, we visually delineated and adjusted these vector elements to accurately reflect the gully extents. Subsequently, each cropped sub-area was categorized by gully type in the attribute table. Using the calculate geometry tool, we quantified the perimeter (P) and area (Ag) of each identified gully. Gullies were sequentially numbered following the increasing latitudinal and longitudinal directions, omitting numbering for nascent gullies.
Post-surface vector interpretation, we generated line vector elements aligned with the remote sensing image’s coordinate system. These line vectors were edited based on the corresponding gully interpretation polygons. For each gully, a polyline was sketched, tracing its lowest points from start to end. Attribute data matching the gully surface vector, including gully type, was appended to the line vector’s attribute table. Finally, we computed the gully length (L) using the calculate geometry tool.
Meanwhile, we calculated the gully shape index SI, GLD (gully length density), GAD (gully area density) [42,43,44] as follows:
SI = 0.25 × P A g
GLD = ∑ i = 1 n L i A s
GAD = ∑ i = 1 n A gi A s
where As is the area of the study region, and subscript i represents serial number of the gully.
In our gully interpretation, initial findings revealed multiple sub-gullies distributed along the drainage line. Subsequent analysis showed these sub-gullies coalesced into a major gully formation along the drainage. Morphological parameters of the gully were computed based on the early interpretation results, which involved merging and processing these sub-gullies to delineate the primary gully structure [45].
Furthermore, leveraging the results from the gully surface vector interpretation in conjunction with remote sensing imagery, we extended our analysis to interpret land use types across the study area [46]. The identified land use categories encompassed gully areas, dry farmland, woodland, paddy fields, grasslands, highways, rural settlements, rural roads, irrigated lands, and bare lands.

2.3. Calculation of Gully Erosion Rates

The use of remote sensing technology and geographic information systems for acquiring historical data on erosion gullies is a more effective method [47]. This involves interpreting remote sensing images from two different periods and summarizing the results in a table. The table includes data on gully length, area, and width (area/length) matched to specific gullies. By subtracting the values and then dividing by the time interval, the erosion rate of each gully can be calculated. Additionally, we observed that in 2013, several smaller gullies gradually merged to form one larger gully by 2023. To account for this, we combined the morphology parameters of the individual smaller gullies from 2013 and compared them with the 2023 results to calculate the erosion rate of the larger gully.

2.4. Topography Threshold of Gully Erosion (S-A Model)

To investigate the impact of slope and drainage area on gully head erosion, we randomly selected gullies from three study areas using the geostatistical analyst tools-utilities-subset features tool in the ArcGIS 10.5 software. Considering the large amount of work involved in full extraction, we constructed the topography threshold model by randomly extracting gullies, taking into account different land uses and different gully morphology, with an extraction ratio of approximately 30%, resulting in 29 gullies for HL, 237 for ML, and 6 for YKS. We used the spatial analyst tools-zonal-zonal statistics as a table tool to analyze the slope of the raster where the gully head point was located. The drainage area of the gully head was extracted using the hydrological analysis tool (Figure 2) and determined based on images combined with field survey data. Considering that the development process of gullies with too small slopes is more influenced by other factors outside the topography, gully samples with slopes less than 0.001 m m−1 were considered outliers. In addition, due to the large difference in the number of gully samples in the three study areas, especially in YKS, where there were only six gully samples, it was not appropriate to use statistical methods such as confidence intervals, orthogonal regression, nonlinear regression, and quantile regression to determine the topography threshold model [43,48,49]. Therefore, we used a manual plotting method to determine the critical line by taking the two left-most points after excluding outliers and then determined the topography threshold model (S = a·A−b), and this method was popularly used in lots of previous studies. This model could be used to predict the position of gully erosion occurrences and guide the layout of gully erosion prevention measures.

2.5. Soil, Climate, and Terrain Data Acquisition

Meteorological data were adopted from monitoring stations at 47.0°N 126.5°E Hailun City, 44.75°N 130.25°E Muling City, and 49.25°N 121°E Yakeshi City, and month-by-month monitoring data were selected to support the study for the ten years from 2013 to 2023, with the main indicators of precipitation and temperature selected, and the data were organized by year using Excel software 2021 to organize the data by year.
Soil data were gathered from in situ soils and ring knives by placing over 50 points in the sub-watershed area [50]. We used a spatially uniform distribution of points to take soil in three areas, with 21 points in HL, 22 points in ML, and 14 points in YKS. In situ soils were utilized to measure soil particle composition and gravel content. Each point was sampled with a minimum of 25 g, which was then analyzed for particle composition using a laser particle size analyzer after passing through a 2 mm sieve [51]. The mass of soil on the sieve after passing through the 2 mm sieve was divided by the total mass of sieved soil, referred to as M0. M1, divided by the total sieved soil mass M0, represented the soil gravel content.
The 12.5 m accuracy DEM [52] is based on data from the ALOS (Advanced Land Observing Satellite), an earth observation satellite launched in 2006. The all-weather land observation data are collected by the Phased Array L-band Synthetic Aperture Radar (PALSAR, JAXA, Tokyo, Japan), providing high-resolution data with a horizontal and vertical accuracy of up to 12.5 m. In the study, the Spatial Analyst Tools-surface-slope tool in the ArcGIS 10.5 software was utilized along with the 12.5 m DEM data to analyze and obtain slope data in the study area. We graded slopes, which incorporate methods from the Northeast Blackland Conservation and Utilization Report of the Chinese Academy of Sciences (2021), into eight classes of slopes: <1°, 1–2°, 2–3°, 3–4°, 4–5°, 5–7°, 7–15°, and 15–25°. This analysis involved using the Spatial Analyst tools-Zonal-Zonal Statistics as Table tool to compute statistics on the average slope of each trench and form a data table. Additionally, the Spatial Analyst Tools-surface aspect tool, combined with the 12.5 m DEM data, was used to analyze and obtain slope aspect data in the study area. This analysis also involved using the Spatial Analyst tools-Zonal-Zonal Statistics as Table tool to form a data table that provided information on the major slope aspect Flat, N, NE, E, SE, S, SW, W, NW (N is for North, S is for South, E is for East, W is for West, NE is for North-East, SE is for South-East, SW is for South-West and NW is for North-West.)of each gully. All data processing and methods are shown in Figure 3.

2.6. Statistical Analysis

Duncan’s test and one-way analysis of variance (ANOVA) were performed to determine the significance of gully morphology parameters and gully erosion rate in different years and different study areas using IBM SPSS Statistics 25 (SPSS Inc., Chicago, IL, USA), and the significance level was set at p < 0.05 for all statistical analyses. The significance level of all analyses was set at p < 0.05. All graphs were plotted by origin2021 software, and the data were analyzed using Microsoft Excel and IBM SPSS Statistics 25.

3. Results

3.1. Gully Morphology Characteristics

Based on the 2023 survey data (refer to Table 2 and Figure 4), the regions of HL, ML, and YKS exhibit varying numbers and densities of gullies. HL hosts 97 gullies at a density of 3.14 No. km−2, whereas ML has 794 gullies at a density of 25.18 No. km−2. Conversely, YKS exhibits only 18 gullies, resulting in a density of 0.82 No. km−2. Gully length also varies significantly among the regions. In HL, gullies range from 51.56 m to 4342.79 m, with an average length of 526.22 m. ML gullies span from 13.13 m to 1790.63 m, averaging 208.64 m in length. YKS gullies range from 28.65 m to 3385.17 m, averaging 614.20 m in length. The average length shows a significant difference: HL gullies are longer than those in YKS and ML (p < 0.05). Regarding gully width, HL gullies have an average width of 13.28 m. In ML, the majority of gullies have widths between 6 and 10 m, while in YKS, width typically ranges from 6 to 12 m. The average width of HL gullies is significantly greater than that of ML and YKS (p < 0.05), but there is no significant difference between ML and YKS (p > 0.05). Gully area also varies widely. HL gullies range from 340.59 m2 to 80,904.78 m2, averaging 7984.44 m2. ML gullies range from 73.08 m2 to 22,779.15 m2, averaging 1969.32 m2. YKS gullies range from 192.55 m2 to 36,458.16 m2, averaging 6058.78 m2. The average gully area differs significantly across the regions: HL > YKS > ML (p < 0.05).
In terms of gully perimeter, HL has an average gully perimeter of 1092.73 m, while ML gullies average 439.47 m and YKS gullies average 1251.74 m. The perimeters of HL and YKS gullies do not significantly differ (p > 0.05), but both are significantly larger than those of ML gullies (p < 0.05). The shape index (SI), reflecting gully morphology, shows moderate variation among the regions. HL gullies have an SI mean of 3.02, ML gullies have a mean SI of 2.44, and YKS gullies have a mean SI of 3.56. The differences in SI among YKS, HL, and ML are statistically significant (p < 0.05), indicating distinct morphological characteristics across the study areas. Overall, HL is characterized by longer and wider gullies, while ML generally features shorter and narrower gullies. YKS stands out with longer and relatively narrower gullies compared to both HL and ML.

3.2. Topography Threshold for Gully Erosion

We surveyed 29 gullies in HL, 237 in ML, and 6 in YKS, encompassing approximately 30% of the gullies in each area to construct the topography threshold model (S = a·A−b). This model calculates the slope (S) based on the gully head point elevation and the upstream drainage area, with outliers excluded. The derived equations are (Figure 5):
S = 0.052A−0.52 (HL)
S = 0.044A−0.27 (ML)
S = 0.12A−0.36 (YKS)
The parameter ‘a’ indicates soil susceptibility to gully erosion, with higher values suggesting greater resistance. Comparing the three regions, YKS exhibits the highest resistance (a-value: YKS > ML > HL), indicating its soil is most resistant to gully formation, whereas HL shows the least resistance. The parameter ‘b’ reflects the influence of drainage area size on gully formation. For HL, the b-value indicates the strongest correlation with drainage area size, followed by YKS and then ML, suggesting that gully formation in HL is most influenced by drainage size, while ML is least affected. These findings highlight significant variations in gully formation dynamics across HL, ML, and YKS, driven by differences in soil susceptibility and drainage area size.

3.3. Gully Development Rate

The study analyzed gully dynamics over the period 2013–2023 across three distinct study areas, focusing on headcut retreat rate (RL), gully expansion rate (RW), and areal gully erosion rate (RA) (Figure 6). Figure 6a illustrates significant variability in RL among the study areas: RL in HL ranged from 0.11 to 84.15 m yr−1 with a coefficient of variation (CV) of 1.25; RL in ML ranged from 0.010 to 69.61 m yr−1 with a CV of 0.98, and YKS ranged from 1.46 to 63.56 m yr−1 with a CV of 1.95. Statistical analysis revealed significant differences in average RL between HL, ML, and YKS (p < 0.05), with YKS exhibiting the highest maximum RL at 17.50 m yr−1, surpassing HL and ML by 1.43 times and 2.46 times, respectively. In Figure 6b, the RW showed narrower ranges: HL ranged from 0.0040 to 1.66 m yr−1 (CV: 0.99), ML from 0.0010 to 1.59 m yr−1 (CV: 0.70); and YKS from 0.030 to 0.94 m yr−1 (CV: 0.80). On average, there were no significant differences in RW among HL, ML, and YKS (p > 0.05), averaging 0.39 m yr−1, 0.43 m yr−1, and 0.37 m yr−1, respectively. Figure 6c presents the variation in RA: HL ranged from 12.47 to 2444.02 m2 yr−1 (CV: 1.31), ML from 2.92 to 1435.60 m2 yr−1 (CV: 1.18), and YKS from 19.26 to 1132.54 m2 yr−1 (CV: 1.13). Average RA for HL (277.79 m2 yr−1) and YKS (243.36 m2 yr−1) were not significantly different (p > 0.05), but were significantly higher than ML’s rates by 1.64 times and 1.31 times, respectively (p < 0.05).

3.4. Factors Influencing Gully Evolution

3.4.1. Land Use

A comparison of the results of the two periods of land use interpretation revealed that no extensive land use changes occurred in the three study areas during the 10 years (Figure 7), so only the differences in the rate of gully development between different land use types were considered in this study. From 2013 to 2023, ML had 260 new gullies, of which 243 developed in arable land, 13 gullies in woodland, and 4 gullies in grassland, HL had 16 new gullies and were all in arable land, and YKS had 4 gullies in arable land and 4 gullies in grassland. In total, 97 gullies in HL were all in arable land, and 739, 4, 51 developed in arable land, grassland, and woodland in ML, respectively. There are only 18 gullies in YKS, of which 13 were in arable land and 5 in grassland.
The comparison of gully development rates across three study areas reveals distinct patterns among various land use types (Figure 8). In the HL area, gullies were exclusively observed in croplands, exhibiting an average RL of 12.21 m yr−1. This rate was marginally lower than YKS croplands (13.80 m yr−1, non-significant difference, p > 0.05) but significantly higher than ML (7.10 m yr−1, p < 0.05). Significant variability was evident across land uses; grasslands recorded the highest RL at 12.80 m yr−1, significantly surpassing rates in forests and croplands by 0.81 m yr−1 and 0.45 m yr−1, respectively (p < 0.05). Figure 8b illustrates the expansion rates of gully banks in permanent gullies across different study locations and land uses. Expansion rates did not significantly differ between HL, ML, and YKS croplands (p > 0.05), with average rates of 0.39 m yr−1, 0.44 m yr−1, and 0.31 m yr−1 respectively. However, in ML, significant variations (p < 0.05) were observed among different land uses, with grasslands exhibiting the highest rate at 0.59 m yr−1. Cultivated land followed with a rate 1.31 times higher than that of forested land. Moreover, YKS grassland showed greater variability (coefficient of variation 0.90) compared to ML (0.71), though mean expansion rates were not significantly different (p > 0.05). As shown in Figure 8c, the variability of permanent gully area erosion rates across study areas and land use types is depicted. The gully area erosion rate in ML cropland (103.66 m2 yr−1) differed significantly from HL (277.79 m2 yr−1) and YKS cropland (247.33 m2 yr−1) (p < 0.05).

3.4.2. Slope Gradient

Overall, RL, RW, and RA across three study areas generally increased and then decreased with slope, with significant variability observed between different slopes and study areas (Figure 9). For RL (Figure 9a), the highest rate (15.94 m yr−1) occurred on 1–2° slopes, with no significant difference observed among other gradients (p > 0.05). Notably, RL peaked at 9.85 m yr−1 on 3–4° slopes, significantly exceeding rates on 5–7° slopes by 19% (p < 0.05). In YKS, RL fluctuated with slope; notably, 2–3° and 5–7° slopes exhibited higher rates compared to others, with significant differences observed primarily against 5–7° slopes (p < 0.05). YKS showed notably higher rates on 2–3° and 5–7° slopes compared to HL and ML (p < 0.05).
Regarding RW (Figure 9b), HL exhibited a decreasing then increasing trend with slope, peaking at 0.56 m yr−1 on 0–1° slopes, not significantly differing from other slopes (p > 0.05). MW showed no significant variation across slopes, with rates ranging from 0.56 m yr−1 (3–4°) to 0.42 m yr−1 (7–15°). In YKS, RW initially decreased from 0.49 m yr−1 to 0.12 m yr−1 before increasing to 0.93 m yr−1 with slope increase. Significant differences were noted primarily against 5–7° slopes (p < 0.05), with ML notably lower than YKS by 43.60% on 5–7° slopes (p < 0.05).
For RA (Figure 9c), both HL and ML exhibited increasing and decreasing trends with slope. HL peaked at 392.25 m2 yr−1 on 1–2° slopes, similar to other slopes (p > 0.05). In ML, RA peaked at 149.21 m2 yr−1 on 5–7° slopes, significantly surpassing rates on 3–4° and 15–25° slopes (p < 0.05). YKS showed significant variability, with RA ranging from 50.56 m2 yr−1 (0–1°) to 471.87 m2 yr−1 (5–7°), significantly higher on 5–7° slopes compared to others (p < 0.05).

3.4.3. Slope Aspect

Figure 10 shows the gully erosion rate from different slope aspects. In HL (Figure 10a), the largest RL (20.26 m yr−1) occurs in W aspect, significantly 2.28, 1.43, and 1.64 times higher than that in the NE, E, and SE aspects, respectively (p < 0.05). ML has the highest RL (8.12 m yr−1) in SE, which is just significantly 2.31 times higher than that in gentle slope areas (p < 0.05). But in YKS, the largest RL (38.09 m yr−1) is in the E aspect, which was significantly 4.71 times, 8 times, and 3.33 times higher than that in the W, WE, and S aspects, respectively (p < 0.05). Furthermore, in the N aspect, the RL in YKS was significantly 2.42 times and 5.43 times higher than that in HL and ML, respectively. Also, in E, SE, and SW aspects, the RL of YKS was significantly higher than that of ML by 4.93 times, 3.64 times, and 1.75 times, and significantly higher than that of HL by 3.56 times, 3.64 times, and 0.79 times, respectively (p < 0.05). In the W, NW, and S, the RL of HL was significantly 3.37 times, 1.80 times, and 2.26 times higher than that of ML, and 3.04 times, 2.17 times, and 2.09 times higher than that of YKS, respectively (p < 0.05).
As shown in Figure 10b, HL had the greatest RW in the NW aspect (0.54 m yr−1), which was significantly 10.6 times higher than that in the S aspect (p < 0.05). The RW of ML in the flat areas was the greatest (0.55 m yr−1), which had no significant difference with the other aspects. The greatest RW of YKS was found in the E aspect and was just significantly 8.72 and 4.07 times higher than that in the S and NW aspects, respectively (p < 0.05). In the N and E aspects, the YKS had higher RW than ML (p < 0.05), but in the NW aspect, the RW of HL was significantly 2.86 times higher than that of YKS (p < 0.05). However, in SE, W, and S aspects, ML showed a significantly higher RW than HL and YKS (p < 0.05).
As shown in Figure 10c, HL had the greatest RA in the W aspect (494.11 m2 yr−1), significantly higher than N, NE, SE, and SW (p < 0.05). YKS peaks in NW (334.06 m2 yr−1), significantly higher than S and flat areas (p < 0.05). Notably, ML shows minimal variation in RA among aspects (p > 0.05). The RA of HL in the flat area, W, and S was significantly 12.83 times, 5.16 times, and 1.57 times higher than that of ML, respectively (p < 0.05), while in the N, E, and SE aspects, YKS had a higher rate than HL and ML.

4. Discussion

4.1. Gully Morphology

The morphological characteristics of gullies varied significantly across the study area, differing notably among HL, ML, and YKS (refer to Table 2 and Figure 4). In HL, gullies exhibited greater length, width, and area compared to ML and YKS, primarily due to their gentler slope (2.3°), larger drainage area, and higher rainfall percentage (refer to Table 1). These factors resulted in increased flow discharge into gully heads, leading to more pronounced gully development over time [53]. Furthermore, HL underwent reclamation earlier, which allowed for longer-term gully development compared to YKS and ML, contributing further to its larger-scale gully morphology. Conversely, gullies in ML were characterized by shorter and narrower dimensions, influenced by its steeper slope gradient (average 9.93°), shorter slope length, and smaller drainage area, which restricted water inflow and thereby limited gully length development [5]. Additionally, the thin soil layer and high gravel content in ML (refer to Table 1) reduced the water flow dynamics and incision ability of the gullies [54]. YKS featured two main gullies with lengths of 3385.17 m and 2589.25 m, significantly influencing its average gully length, which surpassed that of HL. Excluding these main gullies, however, the average gully length in YKS was only 317.58 m. Despite being reclaimed later, YKS exhibited gully development in its early stages, comparable in area and length to HL, with long and narrow morphological characteristics (refer to Table 1 and Figure 5). Moreover, YKS presented challenges such as a larger gravel content, which hindered runoff downcutting but promoted lateral erosion in gully channels. The region’s pronounced freeze–thaw cycles also exacerbated the gully bank collapse [55]. Despite these factors, the forested grass cover in YKS’s upstream drainage area mitigated runoff erosion, influencing its gully development dynamics [56]. Therefore, although the YKS was reclaimed late and the gully development is in the early stages, its gully area and gully length are close to the HL and show long and thin morphological characteristics.
Furthermore, our study reveals significant variability in gully morphology across different regions, mirroring findings from global gully morphology studies that highlight the influence of developmental stages and complex environmental factors [4]. We compared the gully morphology parameters obtained in our study with those reported for several regions: South Carolina, USA [57], Chinese Loess Plateau [58], northern Ethiopian Highlands [59], southwestern Spain [60], Yuanmou Dry-hot Valley [61], Upper Blue Nile basin, Ethiopia [62], and Yimeng Mountain Area, China [63]. Our findings indicate that gully lengths and areas in the three study areas were 1.38–11.55 times and 2.16–30.31 times larger, respectively, than those reported for these regions, primarily due to differences in topography. The drainage areas and slope lengths in our study areas are significantly larger, with lower undulation degrees, resulting in higher runoff intensity conducive to extensive gully formation characterized by longer lengths and larger areas. Except for two Ethiopian studies (Table 3), gully widths in our study were generally narrower compared to most other regions such as the Loess Plateau [58], Yuanmou Dry-hot Valley [61], southwestern Spain [60], and Yimeng Mountain Area, China [63]. This difference is attributed to variations in soil layer structure and physicochemical properties. For instance, soils in southwestern Spain are marl-derived and deposited on calcareous sandstones, prone to gully head advancement and bank soil crumbling underwater flow [63]. In the Loess Plateau and Yuanmou Dry-hot Valley, vertical soil joints facilitate bottom erosion on gully sides, leading to bank collapse and intensified gully expansion [61]. The Yimeng Mountain Area features a binary soil-stone structure with high erodibility, contributing to significant gully bank collapses [63], resulting in wider gully widths compared to our findings. Overall, our comparisons underscore substantial differences in gully dimensions globally, but gully density remains relatively low in our study area, especially in the YKS. Gullies in YKS are large and will continue to develop as the intensity of reclamation increases. Compared with other regions, the gullies in the Molliesol region of northeast China are still in the active development stage of continuous development, with great potential for future development. This trend poses significant threats to cropland integrity and food security, demanding urgent attention and effective management strategies.

4.2. Gully Erosion Rate and Factors

The lineal and areal gully erosion rates in these three study areas showed great variation and spatial heterogeneity (Figure 6), which was consistent with previous studies [6,55,64,65,66,67,68]. This also implies significant differences in environmental influencing factors among all gullies. On average, the headcut retreat rate and areal gully erosion rates in YKS and HL were obviously higher than those in ML, but there was no significant difference in widening rate among the three study areas (0.37~0.43 m yr−1, Figure 6). In fact, the combination of topography, land use, climate, and soil determined the gully erosion rates; maybe it accelerates or restrains gully erosion. In view of the land use, the YKS is experiencing the transformation from woodland and grassland to farmland and owns 63.6% of woodland and grassland area (Figure 7), and the gullies were in the initial developing stage in this period with a high developing rate [56]. As shown in Figure 9b, the gully erosion rate of grassland was significantly higher than that of woodland and farmland, which also proved that the gully development in YKS was in the initial developing stage. For HL and ML, the few woodland and grassland areas indicate a greater reclamation intensity than YKS, and the gully development in ML was in the middle and later periods of gully erosion. So, the gully erosion rate in ML was lower, although the greater slope gradient in ML than in YKS and HL would accelerate gully erosion. In addition, the freeze–thaw cycle period in the YKS region is also longer than that in HL and ML (Figure A1), and frequent freeze–thaw cycles aggravate the gully development [55]. This was also proven by the fact that the linear gully erosion rate in YKS was larger than that in HL under the same slope gradients (Figure 9a). In addition, although the slope of ML is large, the drainage area is much smaller than that of YKS and HL, so the flow discharge at the gully head and the resulting runoff erosion force are significantly smaller than those of YKS and HL. Secondly, the gravel content in ML is significantly high (Table 1), which significantly restricts gully undercutting. However, the loose soil structure and the more serious lateral gully erosion caused by the larger slope will significantly reduce the mechanical stability of steep gully banks and promote the expansion of gully banks [53], so the expansion rate (0.43 m yr−1) is slightly higher than that of YKS and HL.
Gully erosion rates are different under different natural and anthropogenic conditions [4]. As shown in Table 4, we compared the differences in head-cut retreat rate and areal gully erosion rate between this study and previous studies in other parts of the world, for example, the northern Ethiopian Plateau [69], Tunisia in Africa [64], Yuanmou Basin in China [67], Loess Plateau [68], southeast Spain [66], southeastern Arizona, USA [65], Negev highlands [6], and the Upper Blue Nile basin, Ethiopia [62]. These published studies mainly focus on the head-cut retreat rate and areal erosion rate, but pay little attention to the rate of gully expansion. We found that the gully head-cut retreat rates in the Negev highland in Israel reached 22.70 m yr−1 and 790 m2 yr−1 [6], which were 1.30~3.19 times and 2.84~7.51 times of that in this study, respectively. This can be explained not only by the combined impact of the highly erodible alluvial component (sandy loess) but also by the larger basin size of 100 km2, which generates more powerful flood events under extreme and regional synoptic conditions [3], and the soils in these three study areas have a relatively lower erodibility (Table 1). However, the headcut retreat rates and areal gully erosion rates in our three study regions were 1.87~388.90 and 3.34~28.58 times than those in other areas, respectively. This fully indicated that the gully erosion rate in northeast China is far higher than the world average level and has strong development potential and destructive power over farmland in the future. This huge difference could be attributed to several factors, such as precipitation, topography, soil, and lithology. The high gravel content in ML determines the high soil erodibility (Table 1), so the gully development is very quick under extreme rainfall conditions [70]. As shown in Table 4, these regions around the world have low annual precipitation (80~500 mm) but the annual evaporation can reach up to 2500 mm. Therefore, the gully development just depended on a few heavy rainfall events. Also, after rainfall, the gully bank develops tension cracks caused by the wetting and drying effects, and these cracks lead to a local collapse along the gully and contribute to high total soil erosion. However, for our study, although the clay content in YKS and HL is higher (Table 1) and represents a high anti-erodibility [50], the higher annual precipitation, lower evaporation, and larger drainage area upstream gully heads than those in other regions of the world would lead to higher gully erosion rates [71,72,73]. For example, in the HL study region, Qi et al. [70] and Zhang et al. [74] reported that the lineal, areal, and volumetric migration rates of gully heads can reach up to 35.11 m yr−1, 562.46 m2 yr−1, and 6626.37 m3 yr−1, respectively. YKS and ML soils are mainly produced by the weathering and deposition of leucogranitic granite, gabbro, granodiorite, and black mica granite. While HL is mainly formed by the weathering of crushed nitrogenous sedimentary rocks, red mudstone, and muddy sandstone. Differences in soil texture produced by the weathering deposition of different rocks affect the rate of gully erosion, especially in gully undercutting. Soils formed by the weathering of sandstones, mudstones, and loose Quaternary sediments favor gully development, whereas the opposite is true for marble and granite [75,76,77]. This may be one of the reasons for the higher rate of gully erosion in HL than in ML and YKS. Moreover, the freeze–thaw effect in our three study areas is a critical factor promoting gully erosion. According to our studies, the freeze–thaw increased the soil erodibility of gully heads and banks by 20.1–37.3%, and the extremely low temperature and temperature rangeability during the freeze–thaw period exhibited the greatest impact on the soil erodibility of the gully heads [78]. The cracks mainly developed in the freeze–thaw period, and 86.3% of them would collapse during the rainy season, contributing 35% of annual gully erosion [34,79,80,81]. The above comparison also fully shows that the gully erosion rate in northeast China is not only higher than the world level but also has great potential for further development. If the gully formation and development are not controlled and prevented, it will intensify the fragmentation of the surface and seriously affect the cultivated land area, posing a threat to food security.

4.3. Topography Threshold of Gully Erosion

The S-A models for gully development in the three study areas exhibited significant variations. The a-values were 0.052 for HL, 0.12 for YKS, and 0.0435 for ML, while the b-values were 0.52 for HL, 0.36 for YKS, and 0.27 for ML. The magnitude of the a-value reflects the susceptibility to gully erosion, with larger values indicating lower susceptibility. The b-value indicates the importance of drainage area in gully formation [82,83]. The order of a-values across the regions is YKS > HL > ML, while for b-values, it is HL > YKS > ML. ML, characterized by the steepest slopes, a higher gravel content in the soil, and a looser soil structure, exhibits greater erodibility [53]. Moreover, ML experiences the highest average annual rainfall among the three regions, and anthropogenic activities such as reclamation and cultivation further enhance the likelihood of gully occurrence [84]. YKS features a larger proportion of grassland and woodland, with less disturbed topsoil compared to HL, indicating higher resistance to erosion, hence the highest value of a. HL, despite having the smallest average slope among the three areas, possesses the largest drainage area and better soil structure relative to ML and YKS, resulting in lower soil erodibility. Additionally, the Quaternary loess sub-clay in HL exhibits a very low infiltration rate [70], and the higher water content in the surface soil during the rainy season enhances its influence on gully formation processes. Thus, HL demonstrates the highest b-value, emphasizing the significance of drainage area in gully formation. Although YKS has a larger average slope compared to HL and is second only to ML, it experiences the lowest average annual precipitation among the three regions. The larger proportion of grassland and woodland in YKS mitigates runoff contribution from the drainage area to some extent, potentially explaining its smaller b-value compared to HL and larger than ML.
Table 5 illustrates a comparison of the a and b values in S-A models between this study and 10 previous studies on gully erosion in croplands across various countries using the same method as our study (Figure 11). The a-values observed in HL, ML, and YKS were found to be lower compared to those in the Upper-Rwizi catchment, Uganda [32], the Loess Plateau, China [68], Yuanmou Dry-hot Valley, China [61], Central-Eastern Sardinia, Italy [16], and Guadalentin and Cerro Tonosa, Spain [85,86], indicating that gully formation in the study areas has a higher susceptibility. The variability in a-values reflects local environmental attributes influenced significantly by climate, soil properties, and vegetation conditions [86]. For instance, in regions such as Alentejo, Portugal [87], Sao Paulo State, Sao Pedro, Brazil [88], New South Wales, Australia [89], Brussels, and Leuven, Belgium [90], the very low a-values (0.018–0.025) suggest heightened sensitivity to environmental conditions and easier gully erosion initiation. Furthermore, regions with higher a-values (No. 4–9 in Table 5) exhibit lower b-values compared to our study, indicating that drainage area has a limited influence on gully formation in these areas. Gully erosion processes in these regions vary widely, encompassing overland flow erosion [68,91], pipe erosion [92], and landslides [93]. For example, in the Yuanmou Dry-hot Valley, China [61], active gully heads with small drainage areas experience gully wall collapse primarily due to steep walls and deep gully beds. Similarly, in the Chinese Loess Plateau [68], pipe erosion and landslides contribute to erosion processes on gully slopes and drainage areas. Conversely, regions with lower a-values (No. 10–13 in Table 5) exhibit b-values (0.35–0.40) similar to our study, suggesting that gullies in these areas drain larger areas influenced predominantly by surface runoff [94].

4.4. Limitations of This Study

Our study focused on the gully development characteristics and erosion rates in three different agro-geomorphic regions. We also analyzed the effects of different land use types and topography (slope gradient and aspect) on the rate of gully erosion, and the effects of climate and soil properties on the rate of gully erosion were briefly analyzed. As we know, the gully erosion rate can be attributed to the interaction of the above-mentioned factors. However, we have not yet considered the effect mechanism of soil characteristics, lithology, tillage direction, precipitation, and other influencing factors and their differences among different regions. For example, the vertical change in soil layer structure and root distribution along the gully headwall and bank and differences among different regions can significantly affect the headcut retreat process and the gully bank stability change process. However, issues like this have not been resolved. These are also the issues we want to study deeply in the future, and the mechanisms of different factors are worth exploring. At the same time, we did not analyze the factors affecting the parameters “a” and ”b” in the S-A model, which is something we need to study next. Despite the above problems, we have made detailed analyses of the gully characteristics and development rates of the three representative areas in Northeast China, which provide a scientific basis for the planning and layout of a national gully control project.

5. Conclusions

This study examined the morphological characteristics, erosion rates, and thresholds of permanent gullies across three agro-geomorphic regions in northeast China. Gully morphology varied significantly among these areas, displaying distinct features: long and wide in HL, short and thin in ML, and long and narrow in YKS. These gullies exceeded global averages in length and area, with densities of 1.65, 5.25, and 0.50 km/km2, respectively, contributing to erosion-induced land losses of 3%, 5%, and 1%. Average rates of gully head retreat, expansion, and areal erosion ranged from 7.11 to 17.50 m yr−1, 0.37 to 0.43 m yr−1, and 105.22 to 277.79 m2 yr−1 across the areas. Gully head retreat and areal erosion were notably higher in HL and YKS compared to ML, though differences were not statistically significant. Factors influencing erosion rates included land use, slope, aspect, and soil properties. YKS, undergoing recent reclamation, exhibited accelerated gully erosion initially. Critical slopes for erosion varied: HL, YKS, and ML experienced peak rates at slopes of 1–2°, 2–3°, and 5–7°, respectively. Overall, gully erosion rates in these areas surpassed global averages, indicating ongoing development and significant hazards. The study identified drainage area as the primary influence on gully formation in HL, followed by YKS and ML. YKS soils showed the highest resistance to gully formation, followed by HL and ML. This research establishes a foundation for assessing erosion risks, planning management strategies, and preventing gully formation in northeast China’s black soil region. Future studies will further explore environmental factors contributing to gully erosion, aiming to enhance erosion management practices.

Author Contributions

Conceptualization, X.Z., M.G. and M.S.; methodology, Z.W.; software, X.L.; validation, Z.W., X.L.; formal analysis, Z.W.; investigation, Z.C.; resources, M.G.; data curation, Z.W. and M.G.; writing—original draft preparation, Z.W. and M.G.; writing—review and editing, M.G.; supervision, M.S. and M.G.; project administration, M.G.; funding acquisition, M.G. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA28010200), and the Young Scientist Group Project of Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences (2023QNXZ03).

Data Availability Statement

Data are contained within the article: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Acknowledgments

We would like to express appreciation to Pengchong Zhou and Lixin Wang of our research team. Moreover, we thank the anonymous reviewers and members of the editorial team for their constructive comments.

Conflicts of Interest

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Appendix A

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Figure A1. Ten years (2013–2023) annual average temperature of three study areas (HL, ML, and YKS).
Figure A1. Ten years (2013–2023) annual average temperature of three study areas (HL, ML, and YKS).
Remotesensing 16 02905 g0a1

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Figure 1. Location of study area. Note: Subfigure (a) is the HL study area, subfigure (b) is the ML study area and subfigure (c) is the YKS study area.
Figure 1. Location of study area. Note: Subfigure (a) is the HL study area, subfigure (b) is the ML study area and subfigure (c) is the YKS study area.
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Figure 2. Changes in gully morphology in two periods and construction of S = a·A−b model. Note: The orange line represents the gully in 2013.
Figure 2. Changes in gully morphology in two periods and construction of S = a·A−b model. Note: The orange line represents the gully in 2013.
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Figure 3. Flow chart of this study.
Figure 3. Flow chart of this study.
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Figure 4. The distribution of gully morphological parameters. Note: Red curves represent the cumulative percentage of gullies. Note: Subfigure (a,f,k) show the gully length in HL, ML and YKS, Subfigure (b,g,l) show the gully width in HL, ML and YKS, Subfigure (c,h,m) show the gully perimeter in HL, ML and YKS, Subfigure (d,i,n) show the gully area in HL, ML and YKS, Subfigure (e,j,o) show the SI in HL, ML and YKS.
Figure 4. The distribution of gully morphological parameters. Note: Red curves represent the cumulative percentage of gullies. Note: Subfigure (a,f,k) show the gully length in HL, ML and YKS, Subfigure (b,g,l) show the gully width in HL, ML and YKS, Subfigure (c,h,m) show the gully perimeter in HL, ML and YKS, Subfigure (d,i,n) show the gully area in HL, ML and YKS, Subfigure (e,j,o) show the SI in HL, ML and YKS.
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Figure 5. S-A model for the three study areas.
Figure 5. S-A model for the three study areas.
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Figure 6. Differential rates of gully erosion in the three study areas: rate of headcut retreat (a), rate of gully area erosion (b), and rate of gully bank expansion (c). Different lowercase letters represent significant (p < 0.05) differences in gully erosion rates between regions.
Figure 6. Differential rates of gully erosion in the three study areas: rate of headcut retreat (a), rate of gully area erosion (b), and rate of gully bank expansion (c). Different lowercase letters represent significant (p < 0.05) differences in gully erosion rates between regions.
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Figure 7. Land use change in HL, ML, and YKS study area from 2013 to 2023. Note: Subfigure (a–c) show the 2013 HL land use, 2023 HL land use and land use change in HL, Subfigure (d–f) show the 2013 ML land use, 2023 ML land use and land use change in ML, Subfigure (g–i) show the 2013 YKS land use, 2023 YKS land use and land use change in YKS.
Figure 7. Land use change in HL, ML, and YKS study area from 2013 to 2023. Note: Subfigure (a–c) show the 2013 HL land use, 2023 HL land use and land use change in HL, Subfigure (d–f) show the 2013 ML land use, 2023 ML land use and land use change in ML, Subfigure (g–i) show the 2013 YKS land use, 2023 YKS land use and land use change in YKS.
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Figure 8. Differences in gully head retreat rate (a), gully expansion rate (b), and areal gully erosion rate (c) among different land uses and study areas. Note: Different capital letters represent significant differences (p < 0.05) in gully erosion rate among different study areas for same land use. Different small letters represent significant differences (p < 0.05) among different land uses in a given study area. DFL is dry farmland, GL is grassland, WL is woodland.
Figure 8. Differences in gully head retreat rate (a), gully expansion rate (b), and areal gully erosion rate (c) among different land uses and study areas. Note: Different capital letters represent significant differences (p < 0.05) in gully erosion rate among different study areas for same land use. Different small letters represent significant differences (p < 0.05) among different land uses in a given study area. DFL is dry farmland, GL is grassland, WL is woodland.
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Figure 9. Differences in gully head retreat rate (a), gully expansion rate (b) and areal gully erosion rate (c) among different slope classes and study areas. Note: Different capital letters represent significant differences (p < 0.05) in gully erosion rate among different study areas for same slope class, and different small letters represent significant differences (p < 0.05) among different slope classes in a given study area.
Figure 9. Differences in gully head retreat rate (a), gully expansion rate (b) and areal gully erosion rate (c) among different slope classes and study areas. Note: Different capital letters represent significant differences (p < 0.05) in gully erosion rate among different study areas for same slope class, and different small letters represent significant differences (p < 0.05) among different slope classes in a given study area.
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Figure 10. Differences in gully head retreat rate (a), gully expansion rate (b) and areal gully erosion rate (c) among different slope aspects and study areas. Note: Different capital letters represent significant differences (p < 0.05) in gully erosion rate among different study areas for the same slope aspect, and different small letters represent significant differences (p < 0.05) among different slope aspects in a given study area.
Figure 10. Differences in gully head retreat rate (a), gully expansion rate (b) and areal gully erosion rate (c) among different slope aspects and study areas. Note: Different capital letters represent significant differences (p < 0.05) in gully erosion rate among different study areas for the same slope aspect, and different small letters represent significant differences (p < 0.05) among different slope aspects in a given study area.
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Figure 11. Modeling of gully criticality in different study areas.
Figure 11. Modeling of gully criticality in different study areas.
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Table 1. Basic characteristics of the study areas.
Table 1. Basic characteristics of the study areas.
IndexHailunMulingYakeshi
Altitude (m asl)205–261307–658685–947
Total area (km2)30.8831.5321.98
Annual rainfall (mm) *649.63719.71553.46
Average slope (°)2.299.936.70
Soil bulk density (g cm−3)1.301.551.26
Annual average temperature (°C) *3.674.24−0.83
Clay (%)33.387.3315.29
Silt (%)57.8368.7667.84
Sand (%)8.7923.9116.87
Total porosity (%)50.8341.8352.44
Gravel concentration/%0.9664.265.93
Note: “*” refers to the mean value of 2013–2023.
Table 2. Morphological characteristics of gullies in the study area (2023).
Table 2. Morphological characteristics of gullies in the study area (2023).
SiteIndexMeanMedianMinMaxSDCVKurtosisSkewnessGLD (km/km2)GAD (km2/km2)
HLL (m)526.22 (a)292.42 51.56 4342.79 666.65 1.27 13.81 3.37 1.65 0.03
B (m)13.28 (a)12.91 3.03 38.74 6.15 0.46 3.17 1.38
Ag (m2)7984.44 (a)3871.67 340.59 80,904.78 11,920.06 1.49 16.61 3.60
P (m)1092.73 (a)689.49 122.96 8823.98 1353.58 1.24 13.75 3.36
SI3.02 (b)2.72 1.27 7.76 1.31 0.43 3.78 1.76
MLL (m)208.64 (b)141.02 13.13 1790.63 213.80 1.02 10.71 2.80 5.250.05
B (m)8.45 (b)8.07 3.01 22.64 2.60 0.31 1.71 1.01
Ag (m2)1969.32 (b)1110.43 73.08 22,779.15 2562.52 1.30 14.93 3.37
P (m)439.47 (b)299.56 36.94 3585.38 433.09 0.99 10.35 2.77
SI2.44 (c)2.31 1.08 6.02 0.86 0.35 1.48 1.10
YKSL (m)614.20 (a)323.07 28.65 3385.17 903.66 1.47 5.78 2.51 0.500.01
B (m)9.32 (b)8.28 4.88 18.90 3.65 0.39 1.29 1.07
Ag (m2)6058.78 (a)2603.51 192.55 36,458.16 8978.69 1.48 7.81 2.67
P (m)1251.74 (a)653.62 75.20 6855.52 1841.08 1.47 5.70 2.51
SI3.56 (a)2.89 1.35 9.80 2.30 0.65 3.62 2.00
Note: L is gully length, B is gully width, Ag is gully area, P is gully perimeter, and SI is gully shape index; GLD is gully length density, GAD is gully area density, and different lowercase letters in the parentheses of the morphology parameters represent different significant differences in the study area (p < 0.05).
Table 3. Gully morphology in different study areas.
Table 3. Gully morphology in different study areas.
Location of Study AreaMethodGully Length (m)Gully Width (m)Gully Area (m2)
This studyHailun, ChinaSatellite images (2.0–2.1 m)526.2213.287984.44
Muling, ChinaSatellite images (2.0–2.1 m)208.648.451969.32
Yakeshi, ChinaSatellite images (2.0–2.1 m)614.29.326058.78
Other studyUpper Blue Nile Basin, Ethiopia [62]Satellite images
(0.50–1.50 m)		5.20–2372.63–13.4520.39–962.24
(67.54)(5.43)(263.29)
Loess Plateau, China [58]3D laser scanning (1 cm)33.60–88.907.8–33.7384.10–2090.30
(58.4)(17.30)(910.3)
Northern Ethiopian Highlands [59]Field measurements (not mentioned)53.192.5NA
Yuanmou Dry-hot Valley, China [61]RTK GPS (<2 cm)NA14.08NA
Yimeng Mountain Area, China [63]UAV, satellite images (0.03 m and 0.5 m)151.3423.814089.02
southwestern Spain [60]Aerial photographs (1:5000)NA13.1NA
South Carolina, USA [57]Field measurements (not mentioned)36–90 (57.25)2.4–9.5 (6.21)NA
Table 4. Gully development rates in different study areas.
Table 4. Gully development rates in different study areas.
Location of Study AreaMethodLinear Rates (m yr−1)Areal Rates (m2 yr−1)Annual Precipitation (mm)Annual Evaporation (mm)Soil Property
this studyHailun, ChinaSatellite images12.21277.79649.63553.46silts and clays
Muling, ChinaSatellite images7.11105.26719.71584.84silts and sands
Yakeshi, ChinaSatellite images17.5243.36553.46450.82silts and clays
other studyNorthern Ethiopia [69]Field measurements3.831.5500–900NAsandstone, limestone, and shale
Tunisia [64]Field measurements0.3810.54<450NAsandstone
Yuanmou Basin, China [67]Field measurements0.045–1.17NA6153569alluvial soil
Loess Plateau [68]Field measurements0.63NA300–500NAloess
southeast Spain [66]Field measurements0.1NA276–379NAQuaternary clays
southeastern Arizona, USA [65]Aerial photographs, Theodolite, LIDAR DEM, Differential GPS0.35–1.50NA311–324NAsilts and clays
Negev highlands, Israel [6]Field measurements22.779080–1202000–2500sandy loess
the Upper Blue Nile basin, Ethiopia [62]Satellite images0.76~3.429.72~12.26850–3424NAclay
Table 5. Parameters of the gully critical model for different study areas.
Table 5. Parameters of the gully critical model for different study areas.
No.LocationSourceab
1Hailun, ChinaThis study0.0520.52
2Muling, ChinaThis study0.0440.27
3Yakeshi, ChinaThis study0.120.36
4Upper-Rwizi catchment, UgandaHamels (2011) [95]0.190.14
5Guadalentin, SpainNachtergaele et al. (2001) [85]0.150.13
6Cerro Tonosa, SpainVandekerckhove et al. (2000) [86]0.230.10
7Loess Plateau, ChinaWu and Cheng. (2005) [68]0.180.24
8Yuanmou Dry-hot Valley, ChinaDong et al. (2013) [61]0.520.09
9Central-Eastern Sardinia, ItalyZucca et al. (2006) [16]0.180.20
10Alentejo, PortugalVandaele et al. (1996) [87]0.020.35
11New South Wales, AustraliaMuñoz-Robles et al. (2010) [89]0.0180.36
12Brussels and Leuven, BelgianVandaele and Poesen (1995) [90]0.0250.40
13Sao Paulo State, Sao Pedro, BrazilAraujo (2011) [88]0.0200.38
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Wang, Z.; Shi, M.; Guo, M.; Zhang, X.; Liu, X.; Chen, Z. Morphological Characteristics and Development Rate of Gullies in Three Main Agro-Geomorphological Regions of Northeast China. Remote Sens. 2024, 16, 2905. https://doi.org/10.3390/rs16162905

AMA Style

Wang Z, Shi M, Guo M, Zhang X, Liu X, Chen Z. Morphological Characteristics and Development Rate of Gullies in Three Main Agro-Geomorphological Regions of Northeast China. Remote Sensing. 2024; 16(16):2905. https://doi.org/10.3390/rs16162905

Chicago/Turabian Style

Wang, Zhengyu, Mingchang Shi, Mingming Guo, Xingyi Zhang, Xin Liu, and Zhuoxin Chen. 2024. "Morphological Characteristics and Development Rate of Gullies in Three Main Agro-Geomorphological Regions of Northeast China" Remote Sensing 16, no. 16: 2905. https://doi.org/10.3390/rs16162905

APA Style

Wang, Z., Shi, M., Guo, M., Zhang, X., Liu, X., & Chen, Z. (2024). Morphological Characteristics and Development Rate of Gullies in Three Main Agro-Geomorphological Regions of Northeast China. Remote Sensing, 16(16), 2905. https://doi.org/10.3390/rs16162905

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