Soil Conservation and Influencing Factors in Xiangyang City, Hanjiang River Basin

Abstract

Xiangyang City is the core area of soil erosion in the Han River Basin, with serious problems of soil erosion and a weak soil conservation capacity. The spatiotemporal evolution characteristics and influencing factors of soil conservation in Xiangyang City, Han River Basin, from 2000 to 2020 were analyzed using the InVEST-SDR model and the PLUS contribution methodology. The results show the following: (1) The amount of soil conservation per unit area in Xiangyang in 2000, 2010, and 2020 was 1.84 × 10⁵ t/km², 1.59 × 10⁵ t/km², and 1.96 × 10⁵ t/km². This was concentrated in some areas, such as Baokang County, Nanzhang County, and Gucheng County. The soil conservation in Zaoyang, Xiangzhou, Yicheng, and Laohekou was relatively low, while the soil conservation capacity in the Xiangcheng and Fancheng areas was weakest. (2) The areas with the highest value of soil conservation were mainly concentrated in the forest areas in the southwest and northwest of Xiangyang, where the vegetation coverage is high and the altitude is low. The areas with low soil conservation were mainly concentrated in the eastern central part of Xiangyang, which is mainly farmland, with less vegetation and relatively flat terrain. (3) The amount of soil conservation is mainly influenced by two factors—vegetation coverage and terrain—indicating that vegetation management strategies should be tailored to local conditions. This article differs from previous watershed research areas by exploring the influencing factors of soil conservation in Xiangyang City and deeply analyzing the changes in importance and the spatiotemporal differentiation of ecosystem service functions. This conclusion can provide data support for environmental management and decision-making in the Xiangyang region, helping to achieve the sustainable development of the regional ecological environment and economic society.

1. Introduction

The soil conservation function refers to the ability of surface vegetation, such as forests and grasslands, in a certain area to reduce soil erosion [1]. It is the foundation of ecological processes such as soil formation, vegetation fixation, and water conservation in a region [2,3,4,5]. Moreover, it is an important regulatory service and component of service functions in an ecosystem. Soil conservation plays an important role in maintaining regional ecological security and sustainable development [6,7]. At present, most evaluation methods for soil conservation functions are based on the perspective of soil erosion, and quality assessment is carried out based on the impact of ecosystems on erosion and sediment production processes. The main methods include the Universal Soil Loss Equation (USLE) published in the US Department of Agriculture’s Agricultural Handbook, the Chinese Soil Loss Equation (CSLE) [8], and the Integrated Valuation and Balancing of Ecosystem Services model (InVEST) jointly developed by Stanford University, the Nature Conservancy, the World Wildlife Fund, and others [9]. The RULSE model considers vegetation cover factors, precipitation, terrain, soil properties, and management factors and is currently the most widely used soil erosion model. It can estimate potential soil erosion, actual soil erosion, and soil conservation. From existing research, the USLE model has not taken into account the ability of the land parcel to intercept upstream sediments, and there are certain issues with soil conservation calculated through the USLE model. This problem has been adequately solved with the InVEST model. The InVEST model has the characteristics of needing only a small amount of input data, a simple operation process, produces accurate calculation results, and has been unanimously recognized by many experts and scholars at home and abroad. Therefore, this article chose the InVEST model to evaluate soil conservation in Xiangyang City.

The InVEST model has been widely applied by experts and scholars worldwide since its release, including in Asia, Europe, South America, North America, Africa, and other regions, with practical research results. The InVEST model was first used abroad. Mansoor D. K. Leh conducted research and analysis on multiple ecosystem services in the Ivory Coast and Ghana in Africa using the InVEST model [10]; Nelson et al. used the Willamette River Basin in North America as their research area and applied the InVEST model to explore the dynamic changes in different ecosystem services under different land use change scenarios [11]; Goldstein et al. [12] used multiple modules from the InVEST model to analyze the changes in ecosystem service functions in Hawaii Island under different land use scenarios; Bagstad et al. compared and analyzed the ecosystem service results of the San Pedro River in Bolivia using the InVEST and ARIES models [13]; Borrelli’s research on global soil erosion found that the expansion of arable land caused by human activities has exacerbated soil erosion [14]; Ain analyzed the impact of suburban expansion in India on land and natural resources [15]; Peng analyzed the impact of Beijing’s ecological security pattern on nature reserves [16]; Dong conducted an ecological security assessment of Hefei City using the Driving Force–Pressure–Status–Impact–Response (DPSIR) model [17]; Nie et al. conducted in-depth analysis at different geographical scales from county to town and used ecological network analysis to provide a reference for ecological protection areas and balanced regional development [18]; Bouguerra conducted a priority assessment of soil conservation ecosystems in the Tunisian watershed [19]; Ougougdal used the InVEST model to divide the soil erosion areas in the Ourika watershed of Morocco [20]; Gashaw evaluated the soil and water conservation situation in the Upper Blue Nile Basin of Abai, Ethiopia to provide recommendations for managers [21]; Guo used the InVEST SDR model to evaluate soil erosion and its driving factors in the Huaihe River region [22]; Abeysingha evaluated the soil erosion and conservation status in the Sri Lanka River Basin [23]; Alaoui analyzed the soil erosion situation in the Moroccan River Basin [24]; and Ureta’s analysis of the Cruz River Basin in the Philippines found that forests are a high-value distribution area for soil conservation [25]. Zhu analyzed soil conservation ecosystem service functions and their spatiotemporal distribution patterns in the water source area of the South-to-North Water Diversion Middle-Route Project based on the InVEST model [26,27]. The research shows that the existing methods have significant effects on soil and water conservation in the region. Other scholars have explored the evolution characteristics of soil erosion in different watersheds such as the Hei River, Sanjiangyuan, the Xin River, the Shiyang River, and the Xinjiang region [28,29,30,31,32,33,34,35]. Overall, the InVEST model is feasible to use in soil conservation research areas at different scales.

Chinese scholars have attempted to use the InVEST model to evaluate ecosystem services such as water conservation [36], water supply [37], water production [38], biodiversity [39], and carbon storage [40]. However, there are still a few research results on using the InVEST model for the assessment of soil conservation function. This article aims to use modeling methods such as InVEST to understand the basic status of soil protection ecosystem services in the Han River Basin of Xiangyang City in 2000, 2010, and 2020 [41,42]. A long time series of dynamic simulations can reveal the long-term evolution laws and key turning points of soil erosion. Combined with remote sensing interpretation data and the soil conservation module of the InVEST model, it quantifies the cumulative effects of different land use/cover changes on soil and water conservation functions.

Traditional research often focuses on natural factors such as precipitation and slope. This article uses the PLUS model contribution analysis to identify the dominant factors affecting soil and water conservation in Xiangyang at different ages [43,44]. It explores the changes in the importance and spatiotemporal differences between six influencing factors, including the NDVI (Normalized Difference Vegetation Index), slope, precipitation, temperature, population density, GDP (Gross Domestic Product), and soil protection ecosystem services [45,46,47,48]. Based on Xiangyang’s subtropical monsoon climate and terrain fragmentation characteristics, the model parameters were optimized to enhance regional applicability.

This article is significant for innovation in long-term dynamic simulations, multi-factor collaborative analysis, and ecological economic collaborative optimization. Combining the spatial heterogeneity results output by the model (such as the distribution of high-risk areas for soil erosion), priority governance strategies are proposed, providing a scientific and objective theoretical basis and practical reference for the management and research of ecological protection and governance, ecological function zoning, ecological security pattern construction, ecological vulnerability assessments, and ecological compensation mechanisms in the Han River Basin in Xiangyang City and other similar areas. This will consolidate and enhance Xiangyang’s position as the central city of the Han River Basin, enhance its comprehensive competitiveness, create a sustainable development environment, and develop a soft environment.

2. Study Area

Xiangyang City, one of the central cities in the Han River Basin, has a total area of approximately 19,700 km² and important ecological functions. In terms of the climate and environment, it belongs to the northern subtropical monsoon climate with four distinct seasons, an annual precipitation of 878.3 mm, and an average annual evaporation rate of 1457.5 mm. Located in the transitional zone between the Jianghan Plain and the central southern hills, the geological structure is complex, and the strata are well-developed. The terrain features are high in the northwest and low in the southeast, including mountainous areas in the west, hilly plains in the middle, and low mountains and hills in the east. In terms of stratigraphy, the western region belongs to the Paleozoic era, including the Cambrian, Ordovician, Silurian, Devonian, Carboniferous, and Permian systems. The terrain is mainly mountainous, with steep mountains, deep valleys, and shallow soil layers. The central region is mainly composed of the Paleozoic strata, with hills and ridges as the main terrain, including undulating waves, deep soil layers, and sparse vegetation. The plain areas in the south are mainly composed of Quaternary alluvial and lacustrine deposits in the Jianghan Plain, with deep soil layers and fertile, mainly cultivated, soil. In terms of its geological structure, the Tongbai Dahongshan fault zone divides Xiangyang City into two structural units from northeast to east, while the Xiangfan Suizhou fault zone and the Hanjiang fault zone control the flow of the Hanjiang River from northwest to southeast in the central part of Xiangyang City. The forest coverage rate in Xiangyang City is as high as 46%, thanks to the diversity of the regional climate and habitats and the abundant natural endowment, which provides a good foundation for the living environment and sustainable development of Xiangyang City (Figure 1).

3. Data Sources and Research Methods

3.1. Data Sources

The basic geographic information data on Xiangyang City, including land use data, were sourced from the High-Performance Spatial Computing Intelligent Laboratory of Wuhan University [47]. DEM (digital elevation data) were sourced from the Geospatial Data Cloud (https://www.gscloud.cn/ (accessed on 16 June 2024)). The slope and aspect were obtained via digital elevation processing, while the population (abbreviated as POP), GDP (Gross Domestic Product), and NDVI (Normalized Difference Vegetation Index) data were sourced from the Resource and Environmental Science Data Center (https://www.resdc.cn/Default.aspx (accessed on 16 June 2024)). The annual average temperature (abbreviated as TEM) and precipitation (abbreviated as PRE) were sourced from the Qinghai–Tibet Plateau Data Center (https://data.tpdc.ac.cn/home (accessed on 2 June 2024)). The railway and highway data were sourced from the 2021 public version of the Basic Geographic Information Dataset released by the National Geographic Information Center (https://www.webmap.cn/commres.do?method=result100W (accessed on 6 July 2023)). The cost distance tool in ArcGIS was used to create cost raster data for railways and highways. The soil type data were sourced from the soil database “Harmonious World Soil Database” jointly established by the Food and Agriculture Organization of the United Nations and the International Institute for Applied Systems Analysis. The above data were subjected to secondary data, with a coordinate system of CGCS2000 and a spatial resolution of 30 m.

3.2. Research Method

3.2.1. Calculation of Soil Conservation

The InVEST model is an ecosystem service tool jointly developed by Stanford University, the World Wildlife Fund, and the Nature Conservancy in the United States. It overcomes the shortcomings of traditional soil conservation estimation research methods, such as static calculation results, high costs, and extensive data requirements. It has the advantages of simple use, flexible parameters, and the spatial visualization of evaluation results. It has been widely applied to assess the impact of land use changes on ecological security [8,49,50]. This article utilized the sediment delivery ratio (SDR) in the InVEST model to quantitatively evaluate soil conservation in Xiangyang City. The potential soil erosion amount and actual soil erosion amount in the study area were quantitatively estimated using the Universal Soil Loss Equation (USLE) equation. The difference between the potential and actual erosion amounts is the soil conservation amount [51]. The unit of measurement for soil conservation used in this article is t/km² (Table 1).

Table 1. Brief information of data used in this study.

Input Model Data	Data Type/Parameter	Data Source	Type	Pixel Size (m)
Land use data	Land type	Earth System Science Data Discussions	Raster	30 × 30
DEM	Altitude	Geospatial Data Cloud	Raster	30 × 30
Rainfall erosivity factor	Precipitation	National Tibetan Plateau/Third Pole Environment Data Center	Raster	30 × 30
Soil erodibility factor	Organic carbon, sand, and sticky soil gravel	Harmonized World Soil Database	Raster	30 × 30
Watersheds	Boundary	National Catalogue Service For Geographic Information	Shape	-
Biophysical table	Table 2	InVEST guidebook; related literature	CSV	-
Threshold flow accumulation	1000	InVEST guidebook; related literature	Constant	-
Borselli k	0.5	InVEST guidebook; related literature	Constant	-
Borselli IC0	0.8	InVEST guidebook; related literature	Constant	-
SDRMAX	0.5	InVEST guidebook; related literature	Constant	-

Table 1. Brief information of data used in this study.

Input Model Data	Data Type/Parameter	Data Source	Type	Pixel Size (m)
Land use data	Land type	Earth System Science Data Discussions	Raster	30 × 30
DEM	Altitude	Geospatial Data Cloud	Raster	30 × 30
Rainfall erosivity factor	Precipitation	National Tibetan Plateau/Third Pole Environment Data Center	Raster	30 × 30
Soil erodibility factor	Organic carbon, sand, and sticky soil gravel	Harmonized World Soil Database	Raster	30 × 30
Watersheds	Boundary	National Catalogue Service For Geographic Information	Shape	-
Biophysical table	Table 2	InVEST guidebook; related literature	CSV	-
Threshold flow accumulation	1000	InVEST guidebook; related literature	Constant	-
Borselli k	0.5	InVEST guidebook; related literature	Constant	-
Borselli IC0	0.8	InVEST guidebook; related literature	Constant	-
SDRMAX	0.5	InVEST guidebook; related literature	Constant	-

Table 2. Water and soil conservation factors (c & p values) of different land use types.

LULC	Lucode	C	p
Cropland	1	0.23	0.75
Forest	2	0.05	0.15
Grassland	3	0.06	0.35
Wetland	4	0	0
Built-up areas	5	0	0
Bare land	6	1	1

Table 2. Water and soil conservation factors (c & p values) of different land use types.

LULC	Lucode	C	p
Cropland	1	0.23	0.75
Forest	2	0.05	0.15
Grassland	3	0.06	0.35
Wetland	4	0	0
Built-up areas	5	0	0
Bare land	6	1	1

3.2.2. Rainfall Erosivity Factor (R)

The R factor represents the potential soil erosion caused by rainfall and is a dynamic indicator for evaluating soil erosion status [52]. This study adopted Wischmeier’s annual scale formula, expressed as follows:

$$R_i=\mathrm{\alpha }\times P_i^{\mathrm{\beta }}$$

(1)

Pi is the annual rainfall in the i-th year (mm), R_i is the erosive force of rainfall in the i-th year,$\mathrm{\alpha }$ is 0.0534, and $\mathrm{\beta }$ is 1.6548. The unit of the R factor calculation result was inconsistent with the standard unit and needed to be multiplied by 0.0534 to convert it into international standard units.

3.2.3. Soil Erodibility Factor (K)

The K factor can reflect the sensitivity of soil to erosion and is an important parameter for soil erosion prediction. Due to the requirement of the soil structure coefficient and soil permeability index in this formula, considering the availability of data, this paper adopted the calculation method proposed by Williams [53] to obtain the grid map data of the soil erodibility factor. The specific calculation Equation (2) is as follows:

$$\begin{gathered} {\mathrm{K}}_{\mathrm{EPIC}}=\left\{0.2+0.3\times \exp \left[-0.0256\times \mathrm{SAND}\left(1-{\displaystyle \frac{\mathrm{SILT}}{100}}\right)\right]\right\}\times {\left({\displaystyle \frac{\mathrm{SILT}}{\mathrm{SILT}+\mathrm{CLAY}}}\right)}^{0.3} \\ \times \left[1-0.25\times {\displaystyle \frac{\mathrm{C}}{\mathrm{C}+\exp \left(3.72-2.95\times \mathrm{C}\right)}}\right] \\ \times \left\{1-0.7{\displaystyle \frac{1-\left(\mathrm{SAND}/100\right)}{\left(1-\mathrm{SAND}\right)+\exp \left[-5.51+22.9\times \left(1-\mathrm{SAND}/100\right)\right]}}\right\} \end{gathered}$$

(2)

In the formula, K represents the modified soil erodibility factor; KEPIC represents the soil erodibility factor before correction; SAND, SILT, and CALY represent the proportions of sand, silt, and clay particles in the soil, respectively; and C represents the proportion of organic carbon content in the soil.

3.2.4. Vegetation Coverage and Soil and Water Conservation Measure Factors (CP)

The vegetation cover and crop management factor (C) refers to the ratio of total soil erosion with vegetation cover to that without vegetation cover under different land use scenarios. The C factor value has a range of [0, 1]. C = 1 indicates that there is no vegetation cover on the ground, and it is in a completely exposed state; on the contrary, C = 0 indicates high ground vegetation coverage. The soil conservation measurement factor (P) refers to the ratio of soil erosion after taking soil and water conservation measures against soil erosion for planting on slopes. The P factor values have a range of [0, 1]. p = 0 indicates that very good soil and water conservation measures have been taken, and the soil has basically no erosion or extremely mild erosion; p = 1 indicates that no soil and water conservation measures have been taken, resulting in severe soil erosion. This article determined the C and p values of the corresponding land use types in this study based on the InVEST user manual [9], the USDA RUSLE manual [54] of the United States Department of Agriculture, the Food and Agriculture Organization of the United Nations, and the relevant literature [41,55,56,57,58,59], as shown in the following table.

3.2.5. Slope Length and Gradient Factor (LS)

The LS factor reflects the impact of the terrain on soil erosion and is a dimensionless composite terrain factor. Fu Suhua et al. developed the LS calculation tool based on DEM raster data, considering the characteristics of soil erosion in China [60,61]. When running InVEST SDR, the model automatically calculates the factor based on the input elevation data. The operating principle is based on DESMET’s two-dimensional surface calculation method, as shown in the following Equation (3):

$$\begin{gathered} {\mathrm{SL}}_{\mathrm{i}}={\mathrm{S}}_{\mathrm{i}}{\displaystyle \frac{{\left({\mathrm{A}}_{\mathrm{i}-\mathrm{in}}+{\mathrm{D}}^2\right)}^{\mathrm{m}+1}-{\mathrm{A}}_{\mathrm{i}-\mathrm{in}}^{\mathrm{m}+1}}{{\mathrm{D}}^{\mathrm{m}+2}·{\mathrm{x}}_{\mathrm{i}}^{\mathrm{m}}·{\left(22.13\right)}^{\mathrm{m}}}} \\ {\mathrm{S}}_{\mathrm{i}}=\left\{\begin{array}{c}10.8·\sin \left(\mathrm{\theta }\right)+0.03, \mathrm{\theta }<9\%\\ 16.8·\sin \left(\mathrm{\theta }\right)-0.50, \mathrm{\theta }\ge 9\%\end{array}\right. \end{gathered}$$

(3)

In the formula, S_i represents the slope factor of the grid unit; i represents the function $\mathrm{\theta }$ about the slope value; ${ \mathrm{A}}_{\mathrm{i}-\mathrm{in}}$ represents the area of sand production above the net inlet of the grid (m²); D represents the grid size; and m; ${\mathrm{x}}_{\mathrm{i}}=\left|{\sin \mathrm{\alpha }}_{\mathrm{i}}\left|+\right|\cos {\mathrm{\alpha }}_{\mathrm{i}}\right|, {\mathrm{\alpha }}_{\mathrm{i}}$ represents the sediment transport direction of the grid unit.

3.2.6. InVEST Model

Based on the Universal Soil Erosion Equation (USLE), the sediment delivery ratio (SDR) in the InVEST model describes the spatial process of soil erosion and sediment migration in watersheds through image metrics [62].

$$\begin{gathered} {\mathrm{SE}}_{\mathrm{i}}={\mathrm{USLE}}_{\mathrm{i}}·{\mathrm{SDR}}_{\mathrm{i}} \\ {\mathrm{SDR}}_{\mathrm{i}}={\displaystyle \frac{{\mathrm{SDR}}_{\max }}{1+\exp \left(\frac{{\mathrm{IC}}_0-{\mathrm{IC}}_{\mathrm{i}}}{{\mathrm{K}}_{\mathrm{b}}}\right)}} \end{gathered}$$

(4)

$${\mathrm{SEDREN}}_{\mathrm{i}}={\mathrm{R}}_{\mathrm{i}}·{\mathrm{K}}_{\mathrm{i}}·{\mathrm{LS}}_{\mathrm{i}}·\left(1-{\mathrm{C}}_{\mathrm{i}}·{\mathrm{P}}_{\mathrm{i}}\right)·{\mathrm{SDR}}_{\mathrm{i}}$$

(5)

where ${\mathrm{SDR}}_{\mathrm{i}}$ is the sediment transport ratio in the presence of vegetation cover and soil conservation measures for any grid i; ${\mathrm{SDR}}_{\max }$ is the maximum theoretical SDR value, which is set to 0.8 in this paper; and ${\mathrm{IC}}_0$ (the ratio of sediment entering the valley compared to the amount of erosion on the slope) and ${\mathrm{K}}_{\mathrm{b}}$ (the degree of spatial connectivity of a given site to runoff) are calibration parameters for determining the shape of the relationship between spatial connectivity and the sediment transport ratio of hydrological processes in a small watershed. The ${\mathrm{IC}}_{\mathrm{i}}$ indicates the probability that a unit of sediment on grid i in the watershed reaches the river. ${\mathrm{K}}_{\mathrm{b}}$ is set to 0.5 and ${\mathrm{IC}}_0$ is set to 0.5 in this paper. ${\mathrm{SE}}_{\mathrm{i}}$ is the sediment export from grid i. ${\mathrm{SEDREN}}_{\mathrm{i}}$ is the soil retention of raster i, which does not take into account upwelling sedimentation and the amount of sediment exported; that is, it is the amount of soil erosion avoided by the current soil and water conservation measures relative to the barren land, and the difference is used as an estimate of soil retention.

3.2.7. PLUS Contribution Analysis

The PLUS model is commonly used to simulate the driving factors of land change [63] and can also be used to analyze the driving factors of certain indicators. This article uses the PLUS model to explore the importance of factors affecting soil conservation. The random forest classification (RFC) algorithm is used to explore the contribution values of various driving factors to farmland changes. This algorithm can solve the problem of multicollinearity between multiple variables by extracting random samples from the original dataset and determining the probability of a farmland type appearing in cell i ${\mathrm{P}}_{\mathrm{i},\mathrm{k}}^{\mathrm{d}}$. Equation (6) is as follows:

$${\mathrm{P}}_{\mathrm{i},\mathrm{k}}^{\mathrm{d}}\left(\mathrm{x}\right)={\displaystyle \frac{{{\displaystyle \sum }}_{\mathrm{n}=1}^{\mathrm{M}}\mathrm{I}=\left[{\mathrm{h}}_{\mathrm{n}}\left(\mathrm{x}\right)=\mathrm{d}\right]}{\mathrm{M}}}$$

(6)

In the formula, the value range of d is 0 or 1. d = 1 indicates that other land use types have changed to k types of land use; d = 0 indicates that the land has changed to other types besides k; X is a vector composed of several driving force factors; I is the indicator function of the decision tree set; ${\mathrm{h}}_{\mathrm{n}}\left(\mathrm{x}\right)$ is the prediction type of the n-th decision tree of vector x; and M is the total number of decision trees.

4. Results

4.1. Temporal and Spatial Evolution Characteristics of Soil Conservation

The land use data, DEM, rainfall erosivity factor, soil erodibility factor, watershed, CP table, and various parameter values of Xiangyang City in 2000, 2010, and 2020 were input into the “SedimentDeliveryRatio” module of the InVEST model for calculation. Then, the “Raster Calculator” tool in the ArcGIS (Pro 3.4) software was used to process and obtain the soil conservation data for the third phase of the study area. Equation (7) is as follows:

$$\mathrm{A} = \mathrm{RKLS} - \mathrm{ULSE}$$

(7)

In the formula, A represents soil conservation, RKLS represents potential soil erosion, and ULSE represents actual soil erosion.

4.2. Characteristics of Interannual Variation in Soil Conservation

The results of soil conservation and soil conservation per unit area are shown in Figure 2. The total soil conservation values in Xiangyang City in 2000, 2010, and 2020 were 3.64 × 10⁹ t, 3.14 × 10⁹ t, and 3.86 × 10⁹ t. The soil conservation per unit area was as follows: 1.84 × 10⁵ t/km², 1.59 × 10⁵ t/km², and 1.96 × 10⁵ t/km². The total amount of soil conservation decreased by 4.91 × 10⁴ t from 2000 to 2010, which is a decrease of 13.5%. The total amount of soil conservation increased by 7.16 × 10⁴ t from 2010 to 2020, with a growth rate of 22.78%. Overall, the soil conservation in the study area showed an interannual variation pattern of first decreasing and then increasing. It is worth noting that differences in research methods and scales can lead to changes in the estimation of soil conservation, which is consistent with the findings of other researchers [64].

4.3. Spatial Variation Characteristics of Soil Conservation

The soil conservation levels in the study areas in 2000, 2010, and 2020 were as follows: Baokang > Nanzhang > Gucheng > Zaoyang > Xiangzhou > Yicheng > Laohekou > Xiangcheng > Fancheng. The high values of soil conservation were mainly concentrated in the southwestern and northwestern regions of the study area, which were mainly composed of forests with high vegetation coverage and terrain. There were also sporadic distributions in other regions. The low values of soil conservation were mainly concentrated in the central and eastern regions of the study area, where there was less vegetation and relatively flat terrain. Overall, the level of soil conservation was mainly influenced by two factors: vegetation coverage and terrain (Figure 3).

4.4. Analysis of Impacts of Different Land Use Types on Soil Conservation

According to

Table 3. Statistics on changes in soil conservation of land use types in Xiangyang.

Year	Soil Conservation	Cropland	Forest	Grassland	Wetland	Built-Up Areas	Bare Land	Total
2000	Total	1.34 × 109	2.05 × 109	0.02 × 109	0.12 × 109	0.10 × 109	0.0003	3.64 × 109
	Proportion	36.81%	56.46%	0.61%	3.37%	2.73%	0.0090%	100%
2010	Total	1.23 × 109	1.66 × 109	0.01 × 109	0.14 × 109	0.10 × 109	0.0000	3.14 × 109
	Proportion	39.18%	52.77%	0.21%	4.60%	3.22%	0.0003%	100%
2020	Total	1.27 × 109	2.32 × 109	0.00 × 109	0.14 × 109	0.13 × 109	0.0000	3.86 × 109
	Proportion	32.84%	60.20%	0.05%	3.53%	3.36%	0.0001%	100%

, soil conservation levels significantly differed between the different land use types in the study area, specifically manifested as forest land > cultivated land > grassland > cropland > wetland > unused land. The soil conservation of forest land was the highest in all years, with a total amount of 1.66–2.32 × 10⁹ t, accounting for 53–60%. Compared with other land types, the well-developed root system of forest land provides it with a good soil consolidation function, which can minimize soil erosion under rainwater erosion. Therefore, the soil conservation of forest land accounted for the largest proportion among all land types. The soil conservation capacity of cropland was second only to forest land. Planting grain or economic crops can also reduce soil erosion, but the effect is far less significant than that of forest land. The coverage density of grasslands in the watershed was generally low with a small area, so their soil conservation was relatively low. The ability of wetland, cropland, and unused land to intercept and resist soil erosion is also almost zero due to there being almost no vegetation coverage in these land types. Therefore, the soil conservation of these three land types was extremely low [65].

The changes in the soil conservation levels of the various land use types from 2000 to 2020 also differed from a time scale perspective. The proportion of soil conservation levels in forest land, wetland, and cropland generally showed an increasing trend, while the proportion of soil conservation levels in cultivated land, grassland, and unused land generally showed a decreasing trend. This was due to the transformations and changes between different land types, whereby the soil conservation content contained in land types with increasing areas increases as a result; by contrast, the soil conservation amount contained in land types with a reduced area decreases accordingly (Figure 4).

4.5. Analysis of Factors Affecting Soil Conservation

As shown in Figure 5 and Figure 6, among the factors influencing soil conservation service functions, DEM, slope, and NDVI consistently ranked among the top three in importance from 2000 to 2020, with the greatest impact on soil conservation function in the study area. The DEM factor had the greatest importance and was the dominant factor affecting soil conservation service functions. Slope ranked second in 2000 and 2020 and third in 2010, showing a first decreasing and then increasing trend. Overall, the slope factor significantly impacted soil conservation services. From 2000 to 2020, the NDVI showed a first increasing and then decreasing trend but remained in the top three factors. The NDVI is very important for soil conservation services. Important changes in the precipitation and temperature factors were consistent, with the precipitation factor always ranking ahead of the temperature factor, and their contributions increased. The impact on soil conservation functions in the study area was enhanced. The importance ranking of the population density and GDP factors fluctuated but showed a stable upward trend. The impact of the GDP factor on soil conservation function increased, and population density showed a first decreasing and then increasing trend. The impact of socioeconomic factors on soil conservation functions in the study area increased in 2010 compared with the previous two periods.

The terrain and geomorphological factors included the DEM, slope, and NDVI, which consistently ranked in the top three during the three periods and significantly impacted soil conservation services. The DEM factor consistently ranked among the top two factors from 2000 to 2020, with the greatest positive impact on the soil conservation service function in the study area. From 2000 to 2020, the NDVI factors ranked third, first, and third, respectively, showing an upward and then downward trend. The NDVI factor had a significant positive impact on the soil conservation service function in the study area. Vegetation can protect surface soil from raindrop impact or runoff stripping and transportation by enhancing rainfall absorption, reducing the kinetic energy of raindrops, improving the soil aggregate stability and soil infiltration capacity, increasing the underlying surface roughness, and increasing the runoff resistance coefficient. It has a soil retention effect, which usually increases with increasing vegetation coverage. At the spatial scale, the high-value areas of the NDVI are mainly distributed in areas such as Gucheng County, Baokang County, and Nanzhang County. This distribution pattern is consistent with the distribution pattern of high-value areas of soil conservation service functions, further verifying the important impact of NDVI factors on soil and water conservation service functions and the correlation between the two. Compared with the NDVI factor, the slope factor also significantly impacted the soil conservation service function. From 2000 to 2020, it ranked second, third, and second, respectively, with a small change in amplitude. At the spatial scale, similar to the NDVI’s distribution pattern, the slope factor was concentrated in areas such as Chengxian, Baokang, and Nanzhang, and is closely related to the overall sustained growth of soil conservation in the region. Due to the relatively stable characteristics of terrain and landform factors, as well as soil conservation service functions, their spatial distribution range was not significant. Overall, terrain and landform factors are important, particularly for stability, and are significant for maintaining soil conservation service functions in the study area (Figure 7).

Socioeconomic factors, including population density and GDP, can be artificially altered. Exploring the trend of the contribution coefficient of socioeconomic factors to soil conservation services is practically significant for protecting soil conservation services in research areas. As shown in Figure 8, the contribution coefficient of the population density factor in 2000 was 0.11, the contribution coefficient in 2010 was 0.09, and the contribution coefficient in 2020 was 0.1. The contribution coefficient of population density to the soil conservation service function was relatively low in the three periods. With the increase in population density, the contribution coefficient of population density showed a downward trend, and population density negatively impacted the soil conservation service function. At the spatial scale, the range of the population density factor showed a continuous and slow expansion trend, and its negative impact on soil conservation services continued to deepen. The contribution coefficient of the soil conservation service function to the GDP factor showed a continuous upward trend, from 0.09 in 2000 to 0.14 in 2010 and then 0.14 in 2020. On the spatial scale, it was concentrated in the central part of Xiangyang City along the Han River and the urban area of Zaoyang City. The high-value areas of GDP developed toward the north and northeast of Xiangyang, which is a region with low soil conservation. Similarly, the negative impact of the GDP factors on soil conservation services continues to deepen. Generally, human activities change the original land properties on the surface, such as urban expansion, the relocation of residential areas, artificial afforestation, and farmland cultivation, which directly alter the surface state and, thus, affect soil conservation. The increase in population and GDP will bring about urbanization, and cities need to provide more housing, infrastructure, and public services, which drives continuous development and expansion of cities to meet the needs of residents. Urbanization will promote economic growth. However, urban expansion leads to the loss of farmland and the destruction of natural landscapes, resulting in the destruction of land surfaces, a decrease in the sustainability of the ecological environment, and a weakening of soil conservation functions. Overall, there is a positive correlation between population GDP and urbanization, but it is negative for soil conservation.

Climate factors, including precipitation and temperature, also significantly impact soil conservation services, but their degree of influence is lower than that of terrain and geomorphological factors. The contribution coefficients of the precipitation factors to soil conservation services from 2000 to 2020 were 0.11, 0.12, and 0.09, respectively, showing a pattern that first increased and then decreased, with a particularly significant increase between 2000 and 2010, which was related to the precipitation factors’ instability. On a spatial scale, the areas with high rainfall were mainly distributed in Baokang County, Nanzhang County, and other places. Rainfall directly affects the separation of soil particles, the decomposition of soil aggregates, and the migration of eroded sediments. The greater the rainfall intensity, the greater the likelihood of erosion occurring. According to the definition of soil conservation, the greater the actual level of soil erosion, the weaker the soil conservation function will be [51]. The contribution coefficients of the temperature factor to the soil conservation service function from 2000 to 2020 were 0.08, 0.11, and 0.09, respectively, showing the same trend as that for rainfall and a slow increase followed by a decrease. Among the four natural influencing factors of the soil conservation service function, the contribution coefficient of the temperature factor was the lowest. This was because the dependence of the soil conservation service function on the other three natural factors gradually deepened, leading to a gradual decrease in the impact of temperature on the soil conservation service function. At the spatial scale, the high-value areas of the temperature factor were distributed in the central part of Xiangyang City, along both sides of the Han River, showing a distribution pattern of low temperatures and high soil conservation. Overall, although the impact of climate factors on soil conservation services was not as high as that of terrain and landform factors and had a certain degree of instability, the proportion of their contribution coefficients was still relatively high, which is of great significance for maintaining soil conservation services in the study area. Overall, contrary to the increasing positive impact of terrain and climate factors on soil conservation services, socioeconomic factors hindered the level of soil conservation services in the study area, increased the burden on these services, and had a significant negative impact.

5. Discussion

5.1. Analysis of Dynamic Changes in Soil Conservation

The overall trend of soil conservation in Xiangyang City from 2000 to 2020 showed a good trend, with a decrease in soil conservation in 2010 compared to 2000. The high-value areas of soil conservation are distributed in Baokang County, Nanzhang County, and Gucheng County, while the low-value areas of soil conservation are distributed in Yicheng, Laohekou, Xiangcheng, Fancheng, and other places. In the distribution map of soil conservation, it can be seen that in Yicheng City, high-value soil conservation areas show an inverted V-shaped distribution along the Han River. The Han River coast belongs to the erosion and dissolution subzone in terms of its landform type and stratigraphic lithology, with ridges as the main rock type, most of which are sandstone. The upper soil layer usually has a rough texture and good permeability but weak water retention performance. The soil layer is loose, vegetation is sparse, and it is easy to erode [66]. Plain areas such as Xiangzhou and Zaoyang are mainly composed of Quaternary alluvial and lacustrine deposits in the Jianghan Plain [67]. The soil is fertile and mainly cultivated, but the erosion caused by cultivation is severe [68], and the vegetation coverage is low, resulting in an overall decrease in soil conservation [69].

Since the implementation of the “Hanjiang Ecological Economic Belt Development Plan” in Hubei Province [70], Xiangyang has faced new demands for landscape development, which has accelerated the evolution of the ecological pattern of Xiangyang City. Xiangyang City is a key area for strategic deployment, such as the development of the Hanjiang Economic Belt and the Hubei Xiangyang Ten City Belt [71]. A series of policies implemented in this area have divided Xiangyang City into zones for management, accelerating the evolution of Xiangyang’s landscape pattern. According to the zoning plan, Gucheng County, Baokang County, Nanzhang County, etc., will be divided into ecological functional zones for water source conservation and soil and water conservation in the middle reaches of the Han River. Ecological restoration will be actively implemented in this region, and efforts will be made to strengthen the return of farmland to forests and grasslands, improving agricultural production conditions and actively developing efficient agriculture and ecological agriculture. The main land type in this area is forest land with high soil conservation levels. In addition, Xiangzhou, Laohekou City, Yicheng City, and Zaoyang City are designated as ecological functional zones for biodiversity and landscape protection [72]. Farming is the main method in these areas, and unreasonable cultivation can disrupt natural soil habitats, alter the composition and distribution of water and nutrients, have negative impacts on soil structure, and damage filamentous organisms, all of which often reduce soil health and erode the soil [73].

5.2. Analysis of Driving Factors for Soil Conservation

The DEM, slope, and NDVI are the main natural factors affecting soil conservation in Xiangyang City.

The impact of altitude on soil erosion is mainly reflected by the synergistic effect of rainfall and slope. Xiangyang City borders the Qinling Daba Mountains in the west and the Tongbai Dabie Mountains in the northeast, with severe terrain cutting and large slope fluctuations. Frequent and concentrated heavy rainfall exacerbates the soil erosion process. The slope is one of the key terrain factors that control slope runoff and soil erosion. The soil erosion mode, soil particle initiation, runoff sand coordination, and erosion ability of the surface layer of the slope are all affected by the size of the slope. Research has shown that as the slope increases, the runoff velocity accelerates, and the shear force of water flow increases, making soil particles easier to strip and transport, thereby exacerbating soil erosion [74]. Through the field experiments, it was found that when the slope increased from 5° to 25°, the amount of soil erosion on the slope increased exponentially, indicating that slope has a significant nonlinear effect on soil erosion. The interaction between vegetation coverage and other factors is significantly higher than its individual impact on soil erosion, indicating that vegetation coverage can enhance its inhibitory effect on soil erosion processes. Plants have different functions in soil conservation. The roots of plants such as forests help to maintain soil and prevent erosion, while fallen leaves and other organic matter increase soil fertility, provide nutrients for forests, and form a biological cycle [22]. This is because vegetation coverage reduces the impact of rainwater on the soil surface, enhances the infiltration capacity of rainwater into the soil, reduces surface runoff, and thus lowers the process of soil erosion [19]. Rainfall has a significant impact on soil erosion without vegetation cover. Areas with rainfall between 3.28 × 10² and 6.43 × 10² mm, slopes greater than 29°, and elevations above 1300 m are high-risk areas for soil erosion. This is consistent with the spatial characteristics of soil erosion in this study and other research areas [75]. The effect of temperature on soil erosion is mainly reflected by its impact on soil moisture and vegetation growth. On the one hand, it accelerates the increase in soil moisture, making the soil surface easier to dry and increasing the possibility of soil erosion. On the other hand, high temperatures affect vegetation growth, reduce soil protection layers, and make the soil more susceptible to erosion. The role of the population and GDP in soil erosion is mainly reflected in their impact on land types, which, in turn, affect soil properties. The soil conservation capacity of densely populated areas is relatively low, which is consistent with the research results of Borrelli [76]. Urban areas are generally well-developed areas with a lack of natural protection on the surface and a weak soil conservation capacity. Soil erosion and soil conservation are not only closely related to natural factors but also greatly influenced by human factors. Many studies have shown that land use types have a significant impact on soil conservation [14]. Under the influence of environmental factors, land use types can affect soil erosion processes to varying degrees. The spatial changes in land use patterns are influenced by rainfall and slope, which can alter hydrological conditions and erosion systems, thereby affecting the retention capacity of soil erosion [77].

5.3. Soil Conservation Recommendations

Xiangyang City is a major producer of billions of kilograms of grain, making it an important national grain production area and a large-scale commodity grain base. The eastern region has abundant arable land resources and diverse agricultural landscapes. Although most of the cultivated land in the eastern region is flat with gentle slopes, suitable for crop cultivation, there are still cultivation modes such as sloping farmland and even steep slope farmland in mountainous and hilly areas such as Yicheng. Although sloping farmland is only sporadically distributed, due to its location in mountainous and hilly areas with fragile ecological environments, agricultural activities are prone to problems such as soil erosion and ecological damage. Therefore, the local ecological balance and soil and water conservation need to be highly valued. Agricultural production in such areas not only needs to consider economic benefits but also needs to balance ecological protection and sustainable development. By implementing measures such as terraced field construction, vegetation restoration, and slope runoff control, we aim to reduce negative impacts on the environment and improve the efficiency and sustainability of agricultural production [78]. In the cultivated areas in the eastern part of Xiangyang City, the cultivation method cannot be sustained, and measures such as fallowing should be implemented to maintain soil activity. Reducing the strong interference of heavy machinery on the soil and increasing straw cover can also be achieved through terracing techniques to enhance soil conservation and fertility. The areas with high forest coverage in Xiangyang City are mainly concentrated in the southeastern hills of the southwestern mountainous region. The forest construction area comprising gentle slopes and slopes is relatively large, but grassland resources are scarce. For mountainous and hilly areas with severe soil erosion, zoning management can be implemented [79,80,81,82]. The low mountain and hilly areas located in the southeastern mountainous region are mostly sloped farmland with sparse vegetation and thin soil layers. Therefore, it is necessary to strengthen vegetation construction, fully utilize the natural recovery ability of vegetation, cultivate drought-resistant and barren forest and grass vegetation, and implement terrace transformation on sloping farmland. We also need to strengthen the supervision of soil erosion prevention and control and build water source conservation forests and soil and water conservation forest areas [83]. However, there are widely distributed barren and rocky mountains in the southwest and northwest regions, with poor vegetation development and low coverage. The forest structure is single, and soil greening technology is difficult. Soil surface erosion, gully erosion, and gravity erosion are serious, making them key areas for soil and water conservation planning. Therefore, vegetation restoration and afforestation projects need to be carried out through measures such as mountain closure and land reclamation, returning farmland to forests, and selecting ecological restoration plants with strong stress resistance and good soil conservation function. A multi-tree species configuration mode combining local and introduced tree species was adopted, and the plant survival rate improved through water conservancy sowing and covering technology [84]. In addition, for long and steep slopes in mountainous areas, interception channels can be constructed in sections to store water sources for agricultural, forestry, and grassland production. Combined with soil and water conservation measures such as terraced fields, flood irrigation, and waterlogging ponds, the farmland under the slope surface can be protected [85]. The above suggestions can help improve soil conservation, increase soil activity and fertility, and build a solid ecosystem barrier.

Although we have carried out a lot of work, our research still has some limitations, especially in the use of the InVEST-SDR model and related research results. Due to limitations such as data accuracy, the predicted soil erosion amount may deviate from the actual value. Soil erosion involves various processes, including the separation, transportation, and deposition of soil particles [86]. Studying the spatial distribution characteristics of sediments can provide scientific insights into the layout of soil and water conservation facilities. However, this study does not include estimates of sediment deposition. In future research, it will be crucial to consider soil sedimentation, select reliable methods to estimate each factor of soil erosion, improve data accuracy, and predict potential changes in soil erosion. Experimental data will also be included to validate soil erosion and soil conservation.

6. Conclusions

The conclusions are as follows:

(1)

Soil conservation in the study area showed an interannual variation pattern of first decreasing and then increasing. The year 2010 was a critical period for urbanization development, during which urban areas rapidly expanded, construction land increased, and surrounding farmland, forests, and other areas were encroached upon. The land type changed from one with a higher soil conservation capacity to one with a lower soil conservation capacity, resulting in a decrease in overall soil conservation in 2010. Since the 12th Five Year Plan of Xiangyang City in 2012 was announced, emphasis has been placed on the development of forest resources in the southwestern mountainous areas. Industrial development is prohibited within protected areas, and an ecological security guarantee system is established to strengthen the ecological barrier. The government attaches great importance to the construction of forest resources in the western mountainous areas while also taking into account the protection of arable land in the eastern region. The increase in forest resources led to an increase in soil conservation in the entire area of Xiangyang city.

(2)

The high-value areas of soil conservation were mainly distributed in the central, southwestern, and northwestern regions of the study area, while the low-value areas were mainly distributed in the eastern region. The soil conservation amount varied among the different land types, specifically manifested as forest land > cultivated land > grassland > cropland > wetland > unused land. The total amount of soil conservation in different administrative regions, from high to low, was as follows: Baokang > Nanzhang > Gucheng > Zaoyang > Xiangzhou > Yicheng > Laohekou > Xiangcheng > Fancheng. Baokang, Nanzhang, Gucheng, and other places are mainly characterized by mountainous landforms, with forest land being the main land use type. In contrast, the eastern regions of Zaoyang and Yicheng are mainly characterized by hilly and plain farmland, while Xiangzhou, Xiangcheng, Fancheng, and other urban areas have construction land as their main land use type. The soil conservation function of forest land is strong, while the soil conservation capacity of farmland and construction land is weak, resulting in different soil conservation levels in different administrative regions.

(3)

In the analysis of factors affecting ecosystem service functions, it was found that the overall importance of natural factors was higher than that of socioeconomic factors. The DEM and NDVI factors had the most significant impact; the slope factor was the most stable factor; and the precipitation and temperature factors were the most unstable. The importance ranking of socioeconomic factors in soil conservation services is rising, with an opposite trend to that of soil conservation. Socioeconomic factors mainly affect soil conservation due to changes in land use caused by human activities. The increase in population means that people need to build more houses to live in, and people will change the surrounding land type, which directly affects soil conservation. The impact of frequent human activities on soil conservation deserves further in-depth discussion. Therefore, in subsequent ecological protection and governance work, the research area should focus on strengthening the supervision and control of human activities.