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Article

Ecosystem Service Assessment and Sensitivity Analysis of a Typical Mine–Agriculture–Urban Compound Area in North Shanxi, China

by
Shufei Wang
1,†,
Yining Zhuang
2,†,
Yingui Cao
1,3,* and
Kai Yang
1
1
School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
2
General Contracting Engineering Limited Company, China Construction Communications Construction Group, Beijing 100142, China
3
Key Laboratory of Land Consolidation and Rehabilitation, Ministry of Natural Resources, Beijing 100035, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Land 2022, 11(9), 1378; https://doi.org/10.3390/land11091378
Submission received: 14 July 2022 / Revised: 13 August 2022 / Accepted: 19 August 2022 / Published: 23 August 2022
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Abstract

:
The production–life–ecology balance in mine–agriculture–urban compound areas is receiving increasing attention in the context of urbanization and industrialization. This study aims to explore the coordinated development modes of ecosystem services and resident well-being in the Pingshuo open-pit mining area and the surrounding mine–agriculture–urban compound area in Pinglu District, Shuozhou City. Relevant models were used to evaluate the ecosystem service value of water and soil conservation, as well as ecological sensitivity. Additionally, using the hierarchical multiple regression method, we analyzed the responses of soil and water conservation services and ecological sensitivity to different land use patterns. The results showed the following. (1) The water conservation function (WCF) and soil conservation function (SCF) were greatly affected by land use and supplied mostly by the natural habitat, followed by the open-pit coal mining area. (2) Ecological sensitivity was greatly affected by land use patterns, with obvious differences in the same land use types in different spatial locations. (3) In order to enhance the WCF and SCF of the study area and reduce ecological sensitivity, the area and diversity of forest and grassland should be increased, and the area of open-pit mining, cultivated land, and urban land, as well as the land use compound degree, should be reasonably controlled. This study will help guide the regional land use layout and provide countermeasures and suggestions for the management of ecosystems in the mine–agriculture–urban compound area.

1. Introduction

With the acceleration of urbanization and industrialization, natural ecosystems are increasingly threatened by various environmental issues, such as soil erosion, environmental pollution, and biodiversity degradation [1,2]. Gradually, intensified human activities have led to a substantial increase in ecological sensitivity in some areas [3,4]. Coal resource mining, especially open-pit coal mining, can seriously disturb the regional ecological environment [5,6,7]. In China, coal resources are mostly distributed in ecologically fragile areas where the ecosystems have been further destroyed by high-intensity mining activities [8,9].
A mine–agriculture–urban compound area refers to an area of development, processing, and utilization of underground mineral resources, with above-ground agricultural production and agricultural biological resources as the main industries in the context of industrialization, urbanization, and urban–rural integration [10]. This type of area cooperates with the development and transformation of resource-based cities and towns [10]. The land use pattern of this compound area is characterized by close coordination of the mining area, urban area, and agricultural area [10,11]. On the one hand, the development of the coal industry could provide abundant employment opportunities for local residents and promote economic development; on the other hand, the relocation of immigrants during open-pit mining processes could increase the local urbanization rate and promote the construction of new rural areas and the integrated development of urban and rural areas [12]. However, a compound area is composed of multiple ecological subsystems, with multiple contradictions and problems. The core issues include (1) sharp land occupation contradictions in agricultural production and mining; (2) social problems, such as damage to and the abandonment of farmland; and (3) environmental problems, such as air and water pollution [10,11,12]. It has been vital for the planning and management of those compound areas to reconcile the relationships between the service functions and the adverse impacts of coal mining [13,14,15]. The ecological environments of functional cities have been severely damaged by industrial pollution, ecosystem degradation, and rapid urbanization expansion [1,2]. The impacts of economic development on ecological environmental protection have been relatively large, and the integrity and systems of ecological protection remain insufficient [16]. Therefore, it is necessary to explore effective modes for the sustainable and coordinated development of the mine–agriculture–urban compound areas.
In order to achieve a comprehensive analysis of natural and artificial ecosystems in the study area, this work uses the concept of ecosystem services. The core of ecosystem services is the sum of the benefits that ecosystems provide to humans [17,18]. To date, many scholars at home and abroad have classified ecosystem services from different research perspectives. For example, Fisher et al. divided ecosystem services into the two categories of direct and indirect services [19], while Ouyang et al. divided ecosystem services into the two categories of products and environmental services from a value perspective [20]. Different classifications have unique advantages for different research purposes. Additionally, from the perspective of generality, the classification method of the Millennium Ecosystem Assessment is widely accepted and used, which divides ecosystem services into the four categories of provisioning, regulating, supporting, and cultural services [21,22,23]. A corresponding ecosystem service assessment model was established [21,22,23,24]. The present study area is located in the key ecological zone of the Yellow River among the national key ecological function areas, where the ecologically sensitive and fragile area is large, and which is also one of the areas with the most serious soil erosion in China [25,26]. At the same time, due to the degradation of ecosystems and the reduction of the water conservation function (WCF) in recent years, a series of water balance problems have emerged [26,27]. For example, the shortage of water resources in the study area and surrounding areas has intensified contradictions of water use between the resource industry, agriculture, and ecology, leading to an insufficient carrying capacity of water resources [25,26,28]. Therefore, combined with the current situation in the study area, the present research proposes that the main development direction should be to improve the water conservation capacity and soil and water conservation capacity in the study area. We selected water and soil conservation as the representative ecosystem service types in the study area.
Ecological sensitivity refers to the ability of ecosystems to resist external disturbances and recover from damage [29,30]. As a part of an ecological environment assessment, ecological sensitivity can effectively reflect the patterns and characteristics of the spatial distribution of sensitive areas in the ecosystem when the ecological process is in an unstable state, thereby determining the priority development areas and areas requiring key ecological protection [31,32]. Ecosystem services provide basic guarantees for human society and maintain the balance between ecosystems and human well-being [17,26]. Ecological sensitivity reflects the reaction of the ecosystem to environmental changes [30,32]. Therefore, it is helpful for the maintenance of ecosystem stability and sustainable development to coordinate the dynamic relationships between land use and the ecological environment based on ecosystem services and ecological sensitivity [33,34].
In summary, this paper aims to propose management recommendations and countermeasures for regional land use management by analyzing regionally representative ecosystem services and ecological sensitivity characteristics, along with their responses to regional land use types and structures. On the one hand, the study area is located in a loess area with fragile original landforms in the ecological environment, which have been severely disturbed or damaged under the multiple influences of agriculture, mining, and urban construction. On the other hand, increasing attention is being paid to the construction of ecological civilizations at home and abroad, where such severely disturbed ecologically fragile areas represent shortcomings that should be carefully considered. Additionally, as land use is one of the most important ways in which people and the ecological environment closely interact, the rational distribution of land use represents an important means of coordinating regional economic development and ecological protection. Thus, combining an analysis of ecosystem services with an analysis of the ecological sensitivity of land use types can both identify the need for ecosystem protection and explain the impacts of human activities on the ecosystem. Thus, this study has both theoretical and practical significance for solving the contradiction between regional development and protection and actively responding to the development trend of attaching great importance to the ecological environment, both at home and abroad.

2. Materials and Methods

2.1. Study Area

The above-mentioned mine–agriculture–urban compound area is located in Pinglu District, Shuozhou City, Shanxi Province (39°21′–39°58′ N, 111°52′–112°41′ E) (Figure 1). This area is located in the Loess Plateau hills of north Shanxi Province, where the topography is higher in the northwest than southeast [10,35]. This area is located in a northern temperate semi-arid continental monsoon climate zone, where the climate is cold, dry, windy, and sandy, with an average annual temperature of 4.50 °C [10]. The zonal soil type is loessial soil and chestnut soil, and the zonal vegetation type is steppe vegetation [10,35]. This region features many mountains, deep ditches, little flat land, serious soil erosion, and water loss, low vegetation coverage, and a fragile ecosystem background [10]. The study area is a traditional agricultural area in northern China, where agricultural development has formed a diversified agricultural product system represented by the famous wheat (Triticum aestivum L.) [9,10]. After the economic reform and opening up, the study area gave full play to the advantages of coal resources, and the economy developed rapidly.
Currently, three large open-pit mines, Antaibao, Anjialing, and East Open-pit Mine, and three large modern underground mines, I, II, and III are the largest and most modern mining areas of the 100 million tons of open-pit and underground mining areas in China [9]. Additionally, cities with perfect infrastructure are formed along with population growth and economic development. The mine–agriculture–urban compound area was developed by relying on China’s first Sino-foreign joint venture coal mining enterprise, which has a research foundation both at home and abroad. At the same time, this area is located in the unique Loess Plateau in China, which is an area representative of the contradiction between resource exploitation and environmental protection. Thus, the study area is a typical agricultural, mining, and urban concentrated development area, as well as a representative area for current research on the contradiction between ecological protection and economic development. On the other hand, in the Territorial Spatial Planning of Shuozhou City (2020–2035), the study area was listed as the ecological development center for Shuozhou City and tasked with realizing the important goal of ecological civilization. Thus, attaching great importance to developing and managing this research area are important steps to ensure the economic development and good ecology of Shuozhou City [36].

2.2. Data Source and Processing

2.2.1. Remote Sensing Image

The remote sensing image was downloaded from the official website of the United States Geological Survey (USGS) (https://earthexplorer.usgs.gov/ accessed on 26 December 2021). Additionally, in this study, Landsat TM remote sensing images from 2019 were selected with a resolution of 30 m. After systematic radiometric correction and geometric correction, software including ENVI and ArcGIS were used to select the required bands for fusion, mosaic, and cropping processes. Considering the highly complex land use types, fragmented landscapes, and complex terrain of the mine–agriculture–urban in the study area, the 5-4-3 standard false color band was used to supervise and classify land use types in the area. The classification standard was mainly based on the Classification Standard of Land Use Status (GB/T 21010-2017) (accessed on 26 December 2021) combined with the actual land use status of the study area. The land use types were divided into 11 categories, including cultivated land, forest land, grassland, urban land, rural settlement, transportation land, opencast area, waste dump, stripping area, industrial site, and water area. Additionally, we manually determined misclassifications and omissions in the interpretation results based on Google Earth to maximize the classification accuracy. The classification results are shown in Figure 2. The land use data were used to analyze the characteristics of the land use and ecological environment in the study area, and to serve as one of the foundations for determining future development and management goals in the study area.

2.2.2. Other Data

The introduction of other basic data is shown in Table 1.

2.3. Research Process

2.3.1. Selection and Processing of Land Use Pattern Indicators

To visualize and present the spatial pattern of land use in the mine–agriculture–urban compound area from a more detailed perspective, grids were constructed for the study area. Due to the small size of the study area and the high degree of compounding in the land use patterns, we divided the study area into 20 horizontal and 20 vertical grids to better reflect the diversity differences in the grid and the characteristics of the spatial pattern. Ultimately, 314 grids were left after we removed the areas not covered by the study area.
The land use type map of the study area was divided by the constructed grid, and Fragstats was used to calculate the Shannon diversity index (SHDI) in each grid. The SHDI is an important indicator used to reflect landscape heterogeneity in the landscape ecology field [37,38], which measures data based on information theory. This index is sensitive to the non-uniform distribution among patches [38] and can specifically emphasize the contribution of a small patch to the whole [38,39]. Compared to other diversity indicators, the SHDI can better reflect the level of landscape fragmentation in the mine–agriculture–urban compound area and establish a relationship with ecosystem services as a sensitivity index to construct a relationship between landscape integration and ecological functions [40].
The SHDI calculation formula is as follows (1):
S H D I = ∑   P i l n P i
where the Pi is the proportion of the area of the i-th landscape to the whole area.
The SHDI expresses the number of land cover types and the uniformity of patch distribution among different land cover types [40,41,42]. The value of SHDI is greater than or equal to 0 [25,40,42]. When SHDI = 0, the diversity index is zero; thus, the whole landscape consists of one land cover type [42,43,44]. An increase in the SHDI value indicates an increase in the number of land cover types or a more uniform patch distribution [42,43,44]. If each patch belongs to different land cover types, the SHDI is the largest; if each patch belongs to the same land cover types, the SHDI is the smallest [40,44].

2.3.2. Ecosystem Service Assessment

First, due to the small size of the study area, water conservation was calculated using the water balance equation [45,46,47,48]. Here, the difference between water input and output (i.e., the difference between precipitation, surface runoff, and evapotranspiration) represents the WCF [45,46,47,48]. The formula is as follows:
Q W C = ∑ i = 1 j ( P i − R i − A T E i ) × A i × 10 3
where the QWC is the water conservation, m3; Pi is the precipitation, mm; Ri is the surface runoff, mm; ATEi is the evapotranspiration, mm; Ai is the area of the i-type ecosystem, km2; i is the i-th type of ecosystem type; j is the number of ecosystem types.
Second, the soil conservation function (SCF) was calculated via the Revised Universal Soil Loss Equation and judged by the difference between the potential soil erosion amount and the actual soil erosion amount [47,48,49]. The formula is as follows:
Q S R = R × K × L S × ( 1 − C × P )
where the QSR is the soil retention, m3/(hm2·a); R is the rainfall erosion factor, MJ·mm/(hm2·h·a), which is calculated using the method proposed by Wischmeier et al. [50]; K is the soil erosion factor, m3·h/(MJ·mm), which is calculated using the method proposed by Williams and Arnold [51]; LS is the slope length and slope factor with no unit, which is extracted from DEM [52]; C is the vegetation coverage factor with no unit, which is calculated using the NDVI [53]; P is the soil conservation measures factor with no unit, which is assigned with reference to existing research [54].

2.3.3. Ecological Sensitivity Analysis

The vegetation coverage factor, slope length and slope factor, soil factor, surface runoff, and precipitation erosion factor were selected as the sensitive ecological environment factors to construct an ecological sensitivity assessment model. Additionally, the analytic hierarchy process (AHP) method was used to perform weighted superposition analysis on each factor, and to determine the weight of each factor. The AHP is a simple, flexible, and practical multi-criteria decision-making method for quantitative analysis of qualitative problems [55,56]. This tool can divide the key influencing factors of complex problems according to general cognitive laws and then compare the importance of different elements to determine their weights and thus assist in quantitative calculations and analysis [55,56,57].
First, the single-factor ecological sensitivity was divided into 3 grades and assigned a value of 1, 3, or 5 from low to high. Secondly, a judgment matrix was constructed to enable a comparison between any two factors through the Delphi method. Then, through the calculation and normalization of the eigenvectors of the judgment matrix, the maximum latent root of the matrix was calculated. Next, the weight of each factor was calculated. Finally, the consistency test of the matrix was carried out. The test result was 0.04 < 0.1, indicating that the judgment matrix and factor weights were set reasonably.
The weight of each factor is shown in Table 2.
The calculation formula for ecological sensitivity analysis is as follows:
S i = ∑ k = 1 n B k i W k
where Si is the ecological sensitivity evaluation value, Bki is the evaluation level of a single ecological factor, and Wk is the weight of this ecological factor.

2.3.4. Hierarchical Multiple Regression Analysis

The hierarchical multiple regression model involves superimposing and analyzing two or more regression models step by step and exploring the influence relationship of multiple independent variables on the dependent variable [32,58]. First, the regression model is divided into several layers to carry out layer-by-layer modeling analysis; each layer puts a new independent variable based on the previous layer [25,32]. Then, by comparing the parameters of the new layer with the previous layer, we can judge the exponential changes caused by increases in the independent variable to discuss the contribution rate of each layer of independent variables to the overall hierarchical model [25,32].
In this study, we used the SPSS software to construct a hierarchical multiple regression model. We successively selected SHDI, the proportion of the mining area, the proportion of cultivated land, the proportion of urban area, and the proportion of forest and grassland area in the grid as independent variables for each layer.

3. Results

3.1. Land Use Pattern in a Typical Mine–Agriculture–Urban Compound Area

The calculation results for the SHDI in the grid of land use types in the study area are shown in Figure 3. To facilitate the analysis of land use patterns, the results were divided into five groups according to the natural discontinuity classification method, and the boundaries were divided into areas with large numerical differences.
Figure 3 shows that the overall diversity of land use types in the central and peripheral areas of the study area is relatively low. The area with low diversity of land use types in the central area is the waste dump, and the areas with low diversity of land use types in the periphery are forest land and grassland. On the whole, except for the central waste dump, the open-pit coal mining area has the highest diversity of land use types. Thus, coal mining activities enhance the diversity and complexity of land use types. Table 3 shows that a high diversity of land use types accounted for 51.35% of the study area, the area with medium diversity accounted for 27.70%, and the area with low diversity accounted for 20.95%. Thus, overall, the types of land use in the study area are diverse and complex.

3.2. Ecosystem Service Assessment in a Typical Mine–Agriculture–Urban Compound Area

Figure 4 shows that, overall, the WCF in the study area gradually increased from northwest to southeast. Due to land use, the spatial distribution of WCF in the study area has obvious heterogeneity and a patchy distribution. The statistical analysis results in Figure 5 show the WCF in the study area, from high to low, is as follows: forest land > water area or grassland > cultivated land > waste dump, industrial site, or opencast area > stripped area > urban land or rural settlement > transportation land. On the whole, the natural habitat WCF is the highest, the WCF of the open-pit coal mining area is the second highest, and the WCF of the land for human life and transportation is the lowest.
Figure 6 shows that, overall, there is no obvious regularity in the spatial distribution of SCF in the study area. The forest land in the southwest has the highest SCF. The SCF in the open-pit coal mining area is obviously higher than that in other areas. The statistical analysis results in Figure 5 show that the SCF in the study area, from high to low, is as follows: forest land > grassland > waste dump, opencast area or stripped area > cultivated land > industrial site or transportation land > rural settlements or urban land > water area. On the whole, the SCF of natural habitats, waste dumps, and opencast areas is higher than that of other areas.

3.3. Ecological Sensitivity Analysis in a Typical Mine–Agriculture–Urban Compound Area

Figure 7 shows that, overall, the ecological sensitivity in the southern part of the study area is higher than that in the northern part. Due to land use, the spatial distribution of ecological sensitivity in the study area has obvious heterogeneity and a patchy distribution. The ecological sensitivity of the forest land in the southwest region is the highest, whereas the ecological sensitivity of the rest of the forest land is lower. The sensitivity of cultivated land in the north is low, but the sensitivity of cultivated land in other regions is high. The ecological sensitivity of grassland is higher than that of cultivated land. Urban land and rural settlements have the lowest ecological sensitivity and are least affected by environmental changes. The ecological sensitivity of the open-pit mining area is moderate, while the ecological sensitivity of the opencast area is slightly higher. Overall, the ecological sensitivity of human production and living space in the study area is lower than that of natural habitats.

3.4. Hierarchical Multiple Regression of Ecosystem Services and Ecological Sensitivity in a Typical Mine–Agriculture–Urban Compound Area

3.4.1. The Hierarchical Multiple Regression of the WCF

Next, we subjected the WCF to hierarchical multiple regression analysis taking SHDI, the proportion of mining area, the proportion of cultivated land, the proportion of urban area, and the proportion of forest and grassland area as independent variables (Table 4).
In the analysis results, if the p value is less than 0.05, then the model passes the F-test, and the model is statistically significant; otherwise, there is no statistical significance. The value of ∆R2 represents how much of the change in the dependent variable can be explained by the independent variable, that is, the degree of influence of the independent variable on the dependent variable. Finally, if ∆p is less than 0.05, then the independent variable can significantly affect the dependent variable, and if ∆p is less than 0.01, then the independent variable can extremely significantly affect the dependent variable. Additionally, according to the positive and negative of B, it can be determined whether the influence of the independent variable on the dependent variable is positive or negative.
In the first layer model, linear regression analysis is performed with SHDI as the independent variable and WCF as the dependent variable. Here, the analysis shows that the model still fails the F-test. Thus, SHDI has no relationship with the WCF.
The second layer model is based on the first layer model and superimposes the proportion of mining area as another independent variable. The analysis shows that the model passes the F-test. However, the proportion of mining areas has no significant effect on the WCF.
The third layer model is based on the second layer model and superimposes the proportion of cultivated land area as the third independent variable. The analysis shows that the model passes the F-test, and the proportion of cultivated land has an extremely significant positive impact on the WCF.
The fourth layer model is based on the third layer model and superimposed the proportion of urban area as the fourth independent variable. The analysis shows that the model passes the F-test, and the proportion of urban areas has an extremely significant negative impact on the WCF.
The fifth layer model superimposes the proportion of forest and grassland area based on the fourth layer model. The analysis shows that the model passes the F-test, and the proportion of natural habitat areas including woodland and grassland has an extremely significant positive impact on the WCF.

3.4.2. The Hierarchical Multiple Regression of the SCF

For this analysis, the process is the same as above. The summary of the analysis results in Table 5 shows that in the first layer model when SHDI is used as the independent variable, the model passes the F-test, and SHDI has a significant positive impact on the SCF.
In the second layer model, the proportion of mining area is superimposed as another independent variable. The results show that the model passes the F-test, and the proportion of mining area has an extremely significant positive impact on the SCF.
In the third layer model, the proportion of the cultivated land area is used as the third independent variable. The results show that the model passes the F-test. However, the proportion of cultivated land has no significant effect on the SCF.
In the fourth layer model, the proportion of the urban area is used as the fourth independent variable. The results show that the model passes the F-test, and the proportion of urban areas has an extremely significant negative impact on the SCF.
In the fifth layer model, the proportion of forest and grassland area is superimposed as the fifth independent variable. The results show that the model passes the F-test, and the proportion of natural habitat areas including forest and grassland has a significant positive impact on the SCF.

3.4.3. Hierarchical Multiple Regression of Ecological Sensitivity

Here, the analysis process is the same as above. As shown in Table 6, in the first to fourth layer models, SHDI, the proportion of mining area, the proportion of cultivated land area, the proportion of urban area, and the proportion of forest and grassland area are superimposed as independent variables. The results all show that the model passes the F-test, and the proportion of mining area has a significant positive impact on ecosystem sensitivity. The other three independent variables have an extremely significant positive impact on ecosystem sensitivity. In the fifth layer model, the proportion of forest and grassland area was superimposed as the fifth independent variable. The results show that the model passes the F-test, and the proportion of forest and grassland area has a significant negative impact on ecosystem sensitivity.

4. Discussion

Compared with urban land and rural settlements, the opencast areas and waste dumps in the study area have higher WCF and SCF values. This is because more water and soil conservation engineering measures have been implemented in these areas [25,59]. At present, opencast areas adopt layered stripping to form slope steps and thereby achieve better soil and water conservation effects [15,35,59]. Similarly, waste dumps adopt an alternate existence of platforms and slopes to form slope steps and achieve better soil and water conservation effects [10]. At the same time, land reclamation on waste dumps can better stabilize soil and water conditions [60].
The ecological sensitivity of natural habitats is higher than that of urban and open-pit mining areas. This heightened sensitivity is related to the natural conditions of the study area. On the one hand, the study area is located in a loess region, where the soil erosion phenomenon of the original ecosystem is very serious, and the ecological sensitivity is high [8,49]. On the other hand, through identification, we found that the forest land and grassland with high sensitivity in the study area contain a single vegetation type [28,61]. In contrast, there are many artificial protection facilities and environmental restoration projects in open-pit mining areas and urban land, where the forest lands and grasslands are rich in species and show low sensitivity to environmental changes [5,56,61]. Therefore, increasing the diversity of natural habitats is one of the most important ways to reduce ecological sensitivity.
Increasing the area and diversity of forest lands and grasslands, and reasonably controlling the area of the open-pit mining area, cultivated land, and urban land can effectively enhance the WCF and SCF in the study area, and effectively reduce ecological sensitivity. The main suppliers of WCF and SCF in the study area are natural habitats such as forest land, which should be highly valued and protected [33]. For areas with intense human activities and low ecological sensitivity, such as open-pit mining areas, cultivated land, urban land, and industrial sites, although ecological sensitivity is low, it depends on more resources and costs [35,62]. That is, such areas are unsustainable in the long term and overall, and their development intensity and size should be strictly controlled.

5. Conclusions

(1)
The study area has a high diversity of land use types, which are mainly caused by coal mining and processing engineering.
(2)
The WCF and SCF in the study area are greatly affected by land use types. WCF increases from northwest to southeast and is mainly provided by forest land, water area, and grassland. There is no obvious spatial distribution of SCF, which is higher in natural habitats, waste dumps, and opencast areas than that in other areas overall.
(3)
The ecological sensitivity of the study area is significantly affected by land use types, showing a decreasing trend from north to south. Ecosystem sensitivities of the same land use type vary greatly in different spatial locations. Additionally, the overall ecological sensitivity of natural habitats is relatively high.
(4)
The increase in land use compounding degree and open-pit coal mine area have positive effects on the SCF and ecological sensitivity. The increase in cultivated land area was observed to have a positive effect on the WCF and ecological sensitivity. Reducing urban space and increasing forests and grassland could help enhance WCF and SCF and help reduce ecological sensitivity in the study area. Therefore, future planning and construction processes in the study area should increase the area and diversity of forest and grassland and should reasonably control the area of open-pit coal mines, cultivated land, and urban areas.

Author Contributions

Conceptualization, S.W. and Y.Z.; data curation, S.W. and Y.Z.; formal analysis, S.W. and Y.Z.; funding acquisition, Y.C.; investigation, S.W., Y.Z. and K.Y.; methodology, S.W., Y.Z. and K.Y.; supervision, Y.C.; validation, K.Y.; visualization, S.W.; writing—original draft preparation, S.W. and Y.Z.; writing—review and editing, S.W. and K.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant number U1810107 and 41701607, and funded by the Fundamental Research Funds for the Central Universities, grant number 2-9-2018-025 and 2-9-2019-307.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Land 11 01378 g001 550
Figure 1. Location of the mine–agriculture–urban compound area. (The base maps of (a,b) are the color version of the map of China that comes with ArcGIS, the base map of (c) is the 2020 Landsat remote sensing image data that was from United States Geological Survey (https://earthexplorer.usgs.gov/ accessed on 3 March 2022).)
Figure 1. Location of the mine–agriculture–urban compound area. (The base maps of (a,b) are the color version of the map of China that comes with ArcGIS, the base map of (c) is the 2020 Landsat remote sensing image data that was from United States Geological Survey (https://earthexplorer.usgs.gov/ accessed on 3 March 2022).)
Land 11 01378 g001
Land 11 01378 g002 550
Figure 2. The status of land use in the typical compound area mine–agriculture–urban.
Figure 2. The status of land use in the typical compound area mine–agriculture–urban.
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Land 11 01378 g003 550
Figure 3. The SHDI distribution value within the grid.
Figure 3. The SHDI distribution value within the grid.
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Land 11 01378 g004 550
Figure 4. Distribution of WCF in the mine–agriculture–urban compound area.
Figure 4. Distribution of WCF in the mine–agriculture–urban compound area.
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Figure 5. Average ecosystem services in different land use types.
Figure 5. Average ecosystem services in different land use types.
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Land 11 01378 g006 550
Figure 6. Distribution of SCF in the mine–agriculture–urban compound area.
Figure 6. Distribution of SCF in the mine–agriculture–urban compound area.
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Figure 7. Distribution of ecological sensitivity in the mine–agriculture–urban compound area.
Figure 7. Distribution of ecological sensitivity in the mine–agriculture–urban compound area.
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Table
Table 1. The description and resource of the study data.
Table 1. The description and resource of the study data.
DataData DescriptionUnitData Source
Meteorological dataRainfallmmNational Meteorological Information Centre
(http://www.nmic.cn/ accessed on 9 February 2022)
Surface runoff
Evapotranspiration
Soil dataSoil organic carbon%Resource and Environment Science and Data Center
(https://www.resdc.cn/ accessed on 22 January 2022)
Content of sand, silt and clay of soil
Topography dataSlope lengthmResource and Environment Science and Data Center
(https://www.resdc.cn/ accessed on 24 January 2022)
Slope°
Vegetation coverage dataNormalized Vegetation Index (NDVI)-Geospatial Data Cloud
(https://www.gscloud.cn/ accessed on 10 February 2022)
Where the “-” means that these data have no units.
Table
Table 2. The table of ecological sensitivity stratification, grade, and weight.
Table 2. The table of ecological sensitivity stratification, grade, and weight.
Ecological FactorsGradeEvaluation ValueWeight
Slope length and slope factor>20%50.2
5–20%3
<5%1
Vegetation coverage factorNone planting50.3
Generally planting3
Intensive planting1
Soil factorHigh permeability50.2
Medium permeability3
Low permeability1
Surface runoff factor>30%50.1
15–30%3
<15%1
Precipitation erosion factorHigh50.2
Medium3
Low1
Table
Table 3. The statistics of SHDI value within the grid.
Table 3. The statistics of SHDI value within the grid.
The SHDI ValueThe Percentage (%)
0.000000–0.1924666.76
0.192467–0.55693114.19
0.556932–0.85535427.70
0.855355–1.17447131.08
1.174472–1.59995520.27
Table
Table 4. The results of hierarchical multiple regression of the WCF.
Table 4. The results of hierarchical multiple regression of the WCF.
LayerOneTwoThreeFourFive
SHDIProportion of Mining AreaProportion of Cultivated LandProportion of Urban AreaProportion of Forest and Grassland Area
R20.0010.0140.2300.5530.631
∆R20.0010.0130.2160.3230.078
F0.15517.21012.06682.05265.398
p0.6940.0000.0000.0000.000
∆F0.15534.2461.690257.15156.744
∆p0.6940.2140.0000.0000.048
B−2.290−0.2070.059 ***−0.922 ***0.008 **
** ∆p < 0.05, *** ∆p < 0.01.
Table
Table 5. The results of hierarchical multiple regression of the SCF.
Table 5. The results of hierarchical multiple regression of the SCF.
LayerOneTwoThreeFourFive
SHDIProportion of Mining AreaProportion of Cultivated LandProportion of Urban AreaProportion of Forest and Grassland Area
R20.0170.0510.0510.0920.335
∆R20.0170.0340.0000.0410.243
F4.6427.1414.7616.7495.531
p0.0320.0010.0030.0000.000
∆F4.6429.4940.05012.1180.691
∆p0.0320.0020.8230.0010.017
B1.275 **0.012 ***−0.001−0.029 ***0.010 **
** ∆p < 0.05, *** ∆p < 0.01.
Table
Table 6. The results of hierarchical multiple regression of ecological sensitivity.
Table 6. The results of hierarchical multiple regression of ecological sensitivity.
LayerOneTwoThreeFourFive
SHDIProportion of Mining AreaProportion of Cultivated LandProportion of Urban AreaProportion of Forest and Grassland Area
R20.0290.0520.2470.5130.763
∆R20.0290.0230.1950.2660.002
F8.0767.35929.06569.78055.953
p0.0050.0010.0000.0000.000
∆F8.0766.47868.742144.7910.826
∆p0.0050.0110.0000.0000.034
B0.190 ***0.001 **0.004 ***0.008 ***−0.001 **
** ∆p < 0.05, *** ∆p < 0.01.
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Wang, S.; Zhuang, Y.; Cao, Y.; Yang, K. Ecosystem Service Assessment and Sensitivity Analysis of a Typical Mine–Agriculture–Urban Compound Area in North Shanxi, China. Land 2022, 11, 1378. https://doi.org/10.3390/land11091378

AMA Style

Wang S, Zhuang Y, Cao Y, Yang K. Ecosystem Service Assessment and Sensitivity Analysis of a Typical Mine–Agriculture–Urban Compound Area in North Shanxi, China. Land. 2022; 11(9):1378. https://doi.org/10.3390/land11091378

Chicago/Turabian Style

Wang, Shufei, Yining Zhuang, Yingui Cao, and Kai Yang. 2022. "Ecosystem Service Assessment and Sensitivity Analysis of a Typical Mine–Agriculture–Urban Compound Area in North Shanxi, China" Land 11, no. 9: 1378. https://doi.org/10.3390/land11091378

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