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Article

Evaluation and Optimization of Landscape Spatial Patterns and Ecosystem Services in the Northern Agro-Pastoral Ecotone, China

by
Yuxin Wu
1,2,
Xiuwen Peng
3,
Guodong Jia
1,2,*,
Xinxiao Yu
1,4,* and
Honghong Rao
5
1
Key Laboratory of State Forestry and Grassland Administration on Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
2
The Metropolitan Area Forest Ecosystem Research Station, School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
3
Shanghai Investigation, Design & Research Institute Co., Ltd., Shanghai 200126, China
4
College of Horticulture and Forestry Sciences, Tarim University, Alar 843300, China
5
School of Science, East China University of Technology, Nanchang 330013, China
*
Authors to whom correspondence should be addressed.
Land 2024, 13(10), 1549; https://doi.org/10.3390/land13101549
Submission received: 11 May 2024 / Revised: 20 June 2024 / Accepted: 1 August 2024 / Published: 24 September 2024
(This article belongs to the Section Landscape Ecology)
Browse Figures
Figure 1
<p>An overall framework based on research objectives and data scenarios.</p> "> Figure 2
<p>Location of the study area (<b>a</b>), the position of small watersheds (<b>b</b>), and its land use distribution (<b>c</b>).</p> "> Figure 3
<p>Spatial match diagram of ecosystem services in the study area. Ecosystem services include water yield (<b>a</b>), sediment retention (<b>b</b>), and carbon storage (<b>c</b>).</p> "> Figure 4
<p>Scatterplot of landscape spatial pattern indices and water yield (<b>a</b>), sediment retention (<b>b</b>), and carbon storage (<b>c</b>). LVIWY: legged variation in water yield, LVISR: legged variation in soil retention, LVICS: legged variation in carbon storage. The red Moran’s I indices mean the spatial correlation between variable spatial pattern indices and the ecosystem services was the highest among all the explanatory variables.</p> "> Figure 5
<p>LISA Aggregation Map of three landscape spatial indices and water yield (<b>a</b>), sediment retention (<b>b</b>), and carbon storage (<b>c</b>) from 2004 to 2020.</p> "> Figure 6
<p>The spatial distribution characteristics of the importance partition of three ecosystem services of water yield (<b>a</b>), sediment retention (<b>b</b>), and carbon storage (<b>c</b>).</p> "> Figure 7
<p>Ecological service importance partitioning and optimization of the study area. (<b>a</b>) Classification of the importance of integrated ecosystem service functions in the watershed in 2020; (<b>b</b>) Map of land use types in the area to be optimized in the watershed; (<b>c</b>) The result of optimal vegetation allocation in the watershed.</p> ">
Review Reports Versions Notes

Abstract

:
The alteration of landscape spatial patterns (LSPs) and ecosystem services (ESs) in watersheds can have detrimental effects on the local environment and community. However, a comprehensive understanding of the current state of LSPs and ESs in watersheds around Winter Olympic venues in China is limited. Here, we assessed current LSPs and ESs and developed optimization strategies for the Xigou watershed around Winter Olympic venues in the northern agro-pastoral ecotone of China. The results indicated that the main land use type was grassland in the Xigou watershed, and landscape types were relatively homogenous. All three ESs (water yield, sediment retention, and carbon storage) generally improved from 2004 to 2020. For ESs, there was the lowest total volume of water yield in 2004 (637.44 × 104 m3). But sediment retention (10.54 × 106 t, 18.13 × 106 t, 13.28 × 106 t, and 16.85 × 106 t) had an upward, then downward, then upward trend before and after ERP. Carbon storage grew steadily. Correlation analysis suggested that the three ESs were closely related to the landscape spatial indices of average patch area (AREA_MN), contagion index (CONTAG), and Shannon’s evenness index (SHEI). AREA_MN, CONTAG, and SHEI in the eastern part of the study area promoted sediment retention and carbon storage, while in the southwestern part of the study area, they inhibited water yield and sediment retention. The results suggest that improving sediment retention by optimizing land use and cover change (LUCC) and LSPs is the main approach to further enhance ESs in the study area. Our study suggests that the inclusion of multiple landscape pattern indices can provide a more comprehensive representation of regional ecosystem service.

1. Introduction

Ecosystem services (ESs) play a crucial role in regulating the balance of ecosystems and ensuring their stability [1]. The watersheds are of significant importance due to their role in providing essential ecosystem services, such as water purification, soil retention, and carbon sequestration. However, these watersheds are increasingly facing pressures from human activities, including urbanization, tourism, and infrastructure development, which can alter landscape spatial patterns and impact ecosystem services [2].
Numerous studies have been conducted to reveal trends in ecosystem status and change by assessing ESs. Zedler et al. (2005) used the InVEST model to assess the dynamic changes of ecosystem services under different LUCCs and landscape patterns [3]. Nelson et al. (2009) assessed ESs (sediment retention, carbon storage, water purification, and biodiversity) and explored the synergistic relationships among them in the Willamette Basin in the United States [4]. Redhead et al. (2016) calculated the water yield of 22 catchments in the UK [5]. There is a lack of comprehensive and incisive assessment of ecosystem services within the confines of small watersheds. As human activities continue to exert mounting ecological pressures on these delicate systems—manifesting in issues such as soil erosion, diminishing biodiversity, and water scarcity—the imperative to develop robust strategies for their preservation and restoration becomes increasingly acute.
Landscape spatial patterns have been assessed by analyzing landscape pattern indices such as landscape fragmentation, patchiness, and connectivity [6]. The spatial configuration of the patch structure has a direct influence on the overall spatial pattern and development process of LUCCs to a certain extent [7]. Describing the type, number, and distribution pattern of patches in space is called the landscape spatial pattern [8]. The landscape pattern and its change process can reflect not only the heterogeneity of landscape elements in time and space but also the influence of natural factors and human activities [9].
In recent years, landscape pattern analysis has emerged as a valuable tool for understanding how human activities affect ecosystems and their services [10,11,12]. The results of these studies suggested that the dynamics of ESs are closely linked to different LUCCs and landscape patterns. These studies can provide insights into the most suitable landscape pattern options for maximizing the benefits of ecosystem services while minimizing the negative impacts of human activity. However, few studies have systematically analyzed the relationship between landscape pattern indices and ecosystem services [13].
The Chongli District of the Xigou watershed is known as the “snow capital” of China. However, it is an ecologically fragile area located in a wind and water erosion zone, where soil erosion and desertification are very serious [14]. Land degradation and desertification inevitably cause the deterioration in provisioning, supporting, regulating, and cultural ecosystem services [15]. Recently, the Chinese government planned and carried out a series of ecological restoration projects in this region, including the Grain for Green Program (GFGP), the Being Tianjin Sand Source Control Project (BTSSCP), and the Beijing-Hebei Water Protection Forest Project [16]. After completion, these projects effectively reduced soil erosion and desertification [17,18]. The study of such a watershed is crucial for understanding the impact of human activities on the environment, especially in areas that will host large-scale events like the Winter Olympics.
This research aimed to fill the above gaps by focusing on a typical watershed in the northern agro-pastoral ecotone, China. Remote sensing and GIS technologies were utilized to conduct spatial analysis and assess landscape spatial patterns, including metrics such as landscape fragmentation, patchiness, and connectivity. Additionally, we employed appropriate methodologies to assess the ecosystem services provided by the watershed, including water yield, sediment retention, and carbon storage. By analyzing them, this study has sought to understand the relationships between LSPs and ESs. Based on the evaluation of landscape spatial patterns and ecosystem services, optimization strategies have been developed to enhance the watershed’s landscape design.

2. Study Area

2.1. Theoretical Framework

The theoretical framework of the article is shown in Figure 1. First, we undertook a quantitative analysis of the effects of the implementation of ecological restoration projects on LUCC, LSPs, and ESs in the study area. Then, we analyzed the relationships between LSPs and ESs. Based on the assessment results, the study developed optimization strategies to enhance the provision and sustainability of these ESs.

2.2. Study Area Selection

The Xigou watershed around the Winter Olympic venues in the northern agro-pastoral ecotone of China was selected as the study area, 114°47′~115°15′ E,40°53′~41°15′ N. The total area is 703 km2 (Figure 2). It belongs to a typical temperate continental monsoon semi-arid climate, with an annual average temperature of 3.5 °C [19]. The annual rainfall ranges from 300 to 600 mm. The average annual evapotranspiration over multiple years is 837.3 mm [14]. The topography of the basin is inclined from northwest to southeast, and the elevation is between 838 and 1967 m. The thickness of the basin soil layer is between 50 and 150 cm, mainly yellow-brown soil and chestnut calcium soil. The watershed is dominated by grasslands. Since ecological restoration projects were implemented (2009), the vegetation cover has increased significantly, reaching over 70%. However, the populations of native plants have diminished and instead included planted trees and shrubs, as well as herbaceous vegetation.

2.3. Data Collection

The general data input for the analysis of the three ESs were land use/land cover data and watershed delimitation in the InVEST model (Table 1) [5]. The water yield module includes precipitation data, reference evapotranspiration, depth to root restricting layer, plant available water content (PAWC) [20]. The sediment retention module includes the rainfall erosivity index (R), topographic factor (LS), soil erodibility (K), and biophysical properties [21]. The carbon stock module includes LUCC and carbon density volume data of the four basic carbon pools (aboveground biomass, belowground biomass, soil, and dead matter) [22]. The biophysical table contains properties that include Kc (plant evapotranspiration coefficient), root depth, C and p values, aboveground biomass, belowground biomass, and carbon density in soil and dead matter (Table 2).

3. Methods

3.1. Detection of LUCC

The remote sensing data for the Xigou watershed were obtained from the Chinese Geospatial Data Cloud (http://www.gscloud.cn/ accessed on 18 June 2024). The spatial resolution of the four remotely sensed images was 30 m. We used ENVI 5.3 remote sensing processing software for pre-processing images from 2004, 2009, 2015, and 2020. It includes radiometric calibration, atmospheric correction, image stitching, image cropping, and band overlay. After image pre-processing, LUCC maps of the Xigou watershed in different periods were obtained based on an object-oriented classification method for interpretation. In this study, a single dynamic attitude model was used to quantify the changes in each LUCC between 2004 and 2020 [23]. The equations are as follows:
K = U b − U a U a × 1 T × 100 %
where K is the dynamic indicator of land use type; Ua and Ub are the initial and final area of land use type, respectively; and T is the time scale.

3.2. Selection of Landscape Spatial Patterns

Here, we calculated the landscape pattern indices of forest vegetation in the Xigou watershed with the help of FRAGSTATS 4.2.1. In this paper, the overall and component characteristics of the watershed landscape were fully considered. We selected 11 factors at two levels of class metrics and patch metrics from the perspective of landscape fragmentation: number of patches (NP); patch density (PD); average patch area (AREA_MN); landscape shape index (LSI); perimeter area fractional dimension (PAFRAC); contagion index (CONTAG); dispersion and juxtaposition index (IJI); aggregation index (AI); maximum patch index (LPI); Shannon’s diversity index (SHDI); Shannon’s evenness index (SHEI) [24].

3.3. Quantification of Ecosystem Services

Since its inception in 2007, the InVEST model has undergone several iterations, evolving from its original ArcGIS module to a standalone version with increasingly refined modules and algorithms [4]. The InVEST model has performed well in assessing the impact of LUCC on various ESs. This study utilized version 3.10.1 of InVEST, which assesses various ecosystem services categorized into three types: supporting ecological services; final ecosystem services; and auxiliary analysis tools. To ensure the safe and smooth operation of the Winter Olympics venues and address the water security issues for residents downstream of the Qing River, this study focused on water supply, soil retention, and carbon storage functions of the Xigou watershed in Chongli. Using the InVEST model, we conducted a quantitative analysis and evaluation of these functions. The water yield of a watershed was obtained by subtracting vegetation transpiration and surface evapotranspiration from the actual precipitation of each grid cell [25]. The specific formula is as follows:
Y x j = 1 − A E T x j P x · P x
where Yxj is the water yield of grid x on land cover type J (mm); AETxj is the actual evapotranspiration (mm); and Px is the annual precipitation (mm). Considering that the actual evapotranspiration is difficult to obtain, the Budyko curve pair was used here for estimation [26], and the equation is as follows:
A E T x j P x = 1 + P E T x P x − 1 + P E T x P x ω 1 ω
P E T x = K c l x · E T 0 x
ω x = Z · A W C x P x + 1.25
where PETx represents potential evapotranspiration; ω(x) is a non-physical parameter of natural climate and soil characteristics; KC(lX) represents the influence of evapotranspiration for a specific land use/cover type; ET0(x) represents the potential evapotranspiration in grid x [27]; Z is an empirical constant, also called “seasonal constant”, with values ranging from 1 to 30; AWCx is the effective soil water content (mm).
The sediment retention module is based on the modified universal soil loss equation (USLE) to derive the potential soil erosion (RKLS) and the actual soil erosion [28]. In this paper, the SDR module of InVEST 3.10.1 was used to assess the sediment retention for four periods. The potential soil erosion was calculated by the following equations [29]:
R K L S = R × K × L S
The actual soil erosion was then calculated by USLE [30]:
U S L E = R × K × L S × P × C
Finally, the soil delivery was calculated by the following equation:
S D = R K L S − U S L E + S E D R
where RKLS, USLE, SD, and SEDR are the potential soil erosion, actual soil erosion, soil delivery, and sediment retention amounts, respectively [31]; R is the rainfall erosion force factor; K is soil the erodibility factor; LS is the topography factor; P is the water conservation measure factor; C is the vegetation cover and management factor.
The module takes different LUCCs as evaluation units and multiplies the carbon density value of each LUCC by the area of the corresponding land type to obtain the carbon stock of the whole region [32]. The total carbon stock of the module was calculated by the following formula:
C t o t a l = C a b o v e + C b e l o w + C s o i l + C d e a d
where Ctotal is the total carbon stock; Cabove is the aboveground carbon stock; Cbelow is the belowground carbon stock; Csoil is the soil carbon stock; and Cdead is the carbon stock of litter.

3.4. Coupling of Landscape Spatial Patterns and Ecosystem Services

This study used spatial autocorrelation analysis to explore the response of three ESs to LSPs at the watershed scale in depth. The global Moran’s I index was used to measure the overall spatial agglomeration characteristics of landscape ecological safety. The local spatial extent between regions was measured using the local Moran’s I index [33]. The calculation equation is as follows:
I = n ∑ i = 1 n ∑ j = 1 n w i j x i − x ¯ x j − x ¯ ∑ i = 1 n ∑ j = 1 n w i j ∑ i = 1 n x i − x ¯ 2
I i = x i − x ¯ S 2 ∑ j w i j x j − x ¯
where I is the Moran index; n is the number of rasters; xi and xj are raster assignments; wij is the spatial weight matrix; and S2 is the squared difference in raster values.

3.5. Classification of the Importance of Ecosystem Services

Water yield, sediment retention, and carbon storage were classified into four levels of importance from low to high (I. mildly important, II. moderately important, III. highly important, and IV. extremely important) [34,35]. The specific and de-quantified classification criteria of ecosystem services are shown in Table 2. We standardized the magnitudes of water yield, sediment retention, and carbon storage. Three ecosystem services were analyzed using a partial-large semi-gradient affiliation function [36,37]. The equations are as follows:
F x = 0 , x < a x − a b − a , a ≤ x ≤ b 1 , b < x
N = X − X m i n X m a x − X m i n
where N is the standardized ecological service function; X, Xmax, and Xmin are the actual, maximum, and minimum values of ecological service function, respectively.

4. Results

4.1. Changes in Landscape Spatial Patterns before and after Ecological Restoration Projects

The Xigou watershed covers a total area of 702.88 km2, with grassland being the predominant land use type, occupying approximately 80% of the entire study area. Broadleaf forest comes in second, covering around 10% of the total area (Table 3). After 2009 (after ecological restoration projects), the land use composition of the Xigou watershed underwent significant changes. The most notable transformation occurred in shrubland, which experienced a net reduction of 9.21 km2. Furthermore, there was a noticeable increase in broadleaf woodland, primarily sourced from the conversion of grassland, which saw an area transfer of 29.21 km2 (Table 3).
Some landscape spatial patterns (LSPs) significantly changed before and after ecological restoration projects (Table 4). Among them, the NP and LSI of the watershed showed a significant decreasing trend before and after the ecological restoration project, while AREA_MN showed the opposite. In general, LSPs of the watershed decreased and stabilized, with a trend of aggregation between patches and a relatively homogeneous landscape type.

4.2. Changes in Ecosystem Services before and after Ecological Restoration Projects

From 2004 to 2020, the water yield in the study area exhibited a pattern of initial growth, followed by decline and subsequent recovery. The water yield was lowest in 2004, followed by 2015 and 2020, with the highest appearing in 2009 (Table 5). The spatial distribution was marked by a higher concentration in the northeast and a lower concentration in the southwest (Figure 3a). Between 2004 and 2020, the grassland experienced the largest increase in water supply, rising from 508.68 × 104 mm to 1294.83 × 104 mm, marking a total increase of 786.15 × 104 mm. This was followed by broadleaf forest and cultivated land, while shrubland saw the smallest increase, with only 2.80 × 104 mm added from 2004 to 2020 (Figure 3a). Overall, the water yield of the study area still improved.
The total sediment retention showed an initial increase to 10.54 × 106 t, followed by a decrease to 18.13 × 106 t, then another increase to 13.28 × 106 t, and finally a further increase to 16.85 × 106 t before and after the implementation of the ecological restoration project (Table 5). In terms of space, the spatial distribution of sediment retention before and after ecological restoration projects was basically consistent. The high-value areas of sediment retention were found in the middle and upper regions of the watershed, while the low-value areas were concentrated in the northern and southwestern parts of the watershed (Figure 3b). From 2004 to 2020, grassland had the greatest increase in soil and water conservation, with a total increase of 448.91 × 104 t, followed by broadleaf woodland and cropland; shrubland and unutilized land had a decrease in the amount of soil conserved. Overall, the sediment retention capacity of the study area increased and improved.
The total carbon storage showed a consistent increase from 5.841 × 106 t in 2004 to 5.935 × 106 t in 2020 (Table 5). The spatial distribution of carbon storage in the study area from 2004 to 2020 was relatively stable (Figure 3c). The high-value areas were mainly concentrated in the middle and upper reaches and southeast of the watershed. During the period of 2004–2020, the carbon stock of broadleaf forest land increased the most, with a total increase of 9.45 × 104 t, followed by cropland and coniferous forest land, and the carbon stock decreased the most in shrub forest land, with a total decrease of 5.36 × 104 t (Figure 3c). In summary, grassland and broadleaf forest land are important carbon sinks in the study area.

4.3. Response of Ecosystem Services to Landscape Spatial Patterns

Water yield negatively correlated with average patch area (AREA_MN) with a Moran index of −0.06. However, water yield positively correlated with the contagion index (CONTAG) and Shannon’s evenness index (SHEI) (p < 0.001), and their Moran indices were 0.103 and 0.121, respectively (Figure 4a). The high–high aggregation areas of water yield with AREA_MN, CONTAG, and SHEI were concentrated in the northern border area of the watershed, while high–low and low–low zones were predominantly found in the lower zones of the watersheds (Figure 5a). In summary, AREA_MN, CONTAG, and SHEI in the northern part of the study area contributed to water yield, while in the southwestern part, they inhibited it.
Sediment retention negatively correlated with AREA_MN, and its Moran index was −0.06. But it positively correlated with the number of patches (NP), landscape shape index (LSI), CONTAG, Shannon’s diversity index (SHDI), and SHEI (p < 0.001) (Figure 4b). Their Moran indices were 0.085, 0.103, 0.112, 0.094, and 0.121, respectively. Soil retention was broadly similar to the aggregation areas of the LSI, CONTAG, and SHEI, with the high–high areas concentrated in the midstream region as well as in the southeastern mountainous forest region (Figure 5b). These areas were important contributors to the soil conservation capacity of the watershed, with good connectivity of dominant patches and higher levels of patch fragmentation. However, the low–low zone was mainly located in the southwestern part of the watershed as well as in the northwestern corner of the watershed, where the spatial structure of the landscape needs to be optimized, and the soil retention capacity needs to be improved (Figure 5b). In summary, LSI, CONTAG, and SHEI in the eastern portion of the study area contributed to soil retention in the study area, while the southwestern portion inhibited it.
Carbon storage showed a negative correlation with AREA_MN, but it was a positive correlation with NP, LSI, CONTAG, SHDI, and SHEI (p < 0.001) (Figure 4c). Their Moran indices were −0.382, 0.363, 0.266, 0.393, 0.412, and 0.441, respectively. The study area was mainly dominated by high–high and low–low areas. The distribution ranges of CONTAG, SHDI, and SHEI were similar. Among them, high–high areas were mainly distributed in the east of the watershed (Figure 5c). To sum up, CONTAG, SHDI, and SHEI in the eastern part of the study area had a promoting effect on carbon storage, while they had an inhibitory effect in the central part of the watershed.

4.4. Classification of the Importance of Ecosystem Services

The spatial distribution characteristics of the importance partition of water yield in the Xigou watershed from 2004 to 2020 were generally consistent (Figure 6a). But the spatial distribution characteristics of the importance partition of both sediment retention and carbon storage showed fragmentation (Figure 6b,c). We combined the highly important area and the extremely important area to become the high functional area of ecosystem services. The Xigou watershed was regarded as the area with the worst soil erosion in the Qingshui River basin. Sediment retention was considered the primary ecosystem service. The weights of sediment retention, water yield, and carbon storage were adjusted to 0.5, 0.3, and 0.2, respectively.
The four importance levels of comprehensive ecosystem services in 2020 were 164.50 km2, 185.46 km2, 189.78 km2, and 163.06 km2, accounting for 23.41%, 26.39%, 27.00%, and 23.20% of the total area, respectively (Figure 7a). The watershed showed a spatially decreasing trend from northeast to southwest. High functional areas were mainly concentrated in the northern, northwestern, and northeastern parts of the watershed (Figure 7a). The values of the comprehensive ecosystem services of different LUCCs were divided into classifications. The contribution of each classification to the whole watershed was in descending order: broadleaf woodland; shrubland; coniferous woodland; cropland; grassland; building land; unused land; and water.

5. Discussion

5.1. Landscape Spatial Patterns and Ecosystem Services Change before and after Ecological Restoration Projects

After 2009, the land use structure of the Xigou watershed has changed. Grassland was still in the majority. The area of broadleaf forest, coniferous forest, farmland, and construction land all steadily increased. Broadleaf forest increased the most, followed by farmland, while grassland, shrubland, and unused land slightly decreased. There were two main reasons for this situation. On the one hand, the strategic restructuring of the region’s economy and the implementation of a series of ecological restoration projects [38,39,40] increased the area of coniferous and broadleaf woodlands. On the other hand, along with the improvement in the economic level of the watershed and the increase in population, the demand for construction land steadily increased [41,42]. In addition, greenhouse farming has been spreading rapidly in the study area. Some grasslands and shrublands have been used to grow crops [43,44].
The main factors influencing the changes in LSP indices in the study area were classified as natural and human factors [45,46]. Together, they interfered or contributed to the individual LSP index. LSP indices of the study area were in a highly fragmented state. But with the background of ecological restoration projects, the degree of landscape fragmentation, landscape shape indices, and landscape dispersion gradually decreased and stabilized from 2009 to 2020. The patches showed a trend of aggregation among themselves, but the landscape types were relatively homogeneous.
In general, the natural factors in short-time studies could cause weak effects on spatial structures through a slow accumulation process of effects [47]. The degree of fragmentation of the landscape was closely related to the disturbance of ecological restoration projects. Socio-economic development and the degree of change in population size were important influencing factors [48,49]. Consequently, human factors such as artificial afforestation and construction land were more active and had a direct impact on the landscape patterns of the watershed. However, the landscape type in this study was relatively homogeneous, which was different from previous studies. The reason may be that ecological restoration projects were dominated by the creation and restoration of artificial pure forests. The situation still cannot form complex community ecosystems in a short period of time. In conclusion, the main driving force of landscape pattern change was human factors in this study.

5.2. Relationship between Landscape Spatial Patterns and Ecosystem Services

All three ecosystem services were influenced by the contagion index (CONTAG), average patch area (AREA_MN), and Shannon’s evenness index (SHEI) to some extent. The main reason was that the land use patterns in the study area were different [50,51,52]. When several ecological restoration projects or reforestation were carried out in the region, the land use types changed, and the fragmentation of the landscape was further intensified, but at the same time, the ecosystem services in the watershed improved. All three ecosystem services positively correlated with CONTAG and SHEI. This was consistent with the findings of many studies [53,54]. That is, the better the connectivity of dominant patches and the richer the landscape diversity, the better the optimization of ecological processes and, thus, the improvement in local ecosystem services.
Despite this, the correlation strengths between different ESs and LSPs varied. For instance, in the study area, water yield had a lower correlation with AREA_MN, CONTAG, and SHEI. This could be because water yield is more influenced by local rainfall intensity, topography, and human activities than by forest landscape patterns. In contrast, sediment retention showed a significant correlation with these LSPs, indicating that changes in the forest landscape pattern within the watershed greatly impacted sediment retention. To improve soil erosion conditions in the watershed, it is essential to increase forest landscape diversity and enhance the complexity of landscape shapes. For carbon storage, the significance levels (P) of all landscape indices were below 0.001, and its correlation with LSPs was similar to that of sediment retention. However, carbon storage had a higher Moran index with landscape pattern factors, indicating the closest relationship between carbon storage and LSPs.

5.3. Optimization Allocation

The above results revealed a disturbed landscape due to human activities, indicating the need for optimization strategies to enhance ecosystem services. Interactions between ecosystem services were distinguished as trade-offs and synergistic effects. A trade-off relationship indicates that one service increases while the other decreases [55]. The synergistic relationship is that both services either increase or decrease [56]. The results showed that the average annual water yield decreased from 2004 to 2020 while both sediment retention and carbon storage increased. The data suggested that there was a trade-off between water yield and other ESs and that there were synergies between sediment retention and carbon storage. This was in line with the findings of many previous studies [57,58,59]. This was mainly attributed to a series of soil and water conservation management measures carried out in the study area. On the other hand, the increase in vegetation cover and effective human activities and management measures were the main reasons for the decline in annual water yield and the increase in sediment retention and carbon storage in the study area. These changes indicate that improving ecosystem management can effectively enhance the quality and stability of ecosystem services. Therefore, it is important to consider the balance between ecosystem services in an integrated manner before proceeding with LUCC planning and decision-making. A better understanding of this relationship allows for more effective development of future sustainable management policies [55,60].
In this paper, comprehensive ecosystem services simulated by the model were used as important partition indicators to improve sediment retention in the study area (Figure 7). Water yield and carbon storage were considered as the objectives of regional optimization. Based on vegetation restoration, the low-value area of comprehensive ecosystem services was determined as the optimization target (Figure 7a). The optimization area included four land-use types to be optimized: grassland; shrubland; farmland; and unused land (Figure 7b). Since broadleaf woodland already has the strongest ecosystem services, it was used as the best choice in the configuration process, followed by shrubs and grasslands (Figure 7c). The selection differed from other previous studies [61,62,63]. This may be mainly due to different climatic environments (e.g., climate, precipitation, topographic conditions, vegetation types) [63,64]. It is worth noting that the optimized grassland area decreased by 140.21 km2. The broadleaf woodland and shrubland increased by 112.37 km2 and 30.27 km2, respectively. From the optimized ecosystem services, the total carbon storage in the study area increased from 16.85 × 106 t to 20.57 × 106 t; the water yield increased from 1586.22 × 104 m3 to 1685.01 × 104 m3; the carbon storage increased from 5.935 × 106 t to 7.465 × 106 t (Table 6). Therefore, our recommendation satisfied the requirements for the optimal allocation of regional spatial structures and ecosystem services.

5.4. Limitations and Significance of This Study

In this study, we focused on landscape pattern indices and ecosystem services and their relationships. In addition, we provided suggestions and patterns for the optimal allocation of vegetation with low functional areas of comprehensive ecosystem services. However, in the process of applying any model, we could not achieve complete consistency with the actual scenario. The model can only be revised and calibrated continuously to make it better and better. The parameters required by the InVEST model (e.g., KC, C, and P, etc.) were basically derived from relevant literature and empirical equations, and lack of actual survey data [26,65,66]. In addition, other ESs (e.g., habitat quality, biodiversity, water purification, etc.) can be studied [67,68]. Therefore, subsequent studies need to integrate multiple ecosystem services and combine them with local economic development so that ecological and economic development can be coordinated [69,70,71].

6. Conclusions

The spatial and temporal changes in land use/land cover, landscape spatial pattern indices, and three ecosystem services in the study area from 2004 to 2020 based on 3 S and InVEST models were analyzed. We also analyzed the relationship between landscape pattern indices and ecosystem services. Finally, the comprehensive ecosystem service was derived by overlay analysis in ArcGIS in the Xigou watershed of the Chongli district of the Winter Olympic Games. The suggestions and models for the optimal allocation of vegetation in low-functioning areas of comprehensive ecosystem service were proposed. The main conclusions can be summarized as:
The study area is dominated by grassland, which accounts for more than 80% of the total area. From 2004 to 2020, landscape fragmentation gradually decreased, and an aggregation trend among patches was observed. The average annual water yield and sediment export from 2004 to 2020 was 1205.83 × 104 m3 and 14.70 × 106 t, while carbon storage increased 94,000 t. All three ecosystem services negatively correlated with the average patch area and positively correlated with the contagion and Shannon’s evenness indices.
The four important classes of comprehensive ecosystem services showed a gradual decreasing trend from northeast to southwest. The broadleaf woodland had the largest contribution value to the comprehensive ecosystem services of the whole watershed. The allocation of landscape spatial pattern indices was optimized to improve the overall ecological environment quality. We will focus on improving sediment retention in the study area as the goal of regional optimization. This could lead to a virtuous cycle and sustainable development of the area.

Author Contributions

Y.W.: Writing-original draft. X.P.: Methodology, Formal analysis, Investigation. G.J.: Project administration, Funding acquisition, Writing—review & editing. X.Y.: Project administration, Funding acquisition, Writing—review & editing. H.R.: Writing—review & editing. All authors have read and agreed to the published version of the manuscript.

Funding

The National Key Research and Development Program of China (No. 2023YFF1305302) (China) and the National Natural Science Foundation of China (No. 42277062).

Data Availability Statement

Data will be made available on request.

Acknowledgments

We are grateful for the grants from the National Key Research and Development Program of China (No. 2023YFF1305302) (China) and the National Natural Science Foundation of China (No. 42277062).

Conflicts of Interest

Author Xiuwen Peng was employed by the company Shanghai Investigation, Design & Research Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. An overall framework based on research objectives and data scenarios.
Figure 1. An overall framework based on research objectives and data scenarios.
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Figure 2. Location of the study area (a), the position of small watersheds (b), and its land use distribution (c).
Figure 2. Location of the study area (a), the position of small watersheds (b), and its land use distribution (c).
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Figure 3. Spatial match diagram of ecosystem services in the study area. Ecosystem services include water yield (a), sediment retention (b), and carbon storage (c).
Figure 3. Spatial match diagram of ecosystem services in the study area. Ecosystem services include water yield (a), sediment retention (b), and carbon storage (c).
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Figure 4. Scatterplot of landscape spatial pattern indices and water yield (a), sediment retention (b), and carbon storage (c). LVIWY: legged variation in water yield, LVISR: legged variation in soil retention, LVICS: legged variation in carbon storage. The red Moran’s I indices mean the spatial correlation between variable spatial pattern indices and the ecosystem services was the highest among all the explanatory variables.
Figure 4. Scatterplot of landscape spatial pattern indices and water yield (a), sediment retention (b), and carbon storage (c). LVIWY: legged variation in water yield, LVISR: legged variation in soil retention, LVICS: legged variation in carbon storage. The red Moran’s I indices mean the spatial correlation between variable spatial pattern indices and the ecosystem services was the highest among all the explanatory variables.
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Figure 5. LISA Aggregation Map of three landscape spatial indices and water yield (a), sediment retention (b), and carbon storage (c) from 2004 to 2020.
Figure 5. LISA Aggregation Map of three landscape spatial indices and water yield (a), sediment retention (b), and carbon storage (c) from 2004 to 2020.
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Figure 6. The spatial distribution characteristics of the importance partition of three ecosystem services of water yield (a), sediment retention (b), and carbon storage (c).
Figure 6. The spatial distribution characteristics of the importance partition of three ecosystem services of water yield (a), sediment retention (b), and carbon storage (c).
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Figure 7. Ecological service importance partitioning and optimization of the study area. (a) Classification of the importance of integrated ecosystem service functions in the watershed in 2020; (b) Map of land use types in the area to be optimized in the watershed; (c) The result of optimal vegetation allocation in the watershed.
Figure 7. Ecological service importance partitioning and optimization of the study area. (a) Classification of the importance of integrated ecosystem service functions in the watershed in 2020; (b) Map of land use types in the area to be optimized in the watershed; (c) The result of optimal vegetation allocation in the watershed.
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Table 1. The water yield, sediment retention, and carbon stock modules in the InVEST model require a biophysical table. Code: A unique integer for each LUCC class. Root_depth: The maximum root depth. Kc: Plant evapotranspiration coefficient. usle_c and usle_p: Cover-management factor and Support practice factor; their values are between 0 and 1. C_above, C_below, C_soil, and C_dead are the aboveground, underground, soil, and dead organic carbon storage, respectively.
Table 1. The water yield, sediment retention, and carbon stock modules in the InVEST model require a biophysical table. Code: A unique integer for each LUCC class. Root_depth: The maximum root depth. Kc: Plant evapotranspiration coefficient. usle_c and usle_p: Cover-management factor and Support practice factor; their values are between 0 and 1. C_above, C_below, C_soil, and C_dead are the aboveground, underground, soil, and dead organic carbon storage, respectively.
LUCCCodeKcroot_depthusle_cusle_pC_abvoeC_belowC_soilC_dead
Farmland10.620000.2316.600.6692.900.00
Broadleaf woodland2170000.020.1530.239.07151.403.00
Coniferous woodland3170000.020.1529.669.79110.801.68
Shrubland40.6520000.0211.711.9994.002.47
Grassland50.6517000.04311.032.6162.900.24
Water area611002.290.0017.160.00
Build-up land70.31007.614.5142.170.00
Unused land80.31119.1014.2022.630.00
Table 2. Criteria for grading the importance of ecosystem services in the Xigou watershed.
Table 2. Criteria for grading the importance of ecosystem services in the Xigou watershed.
Importance PartitionIIIIIIIV
Water yield (mm)<1515–2020–25>25
sediment retention (t/hm2)<9090–180180–450>450
Carbon storage (t/hm2)<5454–6767–100>100
Classification criteria0–0.350.35–0.370.37–0.400.40–0.73
Table 3. Land use structure changes in the Xigou watershed from 2004 to 2020.
Table 3. Land use structure changes in the Xigou watershed from 2004 to 2020.
Land Use Types2004200920152020
Area (km2)Rates (%)Area (km2)Rates (%)Area (km2)Rates (%)Area (km2)Rates (%)
Broadleaf woodland69.629.9072.8310.3677.2710.9977.7111.06
Coniferous woodland9.701.3810.181.4510.701.5210.981.56
Farmland41.815.9542.436.0443.286.1644.886.39
Grassland564.6380.33564.2680.28561.9679.95560.7879.78
Unused land1.500.211.280.181.120.161.060.15
Built-up land0.850.121.000.141.480.211.810.26
Shrubland14.542.0710.671.526.790.975.320.76
Water area0.200.030.220.030.280.040.350.05
Total702.88100.00702.88100.00702.88100.00702.88100.00
Table 4. Changes in landscape spatial patterns indices from 2004 to 2020. NP: Number of patches; PD: patch density; AREA_MN: average patch area; LSI: landscape shape index; PAFRAC: perimeter area fractional dimension; CONTAG: spread index; IJI: dispersion and juxtaposition index; AI: aggregation index; MPI: maximum patch index; SHDI: Shannon diversity index; SHEI: Shannon evenness index.
Table 4. Changes in landscape spatial patterns indices from 2004 to 2020. NP: Number of patches; PD: patch density; AREA_MN: average patch area; LSI: landscape shape index; PAFRAC: perimeter area fractional dimension; CONTAG: spread index; IJI: dispersion and juxtaposition index; AI: aggregation index; MPI: maximum patch index; SHDI: Shannon diversity index; SHEI: Shannon evenness index.
YearNPPDAREA_MNLSIPAFRACCONTAGIJIAIMPISHDISHEI
200420,17328.69853.484563.73531.459771.055547.845685.95579.14030.73620.3540
200917,26424.55874.071959.50931.450771.786046.840786.915179.50010.72910.3506
201514,80121.05624.749255.55011.436472.356345.777687.813178.79380.72810.3502
202012,94518.41525.430353.09621.430672.699944.453588.372179.15070.72990.3510
Table 5. The mean values of the three key ecosystem services in the Xigou watershed, 2004–2020.
Table 5. The mean values of the three key ecosystem services in the Xigou watershed, 2004–2020.
YearWater Yield
/104 m3		Sediment Retention
/106 t		Carbon Storage
/106 t
2004637.4410.545.841
20091779.4918.135.880
2015820.1713.285.927
20201586.2216.855.935
Table 6. Area and proportion of the functional importance zoning of water yield, sediment retention, and carbon storage in the Xigou watershed, 2004–2020.
Table 6. Area and proportion of the functional importance zoning of water yield, sediment retention, and carbon storage in the Xigou watershed, 2004–2020.
ESsClassification Criteria2004200920152020
Area (km2)ProportionArea (km2)ProportionArea (km2)ProportionArea (km2)Proportion
water yieldI161.0522.92%179.0825.48%171.8824.46%179.7325.57%
II194.2927.65%188.4526.81%174.0524.76%183.8926.17%
III170.4424.25%174.4824.83%191.1127.19%173.9324.75%
IV177.0125.19%160.7922.88%165.7623.59%165.2423.51%
Sediment retentionI392.0555.78%283.2340.30%342.7648.77%297.0942.27%
II149.1221.22%141.9320.20%151.5321.56%144.3320.54%
III118.2416.82%178.7825.44%146.5320.85%172.224.50%
IV43.396.17%98.8514.07%61.988.82%89.1812.69%
carbon storageI2.550.36%2.470.35%2.840.40%3.220.46%
II564.6780.35%564.2180.28%562.0979.98%560.9979.82%
III41.985.97%42.456.04%43.466.18%44.916.39%
IV93.613.32%93.6613.33%94.413.43%93.6813.33%
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Wu, Y.; Peng, X.; Jia, G.; Yu, X.; Rao, H. Evaluation and Optimization of Landscape Spatial Patterns and Ecosystem Services in the Northern Agro-Pastoral Ecotone, China. Land 2024, 13, 1549. https://doi.org/10.3390/land13101549

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Wu Y, Peng X, Jia G, Yu X, Rao H. Evaluation and Optimization of Landscape Spatial Patterns and Ecosystem Services in the Northern Agro-Pastoral Ecotone, China. Land. 2024; 13(10):1549. https://doi.org/10.3390/land13101549

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Wu, Yuxin, Xiuwen Peng, Guodong Jia, Xinxiao Yu, and Honghong Rao. 2024. "Evaluation and Optimization of Landscape Spatial Patterns and Ecosystem Services in the Northern Agro-Pastoral Ecotone, China" Land 13, no. 10: 1549. https://doi.org/10.3390/land13101549

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Wu, Y., Peng, X., Jia, G., Yu, X., & Rao, H. (2024). Evaluation and Optimization of Landscape Spatial Patterns and Ecosystem Services in the Northern Agro-Pastoral Ecotone, China. Land, 13(10), 1549. https://doi.org/10.3390/land13101549

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