Land Use and Land Cover Change in the Yellow River Basin from 1980 to 2015 and Its Impact on the Ecosystem Services

Abstract

Land use and land cover change is an important driving force for changes in ecosystem services. We defined several important human-induced land cover change processes such as Ecological Restoration Project, Cropland Expansion, Land Degradation, and Urbanization by the land use/land cover transition matrix method. We studied human-induced land cover changes in the Yellow River Basin from 1980 to 2015 and evaluated its impact on ecosystem service values by the benefit transfer method and elasticity coefficient. The results show that the cumulative area of human-induced land cover change reaches 65.71 million ha from 1980 to 2015, which is close to the total area of the Yellow River Basin. Before 2000, Ecological Restoration Project was the most important human-induced land cover change process. However, due to the large amount of cropland expansion and land degradation, the area of natural vegetation was reduced and the ecosystem value declined. Since 2000, due to the implementation of the “Grain for Green” program, the natural vegetation of upstream area and midstream area of Yellow River Basin has been significantly improved. This implies that under an appropriate policy framework, a small amount of human-induced land cover change can also improve ecosystem services significantly.

1. Introduction

Land use and land cover (LULC) change is an important indicator that characterizes the impact of human activities on the natural ecosystem [1]. It is the link between human social-economic activities and natural ecological processes [2], and it is also an important content of global climate change and environmental change research [2,3,4]. LULC change is of great significance for in-depth study of biosphere-atmosphere interaction, biodiversity, land surface radiative forcing, biogeochemical cycles, and sustainability of resources and environment [2,3,5].

LULC change is one of the important factors driving changes in ecosystem services provision [6,7]. Ecosystem services (ES) are the benefits that humans obtain from nature through direct or indirect approaches [8,9], and are the core contents of regional sustainable development and human well-beings [10,11,12,13,14]. By changing important ecological processes such as energy exchange, water cycle, soil erosion and deposition, and biogeochemical cycle of the ecosystem, LULC change can affect the provision of ES [15]. Furthermore, there are significant differences in the impact of different LULC change on the ES provision [16,17]. For example, ecosystem protection and ecological restoration projects will increase the key ES such as soil and water conservation, water yield, and nutrient cycling [18], whereas the fragmentation of landscapes caused by urbanization will reduce ES [19].

Ecosystem service value (ESV) assessment is one of the important tools to carry out ecosystem service evaluation [8,20,21]. The monetary value evaluation of ecosystem services has greatly promoted the development of research related to ecosystem services, including raising public awareness of the importance of ecosystem services to society [22,23], safeguard the benefits and interests of different stakeholders [24], guiding decision-making and policy-making [14], and developing knowledge about ecosystem management [25]. It can also provide incentives for ecosystem protection and restoration, such as payments for ecosystem services programs [26,27,28].

There have been many studies about the impact of LULC change on ES [29,30,31], and most of them focus on assessing the overall sensitivity of ES to LULC change [32,33], or analyzing the driving process of LULC changes affecting ES based on temperature, precipitation, population, and economy [34,35,36]. However, most studies only regard LULC changes as the result of land policy or human activities, and fail to distinguish between natural and human-induced processes in land cover changes. Land use (LU) is the use of land by humans for economic and social purposes [14], while land cover (LC) refers to the physical and biological cover over the surface of land including water, vegetation, bare soil, and artificial structures [37]. Although land use and land cover are intimately linked and are often studied as a whole [38], land cover change is influenced both by natural and human-induced processes. The natural processes that affect LC changes mainly include climate change and wildfires [39,40], while human-induced processes that affect land cover include agricultural expansion, water conservancy construction, urbanization, and ecological restoration projects. Therefore, in order to better understand the impact of land policy or human activities on ecosystem services, it is necessary to distinguish between natural and man-made processes in land cover change. Since few ES studies distinguish between human-induced LC changes and nature-process LC changes, and quantitatively evaluate the impact of human-induced LC changes on ES, our understanding of the impact of human-induced LC changes on ES still needs to be strengthened.

The Yellow River Basin (YRB) is an important ecological barrier in northern China, including the Qinghai-Tibet Plateau, the Loess Plateau, and the North China Plain [41]. The ecosystem of the YRB has undergone major changes due to frequent human activities [19,42,43], which in turn affected the ES provision and social economy and development in the YRB [36,44,45]. Investigating the impact of human-induced LC changes on ES in the YRB has important significance for ecological environmental protection and sustainable development policy making in the YRB. The objectives of this paper are to (1) investigate the land use/land cover (LULC) trend and human-induced LC changes in the YRB from 1980 to 2015, (2) assess the ES monetary values (ESV) of the YRB based on the benefit transfer method from 1980 to 2015, and (3) evaluate the impact of LULC changes on ESV.

2. Study Area and Methods

2.1. Study Area

The Yellow River originates from the northern foot of the Bayan Har Mountain on the Qinghai-Tibet Plateau, with a total length of 5464 km. It is the second longest river in China with a basin area of 79,500,000 ha. The YRB is located in 96° E–119° E and 32° N–42° N, with a length of about 1900 km from east to west and a width of about 1100 km from north to south. (Figure 1a), the topography of the YRB is high in the west and low in the east (Figure 1b). The annual average temperature of the YRB is −13.1–15.3 °C (Figure 1c), and the annual average precipitation of the YRB is 116.2~1038.7 mm (Figure 1d), with a decreasing trend from southeast to northwest. The Yellow River basin (YRB) can generally be divided into four watersheds: the source area (SA), the upstream area (UA), the midstream area (MA), and the downstream areas (DA).

2.2. Data Sources

The land use and land cover (LULC) data come from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences (RESDC) (http://www.resdc.cn accessed on 20 August 2021) [1,46,47,48]. These data are based on Landsat Thematic Mapper/Enhanced Thematic Mapper Plus remote sensing images from different periods and are generated through visual interpretation based on national field surveys. LULC data were used in this study from the year 1980, 1990, 1995, 2000, 2005, 2010, and 2015. The spatial resolution of LULC data used was 1 km × 1 km, which is resampled from the original dataset (30 m × 30 m) by nearest neighbor method. According to field surveys and records, the overall evaluation accuracy of the LULC primary types is over 94.3%, and the accuracy of the LULC secondary types is over 91.2% [1,46,47,48], which can meet the data requirements of this study. The LULC types of original data were classified into 6 primary categories (i.e., cropland, forestland, grassland, water area, construction land, unused land) and 24 secondary categories. By overlaying the LULC data and Vegetation Map of China (1:1,000,000), the LULC types are reclassified into seven primary categories including cropland, forest, grassland, wetland, bare land, urban land, and water area and 16 secondary categories (

Table A1. Land use and land cover types.

Primary Categories	Secondary Categories
Cropland	Dry land
	Paddy field
Forest	Needle-leaf
	Mixed
	Broadleaf
	Bush
Grassland	Prairie
	Shrub grass
	Meadow
Wetland	Wetland
Urban land	Urban
Barren land	Desert
	Barren
Water area	Water
	Reservoir
	Ice

in Appendix A). The boundary, Vegetation Map of China (1:1,000,000), temperature and precipitation data of the YRB also come from RESDC (http://www.resdc.cn accessed on 20 August 2021), where the temperature and precipitation data are obtained by the thin film spline interpolation method using ANUSPLIN software [49], with a spatial resolution of 1 km × 1 km. The DEM data come from the Geospatial Data Cloud Platform of the Computer Network Information Center of the Chinese Academy of Sciences (http://www.gscloud.cn accessed on 20 August 2021), with a spatial resolution of 90 m × 90 m, and the nearest neighbor method was used to resample the data into the resolution of 1 km × 1 km.

2.3. LULC Change Transition Analysis

The transition matrix method was used to detect the conversion information of LULC change during the study period. The general form of the LULC change transfer matrix is,

$$s_{ij}=\left[\begin{array}{cccc}{s}_{11}& s_{12}& \dots & s_{1n}\\ s_{21}& s_{22}& \dots & s_{2n}\\ \dots & \dots & \dots & \dots \\ s_{n1}& s_{n2}& \dots & s_{nn}\end{array}\right]$$

(1)

where s represents the area (ha); n represents the number of LULC types before and after the transfer; i, j (i, j = 1,2,…, n) represent the LULC types before and after the transfer respectively; s_ij represents the area (ha) of land type i converted to land type j.

2.4. Estimation of Human-Induced LC Changes

In this study, LULC change was divided as human-induced LC changes and natural-process LC changes. Land cover or ecosystem transfer and change detection method [50] is used for LULC changes attribution analysis (

Table 1. Land cover or ecosystem transfer and change detection method.

LC Type before Change	→	LC Type after Change	Definitions
Desert/Barren/Ice	→	Natural Vegetation *	Ecological Restoration Project
Cropland	→	Natural Vegetation
Desert/Barren/Ice	→	Reservoir	Water conservancy construction
Natural Vegetation	→	Reservoir
Cropland	→	Reservoir
Desert/Barren/Ice	→	Urban	Urbanization
Natural Vegetation	→	Urban
Cropland	→	Urban
Reservoir	→	Urban
Natural Vegetation	→	Cropland	Cropland expansion
Desert/Barren/Ice		Cropland
Natural Vegetation	→	Desert/Barren/Ice	Land degradation
Reservoir	→	Desert/Barren/Ice
Cropland	→	Desert/Barren/Ice
Urban	→	Desert/Barren/Ice
Desert/Barren/Ice	→	Desert/Barren/Ice	Natural process
Natural Vegetation	→	Natural Vegetation

* Natural Vegetation includes Forest, Grassland, Wetland, and Water land cover types.

). Different natural and human-induced land cover transition processes are defined through LULC change transition analysis described in Section 2.3. For example, cropland expansion (CE) is defined as the LC transition process from natural vegetation/desert/barren/ice to cropland; urbanization (UR) is defined as the LC transition process from natural vegetation/desert/barren/ice to urban; Ecological Restoration Project (ERP) is defined as the LC transition process from desert/desert/ice to natural vegetation; land degradation (LD) is defined as the LC transition process from natural vegetation/farmland/urban to desert/barren/ice. The details of LC changes definitions can be found in Figure A1. A summary of land policies implemented in the YRB can be found in

Table A7. Summary of the land policy implemented in the Yellow River Basin.

No.	Programs	Planned Timeframe	Aims and Objectives
1	Three-North Shelterbelt Development Program	1978–2050	Control the expansion of sandy/desertified land, and mitigate wind erosion of sand/soil and dust storms in northern China via forest plantation, mountain closure, and sandy area regeneration.
2	Natural Forest Conservation Program	1998–2020	Increase the area of cultivated land and revenues via consolidation (reorganizing and merging fragmented and underused land), reclamation, constructing high-quality cropland, and improving land use and management.
3	Grain for Green Program	1999–2020	Prevent soil erosion, mitigate flooding, store carbon, and improve livelihoods by increasing forest and grassland cover on cropped hillslopes and converting cropland, barren hills and wasteland to forest.
4	Beijing-Tianjin Sand Source Control Engineering	2001–2022	Reduce desertification and dust storms, and improve the environment in the Beijing/Tianjin area via reforestation, grassland management, and water conservation, relocating affected people and establishing basic governance of desertified lands.
5	Comprehensive Agricultural Development Program	1988–2020	Raise rural quality of life, incomes and food security through land reform, land management, ecological construction, agricultural infrastructure and industry development, and production/efficiency gains using science and technology.
6	National Land Consolidation Program.	1997–2020	Increase the area of cultivated land and revenues via consolidation (reorganizing and merging fragmented and underused land), reclamation, constructing high-quality cropland, and improving land use and management.

.

Ecological restoration projects (ERP), water conservancy construction (WCC), urbanization (UR), and land degradation (LD) are considered as human-induced LC change processes. It should be noted that, drawing on the concept of “human influence on land” proposed by Zalles et al. [5], human-induced LC change only considers human production and life processes that directly affect the land, which is different from human occupation of net primary production [51] and human footprint [52], and did not consider factors such as population density and spatial distance [53] as an indicator of human activity.

2.5. Estimation of ESV

The Benefit Transfer Method refers to applying existing values or information from one or multiple previously established study sites to other unstudied sites with similar characteristics where the site-specific primary valuation is lacking [30,54]. The method proposed by Xie et al. [21] was used to evaluate the ecosystem service value (ESV) of the Yellow River Basin. The formula is as follows,

$$ESV={\displaystyle \sum _k^n{A}_{k}DV{C}_k}$$

(2)

where ESV is the value of ecosystem services, in US dollar (USD); A_k is the area of the k-th LULC type, in ha; VC_k is the equivalent coefficient of the k-th ESV, in USD ha⁻¹.

In the method proposed by Xie et al. [21], the VC_k was estimated as the product of the standard equivalent factor and the equivalent coefficients. The standard equivalent factor, which serves as the benchmark for other ecosystem services, refers to the value provided by the natural ecological components of a certain ecosystem per unit land area. Based on the labor value theory [55], Xie et al. [21] take the average net benefit excluding the cost value of human input as the standard equivalent factor of the Chinese ecosystem and estimate the value of the standard equivalent coefficient of 503.2 USD ha⁻¹ in 2010. Equivalent coefficient is the weight coefficient of the service value of each ecosystem. Through a professional questionnaire survey of 500 Chinese ecologists and scholars in related disciplines, Xie et al. [21,56] proposed an ESV equivalent coefficient table. In the ESV equivalent coefficient table, the ecosystem includes six primary types (i.e., cropland, forest, grassland, wetland, wasteland, and waters) and 14 secondary types. Ecosystem services include four primary classifications (i.e., provisioning services, regulating services, habitat services, and culture and amenity services) [57] and 11 secondary classifications (

Table 2. The equivalent coefficients table for ecosystem service value (ESV) per unit area for the 6 ecosystems and 4 ecosystem services. [21].

Ecosystem Classification		Provisioning Services			Regulating Services						Habitat Services	Cultural & Amenity Services
Primary	Secondary	Food	Materials	Water	Air Quality Regulation	Climate Regulation	Waste Treatment	Regulation of Water Flows	Erosion Prevention	Maintenance of Soil Fertility	Habitat Services	Cultural & Amenity Services
Cropland	Dry land	0.85	0.40	0.02	0.67	0.36	0.10	0.27	1.03	0.12	0.13	0.06
	Paddy field	1.36	0.09	−2.63	1.11	0.57	0.17	2.72	0.01	0.19	0.21	0.09
Forest	Needle-leaf	0.22	0.52	0.27	1.70	5.07	1.49	3.34	2.06	0.16	1.88	0.82
	Mixed	0.31	0.71	0.37	2.35	7.03	1.99	3.51	2.86	0.22	2.60	1.14
	Broadleaf	0.29	0.66	0.34	2.17	6.50	1.93	4.74	2.65	0.20	2.41	1.06
	Bush	0.19	0.43	0.22	1.41	4.23	1.28	3.35	1.72	0.13	1.57	0.69
Grassland	Prairie	0.10	0.14	0.08	0.51	1.34	0.44	0.98	0.62	0.05	0.56	0.25
	Shrub grass	0.38	0.56	0.31	1.97	5.21	1.72	3.82	2.40	0.18	2.18	0.96
	Meadow	0.22	0.33	0.18	1.14	3.02	1.00	2.21	1.39	0.11	1.27	0.56
Wetland *	Wetland	0.51	0.50	2.59	1.90	3.60	3.60	24.23	2.31	0.18	7.87	4.73
Barren land	Desert	0.01	0.03	0.02	0.11	0.10	0.31	0.21	0.13	0.01	0.12	0.05
	Barren	0.00	0.00	0.00	0.02	0.00	0.10	0.03	0.02	0.00	0.02	0.01
Water area **	Water	0.80	0.23	8.29	0.77	2.29	5.55	102.24	0.93	0.07	2.55	1.89
	Glacier and snow	0.00	0.00	2.16	0.18	0.54	0.16	7.13	0.00	0.00	0.01	0.09

* Wetland refers to marshland, swampland and beach. ** Water area refers to terrestrial aquatic ecosystems including natural river, lake, ditch and canal, etc.

). The method proposed by Xie et al. [21] has been used to calculate the monetary value of china’s ecosystem services, and researchers have used this method to calculate the monetary value of ecosystem services in the YRB. Therefore, it is feasible to apply this method to study the monetary value of ecosystem services in the YRB.

2.6. Elasticity-Sensitivity Analysis

Elasticity coefficient (E), a concept from economics, was used to study response of ESV to LULC changes [30], and its formula is as follows,

$$E=\left|\frac{(ES{V}_j-ES{V}_i)/ES{V}_i}{{\displaystyle \sum \Delta A_k}/{\displaystyle \sum A_k}}\right|$$

(3)

where ESV is the total estimated value of all ES, the unit is USD; ΔA_k is the land transfer area of k-th LULC type, the unit is ha; A_k is the land area of the k-th LULC type, the unit is ha; i and j represent the beginning and end of the study period, respectively; k represents LULC category. Elasticity (E) is a measure about percentage change in ESV as a result of percentage change in LULC. If a small area conversion resulted in a significant ESV change, then the elasticity is large. According to Jiang et al. [30], elasticity (E) can be divided into three categories, 0 < E < 0.5, 0.5 < E < 1, and E > 1, indicating that response of ESV to LULC change is inelastic, elastic, and very elastic.

3. Results

3.1. LULC Areas from 1980 to 2015

3.1.1. Basin Scale

In 1980, the area of grassland, cropland, and woodland in the YRB is about 61.36 million hectares (Mha), accounting for 86.76% of the total area of the basin; the area of barren land, wetland, and water areas is about 7.88 Mha, accounting for 11.14% of the total area of the basin; and the area of urban land is about 1.49 Mha, accounting for 2.10% of the total area of the basin (Figure 2). Croplands are mainly distributed in the UA (26.45%) and MA (63.06%); forests are mainly distributed in the UA (24.83%) and MA (63.95%); grasslands are widely distributed in the SA (24.27%), UA (44.07%), and MA (30.82%); wetlands are mainly distributed in the SA (26.79%) and UA (50.74%); urban land are mainly distributed in the UA (34.66%), MA (42.02%), and DA (22.42%); barren land are mainly distributed in the SA (20.69%), UA (63.87%), and MA (14.99%); and water bodies of the YRB are widely distributed. Details of LULC areas in YRB from 1980 to 2015 can be found in

Table A2. LULC in the Yellow River Basin from 1980 to 2015.

Region	LULC	1980	1990	1995	2000	2005	2010	2015
YRB	Cropland	18.84	18.97	18.94	19.11	18.79	18.73	18.60
	Forest	9.00	8.96	8.53	8.99	9.20	9.22	9.23
	Grassland	33.52	33.53	34.30	33.35	33.16	33.19	33.08
	Wetland	1.71	1.57	1.50	1.62	1.61	1.60	1.58
	Urban land	1.49	1.51	1.58	1.65	1.76	1.82	2.16
	Barren land	5.43	5.47	5.20	5.33	5.48	5.43	5.31
	Water area	0.74	0.70	0.68	0.67	0.74	0.73	0.76
SA	Cropland	0.04	0.04	0.04	0.04	0.04	0.04	0.04
	Forest	0.80	0.79	0.79	0.80	0.80	0.80	0.80
	Grassland	8.14	8.18	8.27	8.15	8.08	8.08	8.08
	Wetland	0.46	0.44	0.45	0.46	0.46	0.46	0.46
	Urban land	0.00	0.00	0.00	0.00	0.00	0.00	0.01
	Barren land	1.12	1.14	1.02	1.13	1.19	1.19	1.19
	Water area	0.20	0.18	0.18	0.18	0.18	0.18	0.18
UA	Cropland	4.98	5.02	5.12	5.16	5.07	5.07	5.04
	Forest	2.23	2.26	2.26	2.24	2.30	2.30	2.32
	Grassland	14.78	14.74	14.65	14.56	14.48	14.50	14.47
	Wetland	0.87	0.83	0.78	0.89	0.88	0.87	0.85
	Urban land	0.52	0.52	0.56	0.56	0.59	0.61	0.77
	Barren land	3.47	3.50	3.50	3.47	3.53	3.50	3.38
	Water area	0.26	0.23	0.24	0.23	0.25	0.25	0.27
MA	Cropland	11.88	11.94	11.79	11.91	11.69	11.64	11.56
	Forest	5.75	5.70	5.25	5.74	5.89	5.91	5.90
	Grassland	10.33	10.32	11.11	10.36	10.35	10.36	10.29
	Wetland	0.25	0.22	0.19	0.22	0.21	0.21	0.21
	Urban land	0.63	0.64	0.66	0.71	0.76	0.79	0.94
	Barren land	0.81	0.81	0.65	0.71	0.74	0.73	0.72
	Water area	0.17	0.18	0.17	0.16	0.19	0.19	0.19
DA	Cropland	1.87	1.90	1.91	1.92	1.92	1.90	1.88
	Forest	0.13	0.13	0.15	0.13	0.13	0.13	0.13
	Grassland	0.18	0.18	0.17	0.18	0.15	0.15	0.15
	Wetland	0.12	0.08	0.08	0.05	0.05	0.05	0.05
	Urban land	0.33	0.34	0.35	0.37	0.39	0.41	0.43
	Barren land	0.01	0.01	0.01	0.01	0.00	0.00	0.00
	Water area	0.11	0.10	0.09	0.10	0.11	0.11	0.11

.

Compared with 1980, the area of urban land, water bodies, and urban lands in the YRB increased by 45.10%, 3.44%, and 2.58% in 2015, while wetland, bare land, grassland, and farmland decreased by 7.18%, 2.27%, 1.31%, and 1.29%, respectively. However, only the average annual change rate (ACR) of urban land area continued to increase (

Table A3. LULC change rate (Mha/a) in different periods.

Region	LULC	1980–1990	1990–1995	1995–2000	2000–2005	2005–2010	2010–2015
YRB	Cropland	0.0133	−0.0075	0.0336	−0.0629	−0.0125	−0.0257
	Forest	−0.0042	−0.0862	0.0932	0.0409	0.0051	0.0016
	Grassland	0.0006	0.1540	−0.1896	−0.0390	0.0061	−0.0208
	Wetland	−0.0132	−0.0140	0.0233	−0.0028	−0.0006	−0.0040
	Urban land	0.0023	0.0132	0.0146	0.0229	0.0117	0.0673
	Barren land	0.0038	−0.0531	0.0263	0.0284	−0.0088	−0.0249
	Water area	−0.0044	−0.0035	−0.0008	0.0125	−0.0010	0.0067
SA	Cropland	−0.0001	0.0013	−0.0010	0.0005	0.0002	−0.0000
	Forest	−0.0016	0.0013	0.0019	−0.0002	0.0000	0.0000
	Grassland	0.0043	0.0186	−0.0254	−0.0123	0.0000	−0.0011
	Wetland	−0.0022	0.0031	0.0015	0.0011	−0.0004	0.0003
	Urban land	0.0000	0.0002	−0.0002	0.0001	0.0001	0.0007
	Barren land	0.0012	−0.0225	0.0219	0.0112	−0.0007	0.0000
	Water area	−0.0020	−0.0012	0.0013	−0.0005	0.0008	0.0001
UA	Cropland	0.0034	0.0193	0.0098	−0.0192	0.0012	−0.0061
	Forest	0.0024	0.0002	−0.0039	0.0126	0.0002	0.0037
	Grassland	−0.0031	−0.0194	−0.0170	−0.0172	0.0045	−0.0065
	Wetland	−0.0035	−0.0104	0.0219	−0.0023	−0.0008	−0.0039
	Urban land	0.0003	0.0077	−0.0005	0.0074	0.0033	0.0312
	Barren land	0.0027	0.0013	−0.0073	0.0132	−0.0067	−0.0235
	Water area	−0.0030	0.0020	−0.0025	0.0055	−0.0018	0.0051
MA	Cropland	0.0061	−0.0308	0.0246	−0.0454	−0.0095	−0.0152
	Forest	−0.0052	−0.0905	0.0989	0.0287	0.0045	−0.0020
	Grassland	−0.0006	0.1569	−0.1491	−0.0025	0.0018	−0.0133
	Wetland	−0.0029	−0.0062	0.0048	−0.0009	0.0006	−0.0004
	Urban land	0.0012	0.0040	0.0109	0.0102	0.0047	0.0313
	Barren land	−0.0001	−0.0320	0.0120	0.0045	−0.0015	−0.0015
	Water area	0.0008	−0.0004	−0.0021	0.0054	−0.0006	0.0012
DA	Cropland	0.0033	0.0027	0.0004	0.0010	−0.0042	−0.0042
	Forest	0.0003	0.0026	−0.0033	−0.0002	0.0002	−0.0000
	Grassland	0.0000	−0.0021	0.0015	−0.0063	−0.0001	0.0000
	Wetland	−0.0045	−0.0008	−0.0043	−0.0007	0.0000	−0.0000
	Urban land	0.0008	0.0012	0.0040	0.0049	0.0036	0.0039
	Barren land	0.0002	0.0002	−0.0009	−0.0006	0.0000	0.0000
	Water area	−0.0003	−0.0036	0.0026	0.0020	0.0005	0.0003

), with the ACR reach its maximum value in 2010–2015 period (0.0673 Mha/a), while other LU/LC trends showed significant reversals or fluctuations, for example, the LU/LC with the largest variation in the ACR is grassland, and its maximum and minimum ACR values occurred in 1990–1995 period (0.1540 Mha/a) and 1995–2000 period (−0.1896 Mha/a), respectively. Details of average annual LULC change rate in YRB from 1980 to 2015 can be found in Table A3.

3.1.2. Watershed Scale

In 1980, the LULC in SA and UA of the YRB was dominated by grasslands, with an area of 8.14 Mha (75.60% of SA) and 14.78 Mha (54.51% of UA), respectively (Figure 2). The LULC in MA and DA of the YRB was dominated by cropland, with an area of 11.88 Mha (39.84% of MA) and 1.87 Mha (67.81% of DA), respectively. Compared with 1980, the grassland area in the SA and UA decreased by 0.72% and 2.09% respectively in 2015, the area of cropland in the MA decreased by 2.70%, and the area of cropland in the DA increased by 0.61%. Details of LULC areas in SA, UA, MA, and DA from 1980 to 2015 can be found in Table A2.

In the SA of the YRB, all the LULC trends showed fluctuations (Table A2), where the LULC with the largest variation in ACR is barren land, followed by grassland; in the UA of the YRB, all the LULC trends also showed fluctuations, where the LULC with the largest variation in ACR is cropland, followed by grassland; in the MA of YRB, the urban land area continued to increase from 1980 to 2015, with the ACR reached its maximum value in 2010–2015 period (0.0313 Mha/a), while other LULC trends showed significant reversals or fluctuations, and LULC with the largest variation in ACR is grassland, followed by forest; in the DA of the YRB, the urban land area also continued to increase from 1980 to 2015, with the ACR reached its maximum value in 2000–2005 period, while other LULC trends showed significant reversals or fluctuations, and LULC with the largest variation in the ACR is grassland, followed by cropland. Details of average annual LULC change rate in SA, UA, MA, and DA from 1980 to 2015 can be found in Table A3.

3.2. LULC Change Area from 1980 to 2015

3.2.1. Basin Scale

The LULC change area in the YRB showed an increasing trend before the year 2000 (Figure 3) and reached its maximum value (26.02 Mha) in 1995–2000 period; the LULC change area in the YRB showed a decreasing trend after the year 2000, with its minimum value (0.81 Mha) appeared in 2010–2015 period. From 1990 to 2015, the human-induced LC change dominates, with the minimum and maximum percentages appearing in 2005–2010 period (74.19%) and 2010–2015 period (92.39%), respectively; the natural-process LC in the YRB accounted for less than 25% of the total LULC change areas. For all LULC change types, the proportion of ecological restoration projects (ERP) was the highest before the year 2010, with the maximum value appearing in the 1990–1995 period (accounting for 38.08% of the total LULC change areas); the proportion of urbanization (UR) was the highest in 2010–2015, accounting for 43.39% of the total LULC change areas. Details of LULC change areas in YRB from 1980 to 2015 can be found in

Table A4. LULC change area (M ha) in different periods.

Region	LULC Change	1980–1990	1990–1995	1995–2000	2000–2005	2005–2010	2010–2015
YRB	CE *	8.3068	8.3043	8.9735	0.2954	0.0586	0.1604
	ERP *	9.0055	9.4788	9.2847	0.3234	0.0871	0.1644
	UR *	1.2429	1.2175	1.3013	0.1198	0.0609	0.3529
	WCC *	0.1072	0.0915	0.1052	0.0423	0.0114	0.0314
	LD *	2.0922	2.0444	2.4389	0.2104	0.0030	0.0424
	Natural-process	3.8058	3.8361	4.0041	0.1457	0.0769	0.0631
	Total	24.4848	24.8913	26.0247	1.1325	0.2979	0.8134
SA	CE	0.3225	0.2878	0.3403	0.0031	0.0009	0.0002
	ERP	0.9139	0.9092	0.9128	0.0044	0.0034	0.0017
	UR	0.0029	0.0034	0.0027	0.0006	0.0004	0.0035
	WCC	0.0003	0.0003	0.0001	0.0005	0.0001	0.0009
	LD	0.6140	0.5564	0.7298	0.0595	0.0000	0.0017
	Natural-process	0.8794	0.7886	0.8535	0.0173	0.0071	0.0043
	Total	2.7330	2.5457	2.8392	0.0854	0.0119	0.0123
UA	CE	2.6498	2.6662	2.8302	0.1499	0.0354	0.0986
	ERP	3.2929	3.2520	3.5728	0.1682	0.0433	0.1220
	UR	0.4262	0.4314	0.4415	0.0403	0.0172	0.1619
	WCC	0.0476	0.0224	0.0427	0.0105	0.0049	0.0192
	LD	1.1414	1.1977	1.3263	0.1197	0.0012	0.0249
	Natural-process	1.6766	1.6229	1.7496	0.0842	0.0401	0.0411
	Total	9.2345	9.1926	9.9631	0.5728	0.1421	0.4677
MA	CE	4.8586	4.9029	5.3409	0.1006	0.0179	0.0583
	ERP	4.6156	5.1541	4.6081	0.1410	0.0346	0.0391
	UR	0.5344	0.5217	0.5720	0.0526	0.0241	0.1657
	WCC	0.0293	0.0383	0.0283	0.0224	0.0033	0.0084
	LD	0.3232	0.2728	0.3668	0.0312	0.0018	0.0157
	Natural-process	1.1709	1.3562	1.3409	0.0361	0.0223	0.0170
	Total	11.5320	12.2460	12.2570	0.3839	0.1040	0.3042
DA	CE	0.4447	0.4191	0.4304	0.0396	0.0042	0.0033
	ERP	0.1545	0.1331	0.1631	0.0094	0.0054	0.0015
	UR	0.2717	0.2538	0.2758	0.0248	0.0187	0.0211
	WCC	0.0288	0.0300	0.0329	0.0082	0.0027	0.0029
	LD	0.0092	0.0107	0.0067	0.0000	0.0000	0.0001
	Natural-process	0.0622	0.0502	0.0419	0.0077	0.0070	0.0007
	Total	0.9711	0.8969	0.9508	0.0897	0.0380	0.0296

* CE, Cropland Expansion; ERP, Ecological Restoration Project; UR, Urbanization; WCC, Water Conservation Construction; LD, Land Degradation.

.

3.2.2. Watershed Scale

In the SA, UA, MA, and DA of the YRB, the LULC change areas decreased significantly after the year 2000 (Figure 3). Except for the maximum value of DA which appeared in the 1980–1990 period, the maximum values of the other watersheds all appeared in the 1995–2000 period. The highest proportion of LULC change areas in SA was human-induced LC change areas; except that the proportion of human-induced LC change areas in 2005–2010 period was only 40.34%, the proportion of human-induced LC change areas was higher than 65.04% in the rest of time periods; before the year 2000, ecological restoration projects (ERP) areas accounted for the highest proportion of the total LULC change areas (≥32.15%), after the year 2000, land degradation (LD) areas accounted for 69.67% of the total LULC change areas in 2000–2005 period, and urbanization (UR) areas accounted for 28.46% of the total land use area in the 2010–2015 period. The human-induced LC change areas in UA dominated the LULC change areas before the year 2000; ecological restoration projects (ERP) areas accounted for the highest proportion of the total LULC change areas (≥29.36%), and after the year 2000, urbanization (UR) accounted for 34.62% of the total LULC change in the 2010–2015 period. The human-induced LC change areas in MA dominated the total LULC change areas before the year 2010; cropland expansion (CE) areas and ecological restoration projects (ERP) areas accounted for the most proportion of the total LULC change, and after the year 2010, urbanization (UR) areas accounted for 54.47% of the total LULC change in 2010–2015 period. The human-induced LC areas in DA dominated the LULC change areas; before the year 2005, cropland expansion (CE) areas accounted for the highest proportion of the total LULC change areas (≥44.15%), and after the year 2005, urbanization (UR) areas accounted for the largest proportion of human-induced LC change areas, reaching its maximum value (71.28%) in the 2010–2015 period. Details of LULC change areas in SA, UA, MA, and DA from 1980 to 2015 can be found in

Table A5. ESV change rate (billion USD/a) in different periods.

Region	ES	1980–1990	1990–1995	1995–2000	2000–2005	2005–2010	2010–2015
YRB	Provisioning services	−0.0414	0.0350	−0.0699	0.0547	0.0013	0.0247
	Regulating services	−0.3864	−0.1710	−0.0751	0.4801	−0.0382	0.1044
	Habitat Services	−0.0534	0.0015	−0.0035	−0.0146	0.0015	−0.0294
	Cultural & amenity services	−0.0332	−0.0103	0.0138	−0.0034	−0.0000	−0.0128
	Total	−0.5143	−0.1448	−0.1347	0.5168	−0.0355	0.0869
SA	Provisioning services	−0.0051	0.0042	−0.0010	−0.0035	0.0026	0.0005
	Regulating services	−0.0602	0.0652	−0.0090	−0.0450	0.0333	0.0044
	Habitat Services	−0.0059	0.0206	−0.0085	−0.0019	−0.0005	0.0004
	Cultural & amenity services	−0.0041	0.0103	−0.0024	−0.0004	−0.0002	0.0004
	Total	−0.0752	0.1003	−0.0210	−0.0508	0.0353	0.0056
UA	Provisioning services	−0.0185	−0.0234	0.0237	0.0158	−0.0078	0.0104
	Regulating services	−0.1987	−0.2354	0.2984	0.1501	−0.0695	0.1213
	Habitat Services	−0.0159	−0.0535	0.0738	−0.0120	−0.0017	−0.0161
	Cultural & amenity services	−0.0099	−0.0289	0.0441	−0.0047	−0.0018	−0.0075
	Total	−0.2430	−0.3411	0.4399	0.1493	−0.0808	0.1082
MA	Provisioning services	−0.0022	0.0679	−0.0861	0.0317	0.0023	0.0075
	Regulating services	−0.0041	0.2243	−0.4020	0.2508	−0.0171	−0.0417
	Habitat Services	−0.0091	0.0409	−0.0539	0.0062	0.0029	−0.0096
	Cultural & amenity services	−0.0053	0.0144	−0.0216	0.0040	0.0014	−0.0040
	Total	−0.0207	0.3475	−0.5636	0.2927	−0.0105	−0.0478
DA	Provisioning services	−0.0102	−0.0178	0.0041	0.0033	0.0038	0.0029
	Regulating services	−0.0767	−0.1883	0.0568	0.0561	0.0180	0.0066
	Habitat Services	−0.0150	−0.0075	−0.0119	−0.0045	0.0002	−0.0002
	Cultural & amenity services	−0.0092	−0.0049	−0.0067	−0.0017	0.0003	0.0000
	Total	−0.1111	−0.2185	0.0422	0.0531	0.0223	0.0093

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3.3. ESV from 1980 to 2015

3.3.1. Basin Scale

In 1980, the ESV of the Yellow River Basin (YRB) was 394.25 billion USD (Figure 4). From the perspective of ES types, the main ES in the YRB were regulating services (329.07 billion USD) and habitat services (38.17 billion USD), which accounted for 83.47% and 9.68% of ESV, respectively; from the perspective of watersheds, ESV in the YRB mainly come from the UA and MA, which accounted for 31.46% and 34.46% of the ESV in YRB, respectively. Compared with 1980, the ESV of the YRB in 2015 decreased by 0.94%, valued at 390.55 billion USD, with the main ES was still Regulating Services (83.65%) and habitat services (9.58%), and the ESV still mainly come from the UA (31.49%) and MA (34.76%) of the YRB.

The ACR of ESV in the YRB showed fluctuations or variations (Table A5). The maximum ACR of provisioning services value, regulating services value, and total ESV of all appeared in 2000–2005 period, while the maximum ACR of habitat services value and cultural and amenity services value appeared in 2005–2010 period and 1995–2000 period, respectively; the minimum ACR of ESV appeared in 1980–1990 period. Details of annual ESV change rate in YRB from 1980–2015 can be found in Table A5.

3.3.2. Watershed Scale

In 1980, the ESV in the SA was ranked as (Figure 4) regulating services > habitat services > provisioning services > cultural and amenity service; the ESV in the UA was ranked as regulating services > habitat services > cultural and amenity service > provisioning services; the ESV in the MA was ranked as regulating services > habitat services > cultural and amenity service > provisioning services; the ESV in the DA was ranked as regulating services > habitat services > cultural and amenity service > provisioning services. Compared with 1980, the total amount of ESV in the SA, UA, MA, and DA of YRB decreased by 0.62%, 0.85%, 0.09%, and 11.29% respectively, reaching 873.5, 164.44, 1801.4, and 18.42 billion USD, respectively. The ACR of ESV in the SA, UA, MA, and DA of YRB all showed fluctuations and variations (Table A5). The maximum ACR of ESV occurred in 1990–1995 period, 1995–2000 period, 1990–1995 period, and 2000–2005 period respectively, and the minimum ACR of ESV all appeared in 1980–1990 period. Details of annual ESV change rate in SA, UA, MA, and DA from 1980–2015 can be found in Table A5.

3.4. Elasticity of ESV Changes in Relation to LUCC

During 1980–2015 (Figure 5), the elasticity (E) of the YRB was always less than 0.5, and its minimum and maximum values appeared in 1995–2000 period and 2000–2005 period, respectively. This shows that the elasticity of ESV changes in response to LUCC in the YRB is always an inelastic process. In different watershed regions (

Table A6. Elasticity (E) from 1980 to 2015.

Region	1980–1990	1990–1995	1995–2000	2000–2005	2005–2010	2010–2015
YRB	0.0377	0.0053	0.0047	0.4162	0.1079	0.0968
SA	0.0450	0.0325	0.0061	0.4886	2.4434	0.3763
UA	0.0575	0.0413	0.0499	0.2892	0.6271	0.2561
MA	0.0039	0.0312	0.0499	0.8448	0.1112	0.1724
DA	0.2265	0.2622	0.0523	0.6849	0.6648	0.3526

), elasticity (E) of ESV changes in response to LUCC showed great differences. The elasticity of SA has changed significantly, with its minimum and maximum values appearing in the 1995–2000 period and 2005–2010 period, respectively; the minimum and maximum values of elasticity in UA appeared in 1990–1995 period and 2005–2010 period, respectively; the minimum and maximum values of elasticity in MA appeared in 1980–1990 period and 2000–2005 period; and the minimum and maximum values of elasticity in DA appeared in 1995–2000 period and 2000–2005 period, respectively. This shows that the SA, UA, MA, and DA of the YRB have all experienced the transformation process from inelasticity to elasticity to inelasticity, which mainly occurred in 2000–2005 period and 2005–2010 period. Details of elasticity (E) in YRB, SA, UA, MA, and DA from 1980 to 2015 can be found in Table A6.

4. Discussions

4.1. Implications for Sustainable Development

Humans can significantly alter environments through the conversion of natural vegetation, cropland, reservoir, urban, and other land uses [5]. This process can change the regional climate by adjusting the surface energy, water balance [58,59], and greenhouse gas emissions [60], and it can also lead to a decline in biodiversity through the loss, modification, and fragmentation of habitats [61], and ultimately affect ES and human well-being [6,7].

In the past 35 years, the cumulative area of human-induced LUCC in the YRB has reached 65.71 Mha (i.e., cumulative areas in Figure 3), which is close to the total area of YRB; the ESV of the YRB is between 387.71–394.25 billion USD (Figure 4), which is close to the ESV of the Tibet Autonomous Region in China [21]. Since the 1970s, vegetation restoration and reconstruction has been the important measure for ecological protection and soil erosion control in the YRB [62]. The area of ERP in the YRB in 1980–2000 period was 27.77 Mha (Figure 3), but the area of natural vegetation such as woodland, grassland, and wetland in the YRB declined from 1980 to 2000 (Figure 1), resulting in a decline in ESV (Figure 4). This contradiction shows that ERP has not reversed the decline of natural vegetation and ES in the YRB. The reason is that FR reduces the area of natural vegetation [63]. For a long time, agricultural activities have been an important human activity in the YRB in response to population growth [45,63]. From 1980 to 2000, the area of FR in the YRB reached 25.58 Mha. A large amount of natural vegetation has been replaced with cropland, coupled with the land degradation (about 6.58 Mha) caused by unreasonable human activities such as overexploitation of [64] and overuse of water resources [65]. Eventually, the area of natural vegetation in the YRB decreased from 1980 to 2000.

Since the year 2000, benefit from the implementation of the “grain for green program” (Table A7), the natural vegetation of the YRB, especially the natural vegetation in the UA and MA (i.e., the Loess Plateau) has been significantly improved [66,67]. The area of cropland in the YRB decreased by 0.51 Mha from 2000 to 2015, the area of forest land increased by 0.27 Mha (Figure 1), and ESV increased by 2.84 billion USD (Figure 4). It is worth noting that the area of ERP in 2000–2015 period was only 0.57 Mha, and the elasticity coefficient of ESV to LULC change in 2000–2015 period increased significantly compared with that in the 1980–2000 period (Figure 5). This shows that under the guidance of inappropriate policies, a large amount of LUCC may be harmful to the ecosystem, while under the guidance of appropriate policies, a small amount of LULC change may be beneficial to the ecosystem.

In addition, UR is also an important human-induced LULC change process in YRB, and the area of UR gradually increased from 1980 to 2015 (Figure 1). It is foreseeable that the UR process will become the most important LULC change process in the YRB in the future, and the ecological security issues that may arise from this process need more attention [65,68].

4.2. Uncertainties in ESV Assessment

ESV assessment is usually regarded as an important tool for sustainable land development decision-making and trade-offs [20,69,70]. In this study, the benefit transfer method proposed by Xie et al. [21] was used to calculate the ESV of the YRB. However, recent studies have calculated that the ESV in the YRB is 264.42–266.4 billion USD [71] or 78.17–96.39 billion USD [72]. There is a big difference between these results. It is worth noting that, in these studies, different versions of the value equivalent method [56,73] were used to calculate ESV, and different methods were used to revise the key coefficients [71,72]. We also tried to add revisions to the CPI index in the calculation of ESV to obtain comparable ESV values in different periods (Figure A2). Based on this calculation, the ESV of the YRB increased from US$80.53 billion to US$452.43 billion from 1980 to 2015. However, this result to a large extent only reflects the influence of economic factors and conceals the impact of LULC changes on ESV. Therefore, the result was not used in the study of ESV on LULC changes in the YRB. This shows that the benefit transfer method has huge uncertainties in calculating ESV [74]. Some studies have conducted sensitivity-analysis based on elasticity-sensitivity coefficient to study the impact of changes in ES coefficients on ESV assessment [75,76]. However, Aschonitis et al. [77] pointed out the elasticity-sensitivity coefficient should be used for ranking the importance of land use and land cover (LULC) changes rather than assessing the robustness and sensitivity of the ES coefficients.

ES trade-off refers to the situation where one type of ES increases while the other type of ES decreases [78]. In the current ES research community, ES trade-offs are often regarded as key indicators that guide land policy and management to support the provision of ES [79]. It is generally accepted that all decisions that involve trade-offs involve valuation, whether implicitly or explicitly [8,22]. Due to the ability to examine multiple ESs at the same time, ESV assessment has been used in many studies to study trade-offs between different ESs [80,81]. Since the ES trade-offs can be studied from the perspective of relative value, this can reduce the uncertainty of ESV to a certain extent [79]. However, affected by the temporal and spatial scales [79] and landscape history [82], the uncertainty of ES trade-offs are not well understood.

It is generally accepted that when ESV assessment is used to raise public awareness rather than specific policy guidance, lower confidence is acceptable [55,62]. However, it has been reported that about 82% of ecosystem services mapping studies have cited decision-making purposes [63]. Therefore, it is necessary to construct an uncertainty evaluation framework to increase confidence in ESV assessments and trade-offs to support decision-making in the future.

4.3. Limitations

Human-induced land cover (LC) changes are only defined by land cover (LC) transition, but the impact of different categories and intensities of land use on land cover was not considered. For example, the grazing area and grazing intensity will significantly affect the grassland, but it does not necessarily cause the grassland transfer to other land cover type. This will be one of the important research directions for future LULC changes.

Time-series land use and land cover (LULC) datasets are used to analyze the response of ecosystem services to LULC changes in this study. However, due to the coarse temporal resolution (≥5 years), the time lags of LULC changes to ecosystem services (ES) are unable to be detected. The time lags of ES’s responses to LULC changes is the key direction of future land change sciences.

5. Conclusions

Land use and land cover (LULC) transition matrix and benefit transfer methods were used to study LULC changes in the Yellow River Basin (YRB) and their impact on ecosystem services values (ESV). From 1980 to 2015, the area of urban land, water bodies, and urban lands in the YRB increased by 45.10%, 3.44%, and 2.58%, while wetland, bare land, grassland, and farmland decreased by 7.18%, 2.27%, 1.31%, and 1.29%, respectively. The LULC change area in the YRB showed an increasing trend before the year 2000 and a decreasing trend after the year 2000. From 1990 to 2015, the human-induced LC change dominates in YRB, and the natural-process LC in the YRB accounted for less than 25% of the total LULC change areas. The proportion of ecological restoration projects (ERP) areas was the highest before the year 2010, the proportion of urbanization (UR) was the highest in 2010–2015. From 1980 to 2015, the ESV of the YRB decreased by 0.94%, valued at 390.55 billion USD, and the elasticity of ESV changes in response to LUCC in the YRB is always an inelastic process. We found that under the guidance of inappropriate policies, a large amount of LUCC may be harmful to the ecosystem, while under the guidance of appropriate policies, a small amount of LULC change may be beneficial to the ecosystem.