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Citation: Cheng, Y.; Chen, Y. Spatial
and Temporal Characteristics of Land
Use Changes in the Yellow River
Basin from 1990 to 2021 and Future
Predictions. Land 2024,13, 1510.
https://doi.org/10.3390/land13091510
Academic Editor: Antonio
Miguel Martínez-Graña
Received: 28 July 2024
Revised: 14 September 2024
Accepted: 16 September 2024
Published: 18 September 2024
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land
Article
Spatial and Temporal Characteristics of Land Use Changes in the
Yellow River Basin from 1990 to 2021 and Future Predictions
Yali Cheng and Yangbo Chen *
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China;
chengyli6@mail2.sysu.edu.cn
*Correspondence: eescyb@mail.sysu.edu.cn; Tel.: +86-20-8411-4269
Abstract: Studying spatial and temporal characteristics of land use changes and the driving factors in
the Yellow River Basin as well as simulating and predicting future land use is crucial for resource
management, ecological protection, and regional sustainable development in the Yellow River Basin.
Based on the China Land Cover Dataset (CLCD) of the Yellow River Basin from 1990 to 2021, this
study employs various methods such as the Mann–Kendall test and sliding t-test, land use dynamics,
the land use transfer matrix, the standard deviation ellipse, the center of gravity migration model,
and a geographic detector to explore the spatial and temporal characteristics of land use changes
and driving forces in the Yellow River Basin over the past 30 years. Additionally, the study predicts
land use types in the study area for the year of 2030 by using the Future Land Use Simulation
(FLUS) model. The results show the following: (1) From 1990 to 2021, the area of forest, grassland,
water, and impervious surfaces increased significantly, while the area of cropland, shrub, barren
land, and wetlands decreased significantly. The most actively changing land use types are cropland,
grassland, barren land, and impervious surfaces. (2) The center of gravity for shrub and impervious
surfaces shifted westward, while wetlands showed a trend of obvious concentrated distribution,
and the remaining land use types exhibited stable directional distributions. (3) Economic factors
had a stronger driving effect on land use changes than topographic and climatic factors. The land
use changes in the Yellow River Basin are influenced by the coordinated driving forces of multiple
factors. (4) In 2030, the main land use types in the Yellow River Basin are still expected to be cropland,
grassland, and forest. However, there will be a significant expansion of impervious surfaces and
forest land, with substantial encroachment on cropland and grassland.
Keywords: land use change; Yellow River Basin; FLUS model; standard deviation ellipse;
geographic detector
1. Introduction
Land cover (LC) is a fundamental parameter for environmental and climate change
research [
1
]. Land use/cover change (LUCC) refers to the transformation of land use types
and structures within specific temporal and spatial ranges, encompassing changes in the
quantity, spatial pattern, and function of land use. LUCC directly reflects the interaction
between human activities and the natural environment [
2
] and impacts regional economic
and ecological environmental quality [
3
]. The “International Geosphere and Biosphere
Program” (IGBP) and the “International Human Dimensions Programme on Global En-
vironmental Change” (IHDP) listed LUCC as a core content of global change research
in 1993 [
4
,
5
]. The study of the driving forces of land use change involves analyzing the
factors that lead to changes in land use patterns and purposes, as well as the underling
mechanisms driving these changes [
6
]. In recent decades, rapid economic development
and population growth have led to significant land use changes, exacerbating the conflict
between human needs and land resources [
7
]. Therefore, studying land use change and
its driving forces is crucial for understanding the current status of land use and achieving
coordinated regional development.
Land 2024,13, 1510. https://doi.org/10.3390/land13091510 https://www.mdpi.com/journal/land
Land 2024,13, 1510 2 of 20
In recent years, the rapid development of remote sensing technology has provided
a wealth of satellite image data products for the study of land use change [
8
], including
FROM_GLC [
9
], Globeland30 [
10
], MCD12Q1 [
11
], ESACCI_LC [
12
], and CLCD [
1
]. Among
these, the China Land Cover Dataset (CLCD) stands out, as it is based on all available
Landsat data on Google Earth Engine (GEE). Professor Huang Xin constructed spatiotem-
poral features and combined them with a random forest classifier to obtain classification
results. He also proposed a post-processing method that includes spatiotemporal filtering
and logical reasoning to further improve the spatiotemporal consistency of the CLCD. Wu
and Shao demonstrated that the CLCD dataset has higher recognition accuracy [
13
–
15
].
Therefore, this paper utilizes the CLCD dataset to analyze LUCC.
Many scholars have employed various methods to study land use change and its
driving forces, as well as to simulate and predict land use. They have used methods
such as land use dynamics [16–18], the land use transfer matrix [19], and geo-information
maps [
20
,
21
] to analyze the characteristics of land use quantity and structural changes.
Chord diagrams and Sankey diagrams have been utilized to visualize transfer matrices.
The gravity center migration model [
22
,
23
] and the landscape pattern index [
24
,
25
] have
been applied to study changes in the spatial patterns of land use. Additionally, methods
like principal component analysis [
26
–
28
] and geographic detectors [
29
–
31
] have been
used to explore the driving forces of land use change. Compared with traditional driving
force analysis methods, geographic detectors can not only identify complex non-linear
relationships but also explore the interactions between different driving factors. The FLUS
model is often used to simulate and predict future land use changes. By integrating artificial
neural networks (ANNs) and various geographic information system (GIS) technologies, it
can dynamically adapt to different driving factors of land use change, thereby improving
the accuracy of future land use change simulations [32].
The Yellow River Basin is an important ecological barrier area in China, shouldering
the dual responsibilities of ecological protection and economic development [
33
]. Most
existing studies focus on specific parts of the Yellow River Basin, such as the Ten Tributaries
Basin [
34
], the Henan section [
35
], the Ningxia–Inner Mongolia section [
36
], and the water
conservation zone [
37
]. There is a lack of research on the entire region, and comprehensive
analyses of the spatial and temporal changes in land use, their driving forces, and future
simulation predictions for the Yellow River Basin are scarce. Building on previous research,
this study takes the entire Yellow River Basin as the study area, based on the CLCD dataset
from 1990 to 2021. The Yellow River Basin is divided into upper, middle, and lower reaches,
and the study period is divided into three phases using mutation analysis. This study
combines land use dynamics and a transfer matrix, visualizing the land use transfer matrix
with a Sankey diagram, to comprehensively analyze the quantity and structural changes
in land use in the Yellow River Basin. Furthermore, this study uses the center of gravity
migration model and standard deviation ellipse to analyze changes in spatial patterns,
and employs a geographic detector to analyze the driving effects of economic, climate,
and topographic factors on LUCC in the Yellow River Basin. Based on these analyses, the
FLUS model is used to predict land use changes in the study area for 2030. This research
contributes to a comprehensive understanding of the dynamic evolution of land use in the
basin and provides references for reconciling human–environment conflicts and promoting
sustainable development in the Yellow River Basin.
2. Materials and Methods
2.1. Study Area
The Yellow River is the second largest river in China, originating from the Yugur Basin
at the northern foot of the Bayankala Mountains on the Qinghai–Tibet Plateau. It flows west
to east through nine provinces and regions, including Qinghai, Sichuan, Gansu, Ningxia,
Shaanxi, Henan, and Shandong, finally emptying into the Bohai Sea. The Yellow River Basin
is illustrated in Figure 1. The total basin area is approximately 79.5
×
10
4
km
2
, accounting
for around 8% of the country’s total area. The Yellow River Conservancy Commission of the
Land 2024,13, 1510 3 of 20
Ministry of Water Resources divides the Yellow River into three sections: from the source
of the river to Hekou Town in Tokto County, Inner Mongolia, as the upper reaches, with a
basin area of 42.8
×
10
4
km
2
, from Hekou Town to the Huayuankou area of Taohuayu in
Henan as the middle reaches, with a basin area of 34.4
×
10
4
km
2
, and from Taohuayu to
the estuary as the lower reaches, with a basin area of 2.3
×
10
4
km
2
. The terrain of the basin
is high in the west and low in the east, spanning China’s first, second, and third terraces.
The upper reaches are primarily mountainous, while the lower reaches consist mainly of
plains and hills. The basin mainly belongs to the southern temperate, mid-temperate, and
plateau climate zones. Annual precipitation and average annual temperature increase from
northwest to southeast, and the level of economic development, also show an increasing
trend from the upper reaches to the middle and lower reaches.
Land 2024, 13, x FOR PEER REVIEW 3 of 20
2. Materials and Methods
2.1. Study Area
The Yellow River is the second largest river in China, originating from the Yugur Basin
at the northern foot of the Bayankala Mountains on the Qinghai–Tibet Plateau. It flows west
to east through nine provinces and regions, including Qinghai, Sichuan, Gansu, Ningxia,
Shaanxi, Henan, and Shandong, finally emptying into the Bohai Sea. The Yellow River Basin
is illustrated in Figure 1. The total basin area is approximately 79.5 × 104 km2, accounting for
around 8% of the country’s total area. The Yellow River Conservancy Commission of the
Ministry of Water Resources divides the Yellow River into three sections: from the source
of the river to Hekou Town in Tokto County, Inner Mongolia, as the upper reaches, with a
basin area of 42.8 × 104 km2, from Hekou Town to the Huayuankou area of Taohuayu in
Henan as the middle reaches, with a basin area of 34.4 × 104 km2, and from Taohuayu to the
estuary as the lower reaches, with a basin area of 2.3 × 104 km2. The terrain of the basin is
high in the west and low in the east, spanning China’s first, second, and third terraces. The
upper reaches are primarily mountainous, while the lower reaches consist mainly of plains
and hills. The basin mainly belongs to the southern temperate, mid-temperate, and plateau
climate zones. Annual precipitation and average annual temperature increase from north-
west to southeast, and the level of economic development, also show an increasing trend
from the upper reaches to the middle and lower reaches.
Figure 1. Location of the Yellow River Basin.
2.2. Data
The land-use-type data from 1990 to 2021 in this paper adopt the CLCD data pub-
lished by Professor Huang Xin of Wuhan University, with a spatial resolution of 30 m
(available at https://zenodo.org/records/5816591#.ZAWM3BVBy5c); China’s administra-
tive division data come from CnOpenData (available at https://www.cnopendata.com/);
Figure 1. Location of the Yellow River Basin.
2.2. Data
The land-use-type data from 1990 to 2021 in this paper adopt the CLCD data published
by Professor Huang Xin of Wuhan University, with a spatial resolution of 30 m (available
at https://zenodo.org/records/5816591#.ZAWM3BVBy5c accessed on 15 September 2024);
China’s administrative division data come from CnOpenData (available at https://www.
cnopendata.com/); the river dataset is provided by the Yellow River Data Center (available
at http://huanghe.ncdc.ac.cn); the DEM data use the SRTM data from the Geospatial
Data Cloud platform (available at https://www.gscloud.cn/), with a spatial resolution
of 90 m, and the slope data are from the DEM data processing; population and GDP data
are obtained from the Resource and Environmental Science Data Platform (available at
https://www.resdc.cn/), with a spatial resolution of 1 km; temperature and precipitation
data are from National Earth System Science Data Center, with a spatial resolution of 1 km
(available at http://www.geodata.cn).
Land 2024,13, 1510 4 of 20
2.3. Methodologies
2.3.1. Mann–Kendall and Sliding t-Test
The Mann–Kendall (M-K) test is a quantitative non-parametric method that is par-
ticularly useful for analyzing time series data. It does not require the sample to follow a
specific distribution, can handle missing data without affecting the results, and is robust
against a few outliers. The M-K test is highly practical and is used to detect trends and
identify whether mutations occur in the time series data [
38
]. In this study, the M-K test is
conducted at a significance level of 0.05. If the absolute value of the statistical variable Z
is greater than 1.96, the trend is considered significant; otherwise, it is insignificant. The
sliding t-test is a statistical method used to detect significant changes or “mutations” in data
over time by comparing the average values of different time periods within a dataset [
39
].
This paper combines the M-K test and sliding t-test to analyze the area change trends of
land use types in the Yellow River Basin and to identify the mutation point years.
2.3.2. Land Use Dynamics and Transfer Matrix
Single land use dynamics can directly reflect the average change rate of each land use
type area in the Yellow River Basin over a specific period. Its expression is as follows [20]:
K=Um−Un
Un
×1
t×100% (1)
where
K
is the single dynamic degree;
Un
is the area (km
2
) in the first year of the study
period;
Um
is the area in the last year of the study period; and
t
is the length of the
study period.
Comprehensive land use dynamics can reflect the average change rate of the overall
land use type in the Yellow River Basin within a certain period. Its expression is represented
as follows [40]:
LC =∑n
a=1∆LUa−b
∑n
a=1LUa×1
t×100% (2)
where
LC
is the comprehensive land use dynamics during the study period;
LUa
is the
area (km
2
) in the first year of the
a
-th land use type during the study period;
∆LUa−b
is the
absolute value of the net change area from the
a
-th land use type to the
b
-th land use type
during the study period;
t
is the length of the study period; and
n
is the number of land
use types.
The land use transfer matrix is a method used to analyze land use changes. It can
clearly show the increase or decrease in various types of land use in the form of a matrix,
and can quantitatively describe the transfer amount and transfer direction between different
land use types, which is helpful for accurate analysis of land use changes. Its expression is
typically represented as follows [41]:
Sij =
S11 S12
S21 S22
· · · S1n
· · · S2n
.
.
..
.
.
Sn1Sn2
....
.
.
· · · Snn
(3)
where
Sij
is the area of the
i
-th land use type in the initial period of the study period
converted to the j-th land use type at the end, and n is the number of land use types.
2.3.3. Standard Deviational Ellipse and Center of Gravity Migration Model
The size of the standard deviation ellipse can be used to characterize the degree of
concentration of data distribution. The distribution direction of the major axis reflects
the main trend of spatial distribution, and the length of the minor axis reflects the degree
of concentration. This paper uses the flattening of the ellipse, that is, the ratio of the
difference between the major and minor axes to the major axis, to judge whether the
Land 2024,13, 1510 5 of 20
directionality of data distribution is obvious. The larger the flattening of the ellipse, the
more obvious the directionality of data distribution, which can be used to reflect the
distribution characteristics of the spatial pattern of land use types [42,43].
The center of gravity migration model can analyze the movement characteristics of the
center of gravity for each land use type within a study area. This model is used to illustrate
how the center of gravity of a specific land use type changes over time in spatial evolution
processes. The formula for calculating the center of gravity migration rate is as follows [
44
]:
V(i,m−n)=H×q(Xm−Xn)2+(Ym−Yn)2
tm−tn(4)
where
Xm
,
Ym
,
Xn
, and
Yn
are the longitude and latitude coordinates of the center of gravity
of the land use type in the
m
-th and
n
-th years respectively;
H
is a constant with a value of
111.111 km;
V(i,m−n)
is the migration rate of the center of gravity of the ith land use type
from nto myears; and tmand tnare the last and first years of the study.
2.3.4. Geographical Detector
The geographical detector method is widely recognized for its ability to detect spatial
heterogeneity, making it highly applicable in studying the influencing factors of natural,
economic, and social phenomena. Unlike many other methods, geographical detector
analysis is less constrained by specific assumptions and demonstrates notable advantages
in handling diverse types of data. In this paper, the factor detection module of the geo-
graphical detector is utilized to assess the explanatory power of each influencing factor
on land use change within the Yellow River Basin. Additionally, the interactive detection
module is employed to analyze how these factors interact and collectively influence land
use change. Its expression is as follows [45]:
q=1−1
Nσ2
L
∑
h=1
Nhσ2
h(5)
where
q
represents the explanatory power of the factor on land use change, and its value
range is 0 to 1. The larger the
q
value, the stronger the explanatory power of the factor
on the results of land use change;
L
represents the number of types of impact factors;
h
represents the type quantity;
N
represents the number of samples in the study area;
Nh
represents the number of samples of type
h
; and
σ2
h
and
σ2
represent the discrete variance
of the type quantity hand the variance of land use in the study area, respectively.
In this paper, two core factors from each of the three aspects—economy, climate, and
topography—are selected for analysis using the geographic detector method. These factors
include population and GDP (economic aspect), temperature and precipitation (climate
aspect), and slope and DEM (topography aspect). These factors are processed to match the
resolution of the land use data. Subsequently, 2000 random sampling points are selected,
and both the land use data and corresponding factor data are extracted at these points.
Since the independent variables in the geographic detector must be type quantities, it is
necessary to discretize the six factors using the equal spacing method, quantile method,
geometric interval method, etc. Different discrete methods and number of categories will
have a significant impact on the results of the geographic detector model. Therefore, the
optimal discrete method and number of categories in this paper are automatically calculated
according to the “optidisc()” function provided in the GD package of R language [
46
]. This
function automatically calculates the optimal discretization method and the appropriate
number of categories based on the data, ensuring robust and objective results in the analysis.
2.3.5. FLUS Model
The FLUS model enhances the traditional cellular automata model by incorporating
an adaptive inertia competition mechanism based on roulette wheel selection and using
Land 2024,13, 1510 6 of 20
neural networks to obtain suitability probabilities [
47
]. This approach effectively addresses
the competitive relationships among various land use types, improves simulation accuracy,
and offers high computational efficiency and a wide simulation scope. The FLUS model
primarily consists of the following three modules [48]:
(1) ANN-based Suitability Probability Estimation: Based on 6 driving factors and 2010
land use data, we used an artificial neural network (ANN) algorithm. The sampling pattern
was set to random sampling of 10/1000, with the number of hidden layers in the neural
network set to 12. This approach calculated the suitability probabilities of 9 different land
use types for each pixel within the study area. These probabilities determine the potential
distribution of each land use type across the region.
(2) Self-Adaptive Inertia and Competition-Mechanism-Based Cellular Automata: The
conversion cost matrix indicates the possibility of conversion between different land use
types. A value of 1 is set if conversion is possible, and 0 if not. In this study, all matrix
values were set to 1 to allow mutual conversion between all land use types, reflecting the
actual land use situation in the Yellow River Basin. The neighborhood weight factor ranges
from 0 to 1, with values closer to 1 indicating a stronger expansion capability of the land
use type. Based on previous research and multiple experiments [
49
,
50
], the neighborhood
weight factors were adjusted and ultimately set as shown in Table 1.
Table 1. Neighborhood factor parameters of different land use types.
Land Use Type Neighborhood Factor Parameters
Cropland 0.4
Forest 0.6
Shrub 0.2
Grassland 0.3
Water 0.2
Snow/ice 0.1
Barren 0.1
Impervious 0.8
Wetland 0.3
(3) To validate the simulation accuracy, we used the 2010 land use data and driving
factors to simulate the 2020 land use data, and then performed a Kappa test on the simulated
2020 data and the actual 2020 data to verify the model’s predictive performance. A Kappa
coefficient greater than 0.6 indicates that the model has good overall performance and can
be used to predict future land use data [51].
3. Results and Discussion
3.1. Trend and Mutation Test Analysis
The trend test and mutation test were performed at a given significance level of
α= 0.05
, and the results are shown in Table 2. The area of forest, grassland, water, and
impervious surfaces showed a clear increasing trend. The area of snow and ice increased,
but the trend was not obvious. The area of cropland, shrub, barren land, and wetlands
exhibited a significant decrease. After conducting both the Mann–Kendall (M-K) and
sliding t mutation tests, it was determined that 2002 and 2013 were the mutation point
years. Consequently, the research period from 1990 to 2021 is divided into three distinct
stages: 1990–2002, 2002–2013, and 2013–2021. This segmentation allows for a more detailed
analysis of land use dynamics and changes in the Yellow River Basin over time.
Land 2024,13, 1510 7 of 20
Table 2. Area change trend and mutation test results of various land use types in the Yellow River
Basin from 1990 to 2021.
Land Use Type
M-K Trend Test Mutation Point Test
Statistics Z Value Trend (α= 0.05) M-K Test (α= 0.05) Sliding tTest
(α= 0.05)
Cropland −7.119 significant decrease 1996, 2002, 2015
Forest 8.027 significant rise 2013, 2017
Shrub −5.141 significant decrease 1996, 2002, 2005
Grassland 4.914 significant rise 2002, 2004, 2009, 2013
Water 5.530 significant rise 1996, 2008, 2013
Snow/ice 0.795 insignificant rise 2003 1993, 2005, 2013
Barren −6.697 significant decrease 1998, 2002, 2011, 2013
Impervious 8.027 significant rise 2002, 2013
Wetland −2.514 significant decrease 1995 2001, 2015
3.2. Analysis of the Change in Land Use Quantity Structure
The analysis focused on key years in the Yellow River Basin: the initial study year of
1990, the final year of 2021, and the mutation years of 2002 and 2013. Figure 2illustrates
the distribution of land use types across these pivotal periods. The land use types in the
Yellow River Basin are mainly grassland, cropland, and forest, accounting for around 57%,
25%, and 10%, respectively. Shrubs are mainly concentrated in the southwestern forest area
and its periphery. Snow, ice, and wetlands are mainly located in the western part of the
basin. Barren land is primarily located in the northern part of the upper reaches. Wetlands
are mainly distributed in the southern part of the upper reaches. Impervious surfaces are
prominent in the southeastern part of the basin.
Land 2024, 13, x FOR PEER REVIEW 7 of 20
the trend was not obvious. The area of cropland, shrub, barren land, and wetlands exhib-
ited a significant decrease. After conducting both the Mann–Kendall (M-K) and sliding t
mutation tests, it was determined that 2002 and 2013 were the mutation point years. Con-
sequently, the research period from 1990 to 2021 is divided into three distinct stages: 1990–
2002, 2002–2013, and 2013–2021. This segmentation allows for a more detailed analysis of
land use dynamics and changes in the Yellow River Basin over time.
Table 2. Area change trend and mutation test results of various land use types in the Yellow River
Basin from 1990 to 2021.
Land Use Type
M-K Trend Test
Mutation Point Test
Statistics Z Value
Trend (α = 0.05)
M-K Test (α = 0.05)
Sliding t Test
(α = 0.05)
Cropland
−7.119
significant decrease
1996, 2002, 2015
Forest
8.027
significant rise
2013, 2017
Shrub
−5.141
significant decrease
1996, 2002, 2005
Grassland
4.914
significant rise
2002, 2004, 2009, 2013
Water
5.530
significant rise
1996, 2008, 2013
Snow/ice
0.795
insignificant rise
2003
1993, 2005, 2013
Barren
−6.697
significant decrease
1998, 2002, 2011, 2013
Impervious
8.027
significant rise
2002, 2013
Wetland
−2.514
significant decrease
1995
2001, 2015
3.2. Analysis of the Change in Land Use Quantity Structure
The analysis focused on key years in the Yellow River Basin: the initial study year of
1990, the final year of 2021, and the mutation years of 2002 and 2013. Figure 2 illustrates
the distribution of land use types across these pivotal periods. The land use types in the
Yellow River Basin are mainly grassland, cropland, and forest, accounting for around
57%, 25%, and 10%, respectively. Shrubs are mainly concentrated in the southwestern for-
est area and its periphery. Snow, ice, and wetlands are mainly located in the western part
of the basin. Barren land is primarily located in the northern part of the upper reaches.
Wetlands are mainly distributed in the southern part of the upper reaches. Impervious
surfaces are prominent in the southeastern part of the basin.
Figure 2. Distribution map of land use types in the Yellow River Basin in typical years from 1990 to
2021.
Figure 2. Distribution map of land use types in the Yellow River Basin in typical years from 1990
to 2021.
The proportion of land use in each basin section is shown in Figure 3. The upper
reaches are predominantly grassland, covering approximately 77% of the area. The middle
reaches are marked by a mix of grassland (around 41%) and impervious surfaces (around
35%), and the lower reaches are dominated covered by cropland (around 73%) and im-
pervious surfaces (around 18%). This shows that the upper reaches focus on ecological
Land 2024,13, 1510 8 of 20
protection, the lower reaches emphasize agricultural production and economic activities,
and the middle reaches are a transition area between the two.
Land 2024, 13, x FOR PEER REVIEW 8 of 20
The proportion of land use in each basin section is shown in Figure 3. The upper
reaches are predominantly grassland, covering approximately 77% of the area. The mid-
dle reaches are marked by a mix of grassland (around 41%) and impervious surfaces
(around 35%), and the lower reaches are dominated covered by cropland (around 73%)
and impervious surfaces (around 18%). This shows that the upper reaches focus on eco-
logical protection, the lower reaches emphasize agricultural production and economic ac-
tivities, and the middle reaches are a transition area between the two.
Figure 3. Land use type proportion in each river basin in typical years.
The dynamics of land use in the Yellow River Basin from 1990 to 2021 are illustrated
in Table 3. Over this period, comprehensive land use in different sub-basins showed an
upward trend, indicating increasingly drastic land use changes. The middle reaches ex-
hibit the highest dynamics, with an average rate of 1.24%, highlighting the most active
land use changes in this area. For the entire Yellow River Basin, the dynamics of wetlands
in 1990–2002, 2002–2013, and 2013–2021 were −6.8%, 7.5%, and 9.06%, respectively. This
represents the largest growth rate, suggesting that the “National Wetland Protection Pro-
ject Plan (2002–2030)” enacted in 2003 has significantly contributed to wetland restoration
and protection. Between 2002 and 2013, cropland, forest, shrub, water, snow/ice, and bar-
ren land exhibited the highest dynamics, at −0.65%, 0.91%, −2.13%, 2.08%, 3.42%, and
−2.01%, respectively. The dynamics of impervious surfaces showed a decreasing trend,
with rates of 4.3%, 3.94%, and 2.10% over the three periods, indicating that impervious
surfaces continued to expand from 1990 to 2021, but the rate of change slowed down. In
the upper reaches of the Yellow River, the dynamics of grassland were low and the
changes were not obvious. Although the area decreased slightly, the change rate was gen-
tle. The dynamics of barren land were −0.81%, −1.20%, and 0.39%, respectively. Before
2013, the area of barren land decreased and was continuously developed and utilized at
an accelerated speed, but it showed signs of recovery afterward. Since wetlands are pri-
marily distributed in the upstream area, the dynamics of upstream wetlands are the same
Figure 3. Land use type proportion in each river basin in typical years.
The dynamics of land use in the Yellow River Basin from 1990 to 2021 are illustrated
in Table 3. Over this period, comprehensive land use in different sub-basins showed an
upward trend, indicating increasingly drastic land use changes. The middle reaches exhibit
the highest dynamics, with an average rate of 1.24%, highlighting the most active land
use changes in this area. For the entire Yellow River Basin, the dynamics of wetlands in
1990–2002, 2002–2013, and 2013–2021 were
−
6.8%, 7.5%, and 9.06%, respectively. This
represents the largest growth rate, suggesting that the “National Wetland Protection Project
Plan (2002–2030)” enacted in 2003 has significantly contributed to wetland restoration and
protection. Between 2002 and 2013, cropland, forest, shrub, water, snow/ice, and barren
land exhibited the highest dynamics, at
−
0.65%, 0.91%,
−
2.13%, 2.08%, 3.42%, and
−
2.01%,
respectively. The dynamics of impervious surfaces showed a decreasing trend, with rates
of 4.3%, 3.94%, and 2.10% over the three periods, indicating that impervious surfaces
continued to expand from 1990 to 2021, but the rate of change slowed down. In the upper
reaches of the Yellow River, the dynamics of grassland were low and the changes were not
obvious. Although the area decreased slightly, the change rate was gentle. The dynamics of
barren land were
−
0.81%,
−
1.20%, and 0.39%, respectively. Before 2013, the area of barren
land decreased and was continuously developed and utilized at an accelerated speed, but
it showed signs of recovery afterward. Since wetlands are primarily distributed in the
upstream area, the dynamics of upstream wetlands are the same as those of the entire
basin. The dynamics of impervious surfaces reached a peak value of 6.88% in 2002–2013.
In the middle reaches of the Yellow River, the dynamics of forest were 0.40%, 0.96%, and
0.85%, respectively, with a rapid growth rate and obvious expansion. The dynamics of
shrub were 0.57%,
−
5.16%, and
−
2.6%, respectively, showing a shrinking area. Grassland
expanded before 2013 and then contracted. In the lower reaches of the Yellow River, the
Land 2024,13, 1510 9 of 20
dynamics of cropland were
−
0.33%,
−
0.59%, and
−
0.56% over the three periods, indicating
an accelerated reduction rate and large-scale occupation. The dynamics of impervious
surfaces were 2.82%, 2.67%, and 2.14%, respectively, showing continuous expansion, albeit
at a slower pace.
Table 3. Dynamics of land use in different stages of each river basin from 1990 to 2021.
Area Period
K (%)
LC (%)
Cropland Forest Shrub
Grassland
Water Snow/Ice Barren Impervious Wetland
Entire
Yellow
River
1990–2002 −0.29 0.46 −0.14 0.10 −0.40 −0.19 −1.20 4.30 −6.80 0.95
2002–2013 −0.65 0.91 −2.13 0.16 2.08 3.42 −2.01 3.94 7.50 0.98
2013–2021 −0.03 0.82 −0.84 −0.25 0.68 −3.68 0.09 2.10 9.06 1.04
Upper
Yellow
River
1990–2002 0.59 0.87 −0.43 −0.02 −0.15 −0.19 −0.81 4.23 −6.80 0.74
2002–2013 −0.47 0.64 −0.72 0.10 1.96 3.42 −1.20 6.88 7.50 0.76
2013–2021 0.28 0.61 −0.46 −0.13 0.51 −3.68 0.39 2.74 9.06 0.85
Middle
Yellow
River
1990–2002 −0.55 0.40 0.57 0.34 −0.97 - * −2.29 5.47 −7.69 1.21
2002–2013 −0.73 0.96 −5.16 0.28 2.41 27.03 −4.95 4.12 72.73 1.24
2013–2021 −0.04 0.85 −2.60 −0.47 0.16 −11.90 −1.51 1.91 11.11 1.26
Lower
Yellow
River
1990–2002 −0.33 0.94 −6.59 −2.16 −0.39 - −2.45 2.82 - 0.64
2002–2013 −0.59 0.94 −5.69 −2.02 2.07 - −1.03 2.67 - 0.74
2013–2021 −0.56 0.29 48.31 −3.19 2.17 - −9.74 2.14 - 0.81
* Indicates that there is no such land use type.
The land use data from two periods were spatially superimposed and rasterized in
ArcGIS, and a Sankey diagram of land use transfer was created using Origin visualization,
as shown in Figure 4. From the basin-wide perspective, cropland, grassland, barren land,
and impervious surfaces changed most actively. From 1990 to 2002, the largest net decrease
was in cropland, which decreased by 7474 km
2
, followed by barren land, which decreased
by 5967 km
2
. The reduced cropland was mainly converted to grassland (83.17%), and
the reduced barren land was primarily converted into grassland (92.97%). Grassland saw
the largest net increase of 5224 km
2
, mainly coming from cropland (67.45%) and barren
land (26.48%). This change was related to the launch of the project of returning cropland
to forest and grassland in 1999, converting large areas of sloping cropland unsuitable
for cultivation into herbaceous plants to restore the grassland ecosystem. From 2002 to
2013, the largest net decrease was in barren land, with a reduction of 7846 km
2
, mostly
converted to grassland (94.14%). Grassland experienced the largest net increase of 8115 km
2
,
converted from cropland and barren land. From 2013 to 2021, the largest net decrease
was in grassland (9262 km
2
), with the reduced grassland mainly transitioning to cropland
(60.08%) and barren land (18.09%). This shift was due to the rapid population growth in
China during this period, leading to a sharp increase in food demand and the expansion
of cropland. The largest net increase during this period was in forest (5688 km
2
), which
was transferred from grassland (72.9%) and cropland (20.40%). The rapid expansion of
forest was linked to the project of returning cropland to forest and grassland, as well as the
construction of the shelterbelt system in the middle reaches of the Yellow River starting
in 1995 and subsequent forest ecological protection measures [
33
]. Additionally, cropland
was increasingly occupied by impervious surfaces during all three periods, accounting for
12.37%, 15.01%, and 12.03%, respectively. This was due to rapid economic development,
urbanization, and industrialization in the Yellow River Basin, which led to a significant
increase in impervious surfaces and a corresponding decrease in cropland. In the upper
reaches of the Yellow River from 1990 to 2021, land use changes were mainly conversions
between cropland, grassland, and barren land. The area of wetlands decreased from
915.6 km
2
to 168.9 km
2
, with 83.56% converted to grassland. From 1990 to 2002, the
cropland area increased by 3102 km
2
, primarily from grassland (93.54%), and barren land
area decreased by 2967 km
2
, mainly from grassland (91.62%). From 2002 to 2013, grassland
area significantly increased (3490 km
2
), mainly from cropland (49.74%) and barren land
(44.93%), while barren land continued to decrease (3638 km
2
), mainly transitioning into
grassland (92.53%); the largest net decrease in grassland from 2013 to 2021 was 3101 km
2
,
mainly from cropland (47.50%) and barren land (39.32%). On the one hand, extreme weather
Land 2024,13, 1510 10 of 20
events, such as high temperatures and heavy rains, occurred frequently during this period,
accelerating soil moisture evaporation and causing soil erosion, which hindered grassland
growth and recovery. On the other hand, overgrazing due to the development of animal
husbandry weakened the grassland ecosystem, leading to grassland degradation [
52
]. The
increase in cropland mainly came from grassland (93.67%). Wetland area increased slightly
to approximately 223.6 km
2
, but still decreased by 383.8 km
2
compared to 1990, indicating
that despite recent ecological measures, wetlands still require continued attention. In the
middle reaches of the Yellow River, land use changes mainly involved the conversion of
cropland to grassland, grassland to forest, and barren land to grassland. From 2002 to
2013, the largest net increase was in forest area (7140 km
2
), and the largest net decrease
was in barren land (4186 km
2
). The increase in forest was mainly from cropland (25.85%)
and grassland (66.37%), while the decrease in barren land was mainly due to conversions
to grassland (98.28%). In the lower reaches of the Yellow River, land use changes mainly
involved the conversion of cropland to impervious surfaces. From 1990 to 2002, 2002
to 2013, and 2013 to 2021, cropland area decreased by 1036.48 km
2
, 1615.23 km
2
, and
1051.68 km
2
, respectively. The proportion of reduced cropland converted to impervious
surfaces was 75.77%, 76.99%, and 75.03%, respectively.
Land 2024, 13, x FOR PEER REVIEW 10 of 20
decreased from 915.6 km2 to 168.9 km2, with 83.56% converted to grassland. From 1990 to
2002, the cropland area increased by 3102 km2, primarily from grassland (93.54%), and
barren land area decreased by 2967 km2, mainly from grassland (91.62%). From 2002 to
2013, grassland area significantly increased (3490 km2), mainly from cropland (49.74%)
and barren land (44.93%), while barren land continued to decrease (3638 km2), mainly
transitioning into grassland (92.53%); the largest net decrease in grassland from 2013 to
2021 was 3101 km2, mainly from cropland (47.50%) and barren land (39.32%). On the one
hand, extreme weather events, such as high temperatures and heavy rains, occurred fre-
quently during this period, accelerating soil moisture evaporation and causing soil ero-
sion, which hindered grassland growth and recovery. On the other hand, overgrazing due
to the development of animal husbandry weakened the grassland ecosystem, leading to
grassland degradation [52]. The increase in cropland mainly came from grassland
(93.67%). Wetland area increased slightly to approximately 223.6 km2, but still decreased
by 383.8 km2 compared to 1990, indicating that despite recent ecological measures, wet-
lands still require continued attention. In the middle reaches of the Yellow River, land use
changes mainly involved the conversion of cropland to grassland, grassland to forest, and
barren land to grassland. From 2002 to 2013, the largest net increase was in forest area
(7140 km2), and the largest net decrease was in barren land (4186 km2). The increase in
forest was mainly from cropland (25.85%) and grassland (66.37%), while the decrease in
barren land was mainly due to conversions to grassland (98.28%). In the lower reaches of
the Yellow River, land use changes mainly involved the conversion of cropland to imper-
vious surfaces. From 1990 to 2002, 2002 to 2013, and 2013 to 2021, cropland area decreased
by 1036.48 km2, 1615.23 km2, and 1051.68 km2, respectively. The proportion of reduced
cropland converted to impervious surfaces was 75.77%, 76.99%, and 75.03%, respectively.
Figure 4. Sankey diagram of land use change in the Yellow River Basin from 1990 to 2021.
Figure 4. Sankey diagram of land use change in the Yellow River Basin from 1990 to 2021.
3.3. Analysis of Land Use Spatial Pattern Change
Three land use types that were sensitive to dynamic changes in typical years were
analyzed separately in different watersheds. The results of the standard deviation ellipse
and center of gravity migration trajectories are shown in Figure 5, with parameters detailed
in Table 4. The standard deviation ellipse oblateness of shrubs in the Yellow River Basin
continues to increase, indicating an enhanced significance of directional distribution. The
center of gravity shifted to the southeast before 2002, and then moved to the west with a
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significant increase in rate, suggesting that the overall distribution of shrubs has shifted
westward and spread out. For impervious surfaces, the center of gravity shifted southwest
before 2002, and then northwest, with a decreasing migration rate and gradually decreasing
ellipse oblateness. This indicates that the center of gravity for construction land moved
westward and its distribution became more concentrated. China’s Western Development
Strategy, launched in 2000, promoted economic development in the western region, urban-
ization, and the expansion of impervious surfaces in the Yellow River Basin [
53
], causing the
center of gravity for impervious surfaces to shift westward and become more concentrated.
The center of gravity for wetlands first moved northwest and then southeast, with the
migration rate initially accelerating and then slowing down. The standard deviation ellipse
oblateness changed significantly, from 0.74 in 1990 to 0.34 in 2021, indicating a shift from a
scattered to a more concentrated wetland distribution. During this period, many wetlands
were converted into grasslands due to decreased precipitation in the upper reaches [
54
],
causing water levels to drop and wetlands to dry up. Additionally, the construction of
reservoirs and water diversion projects in the upper reaches intercepted significant water
sources, reducing water supply to the middle and lower reaches and converting wetlands
into grasslands. The standard deviation ellipse oblateness for forest, water, and impervious
surfaces in the upper reaches of the Yellow River, and forest, shrubs, and impervious
surfaces in the middle reaches, remained relatively unchanged. The average oblateness
was 0.573, 0.738, 0.688, 0.353, 0.455, and 0.315, respectively, indicating a directional and rela-
tively stable spatial distribution. The center of gravity for forest and impervious surfaces in
the upper reaches shifted south, while in the middle reaches, the center of gravity for forest
and impervious surfaces shifted north, opposite to the upstream trends. The migration
speed of the center of gravity for impervious surfaces first increased and then slowed.
The center of gravity for water in the upper reaches shifted southwest before 2013, and
then northeast with a sharp increase in migration rate. This change was influenced by the
adjustment of the Xiaolangdi Reservoir and the implementation of the East Route Project
of the South-to-North Water Diversion, affecting downstream water distribution [
55
,
56
].
The center of gravity for shrubs in the middle reaches shifted north overall, with the speed
first increasing and then decreasing. Due to the narrow and elongated shape of the lower
reaches of the Yellow River, the standard deviation ellipse oblateness for grassland, water,
and impervious surfaces was relatively high, averaging 0.595, 0.848, and 0.803, respectively,
showing a pronounced directional distribution. The center of gravity for grassland shifted
southwest at a stable rate, while the center of gravity for water also shifted southwest.
Before 2013, the center of gravity for impervious surfaces shifted northeast at a slower rate,
then shifted southwest at an accelerated rate.
Table 4. Standard deviation ellipses and centroid migration parameters.
Area Land Use Type v * (km/a) α*
1990–2002 2002–2013 2013–2021 1990 2002 2013 2021
Entire
Yellow
River
Shrub 2.67 5.09 6.22 0.62 0.64 0.65 0.67
Impervious 3.61 2.21 2.11 0.37 0.27 0.27 0.26
Wetland 1.56 3.10 1.12 0.74 0.82 0.25 0.34
Upper
Yellow
River
Forest 0.65 0.31 0.56 0.57 0.58 0.58 0.56
Water 1.29 2.84 7.47 0.74 0.73 0.74 0.74
Impervious 0.12 0.57 0.39 0.70 0.69 0.68 0.68
Middle
Yellow
River
Forest 1.57 2.01 2.83 0.33 0.35 0.35 0.38
Shrub 1.01 0.72 5.28 0.46 0.42 0.44 0.50
Impervious 0.67 0.86 0.51 0.30 0.31 0.32 0.33
Lower
Yellow
River
Grassland 0.80 0.81 0.70 0.63 0.58 0.60 0.57
Water 3.53 3.47 4.87 0.82 0.85 0.86 0.86
Impervious 0.68 0.04 0.73 0.81 0.81 0.80 0.79
v * is the migration rate of the center of gravity, in kilometers per year; α* is the flattening of the ellipse.
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Land 2024, 13, x FOR PEER REVIEW 12 of 20
(a) Entire Yellow River
(b) Upper Yellow River
Figure 5. Cont.
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Land 2024, 13, x FOR PEER REVIEW 13 of 20
(c) Middle Yellow River
(d) Lower Yellow River
Figure 5. Standard deviation ellipses and centroid migration trajectories of typical land use types in
different river basin sections from 1990 to 2021.
Figure 5. Standard deviation ellipses and centroid migration trajectories of typical land use types in
different river basin sections from 1990 to 2021.
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3.4. Analysis of Driving Forces of Land Use Change
In different periods, land use type was used as the dependent variable, while popula-
tion, GDP, temperature, precipitation, slope, and DEM were used as independent variables
to detect the impact of each driving factor on the change in land use type. The results
are shown in Table 5. Except for the slope factor during 2013–2021, which did not pass
the significance test, all other factors in the remaining periods had a significant pvalue of 0,
indicating they passed the significance test and could be analyzed as influencing factors. From
1990 to 2002 and 2002 to 2013, the driving factors were ordered from largest to smallest q value
as follows: population > GDP > DEM > temperature > precipitation > slope. From 2013 to
2021, the order changed to population > GDP > temperature > DEM > precipitation > slope.
The explanatory power of temperature on land use exceeded that of DEM. Population
had the strongest explanatory power on land use in all three periods, followed by GDP.
This indicates that economic factors have a significant impact on land use in the Yellow
River Basin, with the driving effect of utilization change being greater than that of topo-
graphic and climatic factors. However, over time, except for precipitation, which showed
an increase in q value after 2013, the q values of other factors exhibited a decreasing trend.
This suggests that the influence of economic, topographic, and climatic factors on land use
change in the Yellow River Basin is gradually weakening. With economic development and
structural adjustments, the economic development model in various regions of the Yellow
River Basin has gradually shifted from high-speed growth to high-quality development,
emphasizing sustainable development and environmental protection. Consequently, the
influence of economic factors on land use has diminished. Since 1990, the state has imple-
mented several major ecological projects in the Yellow River Basin, such as the Taihang
Mountains Greening Project (1994), the Return of Farmland to Forests and Grasslands
(1999), the Second Phase of the Three-North Shelterbelt (2002), and the Landscape, Forest,
Farmland, Lake and Grass Restoration Project (2016) [
33
]. These projects have had an
important impact on the land use structure in the Yellow River Basin. The driving role of
policy factors in land use changes has gradually increased, thereby weakening the relative
influence of economic, topographic, and climatic factors.
Table 5. Detection results of land use change factors in the Yellow River Basin from 1990 to 2021.
Impact Factor 1990–2002 2002–2013 2013–2021
q Statistic pValue q Statistic pValue q Statistic pValue
population 0.2423 0 0.2034 0 0.1930 0
GDP 0.1680 0 0.1584 0 0.1448 0
temperature 0.1512 0 0.1317 0 0.1143 0
precipitation 0.1090 0 0.0899 0 0.0980 0
slope 0.0164 0 0.0135 0 0.0058 0.3816
DEM 0.1573 0 0.1336 0 0.1057 0
Interactive detection of the six driving factors was conducted, and the results are
shown in Figure 6. Each driving factor significantly interacts with land use change, demon-
strating either a double-factor enhancement or non-linear enhancement relationship. This
indicates that land use in the Yellow River Basin is driven by the coordinated influence of
multiple factors, with a close relationship between them. From 1990 to 2002, the interaction
between precipitation and DEM had the strongest explanatory power, with a q value of
0.303, which is greater than the sum of q values for each factor individually, indicating
a non-linear enhancement relationship. This was followed by the interaction between
precipitation and population, with a q value of 0.3, showing a double-factor enhancement
relationship. From 2002 to 2013, the interaction between precipitation and GDP had the
strongest explanatory power, with a q value of 0.287, also indicating a non-linear enhance-
ment relationship. From 2013 to 2021, the interaction between precipitation and population
had the strongest explanatory power, with a q value of 0.269, showing a double-factor
Land 2024,13, 1510 15 of 20
enhancement relationship. The single-factor explanatory power of slope was the weakest
in all three periods. However, its explanatory power was significantly enhanced after
interaction with other factors, especially with the population factor, followed by its interac-
tion with GDP. Additionally, the explanatory power was stronger after the interaction of
precipitation and population with other factors.
Land 2024, 13, x FOR PEER REVIEW 15 of 20
Table 5. Detection results of land use change factors in the Yellow River Basin from 1990 to 2021.
Impact Factor
1990–2002
2002–2013
2013–2021
q Statistic
p Value
q Statistic
p Value
q Statistic
p Value
population
0.2423
0
0.2034
0
0.1930
0
GDP
0.1680
0
0.1584
0
0.1448
0
temperature
0.1512
0
0.1317
0
0.1143
0
precipitation
0.1090
0
0.0899
0
0.0980
0
slope
0.0164
0
0.0135
0
0.0058
0.3816
DEM
0.1573
0
0.1336
0
0.1057
0
Interactive detection of the six driving factors was conducted, and the results are
shown in Figure 6. Each driving factor significantly interacts with land use change,
demonstrating either a double-factor enhancement or non-linear enhancement relation-
ship. This indicates that land use in the Yellow River Basin is driven by the coordinated
influence of multiple factors, with a close relationship between them. From 1990 to 2002,
the interaction between precipitation and DEM had the strongest explanatory power, with
a q value of 0.303, which is greater than the sum of q values for each factor individually,
indicating a non-linear enhancement relationship. This was followed by the interaction
between precipitation and population, with a q value of 0.3, showing a double-factor en-
hancement relationship. From 2002 to 2013, the interaction between precipitation and
GDP had the strongest explanatory power, with a q value of 0.287, also indicating a non-
linear enhancement relationship. From 2013 to 2021, the interaction between precipitation
and population had the strongest explanatory power, with a q value of 0.269, showing a
double-factor enhancement relationship. The single-factor explanatory power of slope
was the weakest in all three periods. However, its explanatory power was significantly
enhanced after interaction with other factors, especially with the population factor, fol-
lowed by its interaction with GDP. Additionally, the explanatory power was stronger af-
ter the interaction of precipitation and population with other factors.
Figure 6. Interactive detection results of land use change in the Yellow River Basin from 1990 to
2021.
Figure 6. Interactive detection results of land use change in the Yellow River Basin from 1990 to 2021.
3.5. Prediction of Future Land Use Changes
This study used the FLUS model to predict land use changes in the Yellow River
Basin for the year 2030. We used the 2010 land use data to simulate the 2020 land use
situation; the model achieved a Kappa coefficient of 0.726. This indicates good predictive
performance and high applicability for the study area, making it suitable for predicting
future land use changes. The predicted land use for the Yellow River Basin in 2030 is shown
in Figure 7. The changes in land use are illustrated in Figure 8. Compared to 2020, the
land use types in the Yellow River Basin in 2030 will still be dominated by cropland, forest,
and grassland. The main changes will occur in the middle and lower reaches, affecting
cropland, forest, grassland, and impervious surfaces. The expansion trends of forest and
impervious surfaces are significant, with increases of 6136 km
2
and 4891 km
2
, respectively.
Conversely, cropland and grassland will experience substantial decreases, losing 4723 km
2
and 6824 km
2
, respectively. This suggests that in the coming years, due to human activities
and reforestation efforts, there will be a rapid growth in impervious surfaces to meet
socio-economic development needs, and an increase in forest area to support ecological
protection and restoration in the Yellow River Basin. Consequently, the significant loss of
cropland and grassland will pose a threat to food security. This issue requires the attention
of relevant authorities, as future policies should not sacrifice cropland to achieve economic
development and ecological protection goals.
Land 2024,13, 1510 16 of 20
Land 2024, 13, x FOR PEER REVIEW 16 of 20
3.5. Prediction of Future Land Use Changes
This study used the FLUS model to predict land use changes in the Yellow River
Basin for the year 2030. We used the 2010 land use data to simulate the 2020 land use
situation; the model achieved a Kappa coefficient of 0.726. This indicates good predictive
performance and high applicability for the study area, making it suitable for predicting
future land use changes. The predicted land use for the Yellow River Basin in 2030 is
shown in Figure 7. The changes in land use are illustrated in Figure 8. Compared to 2020,
the land use types in the Yellow River Basin in 2030 will still be dominated by cropland,
forest, and grassland. The main changes will occur in the middle and lower reaches, af-
fecting cropland, forest, grassland, and impervious surfaces. The expansion trends of for-
est and impervious surfaces are significant, with increases of 6136 km2 and 4891 km2, re-
spectively. Conversely, cropland and grassland will experience substantial decreases, los-
ing 4723 km2 and 6824 km2, respectively. This suggests that in the coming years, due to
human activities and reforestation efforts, there will be a rapid growth in impervious sur-
faces to meet socio-economic development needs, and an increase in forest area to support
ecological protection and restoration in the Yellow River Basin. Consequently, the signif-
icant loss of cropland and grassland will pose a threat to food security. This issue requires
the attention of relevant authorities, as future policies should not sacrifice cropland to
achieve economic development and ecological protection goals.
Figure 7. (a) The actual land use types for the year 2020; (b) the predicted land use types for the year
2030.
Figure 7. (a) The actual land use types for the year 2020; (b) the predicted land use types for the
year 2030.
Land 2024, 13, x FOR PEER REVIEW 17 of 20
Figure 8. The changes in land use types in the Yellow River Basin from 2020 to 2030.
4. Conclusions and Policy Recommendations
4.1. Conclusions
This study reveals the spatiotemporal characteristics of land use types and their driv-
ing factors in the Yellow River Basin, and predicts land use changes for the year 2030. This
provides important insights for understanding the ecological environment changes and
land resource management in the Yellow River Basin, contributing to the sustainable de-
velopment of the region. The main conclusions of this study are as follows:
1. According to the year of mutation, the period from 1990 to 2021 is divided into three
stages: 1990–2002, 2002–2013, and 2013–2021. The predominant land use types in the
Yellow River Basin are grassland (around 57%), cropland (around 25%), and forest
(around 10%). From 1990 to 2021, the areas of forest, grassland, water, and impervi-
ous surfaces increased significantly, while the areas of cropland, shrubs, barren land,
and wetlands decreased significantly. The middle reaches of the Yellow River exhibit
the highest comprehensive land use dynamics and the most active changes in land
use types.
2. Cropland, grassland, barren land, and impervious surfaces in the Yellow River Basin
are sensitive to changes. The upper reaches of the Yellow River are characterized by
conversions between cropland, grassland, and barren land. In the middle reaches,
conversions primarily involve cropland to grassland, grassland to forest, and barren
land to grassland. In the lower reaches, cropland is primarily converted into imper-
vious surfaces. Shrub and impervious surfaces show a westward shift in their centers
of gravity, while wetland distribution changes from being dispersed to concentrated.
The other land use types exhibit stable directional distributions.
3. Economic factors are the primary driving force for land use changes in the Yellow
River Basin. However, with a shift towards high-quality sustainable development
and the implementation of various ecological projects since 1990, policy factors have
become dominant in influencing land use changes. Consequently, the impact of eco-
nomic, climatic, and topographic factors has weakened. Land use change in the Yel-
low River Basin is driven by the coordination of multiple factors, with significant
Figure 8. The changes in land use types in the Yellow River Basin from 2020 to 2030.
Land 2024,13, 1510 17 of 20
4. Conclusions and Policy Recommendations
4.1. Conclusions
This study reveals the spatiotemporal characteristics of land use types and their
driving factors in the Yellow River Basin, and predicts land use changes for the year 2030.
This provides important insights for understanding the ecological environment changes
and land resource management in the Yellow River Basin, contributing to the sustainable
development of the region. The main conclusions of this study are as follows:
1.
According to the year of mutation, the period from 1990 to 2021 is divided into three
stages: 1990–2002, 2002–2013, and 2013–2021. The predominant land use types in the
Yellow River Basin are grassland (around 57%), cropland (around 25%), and forest
(around 10%). From 1990 to 2021, the areas of forest, grassland, water, and impervious
surfaces increased significantly, while the areas of cropland, shrubs, barren land, and
wetlands decreased significantly. The middle reaches of the Yellow River exhibit
the highest comprehensive land use dynamics and the most active changes in land
use types.
2.
Cropland, grassland, barren land, and impervious surfaces in the Yellow River Basin
are sensitive to changes. The upper reaches of the Yellow River are characterized by
conversions between cropland, grassland, and barren land. In the middle reaches,
conversions primarily involve cropland to grassland, grassland to forest, and barren
land to grassland. In the lower reaches, cropland is primarily converted into impervi-
ous surfaces. Shrub and impervious surfaces show a westward shift in their centers
of gravity, while wetland distribution changes from being dispersed to concentrated.
The other land use types exhibit stable directional distributions.
3.
Economic factors are the primary driving force for land use changes in the Yellow River
Basin. However, with a shift towards high-quality sustainable development and the
implementation of various ecological projects since 1990, policy factors have become
dominant in influencing land use changes. Consequently, the impact of economic,
climatic, and topographic factors has weakened. Land use change in the Yellow River
Basin is driven by the coordination of multiple factors, with significant interaction
between economic and climatic factors enhancing the driving force of land use change.
4.
In 2030, the Yellow River Basin’s predominant land use types will still be grassland,
cropland, and forest. However, forest land and impervious surfaces are expected to
expand significantly, while cropland and grassland will see substantial reductions. It
is crucial to focus on food production while balancing economic development and
ecological protection in the Yellow River Basin.
4.2. Policy Recommendations
Based on the findings of this paper, we propose the following recommendations in
order to provide reference for planning and policy making:
1.
Promoting sustainable land use and ecological balance: In view of the reduction in
cropland and grassland, we recommend the adoption of sustainable land use planning
techniques to maximize land use efficiency and balance the needs of food security,
economic development, and ecological protection. At the same time, ecological projects
and policy interventions such as protection, ecological restoration, and afforestation
should be strengthened to address land degradation and promote environmental
sustainability.
2.
Ensuring food security and high-quality development: In the face of the potential
threat to food security from the increase in impervious surfaces, we recommend
that the protection of cropland be a priority task and strategies be developed to
mitigate the impact of industrialization and urbanization on agricultural resources. In
addition, it is encouraged to shift from rapid economic expansion to a high-quality,
sustainable development model to reduce the negative impact of economic activities
on the environment.
Land 2024,13, 1510 18 of 20
3.
Adapting to climate change and multi-factor coordination: In view of the impact of
climate on land use change, we recommend strengthening environmental change
adaptation strategies, such as implementing water resource management plans to
support wetland protection and reduce the impact of extreme weather events. At the
same time, multi-factor coordination in land use management should be promoted to
improve the efficiency of policy implementation and land use planning.
4.
Continuous monitoring and innovative technology application: In order to more
accurately track land use changes and improve predictions, we recommend continuous
monitoring using advanced remote sensing and modeling technologies. This will
provide decision makers with decision support based on current trends and data,
thereby improving the scientific and forward-looking nature of land use management.
Through the implementation of these comprehensive recommendations, we expect
to provide more effective strategies for land use management in the Yellow River Basin
and provide valuable references for planners, policy makers, scholars, and researchers in
related fields.
Author Contributions: Conceptualization, methodology, software, validation, formal analysis,
writing—original draft preparation, Y.C. (Yali Cheng). Supervision, funding acquisition, writin
g—re
view
and editing, Y.C. (Yangbo Chen) All authors have read and agreed to the published version of
the manuscript.
Funding: This research was funded by the Natural Science Foundation of China (Grant No. U2243227).
Data Availability Statement: No new data were created or analyzed in this study. Data sharing is
not applicable to this article.
Conflicts of Interest: The authors declare no conflicts of interest.
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