Quantitative analysis of spatiotemporal changes and driving forces of vegetation net primary productivity (NPP) in the Qimeng region of Inner Mongolia

doi:10.1016/j.ecolind.2023.110610

Ecological Indicators

Volume 154, October 2023, 110610

环境科学与生态学TOPEI检索SCI升级版环境科学与生态学2区

https://doi.org/10.1016/j.ecolind.2023.110610 Get rights and content

Under a Creative Commons license

open access

Highlights

•
This paper aims at quantifying the factors affecting vegetation growth in the western of Inner Mongolia, and judging the relative contribution of natural factors and man-made factors.
•
It is obtained through geodetectors (GD) that Rainfall is the most important driving factor, with the q-value of 0.77 and GDP also having a huge impact on vegetation growth, q-value of 0.64.
•
Different from most studies, human activity also has a positive effect on vegetation growth.
•
The relative contribution of human activities reached 51.75%, slightly exceeding the influence of natural factors.

Abstract

Vegetation is an essential component of terrestrial ecosystems, and understanding the drivers of vegetation change is of great importance for ecological management. In recent years, vegetation growth has increased under the combined effect of global warming and human activities in Inner Mongolia. The net primary productivity (NPP) was used as an indicator to study the spatial and temporal changes in vegetation in the Qimeng Region (QR). The residual trend analysis method was used to analyze the relative contributions of climate variations (CV) and human activities (HA) to NPP changes across the QR, while their drivers were explored using a geographical detector approach to quantify the driving forces of NPP. The results show that (1) NPP exhibited a fluctuating growth trend from 2003 to 2020, with an overall growth rate of 2.91%/year. (2) Precipitation, GDP and population density were the dominant driving factors for the spatial distribution of NPP. The combined explanatory power of any two dominant factors exceeded the power of any dominant individual factor, and the interaction between climate and human factors had a significant effect on NPP. (3) The change in NPP was influenced by the combined effect of HA and CV, accounting for 37.69% of the total area, with the relative contribution of HA being 51.75%. Finally, the relative contribution of human activities was slightly higher than that of climate change, confirming the initial success of the Grain to Green Program as well as ecological conservation projects. This paper provides a scientific basis for the local government to carry out the conversion of cropland to forest.

Keywords

NPP

Driving force

Geographical detectors

Inner Mongolia

1. Introduction

As the main component of the terrestrial ecosystem, vegetation plays a pivotal role in protecting the living environment of humans and stabilizing the global carbon cycle (Frank et al., 2015, Negi et al., 2019, Wang et al., 2021b). To research the impact of global warming and industrialization on vegetation, the net primary productivity (NPP) of vegetation is used as an indicator to investigate variation in plant growth (Chen et al., 2021a). The NPP of vegetation refers to the total organic matter produced by vegetation through photosynthesis in a unit of time obtained by subtracting the organic matter consumed by plant respiration from the accumulated organic matter (Lin et al., 2012, Turner et al., 2006). Vegetation is influenced by climate change and human activities, which can be directly reflected in the vegetation NPP in a short time period. Therefore, NPP can be used to analyze the health status and sustainable development of regional ecosystems.

Vegetation changes are affected by numerous factors, most of which can be attributed to climate variations and human activities (Di Pasquale et al., 2020, Pan et al., 2011, Shi et al., 2021). There is no doubt that climate change, associated with temperature and precipitation changes, will have a very large impact on vegetation NPP (Alkama et al., 2022, Guo et al., 2020, Qu et al., 2020, Sun et al., 2015a). For example, Liang He et al. analyzed the most significant factors affecting the NDVI spatial distribution on the Loess Plateau. Studies have shown that precipitation plays a key role in plant growth and the spatial distribution of vegetation, especially in arid and semiarid regions (He et al., 2022). Chenli Liu et al. employed a geographical detector to identify the primary factors affecting vegetation changes, and temperature had the highest q value and explanatory power for the NDVI (Liu et al., 2021b). Vegetation is also affected by human activities, especially government policies. Zenghui Sun's research on deserts proves that the project of returning farmland to forests has been a major and vital influence in restoring deserts (Sun et al., 2021). He Liang and Guo Jianbin's studies on the Loess Plateau also demonstrate that human activities have a key influence on vegetation distribution (Asefa et al., 2020). Finally, other factors also affect vegetation distribution, such as slope, elevation, aspect, and sunshine hours, and the importance of different factors will also be significantly variable among different regions (Liu et al., 2021b, Yang et al., 2020).

According to the above description, we know that vegetation changes will be affected by many factors, divided mainly into two categories: climate change and human activities. Therefore, there are two questions to address. The first question is how to determine the impact of various factors on NPP and select the most influential factors. In previous studies, linear correlation analysis methods were usually used to qualitatively analyze the driving factors of vegetation cover (Liu et al., 2018, Whetton et al., 2017, Zhang et al., 2018b). However, there are many factors that affect NPP, and they are not completely independent. Therefore, the results obtained by simple linear or correlation analysis methods cannot fully reflect what is actually driving the relationship. The geographic detector proposed by Wang et al. can overcome the shortcomings of traditional correlation analysis methods [38]. It can quantify the driving effect of each element and the interaction between each factor and geographic elements, which is based on the relationship of driving factors and target variables in space. It is already used to detect the driving factors affecting vegetation change, such as precipitation, temperature and land use type. The second question is how to evaluate the roles of natural changes and human activities in NPP changes. Some scholars use regression models to establish the relationships between NPP and its drivers and then evaluate the relative importance of each factor (Xin et al., 2008). Others use residual trend analysis methods, such as Yanling Sun's research on the relative role of climate change and human activities in vegetation change in North China from 1998 to 2001 (Sun et al., 2015b). The relative contribution of human activities is 69.18%, and the relative contribution of natural factors is 30.82% (Wu et al., 2015). The direct relationship between natural change and human activities is complex. Both natural factors and human activities represent numerous driving factors. It is inaccurate to use only linear relationships to determine the overall impact of these factors. In this paper, nonlinear residual trend analysis is used to more accurately assess the relative contributions of human activities and climate variations.

In the past few decades, the Qimeng region has undergone a transition from ecological degradation to ecological stability, making it an appropriate study area for investigating the driving factors of ecological changes. Before 2000, an increase in population and the modernization of lifestyles resulted in more energy being consumed, along with more ecological pressure (Wang et al., 2017, Wu et al., 2015). Land degradation due to overgrazing is an increasingly serious problem. After 2000, to solve the problem of land degradation, a series of policies were introduced. Typical examples are the Three-North Shelter Forest and programs to return farmland to forest (Du et al., 2016, Yu et al., 2021). To date, the current ecosystem has been significantly restored. Previous studies on vegetation in Inner Mongolia often covered whole provinces or even the Mongolian Plateau (Yang et al., 2019). The purposes of this paper are to (1) illustrate the spatiotemporal variation in NPP from 2003 to 2020, (2) assess the influence of natural and anthropogenic factors and their interactions on NPP spatial distribution, and (3) quantitatively measure the relative contributions of human activities and climate variations.

2. Materials and methods

2.1. Study area

The Qimeng Region (QR, 37.40 N-43.38 N 97.17 E-114.80E) (Fig. 1.) is in southwestern Inner Mongolia. Inner Mongolia is a province with a considerable longitudinal span. The administrative region includes the seven cities of Hohhot, Baotou, Ordos, Ulanqab, Bayannaoer, Wuhai and Alxa League, with a total area of 524,933 square kilometers. It is composed of 59.06% desert and 40.94 grasslands, belonging to arid and semiarid regions (Lu, 2018). It is a crucial area of the ecological protection buffer zone in North China. The topography is higher in the northeastern and middle parts and that of the southwestern part is relatively gentle. The overall altitude ranges from 721 m to 3531 m. The QR is located at a high latitude far from the ocean and is blocked by mountains along the edge. The climate is mainly a temperate continental monsoon climate. The average annual precipitation in the QR is less than 400 mm in most areas, with precipitation gradually decreasing from the southeast to the northwest. Approximately 60% to 80% of the precipitation occurs in the summer, and the distribution of precipitation is also uneven over time. The average yearly temperature of the QR in Inner Mongolia is 6.3–7.6 °C, and the temperature difference between day and night is considerable. This contrasts with the spatial precipitation distribution in that the temperature gradually decreases from northwest to southeast (Mu et al., 2013). Therefore, the QR region is characterized by two main types of belt-shaped grasslands: typical grasslands and desert grasslands. Typical grasslands usually grow under semiarid conditions with an annual precipitation of 200 to 400 mm and an average annual temperature of 0 to 8 °C. They are primarily in the northeastern part of the QR. These grasslands are rich in herbivorous animals, such as Mongolian gazelles, wild horses, sheep, and cattle. Desert grasslands, on the other hand, receive an annual precipitation ranging from 150 to 200 mm and have an average annual temperature of 5 to 10 °C. The biomass in these areas is minimal. In even drier desert regions, the characteristic features include sand dunes, sparse vegetation, and extreme temperature fluctuations. The biodiversity is low, with only a few plant and animal species adapted to desert conditions, such as saxaul trees, jerboas, and Bactrian camels. In recent years, a series of ecological protection projects, such as returning farmland to forest and returning grazing land to grassland, has been carried out in the QR, which has significantly improved the ecological environment.

2.2. Data sources and processing

The net primary productivity (NPP) dataset used comes from MOD17A3HGF (https://lpdaac.usgs.gov) and covers the period 2003 to 2020. It has a spatial resolution of 500 m and a temporal resolution of 1 year. The preprocessing of the NPP dataset was implemented through Google Earth Engine by the JavaScript application programming interface. Google's platform provides a wealth of free satellite data and products to help researchers perform better research. The elevation data were obtained from the SRTM dataset with a resolution of 30 m, and the slope and aspect were obtained using the functions of Google Earth Engine (https://cmr.earthdata.nasa.gov). The landcover data were derived from the MC- D12Q106 dataset, with a 500 m spatial resolution (Rabus et al., 2003).

The annual average precipitation and temperature data of the QR with a resolution of 1 km were selected from the Resource and Environmental Sciences Data Center of the Chinese Academy of Sciences (https://www.resdc.cn/). Night light data were obtained from the global NPP-VIIRS-like nighttime light dataset from the Harvard Dataverse platform (10.7910/ DVN/YGIVCD). This dataset is based on the DMSP-OLS and NPP-VIIRS nighttime light data correction scheme and is the first “NPP-VIIRS-like” nighttime light dataset with a resolution of 500 m in the world (Chen et al., 2021c). Soil type data, economic data, landform data and other data were selected from the National Earth System Science Data Center (https://www.geodata.cn). A total of 38,000 sample points were extracted from the same grid interval, such as extracting one sample point every 20 grids by using MATLAB. More sample points can be obtained faster by using this approach than by using fishing net tools. The raw precipitation and temperature data had a temporal resolution of one month. Projection transformations, synthesis with the average, resampling, and other processing steps were applied to the raw precipitation and temperature data using ArcGIS to ensure that the output results had the same spatiotemporal resolution. NPP is affected by the main climate and human activity factors listed in Table 1.

Table 1. Indicators of climate and human factors. (The symbol/indicates the absence of temporal resolution).

Factor	Factor type	Abbreviation	Original temporal resolution	Original spatial resolution	Source
Temperature	Climate Factor	Temp	Monthly	1 km	RESDC (https://www.resdc.cn/)
Precipitation	Climate Factor	Pre	Monthly	1 km
Landform type	Climate Factor	Landf	/	1 km
Aspect	Climate Factor	Aspect	/	30 m	NASA/CGIAR (https://srtm.csi.cgiar.org/)
Elevation	Climate Factor	Dem	/	30 m
Slope	Climate Factor	Slope	/	30 m
Solar radiation	Climate Factor	Solar	Monthly	27.83 km	ECMWF (https://cds.climate.copernicus.eu/)
Soil temperature	Climate Factor	S_temp	Monthly	27.83 km
Soil moisture	Climate Factor	S_moi	Monthly	27.83 km
Soil type	Climate Factor	Soil_t	/	1 km	National Earth System Science Data Center (https://www.geodata.cn)
Population density	Human Factor	Qmp	Yearly	1 km
GDP	Human Factor	GDP	Yearly	1 km
Nighttime light	Human Factor	Nlt	Yearly	500 m	Harvard Dataverse (https://doi.org/10.7910/DVN/YGIVCD)

Climate factors refer to the various elements or variables that influence the overall climate patterns and conditions in a specific region or on a global scale. In this article, this term mainly refers to the factors that have a significant impact on vegetation growth in the Qimeng city area. These factors operate without direct human intervention or influence. In addition to temperature and precipitation, aspect, elevation and slope were also classified as climate factors. Landform type, solar radiation, soil temperature, soil moisture, and soil type also meet the definition of climate factors. Therefore, this article categorizes these factors as climate factors. Human activity factors refer to the elements directly influenced by human production and daily life. Population density, GDP, and nighttime lights are undisputedly classified as human activity factors.

2.3. Methods

2.3.1. Theil–Sen median trend analysis

The Theil–Sen median method, also known as Sen’s slope estimation, is a robust nonparametric statistical trend method. The linear regression method is susceptible to data noise interference and therefore requires a normal data distribution (Gocic and Trajkovic, 2013, Sharma and Saha, 2017). The Theil–Sen median method is less affected by data noise interference than the other methods. At the same time, it is not clear what the distribution and fluctuations in the annual average NPP are, so it is very suitable to use this method to calculate the trend of NPP (Andaryani et al., 2023, Huang and Kong, 2016). The Sen slope is determined by calculating the permutation and combination for each time point in the time series, where the resulting median of these slopes is the Sen slope. This paper adopts the pixel-by-pixel method to apply the Sen slope, and the calculation formula is as follows:(1)

S_{NPP} = M e d i a n (\frac{NP P_{j} - N P P_{i}}{j - i}) 2003 ⩽ i ⩽ j ⩽ 2020

In the formula,

NP P_{i}

and

NP P_{j}

represent the i and j years of the annual average NPP value corresponding to the pixel, respectively. When

S_{NPP}

is greater than 0, the NPP trends upward during the study period, and the environment is more suitable for vegetation. When it is less than 0, the NPP trends downward during the study period.

2.3.2. Mann–Kendall method

Mann–Kendall's test method is a nonparametric method. The significance of the time series trend is determined based on statistical theory, and the data do not need to conform to a normal distribution (Rusciano et al., 2022, Tosunoglu and Kisi, 2017, Zhang and Zwiers, 2004). Mann–Kendall's test statistic was calculated with the following equations:(2)

Z = \{\begin{matrix} \frac{S - 1}{\sqrt{Var (S)}}, S > 0 \\ 0, S = 0 \\ \frac{S + 1}{\sqrt{Var (S)}}, S < 0 \end{matrix}

(3)

S = \sum_{i = 1}^{n - 1} \sum_{j = i + 1}^{n} sgn (N P P_{j} - N P P_{i})

(4)

sgn (N P P_{j} - N P P_{i}) = \{\begin{matrix} 1 \cdot i f N P P_{j} - N P P_{i} > 0 \\ 0 \cdot i f N P P_{j} - N P P_{i} = 0 \\ - 1 \cdot i f N P P_{j} - N P P_{i} > 0 \end{matrix}

(5)

V a r (S) = \frac{n (n - 1) (2 n + 5) - \sum_{i = 1}^{m} t_{i} (t_{i} - 1) (2 t_{i} + 5)}{18}

In formulas (2) and (3),

NP P_{i}

and

NP P_{j}

represent the i and j years for the annual average NPP value corresponding to the pixel, respectively. n represents the length of the time series, n = 18 in this paper, m is the number of tied groups, sgn is a symbolic function, and the value range of the statistic Z is (-∞, +∞). The studied time series can be considered to have changed significantly when we select

α = 0.05

and

|Z| > u_{1 - \frac{α}{2}}

to reject the null hypothesis. The results are divided into two categories: significant changes (

|z| > 1.96

) and slight changes (

|z| < 1.96

2.3.3. Geographical detector

Geographical detectors (Geodetectors) are a set of statistical methods used to detect spatial variability and reveal the driving forces behind it (Liu et al., 2017a, Su et al., 2011, Zhang et al., 2019). In essence, they compare the local variance and the overall variance. If the study area is divided according to a certain driving factor, the local variance of the divided area is calculated. If the value of the dependent variable NPP is close to or similar to the divided area variance, the local variance is much smaller than the global variance. This means that the driving factor has a strong influence on, or correlation with, the NPP. Accordingly, the q value is measured by the relationship between the factor and the analysis variable. This paper focuses on the use of factor detectors and interaction detectors. The factor detector is used to find the most critical driving factors that change the spatial differentiation of NPP, and the calculation formula is as follows:(6)

q = 1 - \frac{1}{n σ_{H}^{2}} \sum_{i = 1}^{m} n_{i} σ_{i}^{2}

In the formula, the q value indicates the explanatory power of each driving factor for the spatial distribution of NPP, and a larger q value indicates a larger impact of the driving factor on NPP. n refers to all samples in the study area,

σ_{H}^{2}

refers to the overall variance of the dependent variable NPP in the entire study area,

n_{i}

is the number of samples in the zone (category) of driving factor X, m is the number of small segmented regions according to a certain driving factor, and

σ_{i}^{2}

is the local variance in the small segmented region. The interaction detector is used to identify the impact on the dependent variable NPP of the interaction between different driving factors and whether the combined effect of the driving factors will enhance or weaken the explanatory power of the driving factors. The ecological detector is used to assess the significant impact of factors

X_{1}

and

X_{2}

on the spatial distribution of NPP, and the F test is employed to measure this impact. The corresponding formula is as follows:(7)

F = \frac{N_{X 1} (N_{X 2} - 1) S S W_{X 1}}{N_{X 2} (N_{X 1} - 1) S S W_{X 2}}

(8)

SS W_{X 1} = \sum_{h = 1}^{L 1} N_{h} σ_{h}^{2} S S W_{X 2} = \sum_{h = 1}^{L 2} N_{h} σ_{h}^{2}

N_{X 1}

and

N_{X 2}

represent the sample sizes of two factors,

X_{1}

and

X_{2}

, respectively. The numbers of categories formed by factors

X_{1}

and

X_{2}

are labelled

L_{1}

and

L_{2}

SS W_{X 1}

and

SS W_{X 2}

represent the sum of within-group variances formed by

X_{1}

and

X_{2}

, respectively. The null hypothesis assumes that the two factors have the same sum of within-group variances, indicating similar effects on the spatial distribution of NPP. If the null hypothesis is rejected at the significance level α, a significant difference exists between the effects of the two factors on NPP distribution.

2.3.4. Relative contributions of driving forces

The residual trend analysis method was first proposed by Evans and Geerken to reflect the relative effects of climate change and human activities on vegetation (Wang et al., 2021a). In previous studies, rainfall, temperature, and solar radiation were used as key natural factors, and multiple linear regression was used to predict these factors pixel by pixel (He et al., 2022). That is, with precipitation, temperature, and solar radiation as independent variables and the NDVI as the dependent variable, the NDVI under the influence of precipitation, temperature, and solar radiation

NDV I_{cli}

is calculated through multiple linear regression. The predicted

NDV I_{cli}

is regarded as the NDVI value affected only by natural factors. In this paper, geographic detectors are used to select the natural factors that have the greatest impact on NPP, including precipitation, soil temperature, and soil type. These factors and the dependent variable NPP do not have simple linear relationships, so traditional linear fitting is replaced by nonlinear fitting to obtain better prediction results. To more accurately describe the influence of natural factors on NPP, which is expressed by

NP P_{cli}

, the corresponding Equation (9). The corresponding formula for linear fitting is given by Equation (10).(9)

\begin{matrix} N P P_{c l i} & = a_{1} \times p r e + a_{2} \times p r e^{2} + a_{3} \times p r e^{3} \\ + b_{1} \times t e m p_{s} + b_{2} \times t e m p_{s}^{2} + b_{3} \times t e m p_{s}^{3} \\ + c_{1} \times S T + c_{2} \times S T^{2} + c_{3} \times S T^{3} \\ + d_{1} \times p r e \times t e m p_{s} + d_{2} \times t e m p_{s} \times S T \\ + d_{3} \times p r e \times S T + d_{4} \times p r e \times t e m p_{s} \times S T + e \end{matrix}

(10)

NP P_{cli} = a \times p r e + b \times t e m p_{s} + c \times S T + d

NP P_{cli}

is the predicted NPP value, Pre refers to the average annual precipitation,

tem p_{s}

is the soil temperature, and ST refers to soil type. The terms a, b, c, d, and e represent the multivariate nonlinear regression coefficients. Then, the observed value of NPP is subtracted from the predicted value to obtain the residual.(11)

NP P_{res} = N P P_{obs} - N P P_{cli}

NP P_{res}

indicates the impact of human activities on NPP,

NP P_{obs}

is the observed value of NPP, and

NP P_{cli}

is the predicted NPP value, representing the response of NPP to climate change. This article studies NPP (net primary productivity) changes from 2003 to 2020. Therefore, the format of each grid corresponding to

{NPP}_{cli}

is 18*1, where 18 represents the 18 years from 2003 to 2020, and 1 refers to the fitted value for each year. The corresponding

slop e_{res}

and

slop e_{cli}

are calculated using a simple linear regression method for the yearly

NP P_{res}

and

NPP_{cli}

. If the calculated slopes are all greater than 0, both human activities and natural factors have promoted the growth of plants. If the slopes are less than 0, these factors have hindered the growth of plants. This paper uses the p test to detect the significance of

slop e_{res}

and

slop e_{cli}

. The relative contribution of the driving forces is calculated according to the method proposed by Sun et al. Table 2 provides specific methods. In Table 2, CV is used to represent climate variations, and HA is used to represent human activities. Based on the table, an objective assessment can be made to determine whether the main driving force is climate variations or human activities.

Table 2. Residual trend analysis formula.

Scenario	$slop e_{cli}$	$slop e_{res}$	Relative role of CV/%	Relative role of HA/%	Driving force
$slop e_{NPP} > 0$	>0	>0	$slop e_{cli} / s l o p e_{NPP}$	$slop e_{res} / s l o p e_{NPP}$	CV&HA
	>0	<0	100	0	CV
	>0	>0	0	100	HA
$slop e_{NPP} < 0$	<0	<0	$slop e_{cli} / s l o p e_{NPP}$	$slop e_{res} / s l o p e_{NPP}$	CV&HA
	<0	>0	100	0	CA
	>0	<0	0	100	HA

2.3.5. Evaluation indicators

The coefficient of determination (

R^{2}

) is commonly used to evaluate the goodness of fit of a model. Its formula is shown below:(12)

R^{2} = 1 - \frac{\sum {(y_{i} - {\hat{y}}_{i})}^{2}}{\sum {(y_{i} - \bar{y})}^{2}}

In this context,

y_{i}

refers to the observed value at time

i

{\hat{y}}_{i}

represents the simulated value at time

i

, and

\bar{y}

denotes the average of the observed values. This metric is used to evaluate the effectiveness of nonlinear fitting. In the formula for residual trend analysis,

{NPP}_{cli}

represents the results obtained by simulating the fitting of NPP values from 2003 to 2020 at each grid using a nonlinear approach. In this study, we utilize

R^{2}

to explain the results of the fitting process. The

R^{2}

value ranges from 0 to 1, where a value closer to 1 indicates a higher level of goodness of fit, implying that the variability of the dependent variable is better explained by the independent variables.

3. Results

3.1. Temporal-spatial variation characteristics of plant NPP

The average value of vegetation NPP showed an increasing trend from 2003 to 2020 in the QR (Fig. 2). The multiyear average NPP in the study area was 167.31

gC \cdot m^{- 2} \cdot a^{- 1}

. Among them, the annual mean value was the lowest in 2005, only 131.17

gC \cdot m^{- 2} \cdot a^{- 1}

. The NPP annual average sharply rose to 195.55

gC \cdot m^{- 2} \cdot a^{- 1}

in 2012 and then fluctuated at approximately 175.48

gC \cdot m^{- 2} \cdot a^{- 1}

. The annual average NPP was generally lower than the multiyear average NPP (167.31) in the study area until 2011. In the QC, the annual average NPP was higher than the multiyear average (167.31), except in 2015.

Fig. 3 shows that the average value of NPP for many years varies greatly in its spatial distribution. S_npp represents the Sen slope of NPP, and |z| is an indicator for statistical tests. Most of the Alxa League area is desert, and its corresponding NPP is extremely low. Overall, the average NPP gradually increases from west to east. The annual average precipitation also increases gradually from west to east, which is consistent with the spatial distribution of NPP. High-NPP value areas with NPP over 400 gC·m⁻²·a⁻¹ were distributed in the south and east of the QC area. The areas that passed the MK significance test were mainly in Ordos city in the southwestern portion of the study area, with an average altitude of 3500 m and a climate characterized by low temperature and rainfall.

The vegetation in that area is mainly typical warm-season grassland vegetation and afforestation vegetation in Ordos city, so its corresponding NPP range is 100–200

gC \cdot m^{- 2} \cdot a^{- 1}

. Most regions of the study area are arid and semiarid, with large temperature differences between day and night, and the rainfall distribution pattern is uneven in time and space, which leads to the vegetation being mainly grassland, shrubs, and the artificial vegetation created by returning farmland to forest.

3.2. Dominant factors in the spatial distribution of NPP for each period

The factor detector in the geographic detector can quantitatively evaluate the influence of each driving factor on the spatial distribution of NPP through the q value. Fig. 5 shows the effect of various driving factors on the spatial differentiation of NPP in 2005, 2010, 2015, and 2019. Due to the lack of GDP data in 2020, 2019 was selected instead of 2020 for the last year. The final results illustrate that the dominant factors are precipitation, population density, and GDP in the years mentioned above. In 2005, factors such as precipitation, population density, and GDP contributed the most to the spatial differentiation of NPP. The influences of these factors on the spatial differentiation of NPP were of the same order of magnitude, and there was no noticeable difference, but in 2019, soil type replaced rainfall as the main driving factor.

The secondary driving factors of NPP are soil temperature, soil moisture, and soil type, which are related to soil properties. The influence of soil type and soil temperature is prominently higher than that of soil moisture on NPP. The effects of soil type and soil temperature on NPP are basically the same. The q values corresponding to slope, aspect, elevation, landform, nighttime light, and solar radiation range from 0.0034 to 0.1732, which have very little influence on the spatial distribution of NPP.

In 2010 and 2015, precipitation, population density, and GDP were the main driving forces for changes in the spatial distribution of NPP, and the q values of these factors were significantly higher than those of other factors. This impact is obviously different from that of soil temperature and soil type on NPP. The highest q value among the driving factors is observed for precipitation, with a q value of 0.7705. Precipitation is the determining factor for NPP distribution, reflecting that water was insufficient for vegetation in QC in 2010. Water is the most critical factor restricting vegetation growth.

3.3. Detection of striking differences in the main driving factors

Ecological factors were considered in the geographical detectors to ascertain whether there were striking differences in the influence of various driving factors on the spatial distribution of NPP. The only nonsignificant relationship is found between GDP and population (Fig. 4). The effect of annual average precipitation on the spatial distribution of NPP is markedly different from that of other factors in the study area. Factor detection also illustrated that average yearly precipitation was the crucial factor in NPP variation in QC. At the same time, ecological driver detection also proved that the impact of precipitation on NPP is much greater than that of other driving factors. Similarly, the q-values of population density and GDP are very close in factor detection. The similarity of the two effects on NPP is fully demonstrated in ecological detection.

3.4. Impacts of climate change and human activities

The impact of climate factors on NPP (Fig. 6). The overall trend of change in the global climate is an increase in temperature, a gradual upward shift of the temperate zone, and an increase in rainfall in North China. In the past 18 years, climate change has been very small, and precipitation has increased slightly to 11.53 mm/10

y r^{- 1}

in QC. The annual precipitation peaked of 18.972 mm during 2018–2020, and the average annual rainfall was 10.277 mm in 2007, which was the lowest value in the past 18 years. The temperatures were generally mild, with the average temperature dropping rapidly from 2009 to 2011. In 2009–2011, the annual average temperature reached the lowest value of 8.17 °C, and outside the period of 2009–2011, the temperature showed a slight upward trend. In 2016–2018, the annual average temperature reached a maximum of 15.56℃. Soil temperature, a factor that has a pronounced effect on the spatial heterogeneity of NPP, was identified by the geodetector and increased steadily over 18 years with a slope of 0.28

y r^{- 1}

. The situation where the annual temperature suddenly drops sharply in some years with a difference between the maximum and minimum values of the annual average temperature reaching more than 7℃ occurred once. The soil temperature peaked of 10.77 °C in the past 18 years. In 2010–2012, the annual average soil temperature reached a minimum value of 9.13 °C, and the difference between the maximum and minimum values was 1.64 °C.

The impact of human activities on NPP. In this paper, the residual trend analysis method is used to study the impact of human activities on NPP driving factors. The increase in population density is more stable, with a slope of 0.26, which corresponds to an increase in the population of the entire study area of 190,000 people per year. In the population density data, the pixel value corresponding to each pixel is the population density of the corresponding area, and the change rate is multiplied by the total number of pixels to obtain the population change in the study area. According to the ecological detector included in the geographic detector, there is no significant difference observed in the impact of population density and GDP on NPP. Fig. 7 shows that from 2003 to 2020, the population continued to increase in the study area, the rate of increase continued to accelerate, and the slope became increasingly steep. At the same time, the environment has not deteriorated due to population growth; in contrast, it has continued to improve. This phenomenon indicates that the government is actively promoting ecological protection and returning farmland to forests, and the construction of the Three-North Shelter Forest Project has achieved remarkable results. The area where NPPF increased due to human activities accounted for 39.377% of the entire study area, as shown in Fig. 6. Among them, the areas with obviously increased NPP are mainly distributed in Baotou city, the central and western parts of Ulanqab and most of Ordos, accounting for 17.362% of the entire study area. The regions with a slight increase in NPP are concentrated mostly in Hohhot and the southeastern part of Ulanqab, accounting for 22.015% of the entire QC. The area with declining NPP accounted for 1.563% of Qimeng city, mainly in the northwestern part of Bayannaoer and the eastern part of Ulanqab.

3.5. Analysis of NPP change drivers

Fig. 8 shows the spatial distribution of the main drivers of NPP, human activities and climate variations in the 2003–2020 period.

Under the combined effect of human activities and the climate variations, 37.695% of the area shows an upwardly trending NPP in the study area, which is mainly distributed in almost all areas of Ordos, Baotou, Hohhot, and Ulanqab. In the abovementioned area, the NPP was distributed in subregions where climate change was the main driving force, and the increasing trend was in the eastern and northernmost parts of Ulanqab and the southern part of Bayannaoer, accounting for 0.927% of the study area. Correspondingly, with human activities as the main driving force, the areas with an increasing trend of NPP are mainly concentrated in the northern part of Ulanqa and the southeastern part of Bayannaoer. Under the combined effect of human activities and the climate variations, 0.422% of the study area shows downwardly trending NPP, and the point-like division is in the southeastern part of Bayannaoer. The areas where NPP has declined mainly due to climate variations are in the southeastern part of Bayannaoer and appear as discrete point-like divisions in the Fig. 8. In particular, the decline in NPP due to human activities is very rare in QC. The corresponding area cannot even be seen on the map, accounting for only 0.214% of the entire study area. In general, the changes in NPP are mainly driven by the combination of human activities and climate variations in Qimeng city. The individual driving effects of human activities and climate change on NPP are almost negligible.

3.6. Relative contribution of drivers to changes in NPP

This study quantitatively analyzes the relative contributions of climate variations and human activities to NPP based on multivariate nonlinear regression and residual analysis. Table 2 presents a detailed description of the calculation method and related parameters. The relative contribution of the different driving factors to NPP is used to analyze the relationships between the original NPP trend, the NPP trend predicted by the environment, and the NPP trend affected by human activities. Then, calculated by the formula, the result is shown in Fig. 9.

The relative contribution of climate change to NPP is 48.25%, while the relative contribution of human activities is 51.75%. In Fig. 9. above, the original trend of NPP is specifically divided into two categories, increase and decrease, and the relative contributions of climate variations and human activities to NPP change are calculated. In the case of an increasing NPP trend, climate variations and human activities accounted for 53.452% and 46.548% of the NPP increase, respectively. Similarly, in the case of a declining NPP trend, the relative contributions of climate change and human activities were 48.310% and 51.689%, respectively. In areas where NPP trends upward, the areas with climate change as the dominant driving factor (relative contribution greater than 50%) are mainly distributed in the eastern part of Ordos, most of Hohhot, and the southeastern part of Ulanqab. Correspondingly, the areas dominated by human activities are distributed in the northwestern part of Ulanqab, the middle part of Baotou and the southwestern part of Bayannaoer.

3.7. Comparison between nonlinear fitting and linear fitting

Equation (9) represents the nonlinear fitting formula for

{NPP}_{cli}

. We calculated the

R^{2}

values between the fitted values of NPP (

{NPP}_{cli}

) and the observed values of NPP (

{NPP}_{obs}

) on a grid-by-grid basis. The corresponding

R^{2}

distribution is shown in Fig. 10. Overall, the

R^{2}

values for nonlinear regression are higher than those for linear regression, and the distribution of

R^{2}

values is relatively uniform. In terms of spatial distribution, the

R^{2}

values for linear regression are unevenly distributed. In regions such as Ordos, Baotou, and the southern part of Ulanqab, the

R^{2}

values are all less than 0.2. In the northern part of Ulanqab and the southern part of Bayannur, the corresponding

R^{2}

values are represented by green color in Fig. 10(b), ranging from 0.4 to 0.8. The remaining areas with

R^{2}

values greater than 0.4 are sporadically distributed in the southeastern part of Wuhai. In contrast, in nonlinear regression, most regions have

R^{2}

values greater than 0.4, with only Baotou, the northern part of Ulanqab, and the border area between Bayannur and Ordos having

R^{2}

values less than 0.4. In the eastern part of Wuhai and the northernmost part of Bayannur, there are sporadic areas with

R^{2}

values exceeding 0.8.

Within the entire study area, three representative regions were selected: Gander Mountain Nature Reserve, Jiufeng Mountain Nature Reserve, and the urban area of Baotou city. Both linear regression and nonlinear regression yielded

R^{2}

values greater than 0.6 for the Gander Mountain Nature Reserve, indicating a good fit. However, in the urban area of Baotou city and the Jiufeng Mountain Nature Reserve, the

R^{2}

values corresponding to linear regression were significantly lower than those of nonlinear regression, suggesting that the nonlinear regression outperformed the linear regression in these regions.

4. Discussion

Global warming has led to climate variations in various regions in the past few decades. Under the combined effect of climate variations and human activities, the degree of greening in North China has generally increased. The NDVI in North China has shown an upward trend in the past four decades, and the degree of vegetation cover has increased. This study shows that the rising trend of NPP in Qimeng city is consistent with the increasing trend of the NDVI on the.

Loess Plateau during the same period (He et al., 2022). In this paper, geographic detectors and residual trend analysis were used to quantify the importance of each driving factor and its impact on NPP and the relative impact of human activities and climate change on NPP in the Qimeng city area. Below, the impacts and relative contributions of human activities and climate change are discussed.

4.1. Influence and relative contribution of climate variations

Many studies have proven that vegetation cover has increased significantly at the middle and high latitudes of the Northern Hemisphere, including most of the northern part of China (Pei et al., 2018, Xiao and Moody, 2004). The temperature of the Mongolian Plateau is rising faster than the rate of global warming (Guo et al., 2021, Hao et al., 2021). Similarly, the vegetation cover of the Mongolian Plateau is also increasing, and this greening trend is being experienced over almost the entire Mongolian Plateau (Zhao et al., 2015). The climate variations of Qimeng City is harsh with the Alxa Desert and severe land desertification in Inner Mongolia. At the same time, approximately 67% or 195 million acres (78 million hectares) of the total land in Inner Mongolia is classified as pasture, which greatly limits the ecological carrying capacity (Lee & Sohn, 2011). Some scholars have studied the influence of precipitation and temperature on NPP in Inner Mongolia, finding that precipitation is more influential (Gerten et al., 2008). This is very different from the drivers of vegetation change in Guangxi and Tibet, where temperature is the determining driver (Liu et al., 2021b, Liu et al., 2017b). The QC area is a typical arid and semiarid area, and the impact of precipitation on vegetation growth is far greater than that of temperature. Water resources are the key factor limiting vegetation growth. The rainfall in this area cannot meet the needs of vegetation growth, so the spatial distribution of NPP is consistent with the spatial distribution of rainfall and increases with increasing rainfall. When the maximum precipitation was 30.6–36.7 mm, the average value of NPP also reached a maximum of 179.39. Higher temperatures will reduce soil moisture, increase evaporation, and make water resources scarcer, so NPP has a significant negative correlation with temperature. The Qimeng city area mainly has two types of land cover: grassland and desert. According to Haiyan Zhang’s research, soil stability is significantly weakened under the influence of climate variations, which indirectly alters the soil environment and affects vegetation growth in the Qimeng city area of Inner Mongolia (Zhang et al., 2018a). The interaction of precipitation and temperature significantly enhanced the effect of precipitation alone on vegetation and considerably improved the hydrothermal conditions for vegetation growth. Yuling Zhao focused on the change in grassland NPP in Inner Mongolia from 2000 to 2014 and concluded that the NPP of grassland showed an increasing trend, which is consistent with the findings of this research (Zhao et al., 2019). However, the research conducted by Jay Angerer concluded that the NPP in Inner Mongolia showed a slow downward trend, which is different from the conclusion of this study (Jay et al., 2008), and this discrepancy is likely caused by the different time periods selected for the research. In general, the Qimeng city area of Inner Mongolia is arid and water deficient, and the vegetation lacks sufficient water sources, so rainfall has always been the dominant influencing factor. Soil temperature will affect soil water content and soil erosion, which will indirectly affect the growth of vegetation. Different soil types have different physical and chemical properties and water-holding capacities, which play an important role in determining vegetation growth. The relative contribution of climate factors was slightly higher than that of human activities. The rainfall in Qimeng city showed a slight increasing trend, but it had a significant impact on the growth of vegetation, so the effect of the climate variations on NPP was meaningful. In some areas with more precipitation, such as the southeastern part of Ordos, climate variations are the dominant factor.

4.2. The impact and relative contribution of human activities

The rapid economic growth of Qimeng city has had an important impact on vegetation, and the significant development of coal, natural gas and oil resources has also brought great challenges to the ecological environment in Inner Mongolia (Liu et al., 2021a). Relevant studies have shown that with economic development, population growth, and urbanization, the vegetation in the urban center and its adjacent areas will be rapidly degraded (Du et al., 2020, Liu et al., 2021a). In contrast, the vegetation coverage in Qimeng city did not decline rapidly due to the population influx but increased with increasing GDP. The economy's improvement allowed the government to invest more funds toward improving the ecological environment (Chen et al., 2021b). This fact is consistent with Hu's view that economic development and ecological protection can form a virtuous circle (Hu & Xia, 2019). First, because a large amount of wasteland has been developed into cultivated land, the NPP of cultivated land is higher than that of unused land. Compared with grasslands, crops tend to grow well with human irrigation and pest control. Second, economic development has played an essential role in the government's ecological protection projects (Kang et al., 2021, Matsushita et al., 2007). According to Liang He's research, the improvement of the NDVI on the Loess Plateau and the cumulative area of farmland converted to forest in various provinces in the Loess Plateau have a strong correlation with the NDVI, and the main factor affecting the change in the NDVI is human activities (He et al., 2022). There are some differences in this study. Specifically, human activities were found to be an important factor driving NPP changes, slightly more important than environmental factors, with a relative contribution of 51.75%. This is because compared with other provinces on the Loess Plateau, Inner Mongolia has a smaller, less dense population, so the relative contribution of human activities to NPP is lower than that on the Loess Plateau. A similar example is observed in the Sanjiangyuan area. The population density of the Sanjiangyuan area is lower, and the conclusion is that environmental factors are much stronger drivers than human activities (Gao et al., 2021). This result shows that the greater the population density is, the greater the relative impact of human activities on vegetation growth. The spatial distribution of population density and economic development is consistent with NPP, not only in terms of the role of humans in improving ecological health but also because the most suitable growth environments for humans and plants overlap, and the environment where humans live is often accompanied by more time for plant growth.

4.3. The evaluation of nonlinear fitting and linear fitting

Nonlinear fitting requires determining the degree of the polynomial to obtain the best possible results. In this study,

R^{2}

was used to evaluate the fitting. In general simulations, a higher

R^{2}

value is considered better. However, in this study, this is not the case.

{NPP}_{cli}

attempted to calculate NPP values based solely on climate factors. In regions with minimal or no human activities, a higher simulated

R^{2}

value is preferred. However, in regions with significant human activities, excessively high

R^{2}

values are not desirable. Two representative regions, the Gander Mountain and Jiufeng Mountain Nature Reserves, were selected and are shown in Fig. 10. In these two regions, human activities are negligible. In the Gander Mountain Nature Reserve, the

R^{2}

value for nonlinear regression is slightly higher than that for linear regression, while in the Jiufeng Mountain Nature Reserve, the

R^{2}

value for linear regression is significantly lower than that for nonlinear regression, even below 0.2 overall. This indicates that linear regression fails to accurately describe the relationship between climate factors and NPP values in these regions. On the other hand, nonlinear regression performs well and better aligns with the actual situation. However, importantly, overfitting can easily occur with nonlinear fitting, especially considering the 18-year duration of this study. In other words, if the degree of the polynomial is too high with too many terms, the

R^{2}

values will be high throughout the entire study area. Therefore, we further compare the

R^{2}

values between linear and nonlinear regression in the urban area of Baotou city. In urban areas, an excessively high

R^{2}

value in nonlinear regression indicates overfitting. Although the

R^{2}

value for nonlinear regression is slightly higher than that for linear regression in the urban area, the overall

R^{2}

values are below 0.4, and the

R^{2}

values are generally low near the urban area. This indicates that nonlinear regression does not suffer from overfitting. Through the comparison of these three representative regions, it can be mathematically demonstrated that nonlinear fitting performs better than linear fitting.

4.4. Effectiveness, limitations, and future directions

There are some limitations in this study. First, the temporal resolution of the NPP dataset used is annual, while the driving factors for NPP can vary in different months within the same region. Second, it does not encompass all the factors that influence vegetation growth changes. We only considered drivers of climate variations and human activities obtained through publicly available datasets, with less consideration given to factors such as built-up land changes and changes in biological populations. Third, in this paper, nonlinear multivariate regression was used in residual trend analysis instead of linear regression, which showed innovation and provided an example of the superiority of nonlinear multivariate regression over linear regression. However, it still needs further validation and recognition by more researchers. In the geographic detector, this study only used parameter optimization algorithms for the discretization of continuous variables without employing natural classification methods. Different discretization methods can yield different detection results; hence, these differences should be explored in future research. There should be greater utilization of the online calculation and analysis capabilities of the Google Earth Engine (GEE) platform, which allows for studying larger areas using data with high spatial and temporal resolutions without considering local device performance issues (Gorelick et al., 2017). Regarding the consideration of human factors, population and GDP were selected, but the driving factors of animal husbandry were not included. The study area of this paper primarily focuses on the southwestern part of Inner Mongolia, which is predominantly characterized by typical grasslands and desert grasslands. The impact of animal husbandry on this study area is limited, but if the study area is expanded to encompass all of Inner Mongolia or even China, then animal husbandry should be included as a driving factor.

The idea of nonlinear residual regression is very interesting. Simulating the value of NPP should consider the influence of only climate variables using polynomial models, and the results should be compared with the actual NPP to assess the impact of human activities on NPP in the area. If we take nonlinear regression further and directly use machine learning or deep learning to simulate NPP based on climate variables while incorporating more driving factors, we can obtain more accurate predictive results (Ahmad et al., 2023). By including all driving factors in deep learning models, we can establish models to simulate and predict NPP, enabling a precise and scientific assessment of the impact of environmental changes on NPP. This represents a transition from qualitative to quantitative analysis and from simulation to prediction.

5. Conclusion

The Qimeng city area is a key area for sand control and is the site of the Three-North Shelter Forest. From 2003 to 2020, when the climate tended to be humid and rainfall increased, combined with the local government's active efforts to convert farmland to forests and invest in ecological protection projects, the NPP in the Qimeng city area increased significantly. The increase in NPP in Qimeng city is mainly due to the combined effect of human activities and the natural environment, accounting for 37.695% of the study area, and the impact of human activities is slightly higher than that of the environment at 51.75%. The influence of each factor on NPP in order of importance is annual precipitation > population density > GDP > soil type > soil temperature > soil moisture > temperature > elevation > solar radiation. Among natural factors, precipitation and soil type played an important role in the spatial distribution of NPP, and rainfall was the dominant factor in QC. The interaction of driving factors enhances its explanatory power for the spatial distribution of NPP through linear or nonlinear effects. The combined effect of soil types and precipitation has the highest explanatory power for the spatial distribution of NPP. Annual precipitation and temperature had a combined explanatory power of more than 70%, and the interaction of soil types and precipitation had the highest explanatory power of more than 80%. Soil type and soil temperature had an explanatory power of more than 40% and explained NPP changes well. Among the human activity factors, the population and economy had similar effects on NPP. Population density and GDP had an explanatory power of more than 60%. With the increases in population density and GDP, the average value of NPP is also rising because locals actively implement afforestation and ecological protection projects.

MOD17A3HGF, SRTM dataset, and MCD12Q106 used in this paper can be downloaded from Google Earth Engine. Precipitation and temperature data are selected from the Resource and Environmental Sciences Data Center of the Chinese Academy of Sciences. The detailed URLs of the data sources are described in Section 2.2. (Data sources and processing). All data are from public datasets.

CRediT authorship contribution statement

Huazhu Xue: Conceptualization, Methodology, Validation, Software. Yunpeng Chen: Data curation, Writing – original draft, Visualization, Software. Guotao Dong: Investigation, Writing – review & editing. Jinyu Li: Visualization.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This research was funded by the National Natural Science Foundation of China (grant number 51779099), the Key Scientific and Technological Project of Henan Province (232102320247), and the Fundamental Research Funds for the Universities of Henan Province (NSFRF230631).

Data availability

All data are from public datasets.

References

Ahmad et al., 2023
R. Ahmad, B. Yang, G. Ettlin, A. Berger, P. Rodríguez-Bocca
A machine-learning based ConvLSTM architecture for NDVI forecasting
Int. Trans. Oper. Res., 30 (2023), pp. 2025-2048, 10.1111/itor.12887
View in Scopus Google Scholar
Alkama et al., 2022
R. Alkama, G. Forzieri, G. Duveiller, G. Grassi, S. Liang, A. Cescatti
Vegetation-based climate mitigation in a warmer and greener World
Nat. Commun., 13 (2022), p. 606, 10.1038/s41467-022-28305-9
View in Scopus Google Scholar
Andaryani et al., 2023
S. Andaryani, V. Nourani, F. Ershadfath, F. Hashemi, A.T. Haghighi, S. Keesstra
The detection and monitoring of pollution caused by gold mining using a vegetation cover index
Environ. Sci. Pollut. Res., 30 (2023), pp. 8020-8035, 10.1007/s11356-022-22773-8
View in Scopus Google Scholar
Asefa et al., 2020
M. Asefa, M. Cao, Y. He, E. Mekonnen, X. Song, J. Yang
Ethiopian vegetation types, climate and topography
Plant Diversity, 42 (2020), pp. 302-311, 10.1016/j.pld.2020.04.004
View PDF View article View in Scopus Google Scholar
Chen et al., 2021a
S.-T. Chen, B. Guo, R. Zhang, W.-Q. Zang, C.-X. Wei, H.-W. Wu, X. Yang, X.-Y. Zhen, X. Li, D.-F. Zhang, B.-M. Han, H.-L. Zhang
Quantitatively determine the dominant driving factors of the spatial-temporal changes of vegetation NPP in the Hengduan Mountain area during 2000–2015
J. Mt. Sci., 18 (2021), pp. 427-445, 10.1007/s11629-020-6404-9
View in Scopus Google Scholar
Chen et al., 2021b
X. Chen, X. Li, J. Yang
The spatial and temporal dynamics of phytoplankton community and their correlation with environmental factors in Wuliangsuhai Lake
China. Arabian Journal of Geosciences, 14 (2021), p. 713, 10.1007/s12517-021-07071-w
Google Scholar
Chen et al., 2021c
Z.Q. Chen, B.L. Yu, C.S. Yang, Y.Y. Zhou, S.J. Yao, X.J. Qian, C.X. Wang, B. Wu, J.P. Wu
An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration
Earth Syst. Sci. Data, 13 (2021), pp. 889-906, 10.5194/essd-13-889-2021
View in Scopus Google Scholar
Di Pasquale et al., 2020
G. Di Pasquale, A. Saracino, L. Bosso, D. Russo, A. Moroni, G. Bonanomi, E. Allevato
Coastal Pine-Oak Glacial Refugia in the Mediterranean Basin: A Biogeographic Approach Based on Charcoal Analysis and Spatial Modelling
Forests, 11 (2020), p. 673, 10.3390/f11060673
View in Scopus Google Scholar
Du et al., 2020
J. Du, Z. Quan, S. Fang, C. Liu, J. Wu, Q. Fu
Spatiotemporal changes in vegetation coverage and its causes in China since the Chinese economic reform
Environ. Sci. Pollut. Res., 27 (2020), pp. 1144-1159, 10.1007/s11356-019-06609-6
View in Scopus Google Scholar
Du et al., 2016
B.Z. Du, L. Zhen, H.M. Yan, R. De Groot
Effects of Government Grassland Conservation Policy on Household Livelihoods and Dependence on Local Grasslands: Evidence from Inner Mongolia, China
Sustainability, 8 (2016), p. 1314, 10.3390/su8121314
View in Scopus Google Scholar
Frank et al., 2015
D. Frank, M. Reichstein, M. Bahn, K. Thonicke, D. Frank, M.D. Mahecha, P. Smith, M. Van Der Velde, S. Vicca, F. Babst, C. Beer, N. Buchmann, J.G. Canadell, P. Ciais, W. Cramer, A. Ibrom, F. Miglietta, B. Poulter, A. Rammig, S.I. Seneviratne, A. Walz, M. Wattenbach, M.A. Zavala, J. Zscheischler
Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts
Glob. Chang. Biol., 21 (2015), pp. 2861-2880, 10.1111/gcb.12916
View in Scopus Google Scholar
Gao et al., 2021
S. Gao, G. Dong, X. Jiang, T. Nie, H. Yin, X. Guo
Quantification of Natural and Anthropogenic Driving Forces of Vegetation Changes in the Three-River Headwater Region during 1982–2015 Based on Geographical Detector Model
Remote Sens. (Basel), 13 (2021), p. 4175, 10.3390/rs13204175
View in Scopus Google Scholar
Gerten et al., 2008
Gerten, D., Luo, Y., Le Maire, G., Parton, W.J., Keough, C., Weng, E., Beier, C., Ciais, P., Cramer, W., Dukes, J.S., Hanson, P.J., Knapp, A.a.K., Linder, S., Nepstad, D., Rustad, L., Sowerby, A., 2008. Modelled effects of precipitation on ecosystem carbon and water dynamics in different climatic zones. Global Change Biology 14, 2365-2379. 10.1111/j.1365-2486.2008.01651.x.
Google Scholar
Gocic and Trajkovic, 2013
M. Gocic, S. Trajkovic
Analysis of changes in meteorological variables using Mann-Kendall and Sen's slope estimator statistical tests in Serbia
Global Planet. Change, 100 (2013), pp. 172-182, 10.1016/j.gloplacha.2012.10.014
View PDF View article View in Scopus Google Scholar
Gorelick et al., 2017
N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, R. Moore
Google Earth Engine: Planetary-scale geospatial analysis for everyone
Remote Sens. Environ., 202 (2017), pp. 18-27, 10.1016/j.rse.2017.06.031
View PDF View article View in Scopus Google Scholar
Guo et al., 2021
D.a. Guo, X. Song, R. Hu, S. Cai, X. Zhu, Y. Hao
Grassland type-dependent spatiotemporal characteristics of productivity in Inner Mongolia and its response to climate factors
Sci. Total Environ., 775 (2021), p. 145644
View PDF View article View in Scopus Google Scholar
Guo et al., 2020
E.L. Guo, Y.F. Wang, Y.H. Bao, Z.Y. Sun, Y.L. Bao, L. Quan
Spatiotemporal variation of heat and cold waves and their potential relation with the large-scale atmospheric circulation across Inner Mongolia, China
Theor. Appl. Climatol., 142 (2020), pp. 643-659, 10.1007/s00704-020-03331-z
View in Scopus Google Scholar
Hao et al., 2021
L. Hao, S. Wang, X. Cui, Y. Zhai
Spatiotemporal Dynamics of Vegetation Net Primary Productivity and Its Response to Climate Change in Inner Mongolia from 2002 to 2019
Sustainability, 13 (2021), p. 13110, 10.3390/su132313310
Google Scholar
He et al., 2022
L. He, J.B. Guo, Q.O. Jiang, Z.Y. Zhang, S.P. Yu
How did the Chinese Loess Plateau turn green from 2001 to 2020? An explanation using satellite data
Catena, 214 (2022), Article 106246, 10.1016/j.catena.2022.106246
View PDF View article View in Scopus Google Scholar
Hu and Xia, 2019
M. Hu, B. Xia
A significant increase in the normalized difference vegetation index during the rapid economic development in the Pearl River Delta of China
Land Degrad. Dev., 30 (2019), pp. 359-370, 10.1002/ldr.3221
View in Scopus Google Scholar
Huang and Kong, 2016
Huang, S., Kong, J., 2016. Assessing Land Degradation Dynamics and Distinguishing Human-Induced Changes from Climate Factors in the Three-North Shelter Forest Region of China. 5, 158. 10.3390/ijgi5090158.
Google Scholar
Jay et al., 2008
A. Jay, H. Guodong, F. Ikuko, H. Kris
Climate Change and Ecosystems of Asia With Emphasis on Inner Mongolia and Mongolia
Rangelands, 30 (2008), pp. 46-51, 10.2111/1551-501X(2008)30[46:CCAEOA]2.0.CO;2
Google Scholar
Kang et al., 2021
Y. Kang, E. Guo, Y. Wang, Y. Bao, Y. Bao, N. Mandula
Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019
Remote Sens. (Basel), 13 (17) (2021), p. 3357
Crossref View in Scopus Google Scholar
Lee and Sohn, 2011
E.-H. Lee, B.-J. Sohn
Recent increasing trend in dust frequency over Mongolia and Inner Mongolia regions and its association with climate and surface condition change
Atmos. Environ., 45 (2011), pp. 4611-4616, 10.1016/j.atmosenv.2011.05.065
View PDF View article View in Scopus Google Scholar
Lin et al., 2012
H.L. Lin, J. Zhao, T.G. Liang, J. Bogaert, Z.Q. Li
A Classification Indices-Based Model for Net Primary Productivity (Npp) and Potential Productivity of Vegetation in China
Int. J. Biomath., 5 (2012), p. 1260009, 10.1142/S1793524512600091
View in Scopus Google Scholar
Liu et al., 2021b
C.L. Liu, W.L. Li, W.Y. Wang, H.K. Zhou, T.G. Liang, F.J. Hou, J. Xu, P.F. Xue
Quantitative spatial analysis of vegetation dynamics and potential driving factors in a typical alpine region on the northeastern Tibetan Plateau using the Google Earth Engine
Catena, 206 (2021), Article 105500, 10.1016/j.catena.2021.105500
View PDF View article View in Scopus Google Scholar
Liu et al., 2017a
C. Liu, Y. Xu, P. Sun, A. Huang, W. Zheng
Land use change and its driving forces toward mutual conversion in Zhangjiakou City, a farming-pastoral ecotone in Northern China
Environ. Monit. Assess., 189 (2017), 10.1007/s10661-017-6218-6
Google Scholar
Liu et al., 2021a
C. Liu, W. Xie, T. Lao, Y.-T. Yao, J. Zhang
Application of a novel grey forecasting model with time power term to predict China's GDP
Grey Syst.-Theory Appl., 11 (2021), pp. 343-357, 10.1108/gs-05-2020-0065
View in Scopus Google Scholar
Liu et al., 2017b
H. Liu, M. Zhang, Z. Lin
Relative importance of climate changes at different time scales on net primary productivity-a case study of the Karst area of northwest Guangxi, China
Environ. Monitor. Assessment, 189 (2017), 10.1007/s10661-017-6251-5
Google Scholar
Liu et al., 2018
H.Y. Liu, M.Y. Zhang, Z.S. Lin, X.J. Xu
Spatial heterogeneity of the relationship between vegetation dynamics and climate change and their driving forces at multiple time scales in Southwest China
Agric. For. Meteorol., 256 (2018), pp. 10-21, 10.1016/j.agrformet.2018.02.015
View PDF View article View in Scopus Google Scholar
Lu, 2018
T.C. Lu
Impacts of changes in rangeland management laws and policies on grazing flexibility and pastoral livelihoods in Inner Mongolia, China
Int J Sust Dev World, 25 (2018), pp. 166-175, 10.1080/13504509.2017.1320596
View in Scopus Google Scholar
Matsushita et al., 2007
B. Matsushita, W. Yang, J. Chen, Y. Onda, G. Qiu
Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to topographic effects: A case study in high-density cypress forest
Sensors, 7 (2007), pp. 2636-2651, 10.3390/s7112636
View in Scopus Google Scholar
Mu et al., 2013
S.J. Mu, S.X. Zhou, Y.Z. Chen, J.L. Li, W.M. Ju, I.O.A. Odeh
Assessing the impact of restoration-induced land conversion and management alternatives on net primary productivity in Inner Mongolian grassland, China
Global Planet. Change, 108 (2013), pp. 29-41, 10.1016/j.gloplacha.2013.06.007
View PDF View article View in Scopus Google Scholar
Negi et al., 2019
V.S. Negi, R. Pathak, R.S. Rawal, I.D. Bhatt, S. Sharma
Long-term ecological monitoring on forest ecosystems in Indian Himalayan Region: Criteria and indicator approach
Ecol. Ind., 102 (2019), pp. 374-381, 10.1016/j.ecolind.2019.02.035
View PDF View article View in Scopus Google Scholar
Pan et al., 2011
Y. Pan, R.A. Birdsey, J. Fang, R. Houghton, P.E. Kauppi, W.A. Kurz, O.L. Phillips, A. Shvidenko, S.L. Lewis, J.G. Canadell, P. Ciais, R.B. Jackson, S.W. Pacala, A.D. Mcguire, S. Piao, A. Rautiainen, S. Sitch, D. Hayes
A Large and Persistent Carbon Sink in the World's Forests
Science, 333 (2011), pp. 988-993, 10.1126/science.1201609
View in Scopus Google Scholar
Pei et al., 2018
F.S. Pei, C.J. Wu, X.P. Liu, X. Li, K.Q. Yang, Y. Zhou, K. Wang, L. Xu, G.R. Xia
Monitoring the vegetation activity in China using vegetation health indices
Agric. For. Meteorol., 248 (2018), pp. 215-227, 10.1016/j.agrformet.2017.10.001
View PDF View article View in Scopus Google Scholar
Qu et al., 2020
S. Qu, L.C. Wang, A.W. Lin, D.Q. Yu, M.X. Yuan, C.A. Li
Distinguishing the impacts of climate change and anthropogenic factors on vegetation dynamics in the Yangtze River Basin China
Ecol. Indicators, 108 (2020), Article 105724, 10.1016/j.ecolind.2019.105724
View PDF View article View in Scopus Google Scholar
Rabus et al., 2003
B. Rabus, M. Eineder, A. Roth, R. Bamler
The shuttle radar topography mission – a new class of digital elevation models acquired by spaceborne radar
ISPRS J. Photogramm. Remote Sens., 57 (2003), pp. 241-262, 10.1016/s0924-2716(02)00124-7
View PDF View article View in Scopus Google Scholar
Rusciano et al., 2022
E. Rusciano, M. Belbeoch, V. Turpin, M. Kramp, L. Jiang, A. Lize, M. Krieger
Odyssey Project: Contributing Actively to the Implementation of the Global Ocean Observing System
Mar. Technol. Soc. J., 56 (2022), p. 132, 10.4031/MTSJ.56.3.27
View in Scopus Google Scholar
Sharma and Saha, 2017
S. Sharma, A.K. Saha
Statistical analysis of rainfall trends over Damodar River basin, India
Arab. J. Geosci., 10 (2017), p. 319, 10.1007/s12517-017-3096-8
Google Scholar
Shi et al., 2021
S.Y. Shi, J.J. Yu, F. Wang, P. Wang, Y.C. Zhang, K. Jin
Quantitative contributions of climate change and human activities to vegetation changes over multiple time scales on the Loess Plateau
Sci. Total Environ., 755 (2021), Article 142419, 10.1016/j.scitotenv.2020.142419
View PDF View article View in Scopus Google Scholar
Su et al., 2011
C.H. Su, B.J. Fu, Y.H. Lu, N. Lu, Y. Zeng, A.N. He, H. Lamparski
Land use change and anthropogenic driving forces: A case study in Yanhe River Basin
Chin. Geogr. Sci., 21 (2011), pp. 587-599, 10.1007/s11769-011-0495-8
View in Scopus Google Scholar
Sun et al., 2021
Z.H. Sun, Z.A. Mao, L.Y. Yang, Z. Liu, J.C. Han, H.Y. Wanag, W. He
Impacts of climate change and afforestation on vegetation dynamic in the Mu Us Desert, China
Ecolog. Indicators, 129 (2021), Article 108020, 10.1016/j.ecolind.2021.108020
View PDF View article View in Scopus Google Scholar
Sun et al., 2015a
W.Y. Sun, X.Y. Song, X.M. Mu, P. Gao, F. Wang, G.J. Zhao
Spatiotemporal vegetation cover variations associated with climate change and ecological restoration in the Loess Plateau
Agric. For. Meteorol., 209 (2015), pp. 87-99, 10.1016/j.agrformet.2015.05.002
View PDF View article View in Scopus Google Scholar
Sun et al., 2015b
Y. Sun, Y. Yang, L. Zhang, Z. Wang
The relative roles of climate variations and human activities in vegetation change in North China
Phys. Chem. Earth, 87–88 (2015), pp. 67-78, 10.1016/j.pce.2015.09.017
View PDF View article View in Scopus Google Scholar
Tosunoglu and Kisi, 2017
F. Tosunoglu, O. Kisi
Trend Analysis of Maximum Hydrologic Drought Variables Using Mann-Kendall and Sen's Innovative Trend Method
River Res. Appl., 33 (2017), pp. 597-610, 10.1002/rra.3106
View in Scopus Google Scholar
Turner et al., 2006
D.P. Turner, W.D. Ritts, W.B. Cohen, S.T. Gower, S.W. Running, M. Zhao, M.H. Costa, A.A. Kirschbaum, J.M. Ham, S.R. Saleska, D.E. Ahl
Evaluation of MODIS NPP and GPP products across multiple biomes
Remote Sens. Environ., 102 (2006), pp. 282-292, 10.1016/j.rse.2006.02.017
View PDF View article View in Scopus Google Scholar
Wang et al., 2017
Z. Wang, X. Deng, W. Song, Z. Li, J. Chen
What is the main cause of grassland degradation? A case study of grassland ecosystem service in the middle-south Inner Mongolia
Catena, 150 (2017), pp. 100-107, 10.1016/j.catena.2016.11.014
View PDF View article View in Scopus Google Scholar
Wang et al., 2021a
H. Wang, S.J. Yan, Z. Liang, K.W. Jiao, D.L. Li, F.L. Wei, S.C. Li
Strength of association between vegetation greenness and its drivers across China between 1982 and 2015: Regional differences and temporal variations
Ecol. Ind., 128 (2021), Article 107831, 10.1016/j.ecolind.2021.107831
View PDF View article View in Scopus Google Scholar
Wang et al., 2021b
S. Wang, Y. Zhang, W. Ju, J.M. Chen, A. Cescatti, J. Sardans, I.A. Janssens, M. Wu, J.A. Berry, J.E. Campbell, M. Fernandez-Martinez, R. Alkama, S. Sitch, W.K. Smith, W. Yuan, W. He, D. Lombardozzi, M. Kautz, D. Zhu, S. Lienert, E. Kato, B. Poulter, T.G.M. Sanders, I. Kruger, R. Wang, N. Zeng, H. Tian, N. Vuichard, A.K. Jain, A. Wiltshire, D.S. Goll, J. Penuelas
Response to Comments on “Recent global decline of CO(2) fertilization effects on vegetation photosynthesis”
Science, 373 (2021), p. eabg7484, 10.1126/science.abg7484
Google Scholar
Whetton et al., 2017
R. Whetton, Y. Zhao, S. Shaddad, A.M. Mouazen
Nonlinear parametric modelling to study how soil properties affect crop yields and NDVI
Comput. Electron. Agric., 138 (2017), pp. 127-136, 10.1016/j.compag.2017.04.016
View PDF View article View in Scopus Google Scholar
Wu et al., 2015
J.G. Wu, Q. Zhang, A. Li, C.Z. Liang
Historical landscape dynamics of Inner Mongolia: patterns, drivers, and impacts
Landsc. Ecol., 30 (2015), pp. 1579-1598, 10.1007/s10980-015-0209-1
View in Scopus Google Scholar
Xiao and Moody, 2004
J.F. Xiao, A. Moody
Trends in vegetation activity and their climatic correlates: China 1982 to 1998
Int. J. Remote Sens., 25 (2004), pp. 5669-5689, 10.1080/01431160410001735094
View in Scopus Google Scholar
Xin et al., 2008
Z. Xin, J. Xu, W. Zheng
Spatiotemporal variations of vegetation cover on the Chinese Loess Plateau (1981–2006): Impacts of climate changes and human activities
Sci. China. Ser. D Earth Sci., 51 (2008), pp. 67-78, 10.1007/s11430-007-0137-2
View in Scopus Google Scholar
Yang et al., 2020
H.F. Yang, D.D. Hu, H. Xu, X.N. Zhong
Assessing the spatiotemporal variation of NPP and its response to driving factors in Anhui province, China
Environ. Sci. Pollut. Res., 27 (2020), pp. 14915-14932, 10.1007/s11356-020-08006-w
View in Scopus Google Scholar
Yang et al., 2019
J. Yang, Z.Q. Wan, S. Borjigin, D. Zhang, Y.L. Yan, Y.L. Chen, R. Gu, Q.Z. Gao
Changing Trends of NDVI and Their Responses to Climatic Variation in Different Types of Grassland in Inner Mongolia from 1982 to 2011
Sustainability, 11 (2019), p. 3256, 10.3390/su11123256
View in Scopus Google Scholar
Yu et al., 2021
Y. Yu, Y. Wu, P. Wang, Y. Zhang, L.E. Yang, X. Cheng, J. Yan
Grassland Subsidies Increase the Number of Livestock on the Tibetan Plateau: Why Does the “Payment for Ecosystem Services” Policy Have the Opposite Outcome?
Sustainability, 13 (2021), 10.3390/su13116208
Google Scholar
Zhang et al., 2018a
H. Zhang, J. Fan, W. Cao, W. Harris, Y. Li, W. Chi, S. Wang
Response of wind erosion dynamics to climate change and human activity in Inner Mongolia, China during 1990 to 2015
Sci. Total Environ., 639 (2018), pp. 1038-1050, 10.1016/j.scitotenv.2018.05.082
View PDF View article View in Scopus Google Scholar
Zhang et al., 2018b
Q. Zhang, D. Kong, P. Shi, V.P. Singh, P. Sun
Vegetation phenology on the Qinghai-Tibetan Plateau and its response to climate change (1982–2013)
Agric. For. Meteorol., 248 (2018), pp. 408-417, 10.1016/j.agrformet.2017.10.026
View PDF View article Google Scholar
Zhang et al., 2019
B. Zhang, P. Sun, G. Jiang, R. Zhang, J. Gao
Rural land use transition of mountainous areas and policy implications for land consolidation in China
J. Geog. Sci., 29 (2019), pp. 1713-1730, 10.1007/s11442-019-1687-x
View in Scopus Google Scholar
Zhang and Zwiers, 2004
X.B. Zhang, F.W. Zwiers
Comment on “Applicability of prewhitening to eliminate the influence of serial correlation on the Mann-Kendall test” by Sheng Yue and Chun Yuan Wang
Water Resour. Res., 40 (2004), 10.1029/2003wr002073
Google Scholar
Zhao et al., 2015
X. Zhao, H. Hu, H. Shen, D. Zhou, L. Zhou, R.B. Myneni, J. Fang
Satellite-indicated long-term vegetation changes and their drivers on the Mongolian Plateau
Landsc. Ecol., 30 (2015), pp. 1599-1611, 10.1007/s10980-014-0095-y
View in Scopus Google Scholar
Zhao et al., 2019
Y. Zhao, H. Liu, A. Zhang, X. Cui, A. Zhao
Spatiotemporal variations and its influencing factors of grassland net primary productivity in Inner Mongolia, China during the period 2000–2014
J. Arid Environ., 165 (2019), pp. 106-118, 10.1016/j.jaridenv.2019.01.004
View PDF View article View in Scopus Google Scholar

Cited by (4)

Spatiotemporal changes in urban forest carbon sequestration capacity and its potential drivers in an urban agglomeration: Implications for urban CO<inf>2</inf> emission mitigation under China's rapid urbanization

2024, Ecological Indicators
Urban forests can absorb carbon dioxide for urban CO₂ emission mitigation. However, the potential capacity of urban forest carbon sequestration (CS) and its drivers remain unclear in urban agglomerations under rapid urbanization. In our study, the net primary productivity (NPP) of built-up areas was reconstructed in the Harbin-Changchun urban agglomeration (HCUA) from 2000 to 2020 to reflect urban forest CS, and the drivers of spatial urban forest CS patterns were further explored using the Geodetector model. Our results showed that the HCUA has experienced rapid urbanization over the past 20 years. Across the urbanization gradient, the CS capacity was higher in new developing built-up areas than in the old developed built-up areas for all years. The CS capacity of urban forests increased gradually from 2000 to 2020, especially in large built-up areas. The urban forest CS was skewed toward low (<100 g·m⁻²) and medium value (100–300 g·m⁻²) class distributions in all years; however, the proportion of high CS (>300 g·m⁻²) show an overall increasing trend from 2000 to 2020, especially in small, low-altitude and old developed built-up areas. The total CS of built-up areas increased from 0.35 Mt·C·yr⁻¹ in 2000 to 2.06 Mt·C·yr⁻¹ in 2020, and the urban forests in the HCUA could offset approximately 2.23 % of urban carbon emissions in 2000, increasing to 5.08 % in 2020. Natural factors, such as temperature, mainly determined changes of the spatial urban forest CS distribution. In addition, we found that urban morphology factors, such as urban build-up area, construction height, population density, and gross national product, can significantly influence urban forest CS. We further found there may exist the threshold of urban built-up area and gross national product significantly affecting urban forest CS variation. The interaction between natural and anthropogenic factors had stronger explanatory power for the spatial variation of CS. Our study can help city managers formulate low-carbon development strategies to address the negative impacts of climate change and realize the low-carbon development of cities.
Quantitative Assessment of the Impact of the Three-North Shelter Forest Program on Vegetation Net Primary Productivity over the Past Two Decades and Its Environmental Benefits in China

2024, Sustainability (Switzerland)
Dynamic Changes in Vegetation Ecological Quality in the Tarim Basin and Its Response to Extreme Climate during 2000–2022

2024, Forests
Enhancing Land Surface Temperature Reconstruction: An Improved Interpolation of Mean Anomalies Based on the Digital Elevation Model (DEM-IMA)

2024, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

[1] Ahmad et al., 2023
R. Ahmad, B. Yang, G. Ettlin, A. Berger, P. Rodríguez-Bocca
A machine-learning based ConvLSTM architecture for NDVI forecasting
Int. Trans. Oper. Res., 30 (2023), pp. 2025-2048, 10.1111/itor.12887
View in Scopus Google Scholar

[2] Alkama et al., 2022
R. Alkama, G. Forzieri, G. Duveiller, G. Grassi, S. Liang, A. Cescatti
Vegetation-based climate mitigation in a warmer and greener World
Nat. Commun., 13 (2022), p. 606, 10.1038/s41467-022-28305-9
View in Scopus Google Scholar

[3] Andaryani et al., 2023
S. Andaryani, V. Nourani, F. Ershadfath, F. Hashemi, A.T. Haghighi, S. Keesstra
The detection and monitoring of pollution caused by gold mining using a vegetation cover index
Environ. Sci. Pollut. Res., 30 (2023), pp. 8020-8035, 10.1007/s11356-022-22773-8
View in Scopus Google Scholar

[4] Asefa et al., 2020
M. Asefa, M. Cao, Y. He, E. Mekonnen, X. Song, J. Yang
Ethiopian vegetation types, climate and topography
Plant Diversity, 42 (2020), pp. 302-311, 10.1016/j.pld.2020.04.004
View PDF View article View in Scopus Google Scholar

[5] Chen et al., 2021a
S.-T. Chen, B. Guo, R. Zhang, W.-Q. Zang, C.-X. Wei, H.-W. Wu, X. Yang, X.-Y. Zhen, X. Li, D.-F. Zhang, B.-M. Han, H.-L. Zhang
Quantitatively determine the dominant driving factors of the spatial-temporal changes of vegetation NPP in the Hengduan Mountain area during 2000–2015
J. Mt. Sci., 18 (2021), pp. 427-445, 10.1007/s11629-020-6404-9
View in Scopus Google Scholar

[6] Chen et al., 2021b
X. Chen, X. Li, J. Yang
The spatial and temporal dynamics of phytoplankton community and their correlation with environmental factors in Wuliangsuhai Lake
China. Arabian Journal of Geosciences, 14 (2021), p. 713, 10.1007/s12517-021-07071-w
Google Scholar

[7] Chen et al., 2021c
Z.Q. Chen, B.L. Yu, C.S. Yang, Y.Y. Zhou, S.J. Yao, X.J. Qian, C.X. Wang, B. Wu, J.P. Wu
An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration
Earth Syst. Sci. Data, 13 (2021), pp. 889-906, 10.5194/essd-13-889-2021
View in Scopus Google Scholar

[8] Di Pasquale et al., 2020
G. Di Pasquale, A. Saracino, L. Bosso, D. Russo, A. Moroni, G. Bonanomi, E. Allevato
Coastal Pine-Oak Glacial Refugia in the Mediterranean Basin: A Biogeographic Approach Based on Charcoal Analysis and Spatial Modelling
Forests, 11 (2020), p. 673, 10.3390/f11060673
View in Scopus Google Scholar

[9] Du et al., 2020
J. Du, Z. Quan, S. Fang, C. Liu, J. Wu, Q. Fu
Spatiotemporal changes in vegetation coverage and its causes in China since the Chinese economic reform
Environ. Sci. Pollut. Res., 27 (2020), pp. 1144-1159, 10.1007/s11356-019-06609-6
View in Scopus Google Scholar

[10] Du et al., 2016
B.Z. Du, L. Zhen, H.M. Yan, R. De Groot
Effects of Government Grassland Conservation Policy on Household Livelihoods and Dependence on Local Grasslands: Evidence from Inner Mongolia, China
Sustainability, 8 (2016), p. 1314, 10.3390/su8121314
View in Scopus Google Scholar

[11] Frank et al., 2015
D. Frank, M. Reichstein, M. Bahn, K. Thonicke, D. Frank, M.D. Mahecha, P. Smith, M. Van Der Velde, S. Vicca, F. Babst, C. Beer, N. Buchmann, J.G. Canadell, P. Ciais, W. Cramer, A. Ibrom, F. Miglietta, B. Poulter, A. Rammig, S.I. Seneviratne, A. Walz, M. Wattenbach, M.A. Zavala, J. Zscheischler
Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts
Glob. Chang. Biol., 21 (2015), pp. 2861-2880, 10.1111/gcb.12916
View in Scopus Google Scholar

[12] Gao et al., 2021
S. Gao, G. Dong, X. Jiang, T. Nie, H. Yin, X. Guo
Quantification of Natural and Anthropogenic Driving Forces of Vegetation Changes in the Three-River Headwater Region during 1982–2015 Based on Geographical Detector Model
Remote Sens. (Basel), 13 (2021), p. 4175, 10.3390/rs13204175
View in Scopus Google Scholar

[13] Gerten et al., 2008
Gerten, D., Luo, Y., Le Maire, G., Parton, W.J., Keough, C., Weng, E., Beier, C., Ciais, P., Cramer, W., Dukes, J.S., Hanson, P.J., Knapp, A.a.K., Linder, S., Nepstad, D., Rustad, L., Sowerby, A., 2008. Modelled effects of precipitation on ecosystem carbon and water dynamics in different climatic zones. Global Change Biology 14, 2365-2379. 10.1111/j.1365-2486.2008.01651.x.
Google Scholar

[14] Gocic and Trajkovic, 2013
M. Gocic, S. Trajkovic
Analysis of changes in meteorological variables using Mann-Kendall and Sen's slope estimator statistical tests in Serbia
Global Planet. Change, 100 (2013), pp. 172-182, 10.1016/j.gloplacha.2012.10.014
View PDF View article View in Scopus Google Scholar

[15] Gorelick et al., 2017
N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, R. Moore
Google Earth Engine: Planetary-scale geospatial analysis for everyone
Remote Sens. Environ., 202 (2017), pp. 18-27, 10.1016/j.rse.2017.06.031
View PDF View article View in Scopus Google Scholar

[16] Guo et al., 2021
D.a. Guo, X. Song, R. Hu, S. Cai, X. Zhu, Y. Hao
Grassland type-dependent spatiotemporal characteristics of productivity in Inner Mongolia and its response to climate factors
Sci. Total Environ., 775 (2021), p. 145644
View PDF View article View in Scopus Google Scholar

[17] Guo et al., 2020
E.L. Guo, Y.F. Wang, Y.H. Bao, Z.Y. Sun, Y.L. Bao, L. Quan
Spatiotemporal variation of heat and cold waves and their potential relation with the large-scale atmospheric circulation across Inner Mongolia, China
Theor. Appl. Climatol., 142 (2020), pp. 643-659, 10.1007/s00704-020-03331-z
View in Scopus Google Scholar

[18] Hao et al., 2021
L. Hao, S. Wang, X. Cui, Y. Zhai
Spatiotemporal Dynamics of Vegetation Net Primary Productivity and Its Response to Climate Change in Inner Mongolia from 2002 to 2019
Sustainability, 13 (2021), p. 13110, 10.3390/su132313310
Google Scholar

[19] He et al., 2022
L. He, J.B. Guo, Q.O. Jiang, Z.Y. Zhang, S.P. Yu
How did the Chinese Loess Plateau turn green from 2001 to 2020? An explanation using satellite data
Catena, 214 (2022), Article 106246, 10.1016/j.catena.2022.106246
View PDF View article View in Scopus Google Scholar

[20] Hu and Xia, 2019
M. Hu, B. Xia
A significant increase in the normalized difference vegetation index during the rapid economic development in the Pearl River Delta of China
Land Degrad. Dev., 30 (2019), pp. 359-370, 10.1002/ldr.3221
View in Scopus Google Scholar

[21] Huang and Kong, 2016
Huang, S., Kong, J., 2016. Assessing Land Degradation Dynamics and Distinguishing Human-Induced Changes from Climate Factors in the Three-North Shelter Forest Region of China. 5, 158. 10.3390/ijgi5090158.
Google Scholar

[22] Jay et al., 2008
A. Jay, H. Guodong, F. Ikuko, H. Kris
Climate Change and Ecosystems of Asia With Emphasis on Inner Mongolia and Mongolia
Rangelands, 30 (2008), pp. 46-51, 10.2111/1551-501X(2008)30[46:CCAEOA]2.0.CO;2
Google Scholar

[23] Kang et al., 2021
Y. Kang, E. Guo, Y. Wang, Y. Bao, Y. Bao, N. Mandula
Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019
Remote Sens. (Basel), 13 (17) (2021), p. 3357
Crossref View in Scopus Google Scholar

[24] Lee and Sohn, 2011
E.-H. Lee, B.-J. Sohn
Recent increasing trend in dust frequency over Mongolia and Inner Mongolia regions and its association with climate and surface condition change
Atmos. Environ., 45 (2011), pp. 4611-4616, 10.1016/j.atmosenv.2011.05.065
View PDF View article View in Scopus Google Scholar

[25] Lin et al., 2012
H.L. Lin, J. Zhao, T.G. Liang, J. Bogaert, Z.Q. Li
A Classification Indices-Based Model for Net Primary Productivity (Npp) and Potential Productivity of Vegetation in China
Int. J. Biomath., 5 (2012), p. 1260009, 10.1142/S1793524512600091
View in Scopus Google Scholar

[26] Liu et al., 2021b
C.L. Liu, W.L. Li, W.Y. Wang, H.K. Zhou, T.G. Liang, F.J. Hou, J. Xu, P.F. Xue
Quantitative spatial analysis of vegetation dynamics and potential driving factors in a typical alpine region on the northeastern Tibetan Plateau using the Google Earth Engine
Catena, 206 (2021), Article 105500, 10.1016/j.catena.2021.105500
View PDF View article View in Scopus Google Scholar

[27] Liu et al., 2017a
C. Liu, Y. Xu, P. Sun, A. Huang, W. Zheng
Land use change and its driving forces toward mutual conversion in Zhangjiakou City, a farming-pastoral ecotone in Northern China
Environ. Monit. Assess., 189 (2017), 10.1007/s10661-017-6218-6
Google Scholar

[28] Liu et al., 2021a
C. Liu, W. Xie, T. Lao, Y.-T. Yao, J. Zhang
Application of a novel grey forecasting model with time power term to predict China's GDP
Grey Syst.-Theory Appl., 11 (2021), pp. 343-357, 10.1108/gs-05-2020-0065
View in Scopus Google Scholar

[29] Liu et al., 2017b
H. Liu, M. Zhang, Z. Lin
Relative importance of climate changes at different time scales on net primary productivity-a case study of the Karst area of northwest Guangxi, China
Environ. Monitor. Assessment, 189 (2017), 10.1007/s10661-017-6251-5
Google Scholar

[30] Liu et al., 2018
H.Y. Liu, M.Y. Zhang, Z.S. Lin, X.J. Xu
Spatial heterogeneity of the relationship between vegetation dynamics and climate change and their driving forces at multiple time scales in Southwest China
Agric. For. Meteorol., 256 (2018), pp. 10-21, 10.1016/j.agrformet.2018.02.015
View PDF View article View in Scopus Google Scholar

[31] Lu, 2018
T.C. Lu
Impacts of changes in rangeland management laws and policies on grazing flexibility and pastoral livelihoods in Inner Mongolia, China
Int J Sust Dev World, 25 (2018), pp. 166-175, 10.1080/13504509.2017.1320596
View in Scopus Google Scholar

[32] Matsushita et al., 2007
B. Matsushita, W. Yang, J. Chen, Y. Onda, G. Qiu
Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to topographic effects: A case study in high-density cypress forest
Sensors, 7 (2007), pp. 2636-2651, 10.3390/s7112636
View in Scopus Google Scholar

[33] Mu et al., 2013
S.J. Mu, S.X. Zhou, Y.Z. Chen, J.L. Li, W.M. Ju, I.O.A. Odeh
Assessing the impact of restoration-induced land conversion and management alternatives on net primary productivity in Inner Mongolian grassland, China
Global Planet. Change, 108 (2013), pp. 29-41, 10.1016/j.gloplacha.2013.06.007
View PDF View article View in Scopus Google Scholar

[34] Negi et al., 2019
V.S. Negi, R. Pathak, R.S. Rawal, I.D. Bhatt, S. Sharma
Long-term ecological monitoring on forest ecosystems in Indian Himalayan Region: Criteria and indicator approach
Ecol. Ind., 102 (2019), pp. 374-381, 10.1016/j.ecolind.2019.02.035
View PDF View article View in Scopus Google Scholar

[35] Pan et al., 2011
Y. Pan, R.A. Birdsey, J. Fang, R. Houghton, P.E. Kauppi, W.A. Kurz, O.L. Phillips, A. Shvidenko, S.L. Lewis, J.G. Canadell, P. Ciais, R.B. Jackson, S.W. Pacala, A.D. Mcguire, S. Piao, A. Rautiainen, S. Sitch, D. Hayes
A Large and Persistent Carbon Sink in the World's Forests
Science, 333 (2011), pp. 988-993, 10.1126/science.1201609
View in Scopus Google Scholar

[36] Pei et al., 2018
F.S. Pei, C.J. Wu, X.P. Liu, X. Li, K.Q. Yang, Y. Zhou, K. Wang, L. Xu, G.R. Xia
Monitoring the vegetation activity in China using vegetation health indices
Agric. For. Meteorol., 248 (2018), pp. 215-227, 10.1016/j.agrformet.2017.10.001
View PDF View article View in Scopus Google Scholar

[37] Qu et al., 2020
S. Qu, L.C. Wang, A.W. Lin, D.Q. Yu, M.X. Yuan, C.A. Li
Distinguishing the impacts of climate change and anthropogenic factors on vegetation dynamics in the Yangtze River Basin China
Ecol. Indicators, 108 (2020), Article 105724, 10.1016/j.ecolind.2019.105724
View PDF View article View in Scopus Google Scholar

[38] Rabus et al., 2003
B. Rabus, M. Eineder, A. Roth, R. Bamler
The shuttle radar topography mission – a new class of digital elevation models acquired by spaceborne radar
ISPRS J. Photogramm. Remote Sens., 57 (2003), pp. 241-262, 10.1016/s0924-2716(02)00124-7
View PDF View article View in Scopus Google Scholar

[39] Rusciano et al., 2022
E. Rusciano, M. Belbeoch, V. Turpin, M. Kramp, L. Jiang, A. Lize, M. Krieger
Odyssey Project: Contributing Actively to the Implementation of the Global Ocean Observing System
Mar. Technol. Soc. J., 56 (2022), p. 132, 10.4031/MTSJ.56.3.27
View in Scopus Google Scholar

[40] Sharma and Saha, 2017
S. Sharma, A.K. Saha
Statistical analysis of rainfall trends over Damodar River basin, India
Arab. J. Geosci., 10 (2017), p. 319, 10.1007/s12517-017-3096-8
Google Scholar

[41] Shi et al., 2021
S.Y. Shi, J.J. Yu, F. Wang, P. Wang, Y.C. Zhang, K. Jin
Quantitative contributions of climate change and human activities to vegetation changes over multiple time scales on the Loess Plateau
Sci. Total Environ., 755 (2021), Article 142419, 10.1016/j.scitotenv.2020.142419
View PDF View article View in Scopus Google Scholar

[42] Su et al., 2011
C.H. Su, B.J. Fu, Y.H. Lu, N. Lu, Y. Zeng, A.N. He, H. Lamparski
Land use change and anthropogenic driving forces: A case study in Yanhe River Basin
Chin. Geogr. Sci., 21 (2011), pp. 587-599, 10.1007/s11769-011-0495-8
View in Scopus Google Scholar

[43] Sun et al., 2021
Z.H. Sun, Z.A. Mao, L.Y. Yang, Z. Liu, J.C. Han, H.Y. Wanag, W. He
Impacts of climate change and afforestation on vegetation dynamic in the Mu Us Desert, China
Ecolog. Indicators, 129 (2021), Article 108020, 10.1016/j.ecolind.2021.108020
View PDF View article View in Scopus Google Scholar

[44] Sun et al., 2015a
W.Y. Sun, X.Y. Song, X.M. Mu, P. Gao, F. Wang, G.J. Zhao
Spatiotemporal vegetation cover variations associated with climate change and ecological restoration in the Loess Plateau
Agric. For. Meteorol., 209 (2015), pp. 87-99, 10.1016/j.agrformet.2015.05.002
View PDF View article View in Scopus Google Scholar

[45] Sun et al., 2015b
Y. Sun, Y. Yang, L. Zhang, Z. Wang
The relative roles of climate variations and human activities in vegetation change in North China
Phys. Chem. Earth, 87–88 (2015), pp. 67-78, 10.1016/j.pce.2015.09.017
View PDF View article View in Scopus Google Scholar

[46] Tosunoglu and Kisi, 2017
F. Tosunoglu, O. Kisi
Trend Analysis of Maximum Hydrologic Drought Variables Using Mann-Kendall and Sen's Innovative Trend Method
River Res. Appl., 33 (2017), pp. 597-610, 10.1002/rra.3106
View in Scopus Google Scholar

[47] Turner et al., 2006
D.P. Turner, W.D. Ritts, W.B. Cohen, S.T. Gower, S.W. Running, M. Zhao, M.H. Costa, A.A. Kirschbaum, J.M. Ham, S.R. Saleska, D.E. Ahl
Evaluation of MODIS NPP and GPP products across multiple biomes
Remote Sens. Environ., 102 (2006), pp. 282-292, 10.1016/j.rse.2006.02.017
View PDF View article View in Scopus Google Scholar

[48] Wang et al., 2017
Z. Wang, X. Deng, W. Song, Z. Li, J. Chen
What is the main cause of grassland degradation? A case study of grassland ecosystem service in the middle-south Inner Mongolia
Catena, 150 (2017), pp. 100-107, 10.1016/j.catena.2016.11.014
View PDF View article View in Scopus Google Scholar

[49] Wang et al., 2021a
H. Wang, S.J. Yan, Z. Liang, K.W. Jiao, D.L. Li, F.L. Wei, S.C. Li
Strength of association between vegetation greenness and its drivers across China between 1982 and 2015: Regional differences and temporal variations
Ecol. Ind., 128 (2021), Article 107831, 10.1016/j.ecolind.2021.107831
View PDF View article View in Scopus Google Scholar

[50] Wang et al., 2021b
S. Wang, Y. Zhang, W. Ju, J.M. Chen, A. Cescatti, J. Sardans, I.A. Janssens, M. Wu, J.A. Berry, J.E. Campbell, M. Fernandez-Martinez, R. Alkama, S. Sitch, W.K. Smith, W. Yuan, W. He, D. Lombardozzi, M. Kautz, D. Zhu, S. Lienert, E. Kato, B. Poulter, T.G.M. Sanders, I. Kruger, R. Wang, N. Zeng, H. Tian, N. Vuichard, A.K. Jain, A. Wiltshire, D.S. Goll, J. Penuelas
Response to Comments on “Recent global decline of CO(2) fertilization effects on vegetation photosynthesis”
Science, 373 (2021), p. eabg7484, 10.1126/science.abg7484
Google Scholar

[51] Whetton et al., 2017
R. Whetton, Y. Zhao, S. Shaddad, A.M. Mouazen
Nonlinear parametric modelling to study how soil properties affect crop yields and NDVI
Comput. Electron. Agric., 138 (2017), pp. 127-136, 10.1016/j.compag.2017.04.016
View PDF View article View in Scopus Google Scholar

[52] Wu et al., 2015
J.G. Wu, Q. Zhang, A. Li, C.Z. Liang
Historical landscape dynamics of Inner Mongolia: patterns, drivers, and impacts
Landsc. Ecol., 30 (2015), pp. 1579-1598, 10.1007/s10980-015-0209-1
View in Scopus Google Scholar

[53] Xiao and Moody, 2004
J.F. Xiao, A. Moody
Trends in vegetation activity and their climatic correlates: China 1982 to 1998
Int. J. Remote Sens., 25 (2004), pp. 5669-5689, 10.1080/01431160410001735094
View in Scopus Google Scholar

[54] Xin et al., 2008
Z. Xin, J. Xu, W. Zheng
Spatiotemporal variations of vegetation cover on the Chinese Loess Plateau (1981–2006): Impacts of climate changes and human activities
Sci. China. Ser. D Earth Sci., 51 (2008), pp. 67-78, 10.1007/s11430-007-0137-2
View in Scopus Google Scholar

[55] Yang et al., 2020
H.F. Yang, D.D. Hu, H. Xu, X.N. Zhong
Assessing the spatiotemporal variation of NPP and its response to driving factors in Anhui province, China
Environ. Sci. Pollut. Res., 27 (2020), pp. 14915-14932, 10.1007/s11356-020-08006-w
View in Scopus Google Scholar

[56] Yang et al., 2019
J. Yang, Z.Q. Wan, S. Borjigin, D. Zhang, Y.L. Yan, Y.L. Chen, R. Gu, Q.Z. Gao
Changing Trends of NDVI and Their Responses to Climatic Variation in Different Types of Grassland in Inner Mongolia from 1982 to 2011
Sustainability, 11 (2019), p. 3256, 10.3390/su11123256
View in Scopus Google Scholar

[57] Yu et al., 2021
Y. Yu, Y. Wu, P. Wang, Y. Zhang, L.E. Yang, X. Cheng, J. Yan
Grassland Subsidies Increase the Number of Livestock on the Tibetan Plateau: Why Does the “Payment for Ecosystem Services” Policy Have the Opposite Outcome?
Sustainability, 13 (2021), 10.3390/su13116208
Google Scholar

[58] Zhang et al., 2018a
H. Zhang, J. Fan, W. Cao, W. Harris, Y. Li, W. Chi, S. Wang
Response of wind erosion dynamics to climate change and human activity in Inner Mongolia, China during 1990 to 2015
Sci. Total Environ., 639 (2018), pp. 1038-1050, 10.1016/j.scitotenv.2018.05.082
View PDF View article View in Scopus Google Scholar

[59] Zhang et al., 2018b
Q. Zhang, D. Kong, P. Shi, V.P. Singh, P. Sun
Vegetation phenology on the Qinghai-Tibetan Plateau and its response to climate change (1982–2013)
Agric. For. Meteorol., 248 (2018), pp. 408-417, 10.1016/j.agrformet.2017.10.026
View PDF View article Google Scholar

[60] Zhang et al., 2019
B. Zhang, P. Sun, G. Jiang, R. Zhang, J. Gao
Rural land use transition of mountainous areas and policy implications for land consolidation in China
J. Geog. Sci., 29 (2019), pp. 1713-1730, 10.1007/s11442-019-1687-x
View in Scopus Google Scholar

[61] Zhang and Zwiers, 2004
X.B. Zhang, F.W. Zwiers
Comment on “Applicability of prewhitening to eliminate the influence of serial correlation on the Mann-Kendall test” by Sheng Yue and Chun Yuan Wang
Water Resour. Res., 40 (2004), 10.1029/2003wr002073
Google Scholar

[62] Zhao et al., 2015
X. Zhao, H. Hu, H. Shen, D. Zhou, L. Zhou, R.B. Myneni, J. Fang
Satellite-indicated long-term vegetation changes and their drivers on the Mongolian Plateau
Landsc. Ecol., 30 (2015), pp. 1599-1611, 10.1007/s10980-014-0095-y
View in Scopus Google Scholar

[63] Zhao et al., 2019
Y. Zhao, H. Liu, A. Zhang, X. Cui, A. Zhao
Spatiotemporal variations and its influencing factors of grassland net primary productivity in Inner Mongolia, China during the period 2000–2014
J. Arid Environ., 165 (2019), pp. 106-118, 10.1016/j.jaridenv.2019.01.004
View PDF View article View in Scopus Google Scholar