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BY 4.0 license Open Access Published by De Gruyter Open Access October 25, 2022

Spatial and temporal variations of vegetation coverage and their driving factors following gully control and land consolidation in Loess Plateau, China

  • Jing Wang , Yi Hu , Liangyan Yang and Qingjun Bai EMAIL logo
From the journal Open Geosciences


Comprehensive management of the ecological environment and sustainable ecological development, such as the gully control and land consolidation (GCLC) project, may affect surface vegetation. The normalized difference vegetation index (NDVI) is a sensitive indicator of vegetation dynamics; however, an in-depth study that continually monitors the spatial and temporal variation of regional vegetation before and after the implementation of the GCLC project is still scarce. To address this issue, we analyzed the spatial and temporal variations of Landsat surface reflectance-derived NDVI data in the Jiulongquan watershed in Yan’an City, China, from 2010 to 2019, and examined the main driving factors for these variations. Results showed high overall vegetation coverage in the evaluated watershed. The NDVI was spatially varied and tended to be low in the gully area and high on the slope. From 2010 to 2019, the NDVI values exhibited an increasing trend, and the most evident changes were concentrated in the gully areas. The changes in NDVI were mainly driven by human activities rather than the evaluated climatic factors. This work indicates that the GCLC project had positive effects on the ecological and agricultural environment at a regional scale.

1 Introduction

Vegetation is a vital constituent of the ecosystem and serves as the hub for mass circulation and energy exchange, directly linking the atmosphere, soil, water, and human activity [1,2]. The normalized difference vegetation index (NDVI) is a measure of the red: near-infrared reflectance ratio and is usually derived from remote-sensing (satellite) data. Since the NDVI is very sensitive to biophysical characteristics of the vegetation, such as the growth status, biomass, and photosynthetic intensity, the relationship between the NDVI and vegetation productivity has been well documented. This has enabled the NDVI to become one of the most widely applied tools for monitoring vegetation dynamics at varied spatiotemporal scales [3,4] and one of the most feasible indicators of eco-environment quality. The development of vegetation and its response to environmental change (e.g., global warming) have been evaluated by many scholars using the NDVI data at regional and global scales in recent years [5,6,7].

Previous studies suggested that spatiotemporal variation of the NDVI is mainly affected by climatic factors and human activities. The change of the NDVI over longer time periods appears to be driven by long-term climate change, but the change in the shorter time scales is related to human activities [8,9]. Precipitation and temperature are two dominant climatic factors that influence vegetation growth [10]. For example, global warming was reported to enhance plant growth in northern mid-latitudes and high latitudes but had a negative impact on vegetation in arid and semi-arid areas due to increased evapotranspiration [11,12]. On the other hand, rainfall generally has a positive correlation with the NDVI [13].

Human activities may also have positive and negative impacts on vegetation simultaneously. In forestry ecological engineering projects, afforestation and soil and water conservation practices could promote the increment of vegetation coverage [14,15]. In contrast, human activities such as overgrazing, deforestation, or urbanization could directly or indirectly destroy the surface vegetation [16].

The gully control and land consolidation (GCLC) project is another major project of ecological environment management in the Loess Plateau following the Grain for Green project. The implementation of a series of projects directly worked on and thus changed the surface vegetation. The GCLC project started in 2011, and some researchers have quantitatively studied the vegetation changes in the area since then. For example, He et al. [17] and Du et al. [18] showed an increasing trend of vegetation coverage in the watershed following the GCLC project.

However, these studies simply compared the vegetation coverage before and after the GCLC project. An in-depth study that continually monitors the spatial and temporal variations of regional vegetation in the area is still scarce, and driving factors for such variations have not been determined. To quantitively analyze the impact of the GCLC project on vegetation in the area over time series, the Jiulongquan project in Nanniwan Town, Yan’an City, China, was chosen as the study area. Based on the Landsat surface reflectance-derived NDVI and meteorological data, we studied the characteristics and trends of the spatiotemporal variation of vegetation in the project area from 2010 to 2019 and analyzed the main driving factors for such variation using trend analysis and correlation analysis. This study aimed to provide a scientific basis for subsequent land engineering and management in this area and places with similar geological and climatic conditions.

2 Overview of the study area

The Baota District of Yan’an City (36.18–37.03°N and 109.23–110.12°E) is characterized by a semi-humid and semi-arid continental monsoon climate with hot and rainy summers and cold and dry winters. The average annual precipitation is 562.1 mm and has an uneven temporal distribution, with 70% concentrated in the summer (June to September). The annual average temperature is 9°C, and the average frost-free period is 179 days.

The Jiulongquan watershed is located in the Fenchuan River basin and belongs to the hilly and gully region of the south Baota District (Figure 1). The terrain mainly comprises river valley and terrace following a southwest–northeast direction. The terrain is fragmented by many crisscrossed gullies that are deep with sharp slopes, and the soil erosion is severe. The soil type in the area is calcic Cambisol as defined by the Food and Agriculture Organization (FAO) of the United Nations. The river channel is about 9.5 km long. The elevation of the river channel decreases from 1,170 m in the north to 1,093 m in the south, with an average gradient of 0.78%. The valley is generally 250–500 m wide, and the total area of the watershed is 62.63 km2.

Figure 1 
               Location and DEM of the Jiulongquan watershed.
Figure 1

Location and DEM of the Jiulongquan watershed.

The vegetation on slopes in the watershed was dominated by grassland and natural shrubs prior to the GCLC projects. Most of the gully land has been reclaimed into rainfed farmland with limited water availability, and consequently, the grain productivity is low. The project started at the beginning of 2013 with a total construction area of 360.91 hm2 and a total investment of CNY 44.12 million. The main project was completed in 2014, and farming began since then.

3 Data acquisition, calibration, and analysis

3.1 Source of data

3.1.1 Remote sensing images

The Landsat data (path 127, row 35 with a spatial resolution of 30 m) managed by the United States Geological Survey were obtained from the Geospatial Data Cloud website ( Landsat images from June to August each year between 2010 and 2019 when the vegetation was vigorously growing and the study area was as cloud-free as possible (e.g., cloudiness <15%) were selected to study the changes in vegetation coverage in the watershed before and after the GCLC project. The data information and key parameters are shown in Table 1. Missing striped data occurred for some images in 2012 and 2018 due to malfunction of the airborne scan line corrector of the Landsat 7 satellite. Therefore, the missing stripes on such images were repaired by pixel spatial interpolation with the gap-fill (triangulation) function of ENVI 5.3 software [19]. Spatial interpolation is one of the most common techniques to reconstruct a single remote sensing image [20].

Table 1

Research data information

Satellite Date of acquisition Sensor type Cloudiness (%)
Landsat 5 2010-06-17 TM 2.48
Landsat 7 2010-08-28 ETM+ 2.98
Landsat 7 2011-06-28 ETM+ 0
Landsat 8 2013-06-25 OLI 0.34
Landsat 7 2013-08-04 ETM+ 2.74
Landsat 8 2014-07-14 OLI 0.30
Landsat 8 2015-07-01 OLI 0.07
Landsat 7 2015-07-25 ETM+ 0.29
Landsat 8 2016-06-17 OLI 0.17
Landsat 8 2016-07-03 OLI 11.27
Landsat 7 2016-07-27 ETM+ 0.15
Landsat 8 2017-06-20 OLI 0.82
Landsat 8 2019-08-13 OLI 6.39
Landsat 8 2019-08-29 OLI 0.65

Note: TM = thematic mapper; ETM+ = enhanced thematic mapper plus; OLI = operational land imager.

The images were radiometric-calibrated and atmospheric-corrected in ENVI 5.3 software and clipped according to the vector boundary of the study area. To eliminate the interference due to cloud, atmospheric effect, and solar altitude angle, the universally adopted maximum value composite method [20,21] was used to obtain the maximized annual NDVI data, which reflects the vegetation coverage in the best season of vegetation growth. Eight images were obtained from 2010 to 2019.

3.1.2 Meteorological data

Monthly precipitation and temperature data from January 2010 to December 2019 from two weather stations in Yan’an City were obtained from the National Meteorological Information Center of China Meteorological Administration (CMA Meteorological Data Centre, Annual mean temperature and precipitation in the study area were calculated using the spatial interpolation method implemented by ANUSPLIN 4.2 software (Australian National University, Canberra, Australia). The grid size was set as 30 m × 30 m.

3.2 Data analysis

3.2.1 Trend analysis

To quantitatively analyze the change of vegetation coverage during the study period, unitary linear regression was employed. Trends of each pixel NDVI in the study area from 2010 to 2019 were calculated using the following formula [22]:

(1) Slope = n × i = 1 n ( i × NDVI i ) i = 1 n i i = 1 n NDVI i n × i = 1 n i 2 i = 1 n i 2 ,

where slope represents the slope of the NDVI regression equation, which is indicative of the rate of change; i represents the year number; n represents the total number of years during the study period; NDVI i represents the NDVI value of the ith year. Slopes >0 and <0 represent an increasing and a decreasing trend of the vegetation coverage, respectively. According to the calculations, the change rates of the slope were divided into seven grades including severely degraded, moderately degraded, slightly degraded, relatively stable, slightly improved, moderately improved, and significantly improved [23].

3.2.2 Correlation analysis

Partial correlation coefficient was calculated to characterize the degree of correlation between climatic factors (i.e., annual precipitation and mean temperature) and the NDVI. The linear correlation coefficient was first calculated using the following formula [24]:

(2) R xy = i = 1 n ( x i x ¯ ) ( y i y ¯ ) i = 1 n ( x i x ¯ ) 2 i = 1 n ( y i y ¯ ) 2 ,

where x is the NDVI value; is the averaged NDVI value of the corresponding pixel from 2010 to 2019; y is the climatic factor, namely annual precipitation and mean temperature; ȳ is the multi-year average of the two climatic factors; i is the year number; and n is the total number of years.

Based on the calculated linear correlation coefficient, the partial correlation coefficient was calculated as follows:

(3) R xy , z = R xy R xz R yz 1 R xz 2 1 R yz 2 ,

where R xy,z is the partial correlation coefficients between the dependent variable x and the independent variable y after the independent variable z is fixed; R xy , R xz , and R yz are the linear correlation coefficients of the corresponding variables. The partial correlation coefficient was statistically analyzed by the t-test using the following equation:

(4) t = R xy , z 1 R xy , z 2 n m 1 ,

where R xz,y is partial correlation coefficients; n is the total number of samples; m is the number of independent variables.

Climatic factors are usually interdependent, and their impacts on vegetation are not opposed. Therefore, multiple correlation analysis was adopted to study the combined effects of the two climatic factors on the NDVI [25]. The calculation formula of multiple correlations is as follows:

(5) R x , yz = 1 ( 1 R xy 2 ) ( 1 R xz , y 2 ) ,

where R x,yz represents the multiple correlation coefficient between the dependent variable x and the independent variables y and z; R xy represents the linear correlation coefficient between x and y; and R xz,y represents the partial correlation coefficient between the dependent variable x and the independent variable z after fixing the independent variable y. The F test was used to test the statistical significance of the multiple correlation coefficient, and the calculation formula is as follows:

(6) F = R x , yz 2 1 R x , yz 2 × n k 1 k ,

where R x,yz is the multiple correlation coefficients, n is the number of samples, and k is the number of independent variables.

4 Results

4.1 Spatial characteristics of vegetation

To study the spatial distribution of the NDVI in the Jiulongquan watershed, the averaged NDVI value of pixels in each image from 2010 to 2019 was calculated, and its spatial distribution map was composed (Figure 2). Together with the digital elevation model (DEM) map of the project area (Figure 1), data showed that the NDVI tended to be low in the gully and high on the slope. The area with the lowest elevation was the main gully and its tributaries, and the area with low NDVI was mainly distributed as strips along the gully bottom of the watershed. The patches with high NDVI were widely distributed on the slopes of the whole watershed, with a maximum value of about 0.9.

Figure 2 
                  Spatial distribution of averaged NDVI in the Jiulongquan watershed from 2010 to 2019.
Figure 2

Spatial distribution of averaged NDVI in the Jiulongquan watershed from 2010 to 2019.

4.2 Spatial and temporal variations of vegetation

4.2.1 Interannual variation

The averaged NDVI value of each image from 2010 to 2019 represented the optimal status of vegetation coverage in the study area and is illustrated in Figure 3. In general, vegetation coverage in the watershed was in good conditions, as indicated by the >0.75 averaged NDVI each year. During the study period, the averaged NDVI value showed an increasing trend (P < 0.05) from 0.788 in 2010 to 0.824 in 2019, with an annual growth rate of 0.004.

Figure 3 
                     Interannual variation of the averaged NDVI in the Jiulongquan watershed.
Figure 3

Interannual variation of the averaged NDVI in the Jiulongquan watershed.

4.2.2 Trend analysis

To better understand the magnitude of variation and spatial distribution of vegetation coverage in the Jiulongquan watershed, unitary linear regression analysis was carried out on each pixel NDVI at each sampling time. The slopes of the regression lines varied between –0.13 and 0.11 during 2010–2019. Classification based on calculated slopes indicated that the improved area was 34.17 km2, accounting for 54.55% of the study area, in which the slightly improved area was the largest, accounting for 50.64% of the study area. The degraded area was relatively small, accounting for only 3.39% (2.12 km2). The other 42.05% was relatively stable (Table 2).

Table 2

Statistics of NDVI change in the Jiulongquan watershed from 2010 to 2019

Slope Classification Area (km2) Percentage
Slope ≤ –0.05 Severely degraded 0.04 0.07
–0.05 < Slope ≤ –0.02 Moderately degraded 0.46 0.74
–0.02 < Slope ≤ –0.005 Slightly degraded 1.62 2.58
–0.005 < Slope ≤ 0.005 Relatively stable 26.34 42.05
0.005 < Slope ≤ 0.02 Slightly improved 31.71 50.64
0.02 < Slope ≤ 0.05 Moderately improved 2.39 3.81
Slope > 0.05 Significantly improved 0.07 0.10

The areas with the most significantly changed slopes of NDVI regression lines overlapped with those having the greatest degradation or improvement and were mainly concentrated as strips in the upper and middle part of the gully (Figure 4). Despite the evident changes in the gully, a majority of the watershed remained relatively unchanged or slightly improved.

Figure 4 
                     Slope grading diagram of the NDVI value in the Jiulongquan watershed.
Figure 4

Slope grading diagram of the NDVI value in the Jiulongquan watershed.

4.3 Analysis of driving factors

4.3.1 Correlation between the vegetation index and climatic factors

Annual precipitation and mean temperature were selected as representative climatic factors for the following analysis. The partial correlation coefficients between the NDVI and annual precipitation were between –0.95 and 0.96 during the study period. The area with positive and negative correlations accounted for 64.98 and 35.02% of the total study area, respectively. However, only 1.3% of the total area had statistically significant partial coefficients (Figure 5a).

Figure 5 
                     Partial correlation between NDVI and (a) annual precipitation and (b) mean temperature and multiple correlation, (c) in the Jiulongquan watershed from 2010 to 2019.
Figure 5

Partial correlation between NDVI and (a) annual precipitation and (b) mean temperature and multiple correlation, (c) in the Jiulongquan watershed from 2010 to 2019.

As shown in Figure 5b, the partial correlation coefficient between the NDVI and mean temperature was between –0.98 and 0.98. A negative correlation existed in 88.43% of the watershed, of which 56.29% reached statistical significance and was mainly distributed on the slopes of the middle and lower reaches of the watershed. In contrast, the area with positive correlation accounted for 11.57% of the total area and was mainly distributed in the gully, of which only 1.38% had significantly positive partial correlation coefficients.

To study the compounding influence of climatic factors on vegetation coverage, multiple correlations among annual precipitation, mean temperature, and the NDVI of each pixel in the Jiulongquan watershed from 2010 to 2019 were performed. The multiple correlation coefficient greatly varied between 0.002 and 0.98. The area with statistically significant multiple correlation coefficients accounted for 13.6% of the total area and was mainly distributed on the downstream slope of the watershed (Figure 5c).

4.3.2 Driving factors partition of vegetation index changes

To further reveal the driving factors of the spatiotemporal changes in vegetation coverage, the driving factors partition method employed by Chen et al. [26] was adopted. The climate background and characteristics of the Loess Plateau were also considered.

According to the grid statistics of the partition results, the change in about 14.05% of the total area was driven by climatic factors (Figure 6). In detail, the area driven by precipitation and both precipitation and temperature accounted for 0.38 and 0.02% of the total area, respectively. Such an area was scattered across the study area. The area driven by the temperature itself accounted for about 13.65% of the study area and was mainly distributed on the southwest downstream slope. The variation of the NDVI in 85.92% of the Jiulongquan watershed was driven by non-climatic factors.

Figure 6 
                     Factor driving zoning of NDVI change in the Jiulongquan watershed.
Figure 6

Factor driving zoning of NDVI change in the Jiulongquan watershed.

5 Discussion

5.1 Distribution and change of vegetation

The low NDVI in the gully bottom of the study area is probably due to perennial crop cultivation and the disturbance of the GCLC project, which had a negative impact on surface vegetation. In contrast, the high NDVI on the slopes may be explained by the vigorous growth of trees and shrubs as a result of the Grain for Green project, which was implemented prior to the GCLC projects.

Our study showed that following the implementation of the Jiulongquan GCLC project, the vegetation coverage of the watershed tended to improve to a limited extent. The improved area was much larger than the degraded area, inconsistent with other land consolidation projects. For example, Zhong and Wang [27] and Shan et al. [28] showed that the regional habitat quality tended to decrease within 3–5 years after land consolidation. This is probably because the objectives of different land consolidation projects are highly varied. The implementation of a series of land consolidation projects in these studies had a large-scale disturbance on surface soil and thus usually destroyed the surface vegetation of gullies as the goal was to create more cultivated land. The monocultural cropping system is another factor leading to the reduction and degradation of the species and quantity of primary and secondary vegetation in the project area [29]. If such a goal is excessively pursued, the area of other ecological land types, such as forestland and grassland, will inevitably decrease, resulting in a reduced vegetation coverage [30,31].

In contrast, the GCLC project in this study aimed to restore, add, and improve the quality of the cultivated land in the gully. As a result, the disturbance by construction was concentrated in the gully and thus the primary vegetation in other areas was not destroyed on a large scale. Although the increase of cultivated land would occupy some grassland and woodland [32], the project greatly restored the vegetation damage caused by the disturbance through a systematic construction of the farmland forest network. Some of the shrub lands were converted to more ecologically suitable native forests due to biological measures that also promoted vegetation restoration [33]. On the other hand, the fragile agricultural ecological environment in and around the project area was improved by hydraulic engineering, which effectively reduced the occurrence of drought and flooding events, prevented soil and water erosion, and thus promoted the protection of vegetation in the whole basin. After the project, the improvement in soil quality and grain yield of the cultivated land effectively reduced the reclamation on the slopes and consolidated the achievements of the Grain for Green project. Such results are consistent with Du et al. [18]. That study performed in the Gutun watershed in Yan’an City showed that the mean NDVI in the watershed increased by about 54% owing to the combined effects of the Grain for Green project and the GCLC project. Taken together, the GCLC project has an overall positive effect on the change of vegetation coverage in this area.

5.2 Driving factors

Our work found that NDVI was positively correlated with precipitation and negatively correlated with temperature in the evaluated watershed. Soil moisture is one of the dominant factors limiting plant growth in the Loess Plateau. Climate change, particularly global warming, may increase evapotranspiration. Uneven spatiotemporal distribution of precipitation subsequently aggravates the water shortage, leading to limited regional vegetation growth. In contrast, the increase in rainfall is conducive to the improvement of soil water status and promotes vegetation growth, which is consistent with Sun et al. [34] and Wang et al. [35]. However, such correlation was statistically significant in only <15% of the total study area, suggesting that the evaluated climatic factor may not be the main driving factor for the development of NDVI in the area.

Our further partition analysis of the driving factors showed that the development of the NDVI during 2010–2019 in >85% of the total area was driven by non-climatic factors such as human activities. The improvement of the NDVI may be attributed to the implementation of the GCLC project. Many studies investigating the change of vegetation coverage in the Loess Plateau also showed that human activities are the main attributes of vegetation change in this area after 2000 [36]. Among all of the human activities, ecological engineering, particularly the Grain for Green project, has largely promoted the increase of vegetation coverage [37,38]. The GCLC project had little disturbance to the region except for the gullies. Meanwhile, the GCLC project changed the factors affecting vegetation growth such as the hydrological environment [39] and soil erosion process [40,41], extending the influence to the whole basin through measures such as field integration, water resource regulation, improvement of agricultural infrastructure and production conditions, and reasonable adjustment of the cropping systems. Li et al. [33] showed that the GCLC project could contribute to the improvement of land use structure and help to optimize the landscape pattern, thus affecting the changes of the geographical environment. Such results suggest that the overall goal of coordinated development of cultivated land quantity, quality, and ecology was achieved.

In the current study, we performed quantitative analysis on the change of regional vegetation coverage and its driving factors before and after the GCLC project on the project scale over time series. In the future, the residual analysis method [42], regression model method [43], and other methods may be used to analyze further the change of vegetation coverage caused by specific human activities in detail.

6 Conclusion

The vegetation coverage of the Jiulongquan watershed was generally high. The spatial distribution of the NDVI tended to be low in the gully area and high on the slope. From 2010 to 2019, vegetation coverage and the ecological environment of the watershed tended to improve following the GCLC project, as indicated by the increased NDVI values, with an annual growth rate of 0.004. The significant NDVI changes were concentrated in the gullies, while a majority of the watershed was relatively stable or slightly improved, indicating that the engineering disturbance had a little negative impact on the whole watershed. The change of NDVI was mainly driven by human activities rather than climatic factors, including annual precipitation and mean temperature.

These results indicated that the GCLC project has significant ecological implications in the Loess Plateau. Comprehensive management and utilization of the gullies could be more effective in increasing cultivated land resources, improving the quality of cultivated land, and ensuring food security than traditional land consolidation projects. The GCLC projects could also consolidate the achievements of the Grain for Green project on slopes of the watershed and thus have positive effects on the local ecological and agricultural environment. Our work could be cited as a reference for other comprehensive ecological control projects in the Loess Plateau.

  1. Funding information: This work was supported by the National Key Research and Development Project of China (Grant No. 2017YFC0504705), the Natural Science Basic Research Project of Shaanxi Province (Grant No. 2022JM-168), and the Internal Research Project of Shaanxi Provincial Land Engineering Construction Group (Grant No. DJNY2021-21).

  2. Author contributions: JW performed the study, analyzed the data, and drafted the original manuscript; YH reviewed and edited the manuscript; LY helped with the visualization of the data; QB managed the entire research project and offered supervision.

  3. Conflict of interest: Authors state no conflict of interest.


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Received: 2022-01-03
Revised: 2022-06-07
Accepted: 2022-07-03
Published Online: 2022-10-25

© 2022 Jing Wang et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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