sustainability

Article Vegetation Change and Driving Factors: Contribution Analysis in the of during 2000–2015

Yunfeng Hu 1,2,* , Rina Dao 3,* and Yang Hu 1,2 1 State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; [email protected] 2 University of Chinese Academy of Sciences, Beijing 100049, China 3 College of Geographical Science, Normal University, Hohhot 010022, China * Correspondence: [email protected] (Y.H.); [email protected] (R.D.)

 Received: 15 November 2018; Accepted: 25 February 2019; Published: 2 March 2019 

Abstract: Judging vegetation change and analyzing the impacts of driving factors on vegetation change are important bases on which to evaluate the effects of ecological engineering constructions on the Loess Plateau and to support ecological construction planning decisions. The authors applied time-section difference analysis and trend analysis methods to analyze the temporal–spatial characteristics of vegetation change on the Loess Plateau from 2000 to 2015. Then, complex linear regression analysis and residual analysis methods were applied to estimate the contribution rates of driving factors to regional vegetation changes. The results showed the following: (1) From 2000 to 2015, most areas of the Loess Plateau became “greener”. These areas were mainly distributed in the southern part of Shanxi Province, the northern and central parts of Shaanxi Province, and the eastern part of Gansu Province. (2) In 2015, the overall contribution rate of meteorological factors (temperature and precipitation) to normalized difference vegetation index (NDVI) in the Loess Plateau was as high as 87.7%. The average contribution rate of non-meteorological factors (mainly referring to human activities) to vegetation NDVI was 6.4%.

Keywords: vegetation index; tendency; meteorological factor; human activity; residual analysis; contribution rate

1. Introduction Terrestrial vegetation is a product of the interactions among the atmosphere, lithosphere, pedosphere, hydrosphere, and biosphere. It is an important link that connects climate change, human activities, and ecosystems. Climate and human activities are the basic drivers for controlling and affecting the spatial distribution of vegetation and its changes [1–3]. Climatic factors control the growth process of vegetation on a long-term scale, restrict the geographical distribution of terrestrial vegetation, and determine the type and magnitude of regional ecosystem services. Human activities have changed the geographical extent and quality of vegetation growth on a short time scale and have had a major impact on regional ecosystem services and functions [4,5]. An in-depth analysis of the impact of climate change and human activities on changes to vegetation cover can provide a quantitative assessment of the contribution rate of the two types of driving factors that have been key issues in global change research but are also challenging to research [6,7]. Some biophysical parameters that have been obtained by satellite remote sensing inversion, such as the normalized difference vegetation index (NDVI), Albedo, and the leaf area index (LAI), can characterize the specific nature of ecosystems so that researchers can use satellite remote sensing inversion and simulation techniques to conduct long-term ecological monitoring and evaluation. In this way, the spatial distribution

Sustainability 2019, 11, 1320; doi:10.3390/su11051320 www.mdpi.com/journal/sustainability Sustainability 2019, 11, 1320 2 of 16 characteristics and the dynamic evolution of terrestrial ecosystems can be revealed [8,9]. Among the parameters, the NDVI is used as a proxy to analyze the impact of climate change and human activities on the NDVI of terrestrial vegetation. This is an important part of applying remote sensing technology to monitor regional vegetation changes and to deepen the driving mechanism [10,11]. The Loess Plateau is one of the regions with the most serious levels of soil erosion in China and in the world. The Loess Plateau has broken terrain, loose soil, concentrated precipitation, and vegetation degradation, and these characteristics make the plateau a typical ecologically fragile area [12–14]. To curb the deteriorating ecological environment of the Loess Plateau, China’s central government and local governments have long carried out land degradation management with soil and water conservation as the core [15,16]. Since 1978, the Chinese government has carried out the “Three-North Protection Forest System Construction Project” in the Loess Plateau. Since 1999, based on the experience of the Chinese Academy of Sciences in soil and water conservation and management in the Yan’an area, the central government has carried out large-scale ecological restoration and treatment projects, such as “returning farmland to forests and grasslands” (or the “Grain for Green” Program (GGP)) and “closing hillsides for afforestation” in the region [17,18]. After long-term unremitting efforts, the vegetation of the Loess Plateau has been significantly restored, and the regional ecological quality and ecological service functions have been significantly improved [19–22]. It is generally believed that the abundant light energy resources and thermal resources in the Loess Plateau can fully meet the basic needs of vegetation growth in the region. Precipitation and the soil moisture status have become the basic limiting factors for land vegetation cover and change. Human activities, including land reclamation, returning farmland to forests and grassland, and other land development and ecological protection and engineering control activities, have affected the composition, growth quality, and growth of the Loess Plateau under the constraints of light, heat, and water conditions. Therefore, the spatial and temporal patterns and changes to the terrestrial vegetation on the Loess Plateau over the past few decades are summarized in this paper. The degree of impact of climate change and human activities on vegetation changes is analyzed in depth. This is an assessment of the effectiveness of the Chinese government in ecological restoration and treatment construction on the Loess Plateau, which is also the basis for assessing where future human interventions can play a role [23,24]. The basic data and basic conclusions that were formed can provide strong scientific support for agricultural development, ecological protection, and governance in the Loess Plateau. Researchers at home and abroad have carried out many studies on the spatial distribution pattern, dynamic change, and the influencing mechanism of vegetation on the Loess Plateau. For example, Xiao [25] used MODIS series products to systematically analyze the impact of the GCP program on the biophysical characteristics of vegetation in the Loess Plateau. Studies have shown that since the implementation of the GGP, the average tree coverage and enhanced vegetation index (EVI), the leaf area index, and the photosynthetically active radiation ratio of vegetation canopy absorption have increased significantly. In addition, the GCP has also significantly reduced the surface albedo and surface temperature during the day, which has had a cooling effect on the climate. Liu [26] extracted the vegetation coverage data based on the MODIS NDVI from 2000 to 2008 and pointed out that the vegetation coverage of the Loess Plateau showed a rapid increase and a partial decline. After comparing the changes in vegetation cover in the soil use/cover change zone and the unaltered zone, the authors pointed out that human activities have an important impact on the vegetation cover change. However, the authors did not further analyze the mechanisms and effects of the factors that drive the spatial and temporal changes in vegetation coverage (climate change and human activities). Shi [27] applied the Carnegie–Ames–Stanford Approach (CASA) model to simulate the net primary productivity (NPP) of the ecosystem and pointed out that before the implementation of the project of returning farmland to forests and grasslands (1982–1998), the NPP changes in most areas of the Loess Plateau were not obvious. However, after the implementation of the project of returning farmland to forests and grasslands, the increase in the vegetation NPP became significant. By comparing the temporal evolution characteristics of vegetation activities and returning farmland Sustainability 2019, 11, 1320 3 of 16 to forests and grassland projects, this study qualitatively points out that ecological protection and treatment projects, such as returning farmland to forests, have an important role in improving regional vegetation conditions. However, this is a correlation-based qualitative analysis. Yi [28] used the SPOT VGT data of the Loess Plateau from 1999 to 2010 to analyze the influence of the NDVI cumulative value and precipitation in the growing season and applied the residual method to analyze climate change in the Loess Plateau and the impact of human activities on the vegetation cover change. However, this study only compared changes in the NDVI and in the precipitation during the same growing season and did not consider the hysteresis effect of vegetation growth relative to precipitation. On the other hand, the study failed to give a quantitative contribution of climate change and human activity factors on regional NDVI changes. To sum up, an in-depth analysis of the effects of climate change and human activities on vegetation cover change has been one of the key elements of global change research. A quantitative assessment of the contribution rates of two types of drivers, climate change and human activities, is a key issue in the study of vegetation change. Most of the past studies have directly described changes in the terrestrial vegetation but have not provided an in-depth explanation of the driving mechanism. The few studies on the driving mechanism were based on a qualitative analysis of correlation and a trend comparison. The aim of the present study was therefore to develop a relatively simple and direct method to isolate the impact of climatic factors on regional vegetation growth and to determine the extent of non-meteorological factors (mainly human activities) on vegetation growth and change. For this, we selected the Loess Plateau as the study area, applied a complex linear regression analysis and residual analysis using MODIS NDVI and surface meteorological station monitoring data from 2000 to 2015, and attempted to separate the contributions of meteorological factors on the vegetation index to achieve a quantitative determination of the NDVI change and the contribution rate of the driving factors in the Loess Plateau in recent years. The study tried to answer the following two sets of questions:

1. What is the trend of vegetation change in the Loess Plateau since 2000? Has the vegetation in those areas become greener? 2. How much does climate change contribute to regional vegetation changes? Where do non-meteorological elements play a role?

2. Research Areas and Methods

2.1. Research Area The Loess Plateau is located in the middle and upper reaches of the Yellow River in China, including the areas west of the Taihangshan Mountains, east of Riyueshan Mountain, and north of the Qinling Mountains, as well as vast areas south of the Great Wall. The north latitude range is roughly 33◦400–41◦200 N, while the east longitude range is roughly 100◦500–114◦400 E. The Loess Plateau has an elevation of 1500–2000 m with an area of approximately 620,000 km2 (Figure1). The Loess Plateau has typical continental climate characteristics. It runs from south to north across the warm temperate zone and the middle temperature zone with two heat zones. It traverses the semihumid and semiarid dry and wet zones from east to west. In most areas, the annual precipitation is approximately 450 mm. The total annual rainfall is low and the rainy season is concentrated, while the annual variability is high. The climate is dry, the evaporation rate is high, and the frost-free period is short [29]. As the world’s largest loess sedimentary area, the Loess Plateau has long been the region with the most serious levels of soil erosion and land degradation in China due to its special climate and soil conditions coupled with land reclamation since the historical period [30]. Sustainability 2019, 11, 1320 4 of 16 Sustainability 2018, 10, x FOR PEER REVIEW 4 of 16

Figure 1.1. Location and topographical features of the studystudy area. The Loess Plateau is located in the middle and upper reaches of the Yellow RiverRiver in China.China. It starts from the Taihangshan Mountains in thethe easteast andand thenthen extends to RiyueshanRiyueshan Mountain in the west, to th thee Qinling Mountains in the south, 2 and to the Great WallWall inin thethe north.north. TheThe totaltotal areaarea isis approximatelyapproximately 620,000620,000 kmkm2.. 2.2. Basic Data 2.2. Basic Data Our research was based on ground meteorological monitoring data for vegetation from 2000 to Our research was based on ground meteorological monitoring data for vegetation from 2000 to 2015. In general, research on regional vegetation trends requires as long a time period as possible 2015. In general, research on regional vegetation trends requires as long a time period as possible for for recording meteorological and vegetation data. At the same time, to reflect the latest conditions of recording meteorological and vegetation data. At the same time, to reflect the latest conditions of the the Loess Plateau, we had hoped to extend the study period from 2000 to 2018. Unfortunately, only Loess Plateau, we had hoped to extend the study period from 2000 to 2018. Unfortunately, only ground meteorological monitoring data (spatial interpolation data) from 1980s to 2015 and MODIS ground meteorological monitoring data (spatial interpolation data) from 1980s to 2015 and MODIS NDVI data from 2010 to 2017 were available. NDVI data from 2010 to 2017 were available. The NDVI data used in this paper was MOD13A2 from January 2000 to December 2015. The data The NDVI data used in this paper was MOD13A2 from January 2000 to December 2015. The data came from NASA’s Earth Observing System (EOS) and had a spatial resolution of 1 km × 1 km with came from NASA’s Earth Observing System (EOS) and had a spatial resolution of 1 km × 1 km with a time resolution of 16 days. Before the production of the MOD13A2 data set, the raw data were a time resolution of 16 days. Before the production of the MOD13A2 data set, the raw data were subjected to geometric precision correction, radiation correction, atmospheric correction, and other subjected to geometric precision correction, radiation correction, atmospheric correction, and other methods of preprocessing. To eliminate the influence and interference of the clouds, atmosphere, and methods of preprocessing. To eliminate the influence and interference of the clouds, atmosphere, and solar elevation angles, the authors used the 16-day NDVI dataset to obtain the maximum NDVI data solar elevation angles, the authors used the 16-day NDVI dataset to obtain the maximum NDVI data on the monthly and annual scales using maximum synthesis composition (MVC) [31,32]. on the monthly and annual scales using maximum synthesis composition (MVC) [31,32]. This study used daily temperature and precipitation observation data provided by the China This study used daily temperature and precipitation observation data provided by the China Meteorological Science Data Sharing Service Network (http://cdc.cma.gov.cn). The above monitoring Meteorological Science Data Sharing Service Network (http://cdc.cma.gov.cn). The above monitoring data included all monitoring of 642 meteorological stations nationwide during the period 2000–2015. data included all monitoring of 642 meteorological stations nationwide during the period 2000–2015. After performing necessary null interpolation and outlier elimination on the above observation data, After performing necessary null interpolation and outlier elimination on the above observation data, the author used the ANUSPLIN method [33] to interpolate the abovementioned site data at the the author used the ANUSPLIN method [33] to interpolate the abovementioned site data at the national scale. Based on the boundary data of the study area, the authors extracted the temperature national scale. Based on the boundary data of the study area, the authors extracted the temperature and precipitation data of the monthly and annual scales for the Loess Plateau. and precipitation data of the monthly and annual scales for the Loess Plateau.

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2.3. Model Approach In this paper, linear fitting was carried out based on the principle of least squares, and the trend of NDVI with time was calculated. The specific formula is as follows:

n  n   n  n × ∑ i × NDVIi − ∑ i × ∑ NDVIi = = = Slope = i 1 i 1 i 1 (1) n  n 2 n × ∑ i2 − ∑ i i=1 i=1

In the formula, NDVIi is the NDVI value of the i-th year (for this study, it was the maximum value of the NDVI year of the i-th year); n is the cumulative number of years of the monitoring period; and Slope is the slope of the change. Slope > 0 indicates that the vegetation growth activity is enhanced and the vegetation coverage is improved; Slope = 0 indicates that the regional vegetation growth shows a stable and invariant situation; and Slope < 0 indicates that the vegetation growth activity is reduced while the vegetation coverage is decreased [34]. This study used the F-test to test the significance of linear regression. Studies have pointed out that climatic factors, such as temperature and precipitation, have a controlling effect on vegetation growth [35]. Therefore, complex linear regression methods can be used to fit the relationship between temperature, precipitation, and NDVI. The fitting equation is as follows:

z = a + bx + cy (2) where z is the NDVI, and x and y are the temperature and precipitation, respectively. The calculation method of coefficients a, b, and c can be referred to in [36]. In-depth studies have also shown that there is a hysteresis effect on vegetation growth relative to climate change [37]. Therefore, when fitting the NDVI to the temperature and precipitation, it is necessary to first calculate the correlation between the monthly meteorological element values (or their combined values) and the annual maximum NDVI value. Then, according to the size of the correlation, the month that best fits the model is selected to build the prediction model of the NDVI. Pearson’s correlation coefficient can be expressed as follows:

n ∑ [(xi − x)(yi − y)] i=1 rxy = (3) s n n 2 2 ∑ (xi − x) ∑ (yi − y) i=1 i=1 where n is the cumulative number of years of the monitoring period; x and y are the two variables of the correlation analysis; x and y are the average of the variable sample values (in this study, x is the temperature or precipitation, and y is the NDVI value). The value of the correlation coefficient is [−1, 1]. The larger the absolute value of the correlation coefficient, the more closely the dependent variable y is related to the independent variable x. The smaller the absolute value of the correlation coefficient, the looser the relationship is between the two [38]. In this study, the authors used six sets of meteorological data (i.e., June, July, August, June–July, July–August, and June–August) to correlate with the largest NDVI during the year (Table1). The results in Table1 show that the correlation coefficients between the annual maximum NDVI and the average temperature and precipitation in July of the Loess Plateau were the highest. Therefore, in the follow-up study, the author selected the spatial and precipitation data of the Loess Plateau for July during the years 2000–2015 for spatial modeling. With the support of geographic information system (GIS), the a, b, and c values in the complex regression equation of the annual maximum NDVI and the average temperature and monthly precipitation in July of that year were calculated pixel by pixel (Figure2). Sustainability 2019, 11, 1320 6 of 16 Sustainability 2018, 10, x FOR PEER REVIEW 6 of 16

Figure 2. Cont. Sustainability 2019, 11, 1320 7 of 16 Sustainability 2018, 10, x FOR PEER REVIEW 7 of 16

Figure 2. 2. ResultsResults of of a complex a complex linear linear regression regression analysis analysis of the of normalized the normalized difference difference vegetation vegetation index index(NDVI) (NDVI) and meteorological and meteorological elements. elements. From From top topto bottom, to bottom, the the spatial spatial dist distributionribution of of the the three ◦ coefficientscoefficients a (without(without unit),unit), b (/(/°C),C), and and cc (/mm)(/mm) are are indicated indicated by by Equation Equation (2). (2). Table 1. Average correlation coefficient between NDVI and meteorological factors in the Loess Plateau. Table 1. Average correlation coefficient between NDVI and meteorological factors in the Loess Plateau. Month NDVI and Precipitation NDVI and Temperature − Month6 NDVI and Precipitation 0.07 NDVI and Temperature0.01 7 0.30 −0.07 8 6 0.07−0.02 −0.01 0.06 6, 77 0.30 0.30 −0.07 −0.05 7, 88 −0.02 0.24 0.06 −0.01 − 6, 7,6, 8 7 0.30 0.27 −0.05 0.02 7, 8 0.24 −0.01 Based on the above6, 7, 8 complex regression 0.27 fitting equation, the−0.02 NDVI prediction value (NDVIc) for a specific year and the specific climate state can be obtained; NDVIc is the contribution of the temperatureBased on and the precipitationabove complex to NDVI.regression On thisfitting basis, equation, the NDVI the NDVI value thatprediction was obtained value (𝑁𝐷𝑉𝐼 by remote) for sensinga specific was year subtracted and the from specific the fitted climate value state of the can meteorological be obtained; element𝑁𝐷𝑉𝐼 model,is the thatcontribution is, the “residual of the difference”temperature ( NDVIand precipitationr) between theto NDVI. true value On this and basi thes, predicted the NDVI value, value which that was can obtained be regarded by remote as the contributionsensing was ofsubtracted human activities from the to vegetation fitted value growth. of the The meteorological specific formula element can be expressedmodel, that as follows:is, the “residual difference” (𝑁𝐷𝑉𝐼) between the true value and the predicted value, which can be regarded as the contribution of human activitiesNDVI to vegetationr = NDVI growth.− NDVI Thec specific formula can be expressed(4) as follows: NDVIc Cc = (5) 𝑁𝐷𝑉𝐼|NDVI =𝑁𝐷𝑉𝐼−𝑁𝐷𝑉𝐼r| + |NDVIc| (4)

NDVIr Cr = 𝑁𝐷𝑉𝐼 (6) 𝐶 =|NDVIr| + |NDVIc | (5) |𝑁𝐷𝑉𝐼| + |𝑁𝐷𝑉𝐼| In the formula, NDVI is the data of satellite remote sensing inversion, NDVIc is the predicted value based on a complex linear regression model, and NDVI𝑁𝐷𝑉𝐼r is the residual between the real NDVI and 𝐶 = (6) |𝑁𝐷𝑉𝐼| + |𝑁𝐷𝑉𝐼| Sustainability 2018, 10, x FOR PEER REVIEW 8 of 16 Sustainability 2019, 11, 1320 8 of 16 In the formula, 𝑁𝐷𝑉𝐼 is the data of satellite remote sensing inversion, 𝑁𝐷𝑉𝐼 is the predicted value based on a complex linear regression model, and 𝑁𝐷𝑉𝐼 is the residual between the real 𝑁𝐷𝑉𝐼 and 𝑁𝐷𝑉𝐼NDVI c. .Correspondingly, Correspondingly, C𝐶c characterizes characterizes the the contribution contribution rate rate of the of climate the climate to NDVI to in𝑁𝐷𝑉𝐼 the currentin the year;currentCr year;characterizes 𝐶 characterizes the contribution the contribution rate of human rate of activities human activities to NDVI toin that𝑁𝐷𝑉𝐼 year. in that year.

3. Result Analysis 3.1. The NDVI Difference between Two Time Sections 3.1. The NDVI Difference between Two Time Sections In 2015, the NDVI value in most areas of the Loess Plateau increased from that in 2000 (Figure3). In 2015, the NDVI value in most areas of the Loess Plateau increased from that in 2000 (Figure The areas with an NDVI increase totaled 523,285 km2, which accounted for 84.1% of the total area of 3). The areas with an NDVI increase totaled 523,285 km2, which accounted for 84.1% of the total area the Loess Plateau. The area ratio of the areas where the NDVI increased by 0–0.2 was 69.0%, and the of the Loess Plateau. The area ratio of the areas where the NDVI increased by 0–0.2 was 69.0%, and area ratio of the areas with an NDVI that increased by 0.2–0.1 was 15.1%. The areas with an NDVI the area ratio of the areas with an NDVI that increased by 0.2–0.1 was 15.1%. The areas with an NDVI reduction totaled 99,159 km2, which accounted for 15.9% of the total area of the Loess Plateau. The area reduction totaled 99,159 km2, which accounted for 15.9% of the total area of the Loess Plateau. The ratio of the areas where the NDVI was reduced by 0–0.2 was 15.5%, and the area ratio of the areas area ratio of the areas where the NDVI was reduced by 0–0.2 was 15.5%, and the area ratio of the where the NDVI was reduced by 0.2–0.7 was 0.4%. areas where the NDVI was reduced by 0.2–0.7 was 0.4%. From a regional perspective, areas in the southern part of the Shanxi Province (Linyi, Yuncheng), From a regional perspective, areas in the southern part of the Shanxi Province (Linyi, Yuncheng), the northern and central parts of Shaanxi Province (Yulin, Yan’an), and the eastern part of Gansu the northern and central parts of Shaanxi Province (Yulin, Yan’an), and the eastern part of Gansu Province (Qingyang, Pingliang and Tianshui) are mainly loess hills and mountains. There was a Province (Qingyang, Pingliang and Tianshui) are mainly loess hills and mountains. There was a significant increase in the NDVI values in these areas. significant increase in the NDVI values in these areas. Areas in the northern part of Shanxi (Datong–Zhangzhou), the Luoyang Basin in Henan (Luoyang), Areas in the northern part of Shanxi (Datong–Zhangzhou), the Luoyang Basin in Henan the Guanzhong Plain in Shaanxi (Xi’an–Xianyang), and the central part of Gansu (Lanzhou–Baiyin) (Luoyang), the Guanzhong Plain in Shaanxi (Xi’an–Xianyang), and the central part of Gansu (these areas are mainly fast-growing urban agglomerations), as well as areas in the western and (Lanzhou–Baiyin) (these areas are mainly fast-growing urban agglomerations), as well as areas in the northern parts of the in Inner Mongolia (Muus Sandy Land, Kubuqi ), showed a western and northern parts of the Ordos Plateau in Inner Mongolia (Muus Sandy Land, Kubuqi significant decline in NDVI. Desert), showed a significant decline in NDVI.

Figure 3. Spatial distribution of the differences in NDVI between 2015 and 2000 in the Loess Plateau. ComparedFigure 3. Spatial with 2000,distribution the NDVI of the value differences of most regionsin NDVI (84.1%) between in the2015 Loess and 2000 Plateau in the increased Loess Plateau. in 2015. InCompared southern with Shanxi 2000, (Linyi, the NDVI Yuncheng), value northern of most region and centrals (84.1%) Shaanxi in the (Yulin, Loess Yan’an), Plateau andincreased eastern in Gansu 2015. (Qingyang,In southern Pingliang Shanxi (Linyi, and Tianshui), Yuncheng), NDVI northern values and increased central significantly. Shaanxi (Yulin, Yan’an), and eastern Gansu (Qingyang, Pingliang and Tianshui), NDVI values increased significantly. Sustainability 2019, 11, 1320 9 of 16 Sustainability 2018, 10, x FOR PEER REVIEW 9 of 16

3.2. Trends in NDVI over the Past 16 Years Since 2000, the NDVI of the Loess Plateau had increased (the average slope of the change was 0.007/a), without without considering considering the the significance significance of of the the change change (Figure (Figure 44).). From the perspective of space, NDVI in most areas had an increasing trend, which indicates that the vegetation on the Loess Plateau has improved. The area with an increasing trend of NDVI was approximately 577,825 km22,, accounting accounting for 92.6% of the total area of the Loess Plateau. Similar to the results of the NDVI difference analysis based on the two time sections, the areas where the NDVI showed an upward trend were mainly distributed in the southern part of of the the Shanxi Shanxi Province Province (Linyi, (Linyi, Yuncheng), thethe northernnorthern andand centralcentral parts parts of of Shaanxi Shaanxi Province Province (Yulin, (Yulin, Yan’an), Yan’an), and and the the eastern eastern part part of Gansuof Gansu Province Province (Qingyang, (Qingyang, Pingliang, Pingliang, and and Tianshui). Tianshui). These These are are mainly mainly loess loess hills hills and and mountains. mountains. The area that showed a reducedreduced NDVINDVI waswas approximatelyapproximately 46,043 km2,, with with an area ratio of approximately 7.4%. Similar to the previous NDVI difference analysis results based on the two time sections, the NDVI that showed a decreasing trend was mainly distributed in the northern part of Shanxi (Datong–Zhangzhou, Changzhi), Henan’s Luoyang Basin (Luoyang), Shaanxi’s Guanzhong Plain (Xi’an–Xianyang), central Gansu (Lanzhou–Silver), and other regions (rapidly developed urban agglomerations), as well as the western and northernnorthern parts of the Ordos PlateauPlateau in Inner Mongolia (Muus Sandy Land, Kubuqi Desert).

Figure 4. NDVI trendstrends inin thethe Loess Loess Plateau Plateau from from 2000 2000 to to 2015. 2015. Between Between 2000 2000 and and 2015, 2015, the the NDVI NDVI of the of Loessthe Loess Plateau Plateau increased increased by anbyaverage an average of 0.007 of 0.007 per per year. year. The The slope slope of NDVI of NDVI varied varied from from−0.046/a −0.046/a to 0.048/ato 0.048/a in in different different regions. regions. A significant test of the p = 0.05 level was performed on the above NDVI trends (Figure5). A significant test of the p = 0.05 level was performed on the above NDVI trends (Figure 5). We We found that the significant increase in the NDVI was still in the mainstream of vegetation change in found that the significant increase in the NDVI was still in the mainstream of vegetation change in the study area, accounting for 64.6% of the total area of the Loess Plateau. The area where the NDVI the study area, accounting for 64.6% of the total area of the Loess Plateau. The area where the NDVI increased but failed the test accounted for 28.0%, the area where the NDVI decreased significantly only increased but failed the test accounted for 28.0%, the area where the NDVI decreased significantly accounted for 1.3% of the Loess Plateau, and the area where the NDVI decreased but not significantly only accounted for 1.3% of the Loess Plateau, and the area where the NDVI decreased but not accounted for 6.1%. The areas that passed the significance test were mainly located in the eastern, significantly accounted for 6.1%. The areas that passed the significance test were mainly located in central, and southern parts of the Loess Plateau; the areas that did not pass the significance test the eastern, central, and southern parts of the Loess Plateau; the areas that did not pass the significance test (including the NDVI rise and the NDVI decline) were mainly located in the eastern, Sustainability 2019, 11, 1320 10 of 16

Sustainability 2018, 10, x FOR PEER REVIEW 10 of 16 (including the NDVI rise and the NDVI decline) were mainly located in the eastern, northern, and northern, and western marginal areas of the study area and were distributed especially in the Ordos western marginal areas of the study area and were distributed especially in the Ordos Plateau and in Plateau and in the area west of Liupanshan Mountain. the area west of Liupanshan Mountain.

Figure 5. Significance test of NDVI trend in the Loess Plateau from 2000 to 2015. At p = 0.05 level, the Figure 5. Significance test of NDVI trend in the Loess Plateau from 2000 to 2015. At p = 0.05 level, the NDVI of the area with a significant increase accounted for 64.6% of the total area of the Loess Plateau, NDVI of the area with a significant increase accounted for 64.6% of the total area of the Loess Plateau, while the area where the NDVI significantly decreased was only 1.3% of the Loess Plateau. while the area where the NDVI significantly decreased was only 1.3% of the Loess Plateau. 3.3. Estimation of the Contribution Rate of Driving Factors 3.3. Estimation of the Contribution Rate of Driving Factors In 2015, the contribution rate of meteorological factors on the vegetation NDVI was highest in the westernIn 2015, part ofthe the contribution study area (Loessrate of Plateau),meteorological slightly factors lower on in thethe easternvegetation and NDVI southern was parts highest of the in studythe western area, and part lowest of the instudy the centralarea (Loess and southwesternPlateau), slightly regions lower (Figure in the6 ).eastern Specifically, and southern in the northern parts of andthe study western area, Ordos and Plateau,lowest in Gansu the central Province and andsouthwestern Qinghai Province, regions (Figure the area 6). west Specifically, of Liupanshan in the Mountain,northern and and western most of Ordos Shanxi Plateau, Province, Gansu southern Prov andince the and central Qinghai part Province, of Shaanxi, the meteorological area west of factorsLiupanshan contributed Mountain, significantly and most to of the Shanxi NDVI. Province, Its contribution southern rate and was the basically central above part 80%.of Shaanxi, In the easternmeteorological part of factors Gansu contributed Province (Qingyang, significantly Pingliang, to the NDVI. Tianshui), Its contribution northwestern rate was Shaanxi basically Province above (Yan’an,80%. In the Yulin), eastern and part southeastern of Gansu Province Ordos, Inner (Qingyang, Mongolia, Pingliang, the contribution Tianshui), ratenorthwestern of meteorological Shaanxi factorsProvince was (Yan’an, small. Its Yulin), contribution and southeastern rate was basically Ordo lesss, Inner than 70%.Mongolia, the contribution rate of meteorologicalSpatial statistics factors showed was small. that Its in contri 2015,bution the average rate was contribution basically less rate than of meteorological70%. factors (temperature,Spatial statistics precipitation) showed to that NDVI in in2015, the Loessthe aver Plateauage contribution was 87.7%, thatrate is,of themeteorological size of the NDVI factors in the(temperature, Loess Plateau precipitation) was generally to NDVI controlled in the by Loess meteorological Plateau was elements. 87.7%, that Among is, the them, size of the the area NDVI of the in meteorologicalthe Loess Plateau element was generally contribution controlled rate that by wasmeteor greaterological than elements. 80% accounted Among forthem, 81.0% the ofarea the of total the areameteorological of the region, element while contribution the area with rate meteorological that was greater element than contribution80% accounted rate for less 81.0% than of 80% the onlytotal accountedarea of the forregion, 19.0% while of the the total area area with of themeteorolog region (Figureical element7). contribution rate less than 80% only accounted for 19.0% of the total area of the region (Figure 7). Sustainability 2018, 10, x FOR PEER REVIEW 11 of 16 Sustainability 2019, 11, 1320 11 of 16 Sustainability 2018, 10, x FOR PEER REVIEW 11 of 16

Figure 6. The contribution rate of meteorological factors to NDVI in the Loess Plateau in 2015. The Figure 6. contributionFigure 6. TheThe rate contribution contribution of meteorological rate rate of of meteorologicalfactors meteorological on the vegetation fact factorsors to NDVINDVI to NDVI wasin the inhighest theLoess Loess in Plateau the Plateau western in 2015. in part 2015. The of The contribution rate of meteorological factors on the vegetation NDVI was highest in the western part thecontribution study area rate (Loess of meteorological Plateau), slightly factors lower on inthe th vegetatione eastern and NDVI southern was highest parts of in thethe studywestern area, part and of of the study area (Loess Plateau), slightly lower in the eastern and southern parts of the study area, lowestthe study in thearea central (Loess and Plateau), southwestern slightly lowerareas. in the eastern and southern parts of the study area, and and lowest in the central and southwestern areas. lowest in the central and southwestern areas.

60.0 60.0 49.3 50.0 49.3

） 50.0 %

） 40.0 （ % 40.0 31.7 （ 30.0 31.7 30.0 20.0 13.6 20.0 13.6 Area Percentage 10.0 3.8 Area Percentage 10.0 0.6 1.0 3.8 0.0 0.6 1.0 0.0 0—50 50—60 60—70 70—80 80—90 90—100 0—50 50—60Contribution 60—70 Rate 70—80（%） 80—90 90—100 Contribution Rate（%） Figure 7. Statistics of the contribution of meteorological factors to the NDVI of the Loess Plateau in Figure 7. Statistics of the contribution of meteorological factors to the NDVI of the Loess Plateau in 2015. In 2015, the average contribution of meteorological factors (temperature, precipitation) to the 2015.Figure In 7. 2015, Statistics the average of the contribution contribution of of meteorological meteorological factors factors to (temperature, the NDVI of precipitation)the Loess Plateau to the in NDVI in the Loess Plateau was 87.7%. The area with a contribution rate greater than 80% accounted for NDVI2015. In in 2015,the Loess the average Plateau contributionwas 87.7%. The of meteorologarea with aical contribution factors (temperature, rate greater thanprecipitation) 80% accounted to the 81.0% of the total area of the region, while the area with a contribution rate of less than 80% accounted forNDVI 81.0% in the of Loessthe total Plateau area wasof the 87.7%. region, The while area withthe areaa contribution with a contribution rate greater rate than of 80%less accountedthan 80% for only 19.0% of the total area of the region. accountedfor 81.0% offor the only total 19.0% area of ofthe the total region, area of while the region. the area with a contribution rate of less than 80% Onaccounted the other for hand,only 19.0% non-meteorological of the total area of factors the region. (mainly human activities) had a significant impact on the vegetation NDVI in different regions (Figure8). The areas where non-meteorological factors

Sustainability 2018, 10, x FOR PEER REVIEW 12 of 16

On the other hand, non-meteorological factors (mainly human activities) had a significant impact on the vegetation NDVI in different regions (Figure 8). The areas where non-meteorological factors weakened the vegetation NDVI (i.e., regions with a Cr less than 0) were mainly distributed in the western part of the study area, namely, the northern and western parts of the Ordos Plateau in SustainabilityInner Mongolia2019, 11 and, 1320 most of the western part of Liupanshan Mountain. At the same time, there12 were of 16 also urban agglomerations in the eastern and southern parts of the Loess Plateau (Northern Shanxi weakenedProvince, Shaanxi the vegetation Guanzhong NDVI Plain, (i.e., Luoyang regions withBasin, a etc.). Cr less Such than areas 0) wereaccounted mainly for distributed 29.3% of the in total the westernarea of the part Loess of the Plateau. study area, namely, the northern and western parts of the Ordos Plateau in Inner MongoliaThe areas and mostwhere of non-meteorological the western part offactors Liupanshan (mainly Mountain. human activities) At the samesignificantly time, there enhanced were alsothe vegetation urban agglomerations index (i.e., regions in the with eastern Cr andgreater southern than 0) parts were of mainly the Loess distributed Plateau in (Northern the eastern Shanxi and Province,southern parts Shaanxi of the Guanzhong study area Plain, (except Luoyang for urban Basin, ag etc.).glomerations Such areas in accounted these areas). for 29.3%This type of the of totalarea areaaccounted of the for Loess 70.7% Plateau. of the total area of the Loess Plateau.

Figure 8. The distribution of non-meteorological factors on the NDVI contribution rate of the Loess Plateau in 2015. The average contribution of non-meteorologicalnon-meteorological factors (mainly(mainly human activities) to NDVI in the Loess Plateau was 6.4%. The areas where non-meteorological factors had weakened the NDVI in the Loess Plateau was 6.4%. The areas where non-meteorological factors had weakened the vegetation NDVI were mainly distributed in the western part of the Loess Plateau (i.e., the western vegetation NDVI were mainly distributed in the western part of the Loess Plateau (i.e., the western and northern parts of the Ordos Plateau in Inner Mongolia and most of the western part of Liupanshan and northern parts of the Ordos Plateau in Inner Mongolia and most of the western part of Mountain) and the urban agglomerations in the eastern and southern parts of the Loess Plateau. Liupanshan Mountain) and the urban agglomerations in the eastern and southern parts of the Loess ThePlateau. areas where non-meteorological factors (mainly human activities) significantly enhanced the vegetation index (i.e., regions with Cr greater than 0) were mainly distributed in the eastern and From a spatial statistical point of view (Figure 9), the average contribution of non-meteorological southern parts of the study area (except for urban agglomerations in these areas). This type of area factors (mainly human activities) to NDVI in the Loess Plateau was 6.4%. In other words, non- accounted for 70.7% of the total area of the Loess Plateau. meteorological factors generally improved the NDVI of the Loess Plateau vegetation. Among them, From a spatial statistical point of view (Figure9), the average contribution of non-meteorological the area with an obvious promotion effect on the vegetation cover (that is, contribution rate of more factors (mainly human activities) to NDVI in the Loess Plateau was 6.4%. In other words, than 20%) accounted for 15.1%. The area that showed significant inhibition of vegetation cover (i.e., non-meteorological factors generally improved the NDVI of the Loess Plateau vegetation. Among contribution rate of less than −20%) accounted for only 3.8%. Obviously, non-meteorological factors them, the area with an obvious promotion effect on the vegetation cover (that is, contribution rate of (mainly human activities) play an important role in improving vegetation in the region. more than 20%) accounted for 15.1%. The area that showed significant inhibition of vegetation cover (i.e., contribution rate of less than −20%) accounted for only 3.8%. Obviously, non-meteorological factors (mainly human activities) play an important role in improving vegetation in the region. Sustainability 2019, 11, 1320 13 of 16 Sustainability 2018, 10, x FOR PEER REVIEW 13 of 16

35.0 32.6

30.0 ）

% 25.0 23.0 （ 20.0 16.8 15.0 10.4 10.0 8.7

Area Percentage 4.7 5.0 3.2 0.6 0.0 -50—-30 -30—-20 -20—-10 -10—0 0—10 10—20 20—30 30—100 Contribution Rate（%）

Figure 9. Area statistics of non-meteorological factors for the actual NDVI contribution rate of the Figure 9. Area statistics of non-meteorological factors for the actual NDVI contribution rate of the Loess Plateau in 2015. In 2015, the average contribution of non-meteorological factors to NDVI in the Loess Plateau in 2015. In 2015, the average contribution of non-meteorological factors to NDVI in the Loess Plateau was 6.4%. The area that significantly promoted vegetation cover was 15.1%. The area Loess Plateau was 6.4%. The area that significantly promoted vegetation cover was 15.1%. The area that had significant inhibition of the vegetation cover accounted for only 3.8%. that had significant inhibition of the vegetation cover accounted for only 3.8%. 4. Discussion 4. Discussion Regardless of the simple difference analysis of NDVI in two time sections (2000 and 2015) or the NDVIRegardless trend analysis of the over simple 16 difference years (2000–2015), analysis of the NDVI results in thattwo weretime sections obtained (2000 by the and two 2015) research or the methodsNDVI trend all reflectedanalysis extremelyover 16 years similar (2000–2015), NDVI spatiotemporal the results that variation were obtain characteristics.ed by the Both two methodsresearch showedmethods that all reflected the vegetation extremely cover similar in most NDVI areas spatio of thetemporal Loess Plateau variation increased characteristics. from 2000 Both to 2015, methods and it wasshowed mainly that distributed the vegetation in the cover southern in most part areas of Shanxi, of the theLoess central Plateau and increased northern partsfrom of2000 Shaanxi, to 2015, and and in theit was eastern mainly part distributed of Gansu. Thisin the is southern consistent part with of the Shanxi, findings the ofcentral other and researchers northern [39 parts,40]. of Compared Shaanxi, withand in Xiao’s the eastern research part results of Gansu. [39], the This authors is consistent believe that with the the area find withings a largerof other area researchers ratio in the [39,40]. Loess PlateauCompared has with become Xiao’s “greener” research (84.6% results vs. [39], 78.6%). the authors This is becausebelieve that the monitoringthe area with period a larger of thisarea paper ratio isin longerthe Loess (2000–2015 Plateau vs.has 2000–2010).become “greener” In the five(84.6% years vs. after 78.6%). 2010, This a number is because of ecological the monitoring construction period projectsof this paper (including is longer the Three-North(2000–2015 vs. Shelterbelt 2000–2010). Project, In the the five Returning years after Farmland 2010, a to number Forests of and ecological Grasses Project,construction the Beijing–Tianjin projects (including Sandstorm the Three-North Source Project, Shelterbelt etc.) continued Project, tothe be Returning implemented, Farmland and the to vegetationForests and on Grasses the Loess Project, Plateau the was Beijing–Tianjin further sustained Sandstorm and improved. Source Project, This means etc.) that continued at least 6%to be of theimplemented, vegetation inand the the Loess vegetation Plateau on was the improved Loess Pl andateau restored was further during sustained these five and years. improved. This meansIn that the at southwestern least 6% of the part vegetation of Shanxi in (Linyi,the Loess Yuncheng), Plateau was central improved and northern and restored Shaanxi during (Yan’an, these Yulin),five years. and the Liupanshan Mountain area (Qingyang, Pingliang, Tianshui) in eastern Gansu, human activitiesIn the actually southwestern increased part the of NDVI Shanxi value (Linyi, of vegetation. Yuncheng), The central loess and types northern in these Shaanxi areas are (Yan’an, mainly loessYulin), hills and and the mountains.Liupanshan InMountain the past area few decades,(Qingyang, especially Pingliang, since Tianshui) the implementation in eastern Gansu, of the human GCP inactivities 1999, the actually central increased government the NDVI has implemented value of vegetation. the project The ofloess returning types in farmland these areas to forestsare mainly and grasslandsloess hills and in the mountains. Loess Plateau. In the For past areas few withdecades, a slope especially of more since than the 25◦ ,implem the originalentation cultivated of the GCP land in is required1999, the to central be withdrawn, government and fruithas treesimplemented and shrubs the have project been of planted. returning At the farmland same time, to forforests a certain and periodgrasslands of time, in the farmers Loess willPlateau. be provided For areas with with appropriate a slope of subsidizedmore than food,25°, the seedling original afforestation cultivated land fees, andis required cash (living to be expenses)withdrawn, subsidies and fruit for trees free. and Each sh acrerubs ofhave farmland been planted. subsidizes At the 100 same kg of time, grain for and a 20certain yuan period of cash perof time, year. Thefarmers afforestation will be fee provid of 50ed yuan with is subsidized appropriate for eachsubsidized acre of cultivatedfood, seedling land, Yilinafforestation barren hills, fees, andand wasteland.cash (living There expenses) are different subsidie subsidiess for free. forEach different acre of postrecoveryfarmland subsidizes vegetation. 100 Thekg of ecological grain and forest 20 yuan subsidy of cash is eight per year. years, The the afforest economication forest fee subsidyof 50 yuan is five is subsidized years, and thefor grasslandeach acre subsidyof cultivated is two land, years. Yilin Obviously, barren the hills, central and government’s wasteland. There returning are farmlanddifferent tosubsidies forests and for grasslanddifferent activitiespostrecovery have vegetation. played a key The role ecological in the restoration forest subsid ofy regional is eight vegetation.years, the economic In the western forest partsubsidy of the is Loessfive years, Plateau, and thethenorthern grassland and subsidy western is two parts year of thes. Obviously, Ordos Plateau the central in Inner government’s Mongolia, most returning of the areafarmland west ofto Liupanshanforests and Mountain,grassland andactivities urban have agglomerations played a key in the role eastern in the and restoration southern partsof regional of the vegetation. In the western part of the Loess Plateau, the northern and western parts of the Ordos Plateau in Inner Mongolia, most of the area west of Liupanshan Mountain, and urban agglomerations Sustainability 2019, 11, 1320 14 of 16

Loess Plateau (Northern Shanxi Province, Shaanxi Guanzhong Plain, Henan Luoyang Basin, etc.; e.g., Xi’an–Xianyang–Weinan, Lanzhou–Baiyin, Luoyang, Datong–Zhangzhou–Jinzhong), human activities have actually reduced the NDVI value of vegetation based on meteorological elements. Since 2005, China’s economy has developed rapidly, and large-scale “making cities” have appeared in vast areas of China, including the Loess Plateau. The rapid development of new towns has taken up existing farmland, grassland, and woodland, which has resulted in a reduction in the surface NDVI. Therefore, the phenomenon of NDVI reduction in these urbanized areas is completely understandable. Vegetation changes in the Loess Plateau are generally subject to meteorological elements. However, in some areas, the influence of non-meteorological factors on the vegetation NDVI dominates. This conclusion is consistent with the findings of Xin [40]. However, the research by Xin only qualitatively analyzed the relationship between vegetation NDVI and various possible driving factors (temperature, precipitation, land use, agricultural planting, vegetation construction), while this study quantitatively gave climatic factors and an estimation of the contribution rate of two major types of non-meteorological factors. Therefore, this study is a more in-depth study of the driving mechanism of vegetation change. As a rapid and simple method for assessing the contribution rate, the residual method is widely used in the study of vegetation degradation in arid and semiarid areas. However, the premise of this method is to assume that there is a fixed linear dependence on the vegetation NDVI and climatic factors. From the perspective of the biological growth mechanism, this premise is not strictly established. Moreover, the residual method can only give the contribution of the two driving factors in a certain year and cannot comprehensively evaluate the contribution of one or more driving factors over a long period of time. Therefore, in the future, it is necessary to continue to deepen the research on the contribution rate of vegetation change factors based on the NDVI.

5. Conclusions In this paper, two time-section NDVI difference analysis methods and a 16-year time series NDVI data trend analysis method were applied to identify the temporal and spatial variation characteristics of the vegetation NDVI in the Loess Plateau. Then, complex linear regression and residual analysis methods were applied to separate the contribution of meteorological elements to NDVI, and the contribution rate of non-meteorological elements (mainly human activities) to NDVI changes was obtained. The results of the study are consistent with the results of other researchers and were improved quantitatively. Our main conclusions are as follows: (1) Most areas of the Loess Plateau from 2000 to 2015 (84.1% for the cross-section difference method and 64.6% for the significant increase in the time-series trend analysis results) became “greener” (the vegetation NDVI increased). These “greener” areas were mainly distributed in the southwestern part of the mountain, in the central and northern parts of Shaanxi, and in eastern Gansu. However, in the rapidly developing urban agglomerations and in the Muus Sandy Land and Kubuqi Desert in the western and northern parts of the Ordos Plateau in Inner Mongolia, the vegetation NDVI showed a significant decline. (2) The contribution rate of meteorological factors on the vegetation NDVI was highest in the western part of the study area (Loess Plateau), slightly lower in the eastern and southern parts of the study area, and lowest in the central and southwestern areas. The non-meteorological factors that weakened the vegetation NDVI were mainly distributed in the northern and western parts of the Ordos Plateau in Inner Mongolia and in most of the western part of Liupanshan Mountain. These non-meteorological factors were also distributed in the eastern and southern urban agglomerations of the Loess Plateau. The overall contribution rate of meteorological factors (temperature and precipitation) to the NDVI in the Loess Plateau was as high as 87.7%, while the average contribution rate of non-meteorological factors (mainly human activities) to the vegetation NDVI was 6.4%. Non-meteorological factors generally improved the NDVI of the Loess Plateau vegetation.

Author Contributions: Conceptualization, Y.H. (Yunfeng Hu); methodology, Y.H. (Yunfeng Hu); software, Y.H. (Yang Hu) and R.D.; validation. Y.H. (Yang Hu) and R.D.; formal analysis, R.D.; investigation, R.D.; data Sustainability 2019, 11, 1320 15 of 16 curation, Y.H. (Yang Hu) and R.D.; writing—original draft preparation, R.D.; writing—review and editing, Y.H. (Yunfeng Hu), R.D.; visualization, R.D.; supervision, Y.H. (Yunfeng Hu); project administration, Y.H. (Yunfeng Hu); funding acquisition, Y.H. (Yunfeng Hu). Funding: This research was funded by the Strategic Priority Research Program of Chinese Academy of Sciences, grant number XDA19040301, XDA20010202, XDA23100201; the National Key Research and Development Plan Program in China, grant number 2016YFC0503701, 2016YFB0501502; and the Key Project of High Resolution Earth Observation System in China, grant number 00-Y30B14-9001-14/16. Conflicts of Interest: The authors declare no conflicts of interest.

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