Environmental Earth Sciences (2019) 78:709 https://doi.org/10.1007/s12665-019-8732-z

ORIGINAL ARTICLE

Vegetation response to climatic variation and human activities on the from 2000 to 2016

Qimin Ma1,2 · Yinping Long3 · Xiaopeng Jia1 · Haibing Wang4 · Yongshan Li1,2

Received: 4 March 2019 / Accepted: 25 November 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019

Abstract Determination and analysis of the efects of climatic variation and human activities on vegetation changes since the imple- mentation of intensive ecological projects in 2000 are critically important for the restoration of vegetation on the Ordos Plateau, one of the areas that sufer from the worst vegetation degradation, the most concentrated and gas exploitation, and the highest ecological risks in . This study was performed to establish and validate a nonlinear regression model to express the relationships between the normalized diference vegetation index (NDVI), precipitation, and air temperature in areas with scarce human activities, as identifed from the Global Human Infuence Index Datasets. This model was then applied to the whole plateau with data on cumulative precipitation and average air temperature with the consideration of the delayed efects to simulate temporal NDVI changes induced by climatic variation. The residual trend was then computed as the slope of the diference between the actual NDVI and the simulated value to analyze the human impact on vegetation. The results show that the plateau’s vegetation had experienced a signifcant greening trend from 2000 to 2016 under a warmer and wetter climate and with the impact of human activities; compared to air temperature, precipitation played a leading role in vegetation greening in most parts of the plateau, and human activities had a signifcant positive impact in 22.0% of the plateau and a signifcant negative impact in 0.66% of the plateau (p < 0.1). More efective programs of ecological protection and restoration must still be conducted by the government and other organizations.

Keywords Vegetation variations · Climate change · Human activities · Nonlinear statistical model · Residual trend analysis

Introduction highest ecological risks in China (Fu et al. 2011; Hu et al. 2019), making it a key area for vegetation restoration. The The Ordos Plateau, located in the center of China’s agropas- implementation of ecological projects since 2000, such as toral ecotone, forms an ecological barrier in northern China those for returning the grain plots to forestry and grass (Cao that is ecologically fragile but frequently disturbed by human et al. 2009), the Three-North Shelterbelt Program (Wang activities. Deserts once covered 86% of the plateau (Lv et al. et al. 2010), bans on grazing, and delays of grazing, have 2002), one of the areas sufering from the worst vegetation led to great improvements in the ’s forests and grasses degradation (Liu et al. 1999), the most concentrated coal (Liu 2010). However, due to the impact of climatic varia- and gas exploitation (Guo 1994; Teng et al. 2016), and the tion and uncertainties in the assessment methods, the areas in which human activities play a positive role in vegetation * Qimin Ma growth and those in which they play a negative role remain [email protected] unclear. Determination and analysis of the efects of climatic variation and human activities on vegetation changes are 1 Northwest Institute of Eco-Environment and Resources, hence critically important for the restoration and reconstruc- Chinese Academy of Sciences, 730000, China tion of the Ordos Plateau ecosystem. 2 University of Chinese Academy of Sciences, Beijing 100049, Many researchers have conducted similar studies on the China regional (Vicenteserrano et al. 2004; Muratova et al. 2008; 3 College of Resources and Environment, Chengdu University Xin et al. 2008; Li et al. 2013; Cai et al. 2014; Chen et al. of Information Technology, Chengdu 610225, China 2014; Li et al. 2015; Sun et al. 2015; Wang et al. 2016b; 4 College of Desert Control Science and Engineering, Inner Xu et al. 2017) and global scales (Mueller et al. 2014; Liu Agricultural University, 010011, China

Vol.:(0123456789)1 3 709 Page 2 of 15 Environmental Earth Sciences (2019) 78:709 et al. 2015). Distinguishing vegetation changes caused by to climatic variation with their lag efects considered, and climatic variation and those caused by human activities has to distinguish the impacts of climatic factors and human always involved the residual analysis method (Evans and activities on the vegetation variations. This research tried to Geerken 2004; Geerken and Ilaiwi 2004; Herrmann et al. answer the following questions: 2005; Wessels et al. 2007; Li et al. 2012; Wang et al. 2012, 2015; Fang et al. 2014; Zhang et al. 2014; He et al. 2015; Qu 1. How did precipitation, temperature, and the NDVI et al. 2018; Xiu et al. 2018; Hu et al. 2019), in which the dif- change in the Ordos Plateau since 2000? What are the ference between the actual normalized diference vegetation relationships between them? How to establish a model index (NDVI) value and its predicted value is used to indi- to express their relationships? cate the human-induced change (Evans and Geerken 2004). 2. In which areas human activities played a positive role The predictive equation should be established under the in vegetation greening and those in which they played a assumption that plants grow in a natural state in the absence negative role? of human interference (Cao et al. 2006; Li et al. 2012; Fang et al. 2014), which has always been neglected, leading to uncertainties in the prediction equation (Wang et al. 2012, Study area 2015; Zhang et al. 2014; He et al. 2015; Qu et al. 2018; Xiu et al. 2018; Hu et al. 2019). To solve this problem, some The Ordos region is located in the southern part of China’s studies established the relationship between the NDVI and Autonomous Region, spanning from 37° 41′ climatic factors during a single period in which the infu- to 40° 51′ N in latitude and from 106° 42′ to 111° 31′ E in ence of human activities on vegetation change was so little longitude (Fig. 1) and covering an area of over 87,400 km2. It that it could be ignored, and then applied the equation to is a complicated transition region between a warm temperate another period (Cao et al. 2006; Li et al. 2012; Fang et al. zone and a temperate zone, between the east China conti- 2014). However, it is difcult to determine the length and nental monsoon climate and the northwest arid climate, and change point of the two periods because of the infuences of between forest and grassland areas and steppe, desert steppe, land-use policies and the diferent responses of policies in and desert areas. Three sides of the plateau, at altitudes rang- various . In most cases, the relationship between the ing from 1000 to 1500 m, are surrounded by the . NDVI and climatic factors has been established using lin- The eastern part is the loess hilly area of Jungar, the western ear regression models (Evans and Geerken 2004; Cao et al. part includes mild slopes of the Zhuozi Mountains and Ordos 2006; Wessels et al. 2007; Muratova et al. 2008; Li et al. Highlands, and to the north and south lie the Hobq Desert and 2012; Liu et al. 2013; He et al. 2015; Qu et al. 2018; Xiu the Mu Us Sandy Land. This region has a temperate continen- et al. 2018); however, this type of model neglects the non- tal climate with an annual mean air temperature of 5.3–8.7 °C linear responses of vegetation dynamics to climatic variation and annual precipitation of 150–450 mm, most of which falls and some studies take no account of the lag efects of cli- between July and September. Due to the diferences in the matic variation on vegetation growth. In addition, few stud- annual precipitation and moisture index between the eastern ies have conducted model validation, which is a necessary and western areas, vegetation in this region have signifcant step before application. Only a few studies have indirectly zonal characteristics (Zhang et al. 2014). The major land use verifed their models with statistics such as livestock volume types are grassland, sandy land, forest land, cultivated land, and land-use changes (Cao et al. 2006; Muratova et al. 2008; and residential land. From 2000 to 2015, the grassland area Li et al. 2012; Wang et al. 2012; Fang et al. 2014). To sum increased from 61.28 to 68.71%, the sandy land decreased up, the residual analysis method is the basis for distinguish- from 24.68 to 16.95%, the forest area increased from 1.66 to ing the impacts of climatic factors and human activities on 4.83%, the cultivated land decreased from 5.49 to 4.51%, and the vegetation variations in most studies. However, the fol- the residential land increased from 1.09 to 1.36% (Fig. 2). lowing issues should be further considered when applying From the Inner Mongolian Statistical Yearbooks, the the method: (1) the regression model expressing the relation- amount of livestock continued to increase between 2000 and ship between vegetation and climatic variations should be 2004, and stabilized at around 8 million animals after 2004. established in areas with little or without human activities; From 2000 to 2012, coal production continued to increase (2) the nonlinear responses of vegetation dynamics to cli- from 26.8 million to 639.4 million tons, followed by a slight matic variation and the lag efects should be taken account; downward trend. The farmland area fuctuated greatly before and (3) the regression model should be comprehensively 2005 and then stabilized at around 0.39 million ha, while verifed before application. food production have increased signifcantly. The average Hence, the objectives of this research were to establish annual aforestation area from 2000 to 2016 is 0.119 million and comprehensively validate a nonlinear regression model ha, resulting in a continuous increase in forest area, from to express the nonlinear responses of vegetation dynamics 1.058 to 2.32 million ha (Fig. 3).

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Fig. 1 Location of and geographic information on the Ordos plateau

Fig. 2 Spatial distributions of various land use types in a 2000 and b 2015

Materials and methods gov/datas​et_disco​very). It is considered to be of high qual- ity, suitable for capturing vegetation dynamics, and able to Data sources and processing improve the accuracy of analysis and provide more explana- tory power (Huete et al. 2002; Fensholt and Proud 2012; The NDVI dataset was taken from the MOD13Q1 product, Jiang et al. 2015). The yearly maximum NDVI (NDVI­ max) with a spatial resolution of 250 m and a temporal resolution was used to evaluate interannual vegetation variability. To of 16 days, spanning from 2000 to 2016 (https://lpdaa​ c.usgs.​ flter out outliers, we frst used the quality assurance layer

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Fig. 3 Interannual variations in a livestock, b coal yield, c farmland area, d food production, e annual aforested area, and f forestland area between 2000 and 2016 of the MOD13Q1 product to mask clouds and low quality with a satellite and rain gauge data-merging framework that values, and then calculated ­NDVImax as the upper 5% of the combined the techniques of statistical spatial downscaling, global NDVI histogram during the growing season for each double kernel smoothing, shufed complex evolution (Duan pixel (Gutman and Ignatov 1997). The census data for the et al. 1993), and indicator kriging (Long et al. 2016). The number of livestock, farmland and forested area, coal yield, satellite precipitation product was obtained from the Tropi- etc., were obtained from the Inner Mongolian Statistical cal Rainfall Measuring Mission Multisatellite Precipitation Yearbooks. Land use data for 2000 and 2015 were obtained Analysis, version 7, with a spatial resolution of 0.25° and from the Resource and Environment Data Cloud Platform a temporal resolution of 3 h. The 0.25° daily precipitation (http://www.resdc​.cn/Defau​lt.aspx). feld was frst downscaled to 1 km based on the relationships Meteorological data, including daily precipitation and between precipitation, weather conditions, and topographi- average air temperature, were taken from the China Mete- cal characteristics. Second, the downscaled Tropical Rain- orological Data Service Center. The air temperature was fall Measuring Mission and gauged precipitation data were interpolated with a modified inverse distance weighted merged with a minimum cross-validation error using the method that considers elevation efects on the temperature nonparametric merging technique of double kernel smooth- (Lu and Wong 2008; Zhuang and Wang 2003). According ing and the global optimization method of shufed complex to the cross-validation, the interplation results had a mean evolution. Finally, to consider the spatial intermittency of error of 0.18 °C, a bias of 1.95%, a root mean square error the daily precipitation, the merged result was multiplied by of 1.42 °C, and a Nash–Sutclife efciency of 0.98. Precipi- an indicator feld that represents the presence and absence tation is the main constraint factor for vegetation dynam- of precipitation generated using the indicator kriging tech- ics in arid and semiarid regions (Xin et al. 2008). Due to nique. A daily precipitation dataset at a resolution of 1 km its strong spatial heterogeneity, we estimated precipitation was generated using this framework and performed well,

1 3 Environmental Earth Sciences (2019) 78:709 Page 5 of 15 709 with a mean error of 0.03 mm, a bias of 3.9%, a root mean indicates no spatial heterogeneity, and higher value indi- square error of 2.10 mm, and a Nash–Sutclife efciency of cates stronger heterogeneity. The q-statistic was 0.945 and 0.72. The daily estimates were then aggregated into monthly 0.970 in the evaluation area (HII ≤ 5) and the entire study values. area, respectively, indicating good representativeness of the The Global human infuence index (HII) dataset, pro- evaluation area. duced by the Wildlife Conservation Society and the Colum- bia University Center for International Earth Science Infor- Methods mation Network (http://sedac​.ciesi​n.colum​bia.edu/data/ colle​ction​/wilda​reas-v2/sets/brows​e), was used as a proxy The Mann–Kendall nonparametric test was used to test for for the overall extent of human efects. It is a comprehensive randomness against trends in the climatological time series. index that represents the efects of human activities on the A positive value for slope indicates an upward trend, and environment with a spatial resolution of 1 km, created from a negative value indicates a downward trend. A detailed nine global data layers that cover human population pres- description of the method can be found in the literature sure (population density), human land use and infrastructure (Burn and Elnur 2002; Sang et al. 2014). (built-up areas, nighttime lights, land use/land cover), and Precipitation and air temperature are the main constraint human access (coastlines, roads, railroads, navigable riv- factors for the spatial and temporal variations in vegetation ers). It ranges from 0 to 64; a score of 0 indicates no direct dynamics (Mu et al. 2012; Xu et al. 2014). Partial correla- human infuence, and higher values indicate an increas- tion coefcients measure the degree of association between ingly strong human activities. Sanderson et al. (2002)and two random variables with the removal of the efect of other Rodríguezrodríguez and Bomhard (2012)both defned areas controlling random variables. The partial correlation coef- with an HII score of 10 or lower as the least infuenced or fcients between the two meteorological factors and NDVI wildest areas in all biomes. However, in our study area, the were thus calculated at each pixel to explore their relation- areas with 5 < HII ≤ 10 involve in residential land. We hence ships. A detailed description of the partial correlation can used this dataset to identity the least human-infuenced areas be found in the literature (Song and Ma 2011). (HII ≤ 5), with which a regression model between the NDVI Various regression models between ­NDVImax and the cli- and climate factors was calibrated and verifed. As shown matic factors using the plateau averaged yearly NDVI­ max, in Fig. 4, the calibration area is highlighted by the black precipitation, and air temperature time series were estab- coiled line and the validation area by the red coiled line. lished to obtain the coefcient of determination (R2), p The representativeness of the calibration and validation value, and root-mean-square error (RMSE) for each model. areas was evaluated through a q-statistic method (Wang It was found that multilevel nonlinear regression was the et al. 2016a). The value of the statistic is within [0,1], 0 most accurate, consistent with the results of Zhang (Zhang

Fig. 4 Spatial distributions of human infuence index (HII) refecting human activity inten- sities, and the calibration and validation areas (HII ≤ 5) for the evaluation of the multilevel nonlinear regression equation model

1 3 709 Page 6 of 15 Environmental Earth Sciences (2019) 78:709 et al. 2014). Table 1 shows the regression equation and the with the lowest value of 0.2233 in 2001, highest value of evaluation results. Hence, a nonlinear regression model with 0.3487 in 2016, and its average value of 0.2834 (Fig. 5a). consideration of the delayed efects was used to simulate Precipitation exhibited a slightly but insignifcant increas- the potential NDVI­ max in natural status (Wang et al. 2012; ing trend, had an annual average value of 339 mm, and was Li et al. 2012): most in 2012 and 2016, respectively, 482 mm and 445 mm (Fig. 5b). In the same 2 years, ­NDVI also reached a maxi- NDVI = a + a P + a T + a P2 + a T2 + a P T , max max 1 2 i 3 j 4 i 5 j 6 i j (1) mum of 0.3487 and 0.3481, respectively. Air temperature exhibited an insignifcant increasing trend at an annual time- where a1 through a6 are coefcients of the nonlinear regres- step (Fig. 5c). The temporal patterns of ­NDVImax were mod- sion; Pi is i months of accumulated precipitation (mm); Tj is 2 erately correlated with precipitation (R = 0.58, p < 0.001) j months of averaged air temperature (°C); and i and j corre- (Fig. 5d), and the correlation was better after 2004. Air spond to varying periods before the occurrence of ­NDVImax, temperature was very weakly correlated with the ­NDVImax which is in early August for the study area. The lag times 2 (R = 0.01, p = 0.676) (Fig. 5e). range from the previous January to August in 1-month incre- ments, producing 400 combinations of cumulative precipita- Spatial patterns and trends in ­NDVImax, tion and average temperature for each pixel. When i = 1, the precipitation, and air temperature precipitation accumulation period is August; when i = 2, the period includes July and August; when i = 3, the period lasts Figure 6 shows the spatial distributions of the mean annual from June to August; and so on until i = 20, when the period ­NDVI , precipitation, and air temperature between lasts from the previous January to August. The calculations max 2000 and 2016 on the plateau. The spatial distributions are similar for the average temperature and j. of NDVI­ showed a signifcant diference between the At each pixel, the combination resulting in the highest R2 max west and east, consistent with the spatial patterns of pre- and passing the 0.05 signifcant level test was selected for cipitation. The pixels with an ­NDVI value greater than the generation of residuals (i.e., the diference between the max 0.4 accounted for 24.9% of the total area of the region and observed ­NDVI and its simulated value). If the annual max were generally distributed along the Yellow River region changes in residuals show random variation around zero, the and over the northeast and southeast parts of the plateau, observed changes in vegetation are thought to be attributed in areas of medium-density crops, arbor, and shrub forest. to climatic variation; an upward trend in the residuals sug- Higher values between 0.2 and 0.4 were mainly observed gests improved vegetation conditions, which is presumably in the central and western parts, in which grassland induced by conservation and restoration eforts; and a down- accounts for 58.9% of the area. NDVI values of less than ward trend in the residuals indicates vegetation browning, 0.2 were generally distributed in the Hobq Desert and the which may be attributed to urban expansion, overgrazing, Mu Us Sandy Land and accounted for 16.2% (Fig. 6a). ore mining, and the abandonment of cultivated land. The annual precipitation ranged from less than 250 mm in the west to more than 350 mm in the east, and was Results mainly concentrated in the northeastern part of the pla- teau (Fig. 6b). The annual mean temperature was primarily altitude-dependent, with a spatial pattern that was low in Temporal trends in ­NDVImax, precipitation, and air temperature the center and high in the north and south (Fig. 6c). As shown in Fig. 7a, NDVI­ max was increasing in most areas, with an area accounting for 93.1% of the entire pla- Figure 5 presents the average yearly NDVI­ , precipita- max teau. Specifcally, vegetation cover was signifcantly increas- tion, and air temperature time-series data over the plateau. ing in 43.2% of the plateau, primarily in the northern and ­NDVI displays a signifcant positive trend throughout max eastern parts (Fig. 7b). In contrast, only 6.9% of the plateau the study period, increasing by 0.0053 per year (p < 0.001), experienced vegetation browning, with 0.24% of the plateau

Table 1 Regression models Model R2 p RMSE between ­NDVImax and climatic factors, i.e., annual cumulative Model_1: ­NDVImax = a1 + a2P (Evans and Geerken 2004; Xiu et al. 2018) 0.49 0.002 0.0248 precipitation (P) and average Model_2: ­NDVI = a + a P2 + a P (Wang et al. 2015) 0.50 0.008 0.0246 temperature (T) max 1 2 3 Model_3: ­NDVImax = a1 + a2P +a3T (Liu et al. 2013; He et al. 2015) 0.52 0.006 0.0240 2 2 Model_4: ­NDVImax = a1 + a2P + a3P +a4T + a5T +a6PT (Wang et al. 2012; 0.54 0.009 0.0237 Fang et al. 2014)

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Fig. 5 Interannual variations in the plateau averaged a ­NDVImax, b precipitation, and c air temperature, and relationships between d ­NDVImax and precipitation and e ­NDVImax and temperature

Fig. 6 Average annual a ­NDVImax, b precipitation, and c air temperature between 2000 and 2016

showing signifcant downward trends, mainly in areas along However, precipitation that exhibited signifcant increase the Yellow River and between Dongsheng and Ejin Horo only accounted for 11.2% of the plateau, primarily in the Banner. Precipitation showed upward trends throughout the northeast (Fig. 7d). The areas with increased temperature plateau, particularly in the northeast (Fig. 7c). Precipitation were mainly distributed in the south and northeast of the exhibited downward trends only in the northwest and central plateau, whereas the reduced areas were mainly distributed parts of the plateau, accounting for 5.5% of the plateau area. in the northwest and center (Fig. 7e). However, the trends

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Fig. 7 Trends in a ­NDVImax, c precipitation, and e air temperature, and b, d, f signifcant trends at a confdence level of 0.1 were not signifcant, only a small portion of the western scale over the past 17 years (Fig. 8c). In the center and west part of showed a signifcant increase (Fig. 7f). of the region, the partial correlation coefcients between the annual variations in the ­NDVImax and air temperature Relationship between ­NDVImax and climatic factors were negative, accounting for 50.1% of the region. Specif- cally, the western part of and the center of Partial correlation coefcients were calculated for each Otog Banner had strong negative coefcients. In the north- pixel to indicate the relationships between the annual vari- east, ­NDVImax and air temperature are positively correlated ations in ­NDVImax and the climatic variation between 2000 in most areas. The areas where ­NDVImax was signifcantly and 2016 (Fig. 8). The average partial correlation coef- negatively correlated with air temperature were mainly dis- cient between annual precipitation and NDVI­ max was 0.47 tributed in the west of Hanggin Banner and in the middle throughout the plateau. Approximately 95.7% of the plateau of Otog Banner (Fig. 8d). The areas of signifcant positive had a positive partial correlation coefcient. In particular, a correlation were mainly at the intersection of Dongsheng, relatively strong positive correlation was observed between Jungar and . precipitation and NDVI­ max in 55.7% of the plateau, including the southern part of Hanggin Banner, the western parts of Vegetation variation impacted by human activities Otog and , and the entire areas of Dalad, Dongsheng, Jungar, and Ejin Horo Banner (Fig. 8a). The Evaluation of the regression model weak correlation coefcients were mainly distributed in the northern Hanggin Banner, which is the main distribution To evaluate the multilevel nonlinear regression model, we area of the Hobq Desert, the eastern parts of Otog and Otog set up a model in the calibration area, and applied the same Front Banner and the entire areas of , where model with the same regression coefcients to the valida- distributed the Mu Us Sandy Land. 89.7%, 56.2% and tion area. The determination coefcient of the calibration 32.1% of the partial correlation coefcients over the plateau area was 0.69, with a confdence level of 0.014, and the had, respectively, passed 0.1, 0.05 and 0.01 signifcant test corresponding values for the validation area were 0.46 (Fig. 8b). and 0.002, respectively. To further evaluate the nonlinear We found both positive and negative correlations between regression method, we also calculated the R2 of the regres- the NDVI­ max and the average air temperature on the pixel sion model established at each pixel with varying months

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Fig. 8 Spatial distributions of partial correlation coefcients between a ­NDVImax and precipitation, and c ­NDVImax and air temperature, and spa- tial distributions of p values b, d of accumulated precipitation and averaged air temperature average value of 0.002, which suggests that human activi- (Fig. 9). The averaged R2 of the entire plateau reached 0.8, ties had an overall strong efect on vegetation growth over 95.5% of the pixels had a value greater than 0.6, and 98.0% the last 17 years and that the positive efects of human of the pixels passed the signifcance test at the 0.1 level. activity on vegetation cover were greater than the negative Overall, these statistics indicate that the multilevel nonlin- efects. The distributions of the residual slopes at a signif- ear regression model can be used to simulate the vegetation cance level of 0.1 difered greatly from the trends for the changes in response to climatic fuctuations in this region. whole plateau mentioned above (Fig. 10b). Around 22.0% of the residual trends were signifcantly positive, particu- Residual trend analysis larly in the Hobq Desert, which indicates that human activities had a widespread positive efect on vegetation Climate change is one of the most important infuences growth and that ecological projects and aforestation had on spatiotemporal variations of vegetation, but the efects been implemented efciently in these areas. Signifcantly of anthropogenic activities cannot be ignored (Xin et al. negative residual slopes accounted for only 0.66% of the 2008). We analyzed the efects of human activities on entire plateau area and were mostly found in the regions vegetation variations via residual trend analysis. A high bordering the Yellow River and between Dongsheng and degree of heterogeneity existed in the relationship between Ejin Horo Banner, which indicates that human activities human activities and vegetation cover (Fig. 10a). The had a strong negative impact on vegetation changes in slopes of residuals were between − 0.023 and 0.02, with an these regions.

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Fig. 9 Spatial distributions of a coefcients of determination for the regression analysis and b the p value between 2000 and 2016

Fig. 10 (a) NDVI residual trends in the whole plateau, and (b) values that show signifcance at a level of 0.1 between 2000 and 2016

Discussion improved vegetation cover in the eastern part of the Ordos Plateau (Wang et al. 2010). Airplane-based grass seeding We analyzed the impacts of climate variation and anthro- during the 1980s and 1990 s in the eastern plateau also pogenic activities on vegetation variations over the Ordos improved vegetation cover and stabilized the sand dunes Plateau, by determining the spatiotemporal patterns in (Runnstrom 2000). Human-induced vegetation greening these factors to provide a theoretical basis for the restora- in most parts of the plateau was a direct consequence of tion of vegetation. Residuals, which result from the difer- reformation of local household land management stimu- ence between the actual NDVI­ max and its simulated value lated by the Prohibition of Open Grazing Policy and the via regression analysis, are afected by both climatic varia- Household Production Responsibility System (Zhang et al. tion and human activities such as farming, grazing, urbani- 2014). Statistics show that the area of aforestation and the zation, and ecological engineering construction (Ostwald amount of livestock have increased and that the area of and Chen 2006). In general, short-term climatic fuctuation farmland has decreased in the Ordos Plateau over the last does not lead to a directional variation in the residuals, but 17 years, which suggests that the Grain for Green Project strong human activities do (Li et al. 2012). The Three- has been efective (see Fig. 3). Despite the increase in the North Shelterbelt Program initiated in 1978 has greatly amount of livestock, which is thought to be directly related

1 3 Environmental Earth Sciences (2019) 78:709 Page 11 of 15 709 to the degradation of vegetation (Cao et al. 2006; Liu et al. estimation methods and analysis periods. Third, the use of 2013; Fang et al. 2014), the Prohibition of Open Graz- diferent linear and nonlinear regression models to simu- ing Policy has played a positive role. However, urbaniza- late the potential NDVI was a source of inconsistency. We tion, ore mining, and the abandonment of cultivated land replaced the nonlinear regression model (model_4) with caused extensive damage to vegetation. Figure 3b shows the frst three models in Table 1, applied the same proce- the increasing trend in the raw coal yield. Figure 2 shows dure to produce three more sets of residual trend results, land use changes, including a signifcant decrease in areas and found consistent spatial patterns in vegetation green- of sand land in Mu Us Sandy Land, an obvious increase in ing and browning induced by human activities (Fig. 11), forested area in Hobq Desert, expansion of residential land but big diferences in their percentage of area (Table 2). in Dongsheng and Ejin Horo Banner, and a decrease in In addition, the frst three models tended to overestimate farmland area in the northeastern plateau but an increase contributions of human activities on vegetation greening, along the Yellow River, which further explains the reli- essentially caused by their big residuals. ability of the residual trend results. We can hardly deny that the regression model has After the analysis of NDVI changes on the Ordos Pla- potential uncertainties due to the model assumptions, teau in recent decades, the consensus of various stud- data quality, and choices of climatic variables. However, ies is that the vegetation has greatly improved, but the process-based ecological models also have some uncer- improved and degraded areas induced by human activities tainties arising from complex model structures, and a were not completely consistent. Zhang et al. (2014) used large number of parameters (Piao et al. 2013). In addi- NDVI data from SPOT to analyze the infuences of human tion, it is impossible to rigorously evaluate the regression activities on vegetation variations over the plateau and model due to the wide efects of human activities and the determined that areas of vegetation browning induced by various regression coefcients in the nonlinear regression human activities were extremely rare on the Ordos Plateau model for diferent pixels. To comprehensively evaluate during 1998–2012. However, our study found that human- the regression analysis method, we frst compared vari- induced vegetation degradation was sparsely distributed in ous forms of regression models and selected the one that the north along the Yellow River, between Dongsheng and performed best. We then established a regression model Ejin Horo Banner, and in the western part of Otog Front in the form of the chosen one at one patch where the HII Banner and . Vegetation browning in the was 5 or less and validated it at another human rarely infu- Yellow River region was probably because of changes in enced patch. We fnally applied the regression method to crop planting types or abandonment of cultivated land. In each pixel in the study area and computed the correlation the western part of Jungar Banner and the areas between coefcients. This evaluation procedure can not only avoid Dongsheng and Ejin Horo Banner, the deterioration of the arbitrary determination of the length of the baseline vegetation cover was likely a result of urban expansion period in the calibration step, but also evaluate the method and coal mining in those years. Otog Front Banner is on a regional scale, in contrast with the commonly used mainly supported by animal husbandry, and vegetation method of single-point verifcation. However, the specifc degradation was most likely caused by excessive grazing. assessment of the contribution of human activities requires Sun et al. (2015), Yi et al. (2014), Hu et al. (2019), and not only method improvement, but extensive feld survey Li et al. (2014) each studied the relationships between and collection of relevant information. vegetation change, climatic factors and human activities in the Inner Mongolia, , and Ordos using GIMMS, SPOT, and MODIS data. The resulting residual Conclusions trends difered for three reasons. First, the spatial and temporal resolutions difered between the NDVI remote- The vegetation of the Ordos Plateau in the center of Chi- sensing products and retrieval algorithms, which led to na’s agropastoral ecotone has experienced a signifcant diferences in intensity of the trends (Fensholt and Proud greening trend over the last 17 years because of a warmer 2012; Guan et al. 2018; Li et al. 2018). Second, the use of and wetter climate and the efects of human activities. diferent interpolation methods for precipitation and tem- Vegetation was signifcantly greening in the northern and perature led to inconsistent results (Zhang et al. 2014). eastern parts, occupying nearly 43.2% of the plateau, and Precipitation exhibited a slightly increasing trend, which is was signifcantly browning in areas along the Yellow River consistent with the fndings of Zhang (Zhang et al. 2014), and between Dongsheng and Ejin Horo Banner, occupying but inconsistent with those of Li and Zheng (2002), Ren only 0.24% of the plateau. According to the partial cor- et al. (2005), Huang et al. (2011), and Yao et al. (2012), relation coefcients between the NDVI and the climatic who reported a weak downward trend in precipitation. factors, precipitation played a leading role in vegetation This diference could be due to diferences in precipitation greening in most parts of the plateau, particularly in the

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Fig. 11 a–d NDVI residual trends produced by diferent regression models, and e–h values that show signifcance at a level of 0.1

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Table 2 Percentage of area Model NDVI residual NDVI residual NDVI residual trend < 0 at NDVI residual trend > 0 at experiencing vegetation trend < 0 (%) trend > 0 (%) 0.1 signifcant level (%) 0.1 signifcant level (%) greening (NDVI residual trend > 0) or browning (NDVI Model_1 7.37 92.63 1.01 60.66 residual trend < 0) induced by Model_2 7.37 92.63 1.14 61.59 human activities Model_3 9.51 90.49 1.18 55.70 Model_4 14.39 85.61 0.66 22

northeast, compared to air temperature. We used the resid- Qinghai Province. China Front Earth Sci 8:93–103. https​://doi. ual analysis method to fnd that human activities had a org/10.1007/s1170​7-013-0390-y Fensholt R, Proud SR (2012) Evaluation of earth observation based signifcant positive impact on vegetation growth in 22.0% global long term vegetation trends—comparing GIMMS and of the plateau, particularly in the Hobq Desert, where eco- MODIS global NDVI time series. 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