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Vegetation Response to Climatic Variation and Human Activities on the Ordos Plateau from 2000 to 2016

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Vegetation Response to Climatic Variation and Human Activities on the Ordos Plateau from 2000 to 2016 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 Ordos Plateau 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 coal and gas exploitation, and the highest ecological risks in China. 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 region’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, Lanzhou 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 Mongolia Agricultural University, Hohhot 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 Inner Mongolia 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 regions. In most cases, the relationship between the ing from 1000 to 1500 m, are surrounded by the Yellow River. 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.
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