## 1 Introduction  
Trees affect terrestrial energy exchanges such as photosynthesis, transpiration, and net primary production [1], and play an important role within and outside forest ecosystems (e.g., on farms and pastures, and in urban areas) [2; 3]. Tree cover is defined structurally as the proportion of ground by, vertically projected area of woody plants (including leaves, stem, branches, etc.) above a given height [4]. The tree cover variable is important for ecosystem science [5]. Analyzing spatiotemporal tree cover distribution and variation could provide information to manage, conserve, and enhance production of trees within and outside forestland, which will promote sustainable resource use of ecosystems. The Earth's surface has experienced intensive tree cover change [6], largely due to intense human activities. Urbanization, infrastructure construction and expansion of farmland bring about deforestation, leading to tree cover decrease [7]. On the other hand, ecological restoration policy such as afforestation and forest restoration promotes an increase of tree cover [8]. Previous studies focused on tree cover variation under the condition of deforestation [9]; however, the opposite process such as forest restoration and afforestation requires further study. The Chinese government launched a large scale ecological restoration program called 'Grain for Green Project' (GGP) in 1999. The program's aims included conversion of marginal cropland to forestland, the mitigation of soil erosion, and provision of solutions to land deterioration problems [10]. Forestations, including afforestation and reforestation, are the main conservation programs of this project. The Loess Plateau in Northern China is a pilot region of the GGP. Evidence from RS and statistics data showed that vegetation cover has largely improved, and forest coverage increased [11; 12]. Previous studies relied on the vegetation index derived from satellite data or land cover maps to reflect the influence of the project [13; 14; 15]. Nevertheless, these two ways both have some limitations in assessing the effectiveness of the GGP. The result from the vegetation index may overestimate effects by analyzing vegetation index variation at different times without excluding the large area of farmland in the Loess Plateau. On the other hand, by comparing forest land cover temporally, the result acquired from land cover maps overlooked the sparse newly planted trees, which were not included in forest land cover type. The tree cover variation is more likely related to the effectiveness of this project [16], whereas little is known about it, especially in a spatiotemporal sense. In order to monitor tree cover dynamic temporally, multiple temporal tree cover data is needed. RS is superior to ground-based investigation in broad-scale tree cover change studies [17]. Through RS technique, tree cover can be represented directly as a 'continuous field', in terms of fractions or proportions of the total pixel area [4], which enables consistent long-term monitoring and quantification of tree cover changes. Among the continuous field data, Vegetation Continuous Fields (VCF) tree cover data produced from Moderated Resolution Imaging Spectroradiometer (MODIS) data are more objective and economical [18]. This data also has the best temporal resolution, offering much more information and reflecting the process when monitoring tree cover change in large scale [19]. Spatial analysis, trend analysis, and time-series analysis are widely used in spatiotemporal variation analysis. GIS is a powerful tool in spatial analysis, which could efficaciously help study the spatial heterogeneity of tree cover and its change [20; 21]. Theil-Sen Median trend analysis and Mann-Kendall tests are robust trend analysis method [22; 23]. The Hurst exponent could help quantitatively detect the sustainability of time series data [24].