Sciences in Cold and Arid Regions  2016, 8 (4): 325-333 PDF

#### Article Information

Li Xie Jia, Zhen Yan Chang, Xiang Lu Zhi, Li Sen . 2016.
Remote-sensing data reveals the response of soil erosion intensity to land use change in Loess Plateau,China
Sciences in Cold and Arid Regions, 8(4): 325-333
http://dx.doi.org/10.3724/SP.J.1226.2016.00325

### Article History

Accepted: June 29, 2016
Remote-sensing data reveals the response of soil erosion intensity to land use change in Loess Plateau,China
Li Xie Jia1, Zhen Yan Chang1, Xiang Lu Zhi2, Li Sen1
1. Key Laboratory of Desert and Desertification, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China;
2. Key Laboratory of Ecohydrology of Inland River Basin, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
Abstract: Developing an effective approach to rapidly assess the effects of restoration projects on soil erosion intensity and their extensive spatial and temporal dynamics is important for regional ecosystem management and the development of soil conservation strategies in the future. This study applied a model that was developed at the pixel scale using water soil erosion indicators (land use, vegetation coverage and slope) to assess the soil erosion intensity in the Loess Plateau, China. Landsat TM/ETM+ images in 2000, 2005 and 2010 were used to produce land use maps based on the object-oriented classification method. The MODIS product MOD13Q1 was adopted to derive the vegetation coverage maps. The slope gradient maps were calculated based on data from the digital elevation model. The area of water soil-eroded land was classified into six grades by integrating slope gradients, land use and vegetation coverage. Results show that the Grain-To-Green Project in the Loess Plateau worked based on the land use changes from 2000 to 2010 and enhanced vegetation restoration and ecological conservation. These projects effectively prevented soil erosion. During this period, lands with moderate, severe, more severe and extremely severe soil erosion intensities significantly decreased and changed into less severe levels, respectively. Lands with slight and light soil erosion intensities increased. However, the total soil-eroded area in the Loess Plateau was reduced. The contributions of the seven provinces to the total soil-eroded area in the Loess Plateau and the composition of the soil erosion intensity level in each province are different. Lands with severe, more severe and extremely severe soil erosion intensities are mainly distributed in Qinghai, Ningxia, Gansu and Inner Mongolia. These areas, although relatively small, must be prioritised and preferentially treated.
Key words: remote sensing     soil erosion intensity     land use     Loess Plateau
1 Introduction

Changing land use represents the most significant human effect on the surface of the earth and directly changes hydrological processes and sediment yields (Lambin et al., 2001; Foley et al., 2005; Khoi and Suetsugi, 2012; Lu et al., 2015). Soil erosion isan important global environmental problem that substantially affects environmental quality and social economy. Severe erosion can be easily triggered by the lack of vegetation protection (Ludwig et al., 2005; Fu et al., 2011), causing the decline of agricultural productivity and the degradation of water bodies, which may reduce the ability of the soil to mitigate greenhouse effects (Lal and Bruce, 1999; Sun et al., 2014). By protecting the soil from wind and water erosionthrough vegetation restoration and soil and water conservation measures, terrestrial ecosystems provide humans with soil erosion control, which is one of the fundamental ecosystem functions that secure human welfare (Fu et al., 2011).

Assessing water soil erosion intensity is important, and several models were developed to assess soil erosion caused by water, including the universal soil loss equation (Wischmeier and Smith, 1965), revised universal soil loss equation (Renard and Freimund, 1994), erosion productivity impact calculator (Williams, 1985), water erosion prediction project (Flanagan and Laflen, 1997) and soil and water assessment tool (Arnold et al., 1998). The process of soil erosion is controlled by numerous natural and anthropogenic factors (Lal, 2001; Tian et al., 2009); thus, the models refer to many parameters, including rainfall erosivity factor (R), soil erodibility factor (K), slope length factor (L), slope factor (S), vegetation coverage (C) and erosion control practice factor (p) (Fu et al., 2011; Sun et al., 2014; Zhang et al., 2016). However, the applications of these models are limited because of the lack of sufficient data and the low quality of these models (Vrieling, 2006; Tian et al., 2009). For example, the values of p vary from place to place and determining these values are subjective to a certain extent; meteorological stations are limited and not well-distributed; moreover, rainfall erosivity should be calculated using event-based rainfall, but only daily rainfall is recorded in several regions (Fu et al., 2011). Fortunately, remote sensing provides data and related products that cover large regions and are capable of regularly revisiting the same land area, thus significantly contributing to regional soil erosion intensity assessment by rapidly deriving pertinent indices (Fan et al., 2004; Vrieling, 2006; Tian et al., 2009).

The Chinese Loess Plateau is infamous because of its severe soil erosion. This plateau is the main source of sediments in the middle Yellow River, thus drawing international attention. Studies show that anthropic land use or cover change is a key driving force for soil and water loss in this region (Li et al., 2006; Fu et al., 2009). Thus, as early as the 1950s, China initiated soil and water conservation activities on the Loess Plateau to improve the local environment and mitigate sediment accession in the middle Yellow River; these works were expanded to a large scale after the 1970s (Zhang et al., 2011; Shi et al., 2013). As an important countermeasure, the Grain-to-Green Program, which converts farmlands to forests or grasslands, was launched in 1999 on the Loess Plateau (Fu et al., 2011). Thus, land use in the Loess Plateau underwent significant changes. Meanwhile, field monitoring and investigations confirmed the reduction of soil erosion on hill slopes and in small catchments of the Loess Plateau after vegetation restoration (Zheng, 2006; Zhou et al., 2006). Aside from monitoring programs at a few gauged stations, regional-scale assessments of the changes in soil erosion intensity are scarce. A rapid assessment of the variation in soil erosion intensity with the change in land cover is important to understand the efficiency of restoration and provide information for regional ecosystem management and the development of relevant soil conservation strategies in the future. Developing an efficient, rapid and simple method to assess the intensity of water soil erosion and detect the eroded areas in the region directly using remote sensing and geographical information system (GIS) technology is crucial (Tian et al., 2009).

The overall objective of this study is to assess the responses of soil erosion intensity to land use or cover changes in the Loess Plateau, China. The specific objectives are (1) to investigate land use changes from 2000 to 2010 based on Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images in 2000, 2005 and 2010;(2) to assess the soil erosion intensity using information on land use, vegetation coverage and slope gradient; and (3) to analyse the spatial and temporal changes of soil erosion intensity. The results should assist decision-makers promote vegetation restoration on the Loess Plateau.

2 Study area and data 2.1 Study area

The Loess Plateau is located in the middle reaches of the Yellow River and extends to the Qinling Mountain Ranges in the south, Yinshan Mountain in the north, Taihang Mountain in the east and Wushaoling-Riyue Mountain in the west. This region has an area of more than 600, 000 km2 and covers 287 counties of 7 provinces, including Qinghai, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi and Henan (Fu et al., 2005) (Figure 1). The plateau surface is the largest area of loess in the world and is covered by highly erodible loess layers with an average depth of 100 m. The fragility of the loess ecosystem is characterised by its sub-humid and semi-arid climate, with an average annual precipitation ranging from 200 mm in the northwest to 750 mm in the southeast; rainfall mostly occurs as high-intensity rainstorms (Li et al., 2009). The types of surface soil vary from the northwest to the southeast in the order of eolian sand, sandy loess, typical loess and clayey loess (Liu, 1964). In the same direction, the natural vegetation types vary from arid desert to steppe and to broad-leaf deciduous forest. The major crops include wheat, corn, millet, sorghum, soybean and buckwheat (Sun et al., 2014).

 Figure 1 Location of the study area

The vulnerable natural conditions, along with long-term and intensive development of agriculture, urbanisation, energy utilisation and road construction, led to serious soil erosion in the region (Cao, 2008). Since 1949, many measures in ecosystem management, agricultural structure and planting methods and soil and water conservation were implemented in the Loess Plateau, which involved controlling floods, optimising land use structure and configuration, transforming slopes into terraces, restoring the slope cropland into forests and grasslands, enclosing hillsides, banning grazing and building reservoirs and basic farmlands (Yang, 2003). Implementing these programs further enhanced vegetation restoration and ecological conservation and strengthened ecosystem management (Fu et al., 2011).

2.2 Data

Land use data with a spatial resolution of 30 m were obtained in three periods by remote sensing, including 2000 ETM+ data and 2005 and 2010 Landsat TM data (http://glovis.usgs.gov/). The data were converted using the EARDS IMAGE and ARC/INFO software to images based on 1:100, 000 topographic maps. In addition to the TM and ETM+ images, we employed ancillary materials, such as vegetation maps and field survey reports, to assist in labelling the patches of the map in each mapped area during the interpretation and post-processing processes. The types of land use include farmland, forest land, grassland, water body, built-up land and unused land. The source of NDVI data is the MODIS product MOD13Q1 with a spatial resolution of 250 m (https://ladsweb.nascom.nasa.gov/), which was used to derive vegetation coverage. The digital elevation model (DEM) with a spatial resolution of 30 m derived from USGS National Elevation Dataset was used to extract slope data. To calculate soil erosion intensity, the data of land use, vegetation coverage and slope should be resampled into a unified spatial resolution of 250 m.

In order to obtain high precision data of soil erosion intensity, the accuracy of land use data should be strictly checked, and then assessed by confusion matrix. If the accuracy did not satisfy the mapping standards, the maps were reinterpreted and rechecked until the mapping accuracy reached the mapping principles (Li et al., 2015). In the processing of accuracy assessment, the data of field verification point and Google earth images were also used as ancillary materials to confirm the interpretation precision.

3 Methodology 3.1 Soil erosion intensity level system

Water soil erosion is the loss of surface soil caused by rain and runoff water, and its intensity is measured by the amount of annual soil loss, i.e., the modulus of soil loss in China (The Ministry of Water Resources of the People’s Republic of China, 2008). The factors contributing to soil erosion intensity mainly include land use, vegetation coverage and slope gradient. Thus, soil erosion intensity can be defined as a response of the regional environment to changes in these factors (Wang et al., 2005; Tian et al., 2009). According to the National Professional Standard of SL190-2007 Standards (Standards for Classification and Gradation of Soil Erosion, substitution of SL190-96) for the classification and gradation of soil erosion (The Ministry of Water Resources of the People’s Republic of China, 2008), soil erosion intensity is divided into six grades because of these factors (Table 1). The input parameters required by the evaluated model can be easily derived by GIS and remote sensing techniques. With the SL190-96 Standards, water and soil erosion was successively monitored at the national and regional scale in China (Zhao et al., 2002; Chen et al., 2005; Tian et al., 2009).

Table 1 Index cross-table of soil erosion intensity
 Land use Vegetation coverage (%) Slope gradation ＜ 5° 5°~8° 8°~15° 15°~25° 25°~35° ＞35° ＞70 ST ST ST ST ST ST 60~70 ST LT LT LT MT MT Non-farmland 45~60 ST LT LT MT MT SR 30~45 ST LT MT MT SR MS ＜30 ST MT MT SR MS ES Farmland ST LT MT SR MS ES ST-slight, LT-light, MT-moderate, SR-severe, MS-more severe, ES-extremely severe.
3.2 Methods

Soil erosion mainly depends on terrain, vegetation cover, soil taxa, rainfall and land cover (Fan et al., 2004). The intensity, duration and frequency of rainfall affected the amount of soil loss, and the characteristics of rainfall are controlled by climate and external soil loss factors. The composition and bond strength of soil taxa determine the erosibility of land surface, which could be represented indirectly by land cover. Thus, soil erosion intensity can be assessed through land use, vegetation coverage and slope gradient. The information on slope gradient, vegetation coverage and land use was employed using the ARC/INFO and ERDAS software to evaluate the soil erosion intensity of this region. Firstly, the datasets of vegetation coverage and slope gradient were graded on the other land use types according to the classification indices of soil erosion intensity. Then, soil erosion intensity was determined by utilising the data presented in Table 1 for each grid of the other land cover types.

3.2.1 Land use

Land use change can affect soil erosion rate and intensity (Liu et al., 2001). In this study, object-oriented classification (OOC) of Landsat TM/ETM+ was employed to derive the land use datasets. The OOC method includes three steps, namely, segmentation and landform delineation, classification of the delineated landforms and validation process to assess the segmentation results (Kassouk et al., 2014). The steps in object-oriented image analysis include image segmentation and land useinformation extraction based on object features (Yu et al., 2011). In our study, image segmentation followed the OOC method, which was available in eCognition. Also, to accurately acquire the land use map, we performed field verification in 2010 (Figure 1).

3.2.2 Vegetation coverage

Soil loss is significantly related to vegetation coverage in a negative exponential relationship (Fu et al., 2011). The value of radiant energy emitted from each pixel of the land surface could be divided into two parts of energy coming from barren soil and vegetation; thus, the ratio of vegetation energy to total energy can represent the vegetation coverage of the pixel (Tian et al., 2009). By adopting the dimidiate pixel model, vegetation coverage was calculated using the normalised difference vegetation index (NDVI) that was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) image, as follows:

$VC=\frac{NDVI-NDV{{I}_{soil}}}{NDV{{I}_{veg}}-NDV{{I}_{soil}}}$

where NDVIsoil is the NDVI value of bare soil and NDVIveg is NDVI value of areas with complete vegetation cover.

Based on the grading scale of 30, 45, 60 and 75, the vegetation coverage data were reclassified (Figure 2a).

 Figure 2 Vegetation coverage in 2010 and slope gradient of the Loess Plateau, China

Slope gradient significantly affected surface runoff and soil erosion (Tian et al., 2009). The slope gradient dataset was generated using the SLOPE and RECLASS functions in the Spatial Analyst Tools of ArcGIS (Hu et al., 2015), and then to create the gradient grid classified with the values of 5, 8, 15, 25 and 35 (Figure 2b).

4 Results and discussion 4.1 Land use changes from 2000 to 2010

In 2010, the two main types of land use on the Loess Plateau were grassland and farmland, which accounted for 37.65% and 31.95%, respectively. The grasslands are mainly distributed in the central and western regions, and the farmlands are distributed in the valley area and plains, such as the Guanzhong, Hetao, and Ningxia plains and Feihe River Valley (Figure 3c). Forest lands account for approximately 20%, and these lands are mainly distributed in Migang and Ziwuling Mountains in the centre, Qinling Mountains Ranges in the south, Taihang and Lvliang Mountains in the east and Wushaoling Mountain in the west. At the provincial scale, forest and built-up land dominated Henan, Shanxi and the southern part of Shaanxi because the population and precipitation are higher in these areas than the rest of the Loess Plateau. Grassland dominated Inner Mongolia, Central Ningxia and Northern Shaanxi. Water body dominated Inner Mongolia, and the largest water body is Ulansuhai Nur. Farmland dominated the Gansu, Shaanxi and Shanxi regions. Unused land, which mainly included gobi and sand, dominated the Gansu and Inner Mongolia regions.

 Figure 3 Land use maps of Loess Plateau in 2000, 2005 and 2010

The important change processes of land use are modification and conversion. The increase and decrease of areas changed in the same land use types at the same times (Figure 3). The changes in different land uses are a direct and evident indicator of the general changes in a regional eco-environment. Table 2 and Figure 3 indicate that, from 2000 to 2010, the areas of grassland, forest land and built-up land expanded, whereas that of farmland and unused land decreased. The increased areas of grassland and forest land were mainly from farmland as a result of the implementation ofthe "Green for Grain"government conservation program. The expansion of built-up land area was inevitable because of population growth and social development, as well as rapid industrialisation and urbanisation. More than 80% of the expanded area of built-up land was from farmland, and the restwas from grassland, unused land and wetlands.

Table 2 Changes in the area of land use from 2000 to 2010
 Year Parameter Land use category Forest land Grassland Water body Farmland Built-up land Unused land 2000 Area (km2) 119, 420.81 228, 004.98 3, 992.90 212, 375.73 14, 455.15 45, 752.42 Percent (%) 19.14 36.54 0.64 34.03 2.32 7.33 2005 Area (km2) 121, 378.22 236, 242.27 4, 116.25 202, 090.76 16, 278.71 43, 895.79 Percent (%) 19.45 37.86 0.66 32.39 2.61 7.03 2010 Area (km2) 123, 638.25 234, 937.81 4, 119.56 199, 377.24 18, 122.64 43, 806.51 Percent (%) 19.81 37.65 0.66 31.95 2.90 7.02
4.2 Soil erosion intensity changes from 2000 to 2010

Figures 2a, 3c and 4c indicate that, in 2010, lands with moderate, severe, more severe and extremely severe soil erosion intensities were mainly distributed in the regions of sparse grassland and unused land because their vegetation coverage is low; thus, the erosion from rain on the ground cannot be reduced (Fu et al., 2011). Lands with light soil erosion intensity are mainly distributed in the regions of medium coverage grassland with gentle slopes and farmland with slopes, varying from 5 to 8. Lands with slight soil erosion intensity are mainly distributed in the regions of high coverage grassland, medium coverage grassland in flat terrains, flat farmland and forest land. In Figure 4, the non-erosion areas include built-up land, lakes and rivers.

 Figure 4 The soil erosion intensity maps of the Loess Plateau in 2000, 2005 and 2010

The results (Table 3) show that 454, 500 and 115, 679 km2 of land exhibit slight and light soil erosion intensities, which account for 75.4% and 19.2% of the total soil-eroded area in 2010, respectively. Lands with moderate soil erosion intensity account for 4.9%. The area with severe soil erosion intensity is less than 320 km2, which accounts for 0.5% together with lands with more severe and extremely severe soil erosion intensities. At the provincial level, the soil-eroded areas in Shanxi, Shaanxi, Inner Mongolia and Gansu account for 24.9%, 20.8%, 19.8% and 17.9% of the total soil-eroded area in 2010, respectively. However, their proportions vary for lands with different soil erosion intensities. Lands with slight soil erosion intensity in Shanxi, Inner Mongolia, Shaanxi and Gansu account for 25.3%, 24.6%, 21.0% and 12.2% of the total area of this type, respectively. Lands with light soil erosion intensity in Gansu, Shanxi and Shaanxi account for 31.6%, 28.7% and 23.9% of the total soil-eroded lands, respectively. Lands with moderate soil erosion intensity in Gansu, Ningxia and Qinghai account for 49.9%, 17.2% and 10.8% of the total soil-eroded area, respectively. Lands with severe, more severe and extremely severe soil erosion intensities are mainly distributed in Qinghai, Ningxia, Gansu and Inner Mongolia, and their corresponding proportions to the total soil-eroded area are 31.1%, 20.8%, 36.1% and 11.0%, respectively.

Table 3 The area of soil erosion in different grades & in different provinces in 2010(km2)
 Province Soil erosion intensity grades Slight Light Moderate Severe More severe Extremely severe Total Qinghai 23, 427.93 6, 061.07 3, 194.80 686.37 153.13 5.31 33, 528.61 Gansu 55, 565.16 36, 587.34 14, 760.69 928.97 51.30 107, 893.44 Ningxia 37, 023.84 7, 036.62 5, 098.83 475.20 86.38 3.64 49, 724.51 Inner Mongolia 111, 697.45 4, 490.22 2, 879.77 271.22 27.38 0.38 119, 366.42 Shaanxi 95, 503.40 27, 628.80 2, 070.19 5.56 125, 207.96 Shanxi 114, 915.18 33, 252.37 1, 549.44 16.82 0.81 0.06 149, 734.69 Henan 16, 366.88 622.23 30.56 0.75 0.06 17, 020.48 Total 454, 499.83 115, 678.65 29, 584.28 2, 384.89 319.06 9.39 602, 476.11

From 2000 to 2010, the area with eroded soil decreased by 3, 259.72 km2(Table 4). The proportions of lands with slight and light soil erosion intensities to the total soil-eroded area of these provinces vary from 51.5% to 93.6% and from 3.7% to 34.0%, respectively, which is similar to the condition of the entire Loess Plateau. Lands with slight and light soil erosion intensities increased. By contrast, lands with moderate, severe, more severe and extremely severe soil erosion intensities decreased by 64.4%, 73.7%, 58.3% and 56.7% in 2000, respectively, because most of the slope farmlands were converted to grassland and forest land. The total soil-eroded area also decreased by approximately 3, 260 km2. In Figure 4, the decrease of land with moderate soil erosion was evident from 2000 to 2010, particularly in the provinces of Gansu, Shaanxi and Shanxi.

Table 4 The area of soil erosion in different grades from 2000 to 2010(km2)
 Soil erosion intensity grades 2000 2005 2010 2000-2005 2005-2010 Slight 415, 529.87 444, 410.87 454, 499.83 28, 881.01 10, 088.96 Light 97, 207.24 117, 061.06 115, 678.65 19, 853.82 −1, 382.40 Moderate 83, 152.69 39, 633.21 29, 584.28 −43, 519.49 −10, 048.92 Severe 9, 058.70 2, 828.42 2, 384.89 −6, 230.28 −443.52 More severe 765.62 354.18 319.06 −411.45 −35.11 Extremely severe 21.71 10.89 9.39 −10.82 −1.50 Total 605, 735.83 604, 298.62 602, 476.11 −1, 437.21 −1, 822.51

To explain the correlation between the changes of soil erosion intensity and land use, an overlay analysis was carried out. The results show that:(1) in Qinghai, Gansu, Ningxia, and Henan provinces, the increase of erosion area was mainly because some slope cropland turned into woodland and grassland with lower vegetation cover and unused land area increased; in Inner Mongolia and Shaanxi Province, erosion area increased because of some grass turning into woodland and increase of unused land; in Shanxi Province, erosion area increased because of some slope cropland turning into woodland and grassland with lower vegetation cover, decrease of water body area and increase of unused land.(2) on the other hand, in Qinghai Province, the decrease of erosion area was mainly because some slope cropland turned into woodland and grassland with higher vegetation cover and built-up land area increased; and then in Gansu, Shaanxi and Shanxi provinces, erosion area decreased mainly because of some slope cropland turning into woodland and grassland with higher vegetation cover, increase of built-up land area and decrease of unused land area; in Ningxia, Inner Mongolia and Henan provinces, the decrease of soil erosion area was mainly due to increase of built-up land area and decrease of unused land area.

5 Conclusions

In this study, we aimed to investigate the effects of land use changes on the soil erosion intensity in the Loess Plateau, China. The major research findings and their implications on practices and future studies are as follows:

The model, which was developed at the pixel scale and integrated with remote-sensed data with thematic tabular data through GIS, was an effective tool to assess soil erosion intensity and its spatial and temporal dynamics on a large scale rapidly.

The results of the land use changes from 2000 to 2010 demonstrated that the Grain-To-Green Project in the Loess Plateau successfully enhanced vegetation restoration and ecological conservation. Thus, these projects effectively prevented soil erosion, which can be proven by changes in soil erosion intensity, including lands with moderate, severe, more severe and extremely severe soil erosion intensities that significantly decreased and changed into less severe levels, respectively. Although the lands with slight and light soil erosion intensities increased, the total soil-eroded area in the Loess Plateau was reduced.

The contributions of the seven provinces to the total soil-eroded area in the Loess Plateau and the composition of the soil erosion intensity level vary. Lands with severe, more severe and extremely severe soil erosion intensities are mainly distributed in Qinghai, Ningxia, Gansu and Inner Mongolia. These areas, although relatively small, must be prioritised and preferentially treated.

The impacts of land use changes on the soil erosion intensity were obvious. With the expansion of built-up land, more and more impervious surface became non-erosion region; in the regions where slope cropland turned into woodland and grassland, the vegetation coverage mainly affected the soil erosion intensity; decrease of water area aggravated regional soil erosion intensity; the governance of unused land was important for the control of soil erosion intensity, especially in the arid region.

However, the accuracy and reliability of the soil erosion intensity in this model mainly depends on land use data, which was influenced by image quality and the image interpretation method. Image quality refers to the phase and cloud cover of an image. The poor phase of image and cloud cover affected the interpretation of images. As for the image interpretation method, it is affected by the performance of our computer and the subjective knowledge of interpreting staff.

Acknowledgments:

This work was supported by the Key Program of the Chinese Academy of Sciences (KZZD-EW-04-04) and the Chinese Science Academy STS Program:Construction of information platform of field and remote sensing data in northwestern China (KFJ-EW-STS-006)

Reference
 Arnold JG, Srinivasan R, Muttiah RS, et al, 1998. Large area hydrologic modeling and assessment, part I:Model develop-ment. Journal of the American Water Resources Association, 34: 73–89. Cao S, 2008. Historical evaluation of relationship between nature and society:Historical changes of the ecological policy and environment in China's loess plateau. Agricultural Archaeology, 1: 21–28. Chen WH, Liu LY, Zhao C, et al, 2005. The fast method of soil erosion investigation based on remote sensing. Research of Soil and Water Conservation, 12: 8–10. Fan JR, Zhang JH, Zhong XH, et al, 2004. Monitoring of soil erosion and assessment for contribution of sediments to rivers in a typical watershed of the upper Yangtze River basin. Land Degradation & Development, 15: 411–421. Flanagan D, Laflen J, 1997. The USDA water erosion prediction project (WEPP). Eurasian Soil Science, 30: 524–530. Foley JA, Defries RA, Gregory P, et al, 2005. Global consequences of land use science. Science, 309: 570–574. Fu BJ, Zhao WW, Chen LD, et al, 2005. Assessment of soil erosion at large watershed scale using RUSLE and GIS:a case study in the Loess Plateau of China. Land Degradation & Development, 16: 73–85. Fu BJ, Liu Y, Lü YH, et al, 2011. Assessing the soil erosion control service of ecosystems change in the Loess Plateau of China. Ecological Complexity, 8: 284–293. Fu BJ, Wang YF, Lu YH, et al, 2009. The effects of landuse com-binations on soil erosion:a case study in the Loess Plateau of China. Progress in Physical Geography, 33: 793–804. Hu Y, Tian G, Mayer AL, et al, 2015. Risk assessment of soil erosion by application of remote sensing and GIS in Yanshan Reservoir catchment, China. Natural Hazards, 79: 277–289. Kassouk Z, Thouret JC, Gupta A, et al, 2014. Object-oriented classification of a high-spatial resolution SPOT5 image for mapping geology and landforms of active volcanoes:Semeru case study. Indonesia Geomorphology, 221: 18–33. Khoi DN, Suetsugi T, 2012. The responses of hydrological pro-cesses and sediment yield to land-use and climate change in the Be River Catchment, Vietnam. Hydrological Processes, 28(3): 640–652. Lal R, 2001. Soil degradation by erosion. Land Degradation & Development, 12: 519–539. doi: 10.1002/ldr.472 Lal R, Bruce J, 1999. The potential of world cropland soils to sequester C and mitigate the greenhouse effect. Environmental Science & Policy, 2: 177–185. Lambin EF, Turner BL, Geist HJ, et al, 2001. The causes of land-use and land-cover change:moving beyond the myths. Global Environmental Change, 11: 261–269. Li YK, Ni J, Yang QK, et al, 2006. Human impacts on soil erosion identified using land-use changes:a case study from the Loess Plateau, China. Physical Geography, 27: 109–126. Li Z, Liu WZ, Zhang XC, et al, 2009. Impacts of land use change and climate variability on hydrology in an agricultural catch-ment on the Loess Plateau of China. Journal of Hydrology, 377: 35–42. doi: 10.1016/j.jhydrol.2009.08.007 Li N, Yan CZ, Xie JL, 2015. Remote sensing monitoring recent rapid increase of coal mining activity of an important energy base in northern China:a case study of Mu Us Sandy Land. Resources, Conservation and Recycling, 94: 129–135. Liu C, Qi S, Shi M, 2001. Process of study on relationship between land use change and soil erosion. Journal of Soil and Water Conservation, 15: 10–13. Liu DS, 1964. Loess in the Middle Yellow River Drainage Basin. Beijing: Science Press. Lu ZX, Zou SB, Qin ZD, et al, 2015. Hydrologic responses to land use change in the Loess Plateau:case study in the upper Fenhe River Watershed. Advances in Meteorology, 3: 1–10. doi: 10.1155/2015/676030 Ludwig JA, Wilcox BP, Breshears DD, et al, 2005. Vegetation patches and runoff-erosion as interacting ecohydrological processes in semiarid landscapes. Ecology, 86: 288–297. Renard KG, Freimund JR, 1994. Using monthly precipitation data to estimate the R-factor in the revised USLE. Journal of Hy-drology, 157: 287–306. Shi C, Zhou Y, Fan X, et al, 2013. A study on the annual runoff change and its relationship with water and soil conservation practices and climate change in the middle Yellow River basin. Catena, 100: 31–41. Sun W, Shao Q, Liu J, et al, 2014. Assessing the effects of land use and topography on soil erosion on the Loess Plateau in China. Catena, 121: 151–163. The Ministry of Water Resources of the People's Republic of China. 2008. National Professional Standards for Classification and Gradation of Soil Erosion:SL190-2007. (in Chinese) Tian YC, Zhou YM, Wu BF, et al, 2009. Risk assessment of water soil erosion in upper basin of Miyun Reservoir, Beijing, China. Environmental Geology, 57: 937–942. Vrieling A, 2006. Satellite remote sensing for water erosion as-sessment:A review. Catena, 65: 2–18. Wang XD, Zhong XH, Fan JR, 2005. Spatial on the distribution of soil erosion sensitivity on the Tibet Plateau. Pedosphere, 15: 465–472. Williams J. 1985. Physical components of the EPIC model Soil Erosion and Conservation. Soil Conservation Society of America, Ankeny, Iowa, pp. 272-284. Wischmeier WH, Smith DD. 1965. Predicting Rainfall-erosion Losses from Cropland East of the Rocky Mountains-guide for Selection of Practices for Soil and Water Conservation. US Department of Agriculture, Washington, D.C.. Yang ZL, 2003. A summary on the history and research progress of ecological construction in the Loess Plateau. World Forest Re-search, 16: 36–40. Yu HY, Cheng G, Ge XS, et al, 2011. Object oriented land cover classification using ALS and GeoEye imagery over mining area. Transactions of Nonferrous Metals Society of China, 21: 733–737. Zhang L, Bai KZ, Wang MJ, et al, 2016. Basin-scale spatial soil erosion variability:Pingshuo opencast mine site in Shanxi Province, Loess Plateau of China. Natural Hazards, 80: 1213–1230. Zhang QF, Wu FQ, Wang L, et al, 2011. Application of PCA inte-grated with CA and GIS in eco-economic regionalization of Chinese Loess Plateau. Ecological Economics, 70: 1051–1056. Zhao XL, Zhang ZX, Liu B, et al, 2002. Method of monitoring soil erosion dynamic based on remote sensing and GIS. Bulletin of Soil and Water Conservation, 22: 29–32. Zheng FL, 2006. Effect of vegetation changes on soil erosion on the Loess Plateau. Pedosphere, 16: 420–427. Zhou ZC, Shang-Guan ZP, Zhao D, 2006. Modeling vegetation coverage and soil erosion in the Loess Plateau Area of China. Ecological Modeling, 198: 263–268.