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Journal of Geographical Sciences >

2018 , Vol. 28 >Issue 2: 175 - 192

DOI: https://doi.org/10.1007/s11442-018-1466-0

Research Articles

Dynamic identification of soil erosion risk in the middle reaches of the Yellow River Basin in China from 1978 to 2010

  • ZHAO Haigen , 1, 2, 3, 4 ,
  • TANG Yuyu 1 ,
  • YANG Shengtian , 1, 2
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  • 1. School of Geography, Beijing Normal University, Beijing 100875, China
  • 2. State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China
  • 3. Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China
  • 4. Beijing Water Science and Technology Institute, Beijing 100048, China

Author: Zhao Haigen (1983-), PhD, specialized in hydrological simulation and remote sensing. E-mail:

*Corresponding author: Yang Shengtian (1965-), Professor, E-mail:

Received date: 2016-12-06

  Accepted date: 2017-03-21

  Online published: 2018-02-10

Supported by

National Natural Science Foundation of China, No.41701517

National Key Project for R&D, No.2016YFC0402403, No.2016YFC0402409

Copyright

Journal of Geographical Sciences, All Rights Reserved
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Abstract

Soil erosion has become a significant environmental problem that threatens ecosystems globally. The risks posed by soil erosion, the trends in the spatial distribution in soil erosion, and the status, intensity, and conservation priority level in the middle reaches of the Yellow River Basin were identified from 1978 to 2010. This study employed a multi-criteria evaluation method integrated with GIS and multi-source remote sensing data including land use, slope gradient and vegetation fractional coverage (VFC). The erosion status in the study region improved from 1978 to 2010; areas of extremely severe, more severe, and severe soil erosion decreased from 0.05%, 0.94%, and 11.25% in 1978 to 0.04%, 0.81%, and 10.28% in 1998, respectively, and to 0.03%, 0.59%, and 6.87% in 2010, respectively. Compared to the period from 1978 to 1998, the area classed as improvement grade erosion increased by about 47,210.18 km2 from 1998 to 2010, while the area classed as deterioration grade erosion decreased by about 17,738.29 km2. Almost all severe erosion regions fall in the 1st and 2nd conservation priority levels, which areas accounted for 3.86% and 1.11% of the study area in the two periods, respectively. This study identified regions where soil erosion control is required and the results provide a reference for policymakers to implement soil conservation measures in the future.

Key words： dynamic identification; soil erosion risk; multi-criteria evaluation; multi-source remote sensing; Yellow River Basin

Cite this article

ZHAO Haigen , TANG Yuyu , YANG Shengtian . Dynamic identification of soil erosion risk in the middle reaches of the Yellow River Basin in China from 1978 to 2010[J]. Journal of Geographical Sciences, 2018 , 28(2) : 175 -192 . DOI: 10.1007/s11442-018-1466-0

1 Introduction

Soil erosion is a well-known global environmental problem (Belyaev et al., 2005; Deng et al., 2009). It not only seriously threatens natural resources, infrastructure construction, and agricultural production (Pimentel et al., 1995; Lal, 1998; Park et al., 2011; Sharda et al., 2013), but also directly affects human safety and quality of life. Water erosion is one of the most serious types of soil erosion. Water erosion can result in land degradation by removing fertile topsoil layers, create negative downstream effects by depositing soil materials in rivers or reservoirs, and cause non-point pollution by washing pollutants attached to soil particles into natural waters (Vrieling et al., 2008; Morgan, 2005; Wang et al., 2013). Therefore, soil conservation is considered important for the protection of the environment and the economy.
China has suffered some of the most severe soil erosion on the planet. Between 2005 and 2007, almost 14% of the total soil erosion area in the world occurred in China (Li et al., 2009). Water erosion constitutes the primary type of soil erosion in China and accounts for 45% of the total soil erosion area (Li et al., 2008). Although the Chinese government has undertaken numerous soil conservation projects, the overall efficacy at improving environmental conditions is low (Zhang et al., 2010). One reason for this low efficacy is that the allocation of limited human and financial resources for soil conservation is made based on the size of the watershed rather than the erosion conservation prioritization (Zhang et al., 2002; Fan et al., 2008). The identification of soil erosion risk can help map and monitor the spatial dynamics of erosion and conservation prioritization. Therefore, it is important to identify dynamic soil erosion risk to effectively use limited resources to control soil erosion in China.
Soil erosion is related to precipitation, land use, soil taxa, vegetation fractional coverage (VFC), and slope (Beskow et al., 2009; Tian et al., 2009). Water erosion is caused by rain and runoff, and its intensity can be expressed as the annual amount of surface soil loss (MWRC, 1997). Currently, both quantitative and qualitative methods are available for the identification of soil erosion. Among the quantitative methods, the experienced statistical universal soil loss equation (USLE) (Wischmeier and Smith, 1965), revised universal soil loss equation (RUSLE) (Renard et al., 1991), and process-based physical water erosion prediction project model (WEPP) (Baffalt et al., 1996) are widely used to simulate and estimate soil loss. Although quantitative methods can calculate absolute soil erosion amounts, their outcomes are generally applied qualitatively (Vrieling et al., 2008). It is difficult to calculate soil erosion over long time periods or over large regions using quantitative methods because sufficient and accurate field validation measurements are time-consuming and expensive, and standard validation equipment is not easy to obtain (Stroosnijder, 2005;Vrieling et al., 2008). In addition, the complexities in model structure, parameters, and scale effect also cause the calculated and measured results to differ (Boardman, 2006; Ni et al., 2008). Compared to quantitative methods, qualitative approaches synthesize some important factors to show the relative probability of erosion. The identification of soil erosion risk using a qualitative method can usually meet the requirements, and accurate soil erosion determinations are often not necessary (Vrieling, 2006). With the help of a geographic information system (GIS), qualitative methods can also avoid the influence of personal subjective knowledge and can quickly and efficiently describe trends in soil erosion risk (Zhao et al., 2002; Zhou and Wu, 2005; Masoudi and Patwardhan, 2006; Tian et al., 2009; Zhang et al., 2012; Zhu, 2012; Wang et al., 2013).
Remote sensing at a regular spatial-temporal scale can provide detailed surface information and contribute to the assessment of regional and national erosion (Tian et al., 2009; Siakeu and Oguchi, 2000; Chen et al., 2011; Aia et al., 2013; Drzewieckiw et al., 2014; Aiello et al., 2015). Previous research has established the multi-criteria evaluation (MCE) method integrated with GIS and remote sensing as a widespread qualitative method in the field; slope gradient, VFC, and land use are usually considered to be the key factors to assess soil erosion risk and identify priority areas for conservation (Eastman, 2001; Zhou et al., 2005; Boroushaki and Malczewski, 2008; MWRC, 2008; Tian et al., 2009; Beskow et al., 2009; Chen et al., 2011; Zhang et al., 2012). Valente and Vettorazzi (2008) developed the spatial distribution of priority ranking in order to conserve forest resources in a river basin in Brazil. Zhang et al. (2010) identified the priority areas for controlling soil erosion in the Cetian reservoir area in China. Wang et al. (2013) assessed dynamic erosion risk in the Danjiangkou reservoir area in China using GIS and remote sensing data.
The middle reaches of the Yellow River are located on the Loess Plateau, a region with the most serious soil erosion caused by water in the world (Liu and Liu, 2010; Sun et al., 2014). The Chinese government has undertaken numerous soil conservation projects in this region (Gao et al., 2011). However, little research related to the identification of soil erosion risk has been conducted on large scales using multi-source remote sensing data with high spatial resolution. This research is particularly important because the Grain-for-Green Program, which began in 1998 (Fu et al., 2011), has greatly improved the ecological and environmental quality in this region and is expected to have had an effect on soil erosion risk.
This study thus aims to use an efficient and fast MCE method that includes only three important factors to (1) analyze the dynamic trend in the spatial distribution of erosion status and intensity, and (2) identify the dynamic erosion risk in the middle reaches of the Yellow River Basin from 1978 to 2010. The results provide a scientific reference to help policy makers identify erosion control regions, create soil conservation measures, and implement new projects.

2 Study area

The study area (Figure 1) belongs to the middle reaches of the Yellow River Basin in China. It is located between 103°57′01″-112°39′50″E and 33°40′19″-40°35′43″N and covers the region between Hekouzhen and Tongguan hydrological stations. The total area is about 25 × 104 km2. The average annual precipitation is 300 mm in the northwest and 650 mm in the southeast, and most precipitation occurs as heavy rainstorms during the rainy season (Luo et al., 2013). The major soil types distributed from the southeast to the northwest in this region include clayey loess, typical loess, sandy loess, and eolian sand (Liu, 1964). Corresponding to the soil distribution, the major vegetation type changes from broad-leaf deciduous forest to steppe and then to arid desert (Yang and Yuan, 1991). Wheat, corn, and millet are the major crops in this region.
Figure 1 Location of the study area

3 Datasets

A digital elevation model (DEM) with a resolution of 90 m was used to generate the slope gradient. The DEM data were downloaded from the International Scientific and Technical Data Mirror Site, Computer Network Information Center, Chinese Academy of Sciences (http://datamirror.csdb.cn).
The multi-source remote sensing images used to interpret the land-use map and invert the VFC information included HJ-CCD, Landsat-TM, Landsat-MSS, and others. Table 1 gives details of the remote sensing data.
Table 1 Details of the remote sensing images in this study
Name Resolution (m) Acquired time Acquired department
Landsat-MSS 56 July to September, 1978 NASA of the US
Landsat-TM 30 July to September, 1998 NASA of the US
HJ-CCD 32 July to September, 2010 China Center for Resources Satellite Data and Application
KH-11 3 July to September, 1978 NASA of the US
ZY3-CCD 2.1 July to September, 2012 China Center for Resources Satellite Data and Application
SPOT4 10 July to September, 1998 Yellow River Conservancy Commission in China

4 Methodology

4.1 MCE technique

The MCE technique is an evaluation decision-making method based on a series of criteria (Ceballos-Silva and López-Blanco, 2003). The main purpose of MCE is to investigate the complex tradeoffs between alternative choices with different environmental and socio-economic impacts (Krois, 2014). MCE could be used to conduct a quantitative treatment for the less quantifiable criteria and could synthetically consider the impact of multi-criteria on the object when making an objective evaluation. With the incorporation of GIS, MCE provides the optimal scheme for this purpose, using the weight linear combination and Boolean overlay. The integration of MCE within a GIS context could help users improve decision-making processes when solving conflictive situations for individuals or groups interested in spatial context (Malczewski, 1996; Ceballos-Silva, 2013).

4.2 Flowchart of methodology

The slope gradient, VFC, and soil taxa, which are related to land cover, can be used to indicate erosion resistance or risk; thus, land use, VFC, and slope gradient were used to assess the risk of soil erosion in this study (Zhang et al., 2010).
Figure 2 shows a flowchart of the overall methodology used in this study based on the National Professional Standard SL190-2007 (MWRC, 2008), which was used to classify the grade of erosion risk. The slope gradient was calculated from DEM data with the help of ArcGIS software. The data for land use and VFC were mapped using remotely sensed images, as described in Section 3.
Figure 2 Flowchart of general methodology
In this study, soil erosion risk was classified into six grades: slight, light, moderate, severe, more severe, and extremely severe (Table 2) based on SL190-2007.
Table 2 Standards for the classification and gradation of soil erosion risk
Ground cover VFC (%) Slope (º)
< 5 5-8 8-15 15-25 25-35 > 35
Non-farmland >75 Slight Slight Slight Slight Slight Slight
60-75 Slight Light Light Light Moderate Moderate
45-60 Slight Light Light Moderate Moderate Severe
30-45 Slight Light Moderate Moderate Severe More severe
<30 Slight Moderate Moderate Severe More severe Extremely severe
Farmland Slight Light Moderate Severe More severe Extremely severe

4.3 Land use

In this study, the man-machine interactive visual interpretation method was first used to classify land-use types as forest, open woodland, other woodland, shrub, high-cover grassland, medium-cover grassland, low-cover grassland, farmland, water body, built-up land, or unused land, based on the land-use type classification standard of China. Subsequently, areas classified as forest, open woodland, other woodland, and shrub were further reclassified as forestland, and areas classified as high-, medium-, and low-cover grasslands were reclassified as grassland. Figure 3 shows the changes in land-use type over 33 years in the middle reaches of the Yellow River Basin.
Figure 3 Land-use maps in the study area in 1978 (a), 1998 (b), and 2010 (c)
To assess the accuracy of the interpretation of land use, 129 field verification points covering approximately 32% of the study area were collected using GPS. In 2010, 129 samples were validated; the results showed that 121 were correctly identified, giving an interpretation accuracy of 93.8%. For land use in 1998, multiple types of information, including historical documents, maps, and interviews with local residents, were employed to determine the interpretation accuracy. In addition, for land use in 1978, the KH-11 data were used to validate the interpretation accuracy. The results showed that the overall accuracy in both 1998 and 1978 was approximately 85%.

4.4 VFC

Vegetative growth and cover can be reflected by the normalized difference vegetation index (NDVI). The NDVI can eliminate remote sensing irradiation errors to some extent. To retrieve the VFC, the following functions based on NDVI were used in this study:
$VFC=(NDVI-NDV{{I}_{soil}})/(NDV{{I}_{veg}}-NDV{{I}_{soil}})$ (1)
where VFC is vegetation fractional coverage, NDVIsoil is the NDVI of barren soil, and NDVIveg is the NDVI of vegetation;
$NDVI=(NIR-R)/(NIR+R)$ (2)
where NIR is the reflectivity of the near-infrared band, and R is the reflectivity of the red band.
To fully cover the relatively large study area, images for different days were collected and used to address the influence of cloud cover and satellite orbit. To minimize the errors caused by the imaging times of adjacent NDVI images, an adjacent image regression analysis, as described in Zhou et al. (2015), was adopted. The accuracy of the resulting VFC for the three periods was assessed using the methodology of Zhou et al. (2015) and the result indicated that the overall accuracy was more than 86%. In addition, the accuracy of data obtained for land use and VFC was also identified and accepted at a meeting held by experts from the Yellow River Conservancy Commission of the Ministry of Water Resources at Zhengzhou city in China.
Based on SL190-2007, the VFC was reclassified into five classes, with limits of 30%, 45%, 60%, and 75%. Figure 4 shows the changes of VFC from 1978 to 2010.
Figure 4 Vegetation fractional coverage (VFC) maps in the study area in 1978 (a), 1998 (b) and 2010 (c)

4.5 Slope gradient

Slope gradient is an important reflection of surface undulation and can change the hydrological velocity and the direction of surface runoff. Thus, slope gradient has a significant impact on surface hydrological processes and soil erosion (Beskow et al., 2009). In this study, DEM data in the range of 320 to 3296 m were used to generate the slope gradient with ArcGIS software and the algorithm described by Burrough in 1998. In this study, the slope gradient was reclassified with threshold values of 5°, 8°, 15°, 25° and 35° based on SL190-2007 (Figure 5).
Figure 5 Ranks of slope gradient in the study area

5 Results

5.1 Identification of soil erosion risk

The six classes of slope gradient (Table 2) in the middle reaches of the Yellow River Basin account for 29.01%, 12.53 %, 31.57%, 22.87%, 3.47%, and 0.55% of the total study area, respectively.
The proportions of the different classes of VFC and different land-use types in 1978, 1998, and 2010 are listed in Tables 3 and 4. Table 3 shows that from 1978 to 1998 the low-cover classes of vegetation (<30% and 30%-45%) increased by 1.83%, the medium-cover classes (45%-60%) did not significantly change, and the high-cover classes (60%-75% and > 75%) decreased by 2.21%. From 1998 to 2010, the low-cover classes of vegetation decreased by 25.22%, the medium-cover classes increased by 4.67%, and the high- cover classes increased by 20.55%. Compared to the changes in VFC between 1978 and 1998, there was a significant reduction in the low- cover classes of vegetation and an increase in the high-cover classes of vegetation from 1998 to 2010.
Table 3 Proportions of different classes of VFC in the study area from 1978 to 2010 (%)
Proportion (%) 1978 1998 2010
<30 62.23 57.28 39.94
30-45 10.30 17.08 9.20
45-60 7.45 7.83 12.50
60-75 6.16 5.43 12.01
>75 13.86 12.38 26.35
The change in land use described in Table 4 is consistent with the changes in VFC detailed above. The change in land use was not significant from 1978 to 1998. However, between 1998 and 2010, forest and grassland areas increased, and farmland area decreased. The proportion of farmland decreased by 3.90% of the total study area, and the proportion of farmland and forest land increased by 4.07% of the total study area.
Table 4 Proportions of different land-use types in the study area from 1980 to 2010 (%)
Land use type 1980 1998 2010
Farmland 38.57 37.42 33.52
Forestland 15.85 16.70 21.65
Grassland 39.22 39.49 38.61
Water 0.93 0.93 0.59
Built-up land 2.01 2.22 2.34
Unused land 3.42 3.24 3.29
The distributions of soil erosion risk grades in the study area in 1978, 1998, and 2010 were identified using the standards in Table 2. Figure 6 shows the spatial dynamics of erosion risk grades from 1978 to 2010. No significant changes were observed between 1978 and 1998, and the overall erosion status improved between 1998 and 2010, particularly in the high-sediment region. One reason for this phenomenon is the large-scale implementation of the Grain-for-Green Program in the study area.
Figure 6 Soil erosion risk grades in the study area in 1978 (a), 1998 (b), and 2010 (c)
The accuracy of the identified erosion risk in 2010 was assessed based on randomly selected field samples and judged primarily by the knowledge of experts from the Yellow River Conservancy Commission of the Ministry of Water Resources. The results indicate that the overall accuracy of estimated erosion risk was 90.5% in 2010. This accuracy level confirms that the accuracy identified for 1978 and 1998 can also be used because the erosion risk maps for all three periods were generated based on slope, VFC, and land use data, and the accuracies of VFC and land use were satisfactory, as described in the validation result in Section 3.

5.2 Comparison of soil erosion risk

Table 5 shows obvious changes in the proportions of erosion risk grades from 1978 to 1998 and from 1998 to 2010, although the trends in the proportions of the severe, more severe, and extremely severe grades were similar in the two periods.
Table 5 Distributions of soil erosion risk grades in the study area in 1978, 1998, and 2010
Erosion risk grade Erosion risk in 1978 Erosion risk in 1998 Erosion risk in 2010
Area (km2) Proportion (%) Area (km2) Proportion (%) Area (km2) Proportion (%)
Slight 105516.21 42.22 103337.17 41.35 132991.81 53.21
Light 34582.84 13.84 36547.61 14.62 56912.95 22.77
Moderate 79221.49 31.70 82230.37 32.90 41308.32 16.53
Severe 28114.40 11.25 25683.34 10.28 17157.76 6.87
More severe 2356.23 0.94 2033.02 0.81 1472.78 0.59
Extremely severe 129.89 0.05 89.55 0.04 77.42 0.03
The area with a soil erosion risk grade of severe decreased from 28,114.40 km2 (11.25% of the region’s total area) in 1978 to 25,683.34 km2 (10.28%) in 1998, and to 17,157.76 km2 (6.87%) in 2010. The area graded more severe decreased from 2356.23 km2 (0.94%) in 1978 to 2033.02 km2 (0.81%) in 1998, and 1472.78 km2 (0.59%) in 2010. The area with a grade of extremely severe decreased from 129.89 km2 (0.05%) in 1978 to 89.55 km2 (0.04%) in 1998, and 77.42 km2 (0.03%) in 2010. In contrast, the total area with grades of slight, light, and moderate soil erosion risk increased from 219,320.54 km2 (87.76%) in 1978 to 222,115.15 km2 (88.87%) in 1998, and 231,213.08 km2 (82.51%) in 2010. The results shown in Table 2 illustrate that from 1978 to 2010 the overall erosion status improved in the study area.
In order to further understand the transformations in erosion grades occurring in the different time periods, the transformation between successive periods, demonstrated by overlaying the erosion risk results in two adjacent periods pixel by pixel, is expressed as a proportion for each erosion grade (Tables 6 and 7).
Table 6 shows the transformation matrix of the proportional distribution of each erosion grade between 1978 and 1998. The values located diagonally from the upper-left corner to the lower-right corner represent the proportions of unchanged areas in the study area. The values above the diagonal represent the proportions of areas with increased erosion risk, while the values below the diagonal represent the proportions with reduced risk. From 1978 to 1998 (Table 6), the unchanged proportion accounts for 79.63% of the region’s total area, with the unchanged proportions of areas of slight, light, moderate, severe, more severe, and extremely severe erosion risk accounting for 44.33%, 11.11%, 32.54%, 11.10%, 0.88%, and 0.04% of the total unchanged area, respectively. The high proportion of unchanged area illustrates that erosion risk did not change significantly between 1978 and 1998. The grades of slight, light, and moderate risk account for the majority of the changed area, and changes from moderate to slight and moderate to light represent the largest proportion (4.78%) of reduced erosion risk from 1978 to 1998. In contrast, the changes from slight and light to moderate represent the largest proportion (4.86%) of increased erosion risk in this period. These results illustrate that the overall erosion status was stable from 1978 to 1998, and areas of improvement and deterioration coexisted.
Table 6 Proportion of transformation for each erosion grade in the study area between 1978 and 1998
Erosion grade in 1998 (%)
Slight Light Moderate Severe More severe Extremely severe
Erosion grade in 1978 (%) Slight 35.30 2.06 1.72 0.20 0.02 0.002
Light 1.70 8.85 3.14 0.14 0 0
Moderate 1.20 3.58 25.91 0.96 0.05 0.001
Severe 0.16 0.13 2.07 8.84 0.05 0.001
More severe 0.04 0 0.07 0.14 0.70 0.002
Extremely severe 0.01 0 0.004 0.003 0.002 0.03
Table 7 shows that from 1998 to 2010, the unchanged proportion was 68.27% of the total study area, with the unchanged proportions of slight, light, moderate, severe, more severe, and extremely severe risk accounting for 51.71%, 12.86%, 21.06%, 9.54%, 1.03%, and 0.004% of the total unchanged area, respectively. These results indicate clear changes in erosion grade between 1998 and 2010. In this period, the greatest reductions in erosion risk were from moderate to slight and light, severe to slight and light, and light to slight, which accounted for 18.60%, 2.12%, and 1.70% of the study area, respectively. The areas with increased erosion risk were very small, the largest value being 0.29%.
Table 7 Proportion of transformation for each erosion grade in the study area between 1998 and 2010
Erosion grade in 2010 (%)
Slight Light Moderate Severe More severe Extremely severe
Erosion grade in 1998 (%) Slight 38.06 0.35 0.13 0.07 0.02 0.004
Light 4.66 8.78 0.29 0.05 0 0
Moderate 5.86 12.74 14.38 0.25 0.03 0.003
Severe 0.97 1.15 1.72 6.51 0.02 0.001
More severe 0.08 0 0.17 0.05 0.52 0.001
Extremely severe 0.010 0 0.001 0.002 0.001 0.02

5.3 Trend analysis of erosion transformation

Based on Figure 6, the erosion trends between 1978 and 1998 and between 1998 and 2010 were mapped (Figure 7), and the statistical results for the two periods are shown in Table 8.
Figure 7 Changes in erosion risk grades in the study area from 1978 to 1998 (a) and from 1998 to 2010 (b)
Table 8 Deterioration and improvement in erosion grades in the study area between 1980 and 2010
Erosion grade variation 1978 to 1998 1998 to 2010
Area (km2) Proportion (%) Area (km2) Proportion (%)
Deterioration of 1 grade 15508.13 6.21 2303.26 0.92
Deterioration of 2 grades 4780.16 1.91 534.25 0.21
Deterioration of 3 grades 491.20 0.20 193.21 0.08
Deterioration of 4 grades 51.78 0.02 56.92 0.02
Deterioration of 5 grades 4.50 0.002 9.83 0.004
Improvement of 1 grade 19141.65 7.66 50004.85 20.01
Improvement of 2 grades 3481.08 1.39 17731.47 7.09
Improvement of 3 grades 445.28 0.18 2463.94 0.99
Improvement of 4 grades 116.81 0.05 206.87 0.08
Improvement of 5 grades 36.21 0.01 24.08 0.01
Summation of deterioration grades 20835.77 8.34 3097.48 1.23
Summation of improvement grades 23221.03 9.29 70431.21 28.18
Figure 7 shows the spatial distributions of changes in erosion grade from 1978 to 1998 and from 1998 to 2010. From 1978 to 1998, some erosion grades improved, while others deteriorated (Figure 7a), but from 1998 to 2010, there were 20 times more improvements in grade than deteriorations (Figure 7b).
Table 8 shows that most deteriorations and improvements in the grade of soil erosion risk were changes of one or two grades. The total area where the erosion grade improved from 1998 to 2010 (28.18% of the region’s total area) was obviously greater than that from 1978 to 1998 (9.29%). The total area that saw a deterioration in erosion grade from 1998 to 2010 (1.23% of the region’s total area) was obviously less than that from 1978 to1998 (8.34%). These results indicate that the erosion condition has been improving in recent years. Although the area at high risk of erosion is decreasing, erosion risk should not be ignored in government policies related to soil conservation. The results shown in Figure 7 are consistent with those in Table 8 and indicate the spatial areas of soil erosion measures.

5.4 Identification of conservation priorities

The conservation priorities of regions were identified by analyzing changes in erosion risk. Table 9 shows the conservation priorities created based on SL190-2007. A higher conservation priority corresponds to a greater erosion risk and indicates that more attention should be given to that region. Based on Table 9, the proportion and the area of every priority level are shown in Table 10.
Table 9 Multi-criteria decision rules for identifying conservation priorities
Erosion grade in the next period
Slight Light Moderate Severe More severe Extremely severe
Erosion grade in the previous period Slight Ⅳ Ⅳ Ⅱ Ⅰ Ⅰ Ⅰ
Light Ⅳ Ⅳ Ⅲ Ⅱ Ⅰ Ⅰ
Moderate Ⅳ Ⅴ Ⅲ Ⅱ Ⅱ Ⅰ
Severe Ⅳ Ⅴ Ⅳ Ⅲ Ⅱ Ⅰ
More severe Ⅳ Ⅴ Ⅳ Ⅲ Ⅱ Ⅰ
Extremely severe Ⅳ Ⅵ Ⅴ Ⅳ Ⅲ Ⅰ
Table 10 Area and proportion of each priority level in the study area
Priority level 1978 to 1998 1998 to 2010
Area (km2) Proportion (%) Area (km2) Proportion (%)
1st level 627.06 0.25 298.80 0.12
2nd level 9023.28 3.61 2464.15 0.99
3rd level 95046.16 38.03 52536.04 21.02
4th level 32618.80 13.05 27296.90 10.92
5th level 9280.42 3.71 34332.97 13.74
6th level 103325.32 41.34 132992.19 53.21
Table 10 indicates that areas with severe erosion and at the 1st and 2nd priority levels comprised 9650.34 km2 (3.86% of the region’s total area) from 1978 to 1998 and 2762.95 km2 (1.11%) from 1998 to 2010. Although the area at severe risk of erosion is becoming smaller, it should not be ignored and requires constant attention to ensure appropriate erosion measures in future projects. The 3rd and 4th levels accounted for 51.08% and 31.94% of the region’s total area from 1978 to 1998 and from 1998 to 2012, respectively. These two levels represent stable or slightly changing erosion status and indicate the need for only a minor allocation of resources to control soil erosion. The areas represented by the 5th and 6th levels accounted for 45.05% and 66.95% of the study area from 1978 to 1998 and from 1998 to 2012, respectively. These areas have low erosion risk, and the current development intensity should be maintained without the need for extra measures to control soil erosion. Compared to the period from 1978 to 1998, the control of soil erosion has been remarkably successful from 1998 to 2010.
Based on the results shown in Tables 9 and 10, distribution maps of conservation priority levels from 1978 to 2010 were generated (Figure 8). Figure 8 can provide comparative measures for controlling soil erosion from 1978 to 2010 for government, and thus facilitates the efficient use of labor and funds to control soil erosion in the future.
Figure 8 Maps showing the distributions of conservation priority levels in the study area from 1978 to 1998 (a) and from 1998 to 2010 (b)

6 Discussion

Water erosion is known to be the most important factor causing soil degradation worldwide.
The middle reaches of the Yellow River Basin on the Loess Plateau are one of the regions most seriously affected by water erosion in the world. Therefore, this area is an important conservation region for the control of soil erosion. Sedimentation rates and human activity are significantly correlated. From the end of the 1970s to the end of the 1990s, a series of soil and water conservation projects were carried out under the support of government-sponsored programs in the area. At the same time, a strong economy led to increased deforestation in this period; thus, improvements in erosion were accompanied by some deteriorations. After 1999, the implementation of the large-scale Grain-for-Green Program greatly increased the proportions of forest and grasslands in the study area and reduced the overall intensity of soil erosion. Therefore, analyzing the temporal and spatial distribution dynamics of soil erosion risk and identifying priority regions over the past 33 years is important for government policymaking in the future.

6.1 Selection of criteria for the dynamic identification of soil erosion risk

The average slope gradient can change with variation in the spatial resolution of DEM data (David and McCabe, 2000; Thompson et al., 2001; Wang and Wang, 2009; Fu, 2015). Previous studies (Sun et al., 2013; Sun et al., 2014) have shown that DEM data with a resolution of 90 m can be used to calculate the erosion rate at a large scale in the Loess Plateau region. Thus, in order to balance the time cost and accuracy of calculation, a resolution of 90 m was chosen in this study. However, the spatial effects of the DEM resolution on the generation of erosion risk in such a large region should be identified in future research.
Land-use factors were separated into farmland and non-farmland types to calculate the spatial and temporal changes in erosion risk from 1978 to 2010. However, in reality, the soil conservation functions of forestland and grassland are different, even if they have the same VFC. Generally, forest has deeper root length, a larger root distribution area, and a denser canopy than grassland. Thus, forest can more greatly reduce the percussive force of a raindrop, can intercept more rainfall, and is better at controlling soil erosion. Quantitative differences in land-use types should be considered when calculating soil erosion risk in future research.
Three periods of remote sensing data were employed to generate land use, slope gradient, and VFC, which have key effects on the distribution of erosion risk. The remote sensing data used to generate these land-use and VFC data included Landsat TM, Landsat MSS, and HJ-CCD. Although the sensors of Landsat and HJ are different, Landsat and HJ have similar spectral ranges in the first four bands, and the difference in spatial resolution between MSS and TM is acceptable. In addition, the remote sensing data were obtained between July and August, because these data best reflect the growth status of vegetation. Therefore, it is reasonable to compare the risk maps for the three time periods.
When assessing soil erosion, the MCE used in this study does not consider rainfall intensity, unlike the RUSLE model (Lu et al., 2004; Alexakis et al., 2013; Fu et al., 2013). Although the RUSLE function reflects erosion more physically (for example, by considering the impacts of climate and underground and raindrop kinetic energy), it requires more accurate input data and is likely to produce significant errors in a specific rainfall measurement if the region has complex terrain and diverse physiognomy (Wang et al., 2013). The distribution of national meteorological stations that can provide publicly available data in the study area is uneven and scarce; thus, it is difficult to use the geographic interpolation method to obtain high-resolution and continuous raster data that can accurately describe the spatial variations in precipitation (Meusburger et al., 2012). Nevertheless, rainfall intensity and runoff should be considered in future risk assessment models.

6.2 Comparison between soil erosion risk grades and estimated soil loss

To further identify the accuracy of soil erosion risk grades, the distribution of soil risk grades in this study were compared with the results of Fu et al. (2011). Figure 9 shows the distributions of estimated soil loss calculated by the USLE method. The spatial patterns of soil erosion risk grades in Figure 6 are generally consistent with the distributions of estimated soil losses shown in Figure 9. The regions classified as having slight, light, moderate, severe, more severe, and extremely severe erosion risk in Figure 6 basically correspond to the soil loss ranges of 0-500, 500-2500, 2500-5000, 5000-8000, 8000-15000, and >15000, respectively, in Figure 9. However, there are differences between them in some areas.
Figure 9 Spatial distributions of estimated soil loss (t km-2 yr-1) in the study area in 2000 (a) and 2008 (b) (Fu et al., 2011)
Some areas have moderate erosion risk (blue box 1) or slight erosion risk (blue box 2) in Figure 9, but fall in the soil loss range of >8000 in Figure 9. These areas are forestlands, and these discrepancies may be primarily attributed to differences in the vegetation cover data. In this study, the VFC values in 1998 and 2010 were derived respectively from Landsat-TM with a resolution of 30 m and HJ-CCD with a resolution of 32 m. In Figure 9, the maximum 16-day NDVI data were derived from MODIS images with a resolution of 250 m. This higher-resolution VFC data should alleviate the soil erosion risk grade.
Some areas have severe and more severe risk grades in Figure 6, but fall into the soil loss range of 500-2500 in Figure 9 (red box 1). Some areas have moderate or severe risk grades in Figure 6, but fall into the soil loss range of 0-500 in Figure 9 (red box 2).These areas are farmlands, and these discrepancies are caused by the differences in the methods used to treat vegetation and slope. In this study, VFC was not considered, and the absolute slope gradient was used when calculating the soil erosion grades for farmland. In Figure 9, vegetation was considered, and the percentile slope gradient was used when calculating soil erosion. Thus, compared to the results of this study, the soil erosion grades in the literature are lower.
In addition, the time periods considered in this study are different from those in the literature, which could obviously affect the vegetation data and the results of the comparisons among different time periods. Thus, more field data should be collected to validate the study results.
However, the maps showing the distributions of conservation priority levels in this study (Figure 8) demonstrated that the change of soil erosion control as an ecosystem service driven by the vegetation cover change mainly happened on the slopes. In this regard, the results of this study are meaningful to assess the dynamics of soil erosion risk, and thus the evolution of the soil erosion control service on the Loess Plateau.

7 Conclusions

Based on GIS techniques, this study integrated a multi-criteria evaluation approach involving high-spatial-resolution remote sensing data (slope gradient, VFC, and land use) to qualitatively identify the trends in the spatial distribution of soil erosion risk in the middle reaches of the Yellow River Basin from 1978 to 2010.
The results show that erosion risk has decreased over the 33-year study period. From 1978 to 1998, the areas categorized as having extremely severe, more severe, and severe erosion risk decreased by 0.01%, 0.13%, and 0.97% of the study area, respectively; the respective decreases were 0.01%, 0.22%, and 3.41%. The decreasing trend from 1998 to 2010 was more pronounced than that from 1978 to 1998.
The results also indicate that from 1978 to 2010 the total area classed as deterioration grade erosion was smaller than that classed as improvement grade erosion, and that the transformation between these classes between 1998 and 2010 was smaller than that between 1978 and 1998. From 1978 to 1998, the proportions of the region where the erosion grade changed from severe to slight and from severe to light were 0.16% and 0.13%, respectively, and the proportions of regions where the erosion grade changed from slight and light to extremely severe and severe were all less than 0.01%. From 1998 to 2010, the proportions of regions where the erosion grade changed from severe to slight and from severe to light were 0.97% and 1.15%, respectively, and the proportions of regions where the erosion grade changed from slight or light to extremely severe or severe were all less than 0.01%. The proportions of extremely severe and more severe erosion grades were all less than 0.01% of the total area from 1978 to 1998 and from 1998 to 2010, but these values were greater from 1998 to 2010 than from 1978 to 1998.
Compared to the period from 1978 to 1998, the area of improvement grade erosion increased by about 47210.18 km2 from 1998 to 2010, while the area of deterioration grade erosion decreased by about 17738.29 km2. The changes in erosion risk indicated that the regions in which erosion risk increased or decreased significantly were located in the central region of the study area.
The maps of the distributions of conservation priority levels indicate that conservation priority levels are significant for future eco-environment management and policymaking related to water and soil conservation in the Yellow River Basin. The top two conservation priorities accounted for 3.86% and 1.11% of the total study area. These areas should not be ignored; they should be given attention even though their overall erosion intensity has been reduced.
The MCE model integrated with multi-source remote sensing data can be applied in the middle reaches of the Yellow River Basin. The dynamics of the spatial distribution of erosion risk from 1978 to 2010 can provide guidance for government agencies as they plan water conservation efforts and implement soil conservation projects in the future.

The authors have declared that no competing interests exist.

References

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Chen T, Niu R Q, Li P Xet al., 2011. Regional soil erosion risk mapping using RUSLE, GIS, and remote sensing: A case study in Miyun Watershed, North China.Environmental Earth Sciences, 63: 533-541.This paper applied the Revised Universal Soil Loss Equation (RUSLE), remote-sensing technique, and geographic information system (GIS) to map the soil erosion risk in Miyun Watershed, North China. The soil erosion parameters were evaluated in different ways: the R factor map was developed from the rainfall data, the K factor map was obtained from the soil map, the C factor map was generated based on a back propagation (BP) neural network method of Landsat ETM+ data with a correlation coefficient ( r ) of 0.929 to the field collected data, and a digital elevation model (DEM) with a spatial resolution of 3002m was derived from topographical map at the scale of 1:50,000 to develop the LS factor map. P factor map was assumed as 1 for the watershed because only a very small area has conservation practices. By integrating the six factor maps in GIS through pixel-based computing, the spatial distribution of soil loss in the upper watershed of Miyun reservoir was obtained by the RUSLE model. The results showed that the annual average soil loss for the upper watershed of Miyun reservoir was 9.8602t02ha 611 02ya 611 in 2005, and the area of 47.502km 2 (0.3%) experiences extremely severe erosion risk, which needs suitable conservation measures to be adopted on a priority basis. The spatial distribution of erosion risk classes was 66.88% very low, 21.90% low, 6.19% moderate, 2.90% severe, and 1.84% very severe. Among all counties and cities in the study area, Huairou County is in the extremely severe level of soil erosion risk, about 39.6% of land suffer from soil erosion, while Guyuan County in the very low level of soil erosion risk suffered from 17.79% of soil erosion in 2005. Therefore, the areas which are in the extremely severe level of soil erosion risk need immediate attention from soil conservation point of view.

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[14]
Drzewieckiw W, Wężyk P, Pierzchalski Met al., 2014. Quantitative and qualitative assessment of soil erosion risk in Małopolska (Poland), supported by an object-based analysis of high-resolution satellite images.Pure and Applied Geophysics, 171: 867-895.In 2011 the Marshal Office of Ma00opolska Voivodeship decided to evaluate the vulnerability of soils to water erosion for the entire region. The quantitative and qualitative assessment of the erosion risk for the soils of the Ma00opolska region was done based on the USLE approach. The special work-flow of geoinformation technologies was used to fulfil this goal. A high-resolution soil map, together with rainfall data, a detailed digital elevation model and statistical information about areas sown with particular crops created the input information for erosion modelling in GIS environment. The satellite remote sensing technology and the object-based image analysis (OBIA) approach gave valuable support to this study. RapidEye satellite images were used to obtain the essential up-to-date data about land use and vegetation cover for the entire region (15,00002km 2 ). The application of OBIA also led to defining the direction of field cultivation and the mapping of contour tillage areas. As a result, the spatially differentiated values of erosion control practice factor were used. Both, the potential and the actual soil erosion risk were assessed quantificatively and qualitatively. The results of the erosion assessment in the Ma00opolska Voivodeship reveal the fact that a majority of its agricultural lands is characterized by moderate or low erosion risk levels. However, high-resolution erosion risk maps show its substantial spatial diversity. According to our study, average or higher actual erosion intensity levels occur for 10.602% of agricultural land, i.e. 3.602% of the entire voivodeship area. In 2002% of the municipalities there is a very urgent demand for erosion control. In the next 2302% an urgent erosion control is needed. Our study showed that even a slight improvement of P-factor estimation may have an influence on modeling results. In our case, despite a marginal change of erosion assessment figures on a regional scale, the influence on the final prioritization of areas (municipalities) according to erosion control needs is visible. The study shows that, high-resolution satellite imagery and OBIA may be efficiently used for P-factor mapping and thus contribute to a refined soil erosion risk assessment.

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[15]
Eastman J R, 2001. IDRISI 32: Guide to GIS and Image Processing. Worcester, USA: Clark Labs, Clark University.Abstract─ The paper discusses the contribution of image processing techniques to improve cognitive processes, acquire vector data and support decision making. Also the problem of knowledge management is addressed and the different ways of knowledge integration is

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[16]
Fan H M, Wang T L, Cai Q Get al., 2008. Study on the zonation differentiation of soil erosion and the model of soil and water conservation in northeast China.Research of Soil and Water Conservation, 15: 69-72. (in Chinese)There are significant zonation differentiations on the tectonic,the landform,the flow separation,the wind power and the freeze-thaw action in the Northeast China,which cause the significant zonation differentiations on the erosion intensity and the type of soil erosion.At present,in the Northeast China the water erosion is significant in the southeast,the wind erosion in the west and the freeze-thaw erosion in the north respectively.According to the soil erosion zonation differentiation,the North- east China is classified 9 regions of soil erosion types.Furthermore,the models of soil and water conservation for the 9 regions of soil erosion types have been generalized.

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[17]
Fu B J, Liu Y, Lü Y Het al., 2011. Assessing the soil erosion control service of ecosystems change in the Loess Plateau of China.Ecological Complexity, 8: 284-293.Soil erosion in terrestrial ecosystems, as an important global environmental problem, significantly impacts on environmental quality and social economy. By protecting soil from wind and water erosion, terrestrial ecosystems supply human beings with soil erosion control service, one of the fundamental ecosystem services that ensure human welfare. The Loess Plateau was one of the regions in the world that suffered from severe soil erosion. In the past decades, restoration projects were implemented to improve soil erosion control in the region. The Grain-to-Green project, converting slope croplands into forest or grasslands, launched in 1999 was the most massive one. It is needed to assess the change of soil erosion control service brought about by the project. This study evaluated the land cover changes from 2000 to 2008 by satellite image interpretation. Universal Soil Loss Equation (USLE) was employed for the soil erosion control assessment for the same period with localized parameters. Soil retention calculated as potential soil erosion (erosion without vegetation cover) minus actual soil erosion was applied as indicator for soil erosion control service. The results indicate that ecosystem soil erosion control service has been improved from 2000 to 2008 as a result of vegetation restoration. Average soil retention rate (the ratio of soil retention to potential soil loss in percentage) was up to 63.3% during 2000–2008. Soil loss rate in 34% of the entire plateau decreased, 48% unchanged and 18% slightly increased. Areas suffering from intense erosion shrank and light erosion areas expanded. Zones with slope gradient of 8°–35° were the main contribution area of soil loss. On average, these zones produced 82% of the total soil loss with 45.5% of the total area in the Loess Plateau. Correspondingly, soil erosion control capacity was significantly improved in these zones. Soil loss rate decreased from 500002t02km 612 02yr 611 to 360002t02km 612 02yr 611 , 690002t02km 612 02yr 611 to 470002t02km 612 02yr 611 , and 850002t02km 612 02yr 611 to 550002t02km 612 02yr 611 in the zones with slope gradient of 8°–15°, 15°–25°, and 25°–35° respectively. However, the mean soil erosion rate in areas with slope gradient over 8° was still larger than 360002t02km 612 02yr 611 , which is far beyond the tolerable erosion rate of 100002t02km 612 02yr 611 . Thus, soil erosion is still one of the top environmental problems that need more ecological restoration efforts.

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[18]
Fu S H, Cao L X, Liu B Yet al., 2015. Effects of DEM grid size on predicting soil loss from small watersheds in China.Environmental Earth Sciences, 73: 2141-2151.The topography (slope gradient and slope length) has an important effect on soil erosion by water. Slope gradient and slope length factor are the parameters of the universal soil loss equation that is commonly used to predict soil erosion. These factors are usually extracted from the digital elevation model (DEM). Thus the DEM grid size will influence topographic factors and therefore soil loss calculation. The purpose of this study was to investigate the effect of DEM grid size on the topographic factors and therefore soil loss prediction on a typical watershed in northeast of China. The site for the case study was Dontaigou watershed which is located at Huairou district, Beijing, China. Contour lines of 2m interval digitized from 1:2,000 scale of topographic maps were used to generate DEM with grid size of 2m. The DEMs with grid sizes of 3 30m, at 1m intervals, were created using the nearest neighbor resampling method. In total, 29 DEMs with different grid sizes were obtained. Four different scenarios of the channel initiation threshold were used to extract the drainage channel and let the slope length cutoff. Chinese soil loss equation was used to predict the soil loss. The results show that DEM grid size had different effects on the topographic factor. The average calculated slope steepness factor decreased with the increase of the DEM grid size in a linear decay function. The average slope length factor increased with DEM grid size when the drainage channel was not considered. It slightly decreased with DEM grid size and then increased when the drainage channel suitable for DEM grid size was used. However, comparing different grid sizes, the LS factor and the soil loss prediction of DEM grid size less than or equal to 10m were close to those of 2m DEM. The channel initiation threshold had effects on the extraction of slope length and then slope length factor. We found that the effect of DEM grid size on predicting soil loss depends on selection of suitable channel initiation threshold.

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[19]
Gao P, Mu X M, Wang Fet al., 2011. Changes in streamflow and sediment discharge and the response to human activities in the middle reaches of the Yellow River.Hydrology and Earth System Sciences, 15: 1-10.The objectives of this work are: (a) to statistically test and quantify the decreasing trends of streamflow and sediment discharge in the middle reaches of the Yellow River in China during 1950 2008, (b) to identify change points or transition years of the decreasing trends, and (c) to diagnose whether the decreasing trends were caused by precipitation changes or human intervention, or both. The results show that significant decreasing trends in annual streamflow and sediment discharge have existed since the late 1950s in the middle reaches of the Yellow River (P=0.01). Change-point analyses further revealed that transition years existed and that abrupt decline in streamflow and sediment discharge began in 1985 and 1981, respectively, in the middle reaches of the Yellow River (P=0.05). Adoption of conservation measures in the 1980s and 1990s corroborates the identified transition years. Double-mass curves of precipitation vs. streamflow (sediment) for the periods before and after the transition year show remarkable decreases in proportionality of streamflow (sediment) generation. Compared with the period before the transition year, cumulative streamflow and cumulative sediment discharge reduced respectively by 17.8% and 28% during 1985 2008, which was caused by human intervention, in the middle reaches of the Yellow River. It is, therefore, concluded that human activities occupied a dominant position and played a major role in the streamflow and sediment discharge reduction in the middle reaches of the Yellow River.

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[20]
Krois J, Schulte A, 2014. GIS-based multi-criteria evaluation to identify potential sites for soil and water conservation techniques in the Ronquillo watershed, northern Peru.Applied Geography, 51: 131-142.61The suitability of soil and water conservation techniques (SWCTs) is assessed.61A GIS-based multi-criteria-evaluation (MCE) is applied for suitability modeling.6124% of the catchment area of the Ronquillo River is highly suited for bund systems.6144% of the catchment area is highly suited for the implementation of terraces.61GIS-based MCE is a new approach in the promotion of SWCTs in Peru.

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[21]
Lal R,1998. Soil erosion impact on agronomic productivity and environment quality.Critical Reviews in Plant Sciences, 17: 319-464.Soil erosion is a global issue because of its severe adverse economic and environmental impacts. Economic impacts on productivity may be due to direct effects on crops/plants on-site and off-site, and environmental consequences are primarily off-site due either to pollution of natural waters or adverse effects on air quality due to dust and emissions of radiatively active gases. Off-site economic effects of erosion are related to the damage to civil structure, siltation of water ways and reservoirs, and additional costs involved in water treatment. There are numerous reports regarding the on-site effects of erosion on productivity. However, a vast majority of these are from the U.S., Canada, Australia, and Europe, and only a few from soils of the tropics and subtropics. On-site effects of erosion on agronomic productivity are assessed with a wide range of methods, which can be broadly grouped into three categories: agronomic/soil quality evaluation, economic assessment, and knowledge surveys. Agronomic methods involve greenhouse and field experiments to assess erosion-induced changes in soil quality in relation to productivity. A widely used technique is to establish field plots on the same soil series but with different severity of past erosion. Different erosional phases must be located on the same landscape position. Impact of past erosion on productivity can also be assessed by relating plant growth to the depth of a root-restrictive horizon. Impact of current erosion rate on productivity can be assessed using field runoff plots or paired watersheds, and that of future erosion using topsoil removal and addition technique. Economic evaluation of the on-site impact involves assessment of the losses of plant available water and nutrients and other additional inputs needed due to erosion. Knowledge surveys are conducted as a qualitative substitute for locations where quantitative data are not available. Results obtained from these different techniques are not comparable, and there is a need to standardize the methods and develop scaling procedures to extrapolate the data from plot or soil level to regional and global scale. There is also a need to assess on-site impact of erosion in relation to soil loss tolerance, soil life, soil resilience or ease of restoration, and soil management options for sustainable use of soil and water resources. Restoration of degraded soils is a high global priority. If about 1.5 109 ha of soils in the world prone to erosion can be managed to effectively control soil erosion, it would improve air and water quality, sequester C in the pedosphere at the rate of about 1.5 Pg/year, and increase food production. The risks of global annual loss of food production due to accelerated erosion may be as high as 190 106 Mg of cereals, 6 106 Mg of soybeans, 3 106 Mg of pulses, and 73 106 Mg of roots and tubers. The actual loss may depend on weather conditions during the growing season, farming systems, soil management, and soil ameliorative input used. Erosion-caused losses of food production are most severe in Asia, Sub-Saharan Africa, and elsewhere in the tropics rather than in other regions.

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[22]
Li R, Shangguan Z, Liu Bet al., 2009. Advances of soil erosion research during the past 60 years in China.Journal of Soil and Water Conservation, 5: 1-6. (in Chinese)Based on the summarization of the advances of soil erosion research in China during the past 60 years,this paper introduced mainly the great significance achievements of soil erosion research on five aspects such as the temporal and spatial characteristics of national soil erosion dynamics,the soil erosion process,models and its control mechanism,the source of coarse sendment of Yellow River,the mechanism of wind erosion and desertification control,erosion environment evolution and control,and pointed out the three areas in soil erosion research in China which need to be strengthened.

[23]
Li Z, Cao W, Liu Bet al., 2008. Current status and developing trend of soil erosion in China.Journal of Soil and Water Conservation, 1: 57-62. (in Chinese)Based on the third soil erosion survey data,this paper analyzes the present status of soil erosion,the status of water erosion,wind erosion and freeze-thaw erosion,and the soil erosion status in Eastern,Northeast,Middle and Western area,and the erosion risk situation in water erosion area was evaluated.Simultaneously,this paper analyzes the dynamic changes of soil erosion during 15 years,comparing with the first and second soil erosion survey data which were promulgated by Ministry of Water Resources,and the change of soil erosion amount in some river basins was analyzed.

[24]
Lu D, Li G, Valladares G Set al., 2004. Mapping soil erosion risk in Rondônia, Brazilian Amazonia: Using RUSLE, remote sensing and GIS.Land Degradation & Development, 15: 499-512.ABSTRACT This article discusses research in which the authors applied the Revised Universal Soil Loss Equation (RUSLE), remote sensing, and geographical information system (GIS) to the maping of soil erosion risk in Brazilian Amazonia. Soil map and soil survey data were used to develop the soil erodibility factor (K), and a digital elevation model image was used to generate the topographic factor (LS). The cover-management factor (C) was developed based on vegetation, shade, and soil fraction images derived from spectral mixture analysis of a Landsat Enhanced Thematic Mapper Plus image. Assuming the same climatic conditions and no support practice in the study area, the rainfall-runoff erosivity (R) and the support practice (P) factors were not used. The majority of the study area has K values of less than 017 2, LS values of less than 217 5, and C values of less than 017 25. A soil erosion risk map with five classes (very low, low, medium, medium-high, and high) was produced based on the simplified RUSLE within the GIS environment, and was linked to land use and land cover (LULC) image to explore relationships between soil erosion risk and LULC distribution. The results indicate that most successional and mature forests are in very low and low erosion risk areas, while agroforestry and pasture are usually associated with medium to high risk areas. This research implies that remote sensing and GIS provide promising tools for evaluating and mapping soil erosion risk in Amazonia. Copyright # 2004 John Wiley & Sons, Ltd.

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[25]
Liu D S, 1964. oess in the Middle Yellow River Drainage Basin. Beijing, China: Science Press: (in Chinese)

[26]
Liu L, Liu X H, 2010. Sensitivity analysis of soil erosion in the northern Loess Plateau.Procedia Environmental Sciences, 2: 134-148.Soil erosion is one of the most serious ecological and environmental problems in the northern Loess Plateau. The purposes of this research are to assess the spatial difference and explore the spatial pattern of soil erosion sensitivity in the northern Loess Plateau. Based on the universal soil loss equation (USLE), four natural factors (i.e., rainfall, soil texture, slope, and vegetation cover) and two human activity factors (i.e., population density and GDP) in 2000 and 2007 were calculated in spatial scale by ArcMap for assessment. The results showed that the sensitivity of soil erosion reduced significantly from 2000 to 2007. It indicated that regional anti-soil erosion force increased from 2000 to 2007. The sensitivity of soil erosion in the southeast region was higher than the northwest region. The areas with high and extreme sensitivity were mainly distributed in the hills and mountains of the northern Loess Plateau. The extreme sensitivity was mainly caused by the frequent regional human activities and the unscientific farming. The spatial patterns of soil erosion and its sensitivity shows similar features, namely, severe and extreme soil erosion regions were also the areas with high and extreme erosion sensitivity.

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[27]
Luo Y, Yang S T, Zhao C Set al., 2013. The effect of environmental factors on spatial variability in land use change in the high-sediment region of China’s Loess Plateau.Journal of Geographical Sciences, 24: 802-814.

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[28]
Malczewski J A, 1996. GIS-based approach to multiple criteria group decision-making.International Journal of Geographical Information Science, 10(8): 321-339.

[29]
Masoudi M, Patwardhan A M, 2006. Risk assessment of water erosion for the Qareh Aghaj subbasin, southern Iran.Stochastic Environmental Research and Risk Assessment, 21: 15-24.South of the Zagros belt, the entire land of Southern Iran faces problems arising out of various types of land degradation of which water erosion forms a major type. A new model has been developed for assessing the risk of water erosion. Taking into consideration nine indicators of water erosion the model identifies areas with ‘Potential Risk’ (risky zones) and areas of ‘Actual Risk’ as well as projects the probability of the worse degradation in future. The Qareh Aghaj subbasin (1,265,00002ha), which covers the upper reaches of Mond River, has been chosen for a test risk assessment of this kind. The preparation of risk maps based on the GIS analysis of these indicators will be helpful for prioritizing the areas to initiate remedial measures. The different kinds of data for indicators of water erosion were gathered from the records and published reports of the governmental offices of Iran. By fixing the thresholds of severity classes of the nine indicators a hazard map for each indicator was first prepared in GIS. The risk classes were defined on the basis of risk scores arrived at by assigning the appropriate attributes to the indicators and the risk map was prepared by overlaying nine hazard maps in the GIS. Areas under potential risk have been found to be widespread (63%) in the basin and when classified into subclasses with different probability levels the model projects a statistical picture of the risk of land degradation.

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[30]
Meusburger K, Steel A, Panagos Pet al., 2012. Spatial and temporal variability of rainfall erosivity factor for Switzerland.Hydrology and Earth System Sciences, 16: 167-177.

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[31]
Morgan R P C, 2005. Soil Erosion and Conservation, 3rd ed., Blackwell Publishing Company: Malden, MA, USA.

[32]
MWRC (Ministry of Water Resources of China), 1997. National Professional Standards for Classification and Gradation of Soil Erosion, SL 190-1996. Beijing, China. (in Chinese)

[33]
MWRC (Ministry of Water Resources of China), 2008. SL190-2007: Standards for Classification and Gradation of Soil Erosion. Beijing, China: Water Resources & Hydropower Press of China. (in Chinese)

[34]
Ni G H, Liu Z Y, Lei Z Det al., 2008. Continuous simulation of water and soil erosion in a small watershed of the Loess Plateau with a distributed model.Journal of Hydrologic Engineering, 13: 392-399.A physically based distributed hydrological model (THIHMS-SW, TsingHua Integrated Hydrological Modeling System-for Small Watershed) was developed and applied to a 187 km(2) watershed in the severe soil erosion region of the Loess Plateau. In the model, calculations of water and sediment transport were coupled in each grid, and the modeling of water and soil conservation measures, especially the silt-trapping dam, was also included. Continuous simulation for a period of eight years undisturbed by human activities was carried out and the results indicate that the model worked well in terms of estimating water/sediment peak discharge, time to peak, and total volume at different locations. Continuous simulation for three years after the installation of hundreds of silt-trapping dams was also carried out and fairly good results were obtained. The model is physically based and can be used in water resources planning, land use management, flood control, as well as water and soil conservation planning in small watersheds.

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[35]
Park S, Oh C, Jeon Set al., 2011. Soil erosion risk in Korean watersheds, assessed using the revised universal soil loss equation.Journal of Hydrology, 399: 263-273.Soil erosion reduces crop productivity and water storage capacity, and, both directly and indirectly, causes water pollution. Loss of soil has become a problem worldwide, and as concerns about the environment grow, active research has begun regarding soil erosion and soil-preservation policies. This study analyzed the amount of soil loss in South Korea over a recent 20-year period and estimated future soil loss in 2020 using the revised universal soil loss equation (RUSLE). Digital elevation (DEM) data, detailed soil maps, and land cover maps were used as primary data, and geographic information system (GIS) and remote sensing (RS) techniques were applied to produce thematic maps, based on RUSLE factors. Using the frequency ratio (FR), analytic hierarchy process (AHP), and logistic regression (LR) approaches, land suitability index (LSI) maps were developed for 2020, considering the already established Environmental Conservation Value Assessment Map (ECVAM) for Korea. Assuming a similar urban growth trend and 10-, 50-, and 100-year rainfall frequencies, soil loss in 2020 was predicted by analyzing changes in the cover-management factor and rainfall–runoff erosivity factor. In the period 1985–2005, soil loss showed an increasing trend, from 17.102Mg/ha in 1985 to 17.402Mg/ha in 1995, and to 20.002Mg/ha in 2005; the 2005 value represents a 2.802Mg/ha (16.6%) increase, compared with 1985 and is attributable to the increased area of grassland and bare land. In 2020, the estimated soil loss, considering the ECVAM, was 19.2–19.302Mg/ha for the 10-year rainfall frequency, 36.4–36.602Mg/ha for the 50-year rainfall frequency, and 45.7–46.002Mg/ha for the 100-year rainfall frequency. Without considering the ECVAM, the amount of soil loss was about 0.4–1.602Mg/ha larger than estimates that did consider the ECVAM; specifically, the values were 19.6–19.902Mg/ha for the 10-year rainfall frequency, 37.1–37.802Mg/ha for the 50-year frequency, and 46.7–47.502Mg/ha for the 100-year frequency. In 2010, without considering the ECVAM, the soil loss was 0.3–1.802Mg/ha more than that estimated when considering the ECVAM. These results indicate that if urban areas are developed such that they damage areas of high value, as defined environmentally and legislatively, the amount of soil loss will increase, whereas if such areas are preserved, erosion will decrease slightly. Thus, when planning urban development, the environmental and legislative value of preservation should be considered to minimize erosion and allow for more sustainable development.

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[36]
Pimentel D, Harvey C, Resosudarmo Pet al., 1995. Environmental and economic costs of soil erosion and conservation benefits.Science, 267: 1117-1123.Abstract Soil erosion is a major environmental threat to the sustainability and productive capacity of agriculture. During the last 40 years, nearly one-third of the world's arable land has been lost by erosion and continues to be lost at a rate of more than 10 million hectares per year. With the addition of a quarter of a million people each day, the world population's food demand is increasing at a time when per capita food productivity is beginning to decline.

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[37]
Renard K G, Foster G R, Weesies G Aet al., 1991. RUSLE, revised universal soil loss equation.Journal of Soil and Water Conservation, 1: 30-33.ABSTRACT Over the last 3yr, a cooperative effort between scientists and users to update the USLE is nearing completion and will produce a revised version of the USLE known as the RUSLE. Some of the improvements in the RUSLE will include: A greatly expanded erosivity map for the western United States. Minor changes in R factors in the eastern United States. Expanded information on soil erodibility. A slope length factor that varies with soil susceptibility to rill erosion. A nearly linear slope steepness relationship that reduces computed soil loss values for very steep slopes. A subfactor method for computing values for the cover-management factor. Improved factor values for the effects of contouring, terracing, stripcropping, and management practices for rangeland. -from Authors

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[38]
Sharda V N, Mandal D, Ojasvi P R, 2013. Identification of soil erosion risk areas for conservation planning in different states of India.Journal of Environmental Biology, 34: 219-226.Assessment of soil risks, especially in the developing countries, is a challenging task mainly due to non-availability or insufficiency of relevant data. In this paper, the soil risks have been estimated by integrating the spatial data on potential rates and soil loss tolerance limits for conservation planning at state level in India. The risk classes have been prioritized based upon the difference between the prevailing rates and the permissible limits. The analysis revealed that about 50% of total geographical area (TGA) of India, falling in five priority risk classes, requires different intensity of conservation measures though about 91% area suffers from potential rates varying from 40 t ha(-1) yr(-1). Statewise analysis indicated that Andhra Pradesh, Maharashtra and Rajasthan share about 75% of total area under priority Class 1 (6.4 M ha) though they account for only 19.4% of the total area (36.2 M ha) under very severe potential rate category (> 40 t ha(-1)yr(-1)). It was observed that about 75% of total geographical area (TGA) in the states of Bihar, Gujarat, Haryana, Kerala and Punjab does not require any specific soil conservation measure as the potential rates are well within the tolerance limits. The developed methodology can be successfully employed for prioritization of risk areas at watershed, region or country level.

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[39]
Siakeu J, Oguchi T, 2000. Soil erosion analysis and modelling: A review.Transactions of the Japanese Geomorphological Union, 21: 413-429.Various mathematical models have been proposed to predict the rates of soil erosion using physical parameters such as topography, rainfall, and land cover. GIS and remote sensing have facilitated application of these models to large areas. Although the new technologies have contributed to improving the original models, problems such as the inappropriate use of remote data and spatial interpolation remain. This paper reviews the characteristics of the most common soil erosion models, discusses difficulties related to their application, and speculates on the future of erosion modelling as carried out through GIS and remote sensing.

[40]
Stroosnijder L, 2005. Measurement of erosion: Is it possible?Catena, 2/3: 162-173.Reasons for erosion measurements are: (1) to determine the environmental impact of erosion and conservation practices, (2) scientific erosion research; (3) development and evaluation of erosion control technology; (4) development of erosion prediction technology and (5) allocation of conservation resources and development of conservation regulations, policies and programs. A handicap for the control of the insidious erosion process is the difficulty of determining its magnitude. Four causes are often mentioned in the literature: the large temporal and spatial variation of erosion, the paucity of accurate erosion measurements, the problem of extrapolating data from small plots to higher scales and the conversion of erosion into production and monetary units (impact). It is an illusion to think that the role of measurements can be taken over by the application of erosion prediction technology. Measurements are needed to develop, calibrate and validate that technology. Measurement techniques differ in accuracy, equipment and personnel cost. The most accurate (and often most expensive) techniques do not always serve the measurement purpose. This paper gives a critical overview of current measurements techniques for erosion at different spatial and temporal scales. Examples are presented of techniques for direct measurements as well as for indirect measurements, i.e. measurements of soil properties that serve as input for models. The paper is concluded with a critical evaluation.

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[41]
Sun W Y, Shao Q Q, Liu J Y, 2013. Soil erosion and its response to the changes of precipitation and vegetation cover on the Loess Plateau.Journal of Geographical Sciences, 23: 1091-1106.

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[42]
Sun W Y, Shao Q Q, Liu J Yet al., 2014. Assessing the effects of land use and topography on soil erosion on the Loess Plateau in China.Catena, 121: 151-163.中国科学院机构知识库(中国科学院机构知识库网格（CAS IR GRID）)以发展机构知识能力和知识管理能力为目标，快速实现对本机构知识资产的收集、长期保存、合理传播利用，积极建设对知识内容进行捕获、转化、传播、利用和审计的能力，逐步建设包括知识内容分析、关系分析和能力审计在内的知识服务能力，开展综合知识管理。

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[43]
Thompson J A, Charles B, Butler A, 2001. Digital elevation model resolution: effects on terrain at tribute calculation and quantitative soil-landscape modeling.Geoderma, 100: 67-89.The accuracy of digital elevation models (DEM) and DEM-derived products depends on several factors, including the horizontal resolution and vertical precision at which the elevation data are represented, and the source of the elevation data. This accuracy becomes increasingly important as we extend the use of DEM data for spatial prediction of soil attributes. Our objective was to compare terrain attributes and quantitative soil-landscape models derived from grid-based DEM represented at different horizontal resolutions (10 and 30 m), represented at different vertical precisions (0.1 and 1 m), and acquired from different sources. Decreasing the horizontal resolution of the field survey DEM produced lower slope gradients on steeper slopes, steeper slope gradients on flatter slopes, narrower ranges in curvatures, larger specific catchment areas in upper landscape positions, and lower specific catchment areas values in lower landscape positions. Overall, certain landscape features were less discernible on the 30-m DEM than on the 10-m DEM. Decreased vertical precision produced a large proportion of points with zero slope gradient and zero slope curvature, and a large number of steeply sloping and more highly curved areas. Differences among DEM from different sources were more significant, with less accurate representation of depressions and drainage pathways with the USGS DEM as compared to the field survey DEM. Empirical models developed from different DEM included similar predictive terrain attributes, and were equally successful in predicting A-horizon depth (AHD) in the validation data set.

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[44]
Tian Y, Zhou Y, Wu Bet al., 2009. Risk assessment of water soil erosion in upper basin of Miyun Reservoir, Beijing, China.Environmental Geology, 4: 937-942.This research selected water soil erosion indicators (land cover, vegetation cover, slope) to assess the risk of soil erosion, ARCMAP GIS ver.9.0 environments and ERDAS ver.9.0 were used to manage and process satellite images and thematic tabular data. Landsat TM images in 2003 were used to produce land/cover maps of the study area based on visual interpreting method and derived vegetation cover maps, and the relief map at the scale of 1:50,000 to calculate the slope gradient maps. The area of water soil erosion was classified into six grades by an integration of slope gradients, land cover types, and vegetation cover fraction. All the data were integrated into a cross-tabular format to carry out the grid-based analysis of soil erosion risk. Results showed that the upper basin of Miyun Reservoir, in general, is exposed to a moderate risk of soil erosion, there is 715,848 ha of land suffered from water soil erosion in 2003, occupied 46.62% of total area, and most of the soil erosion area is on the slight and moderate risk, occupied 45.60 and 47.58% of soil erosion area, respectively.

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[45]
Valente R O A, Vettorazzi C A, 2008. Definition of priority area for forest conservation through the ordered weighted averaging method.Forest Ecological Management, 256: 1408-1417.The general objective of this study was to evaluate the ordered weighted averaging (OWA) method, integrated to a geographic information systems (GIS), in the definition of priority areas for forest conservation in a Brazilian river basin, aiming at to increase the regional biodiversity. We demonstrated how one could obtain a range of alternatives by applying OWA, including the one obtained by the weighted linear combination method and, also the use of the analytic hierarchy process (AHP) to structure the decision problem and to assign the importance to each criterion. The criteria considered important to this study were: proximity to forest patches; proximity among forest patches with larger core area; proximity to surface water; distance from roads; distance from urban areas; and vulnerability to erosion. OWA requires two sets of criteria weights: the weights of relative criterion importance and the order weights. Thus, Participatory Technique was used to define the criteria set and the criterion importance (based in AHP). In order to obtain the second set of weights we considered the influence of each criterion, as well as the importance of each one, on this decision-making process. The sensitivity analysis indicated coherence among the criterion importance weights, the order weights, and the solution. According to this analysis, only the proximity to surface water criterion is not important to identify priority areas for forest conservation. Finally, we can highlight that the OWA method is flexible, easy to be implemented and, mainly, it facilitates a better understanding of the alternative land-use suitability patterns.

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[46]
Vrieling A, 2006. Satellite remote sensing for water erosion assessment: A review.Catena, 65: 2-18.Water erosion creates negative impacts on agricultural production, infrastructure, and water quality across the world. Regional-scale water erosion assessment is important, but limited by data availability and quality. Satellite remote sensing can contribute through providing spatial data to such assessments. During the past 30 years many studies have been published that did this to a greater or lesser extent. The objective of this paper is to review methodologies applied for water erosion assessment using satellite remote sensing. First, studies on erosion detection are treated. This comprises the detection of erosion features and eroded areas, as well as the assessment of off-site impacts such as sediment deposition and water quality of inland lakes. Second, the assessment of erosion controlling factors is evaluated. Four types of factors are discussed: topography, soil properties, vegetation cover, and management practices. Then, erosion mapping techniques are described that integrate products derived from satellite remote sensing with additional data sources. These techniques include erosion models and qualitative methods. Finally, validation methods used to assess the accuracy of maps produced with satellite data are discussed. It is concluded that a general lack of validation data is a main concern. Validation is of utmost importance to achieve regional operational monitoring systems, and close collaboration between the remote sensing community and field-based erosion scientists is therefore required.

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[47]
Vrieling A, de Jong S M, Sterk Get al., 2008. Timing of erosion and satellite data: A multi-resolution approach to soil erosion risk mapping.International Journal of Applied Earth Observation and Geoinformation, 3: 267-281.Erosion reduces soil productivity and causes negative downstream impacts. Erosion processes occur on areas with erodible soils and sloping terrain when high-intensity rainfall coincides with limited vegetation cover. Timing of erosion events has implications on the selection of satellite imagery, used to describe spatial patterns of protective vegetation cover. This study proposes a method for erosion risk mapping with multi-temporal and multi-resolution satellite data. The specific objectives of the study are: (1) to determine when during the year erosion risk is highest using coarse-resolution data, and (2) to assess the optimal timing of available medium-resolution images to spatially represent vegetation cover during the high erosion risk period. Analyses were performed for a 100-km 2 pasture area in the Brazilian Cerrados. The first objective was studied by qualitatively comparing three-hourly TRMM rainfall estimates with MODIS NDVI time series for one full year (August 2002 ugust 2003). November and December were identified as the months with highest erosion risk. The second objective was examined with a time series of six available ASTER images acquired in the same year. Persistent cloud cover limited image acquisition during high erosion risk periods. For each ASTER image the NDVI was calculated and classified into five equally sized classes. Low NDVI was related to high erosion risk and vice versa. A DEM was used to set approximately flat zones to very low erosion risk. The six resulting risk maps were compared with erosion features, visually interpreted from a fine-resolution QuickBird image. Results from the October ASTER image gave highest accuracy (84%), showing that erosion risk mapping in the Brazilian Cerrados can best be performed with images acquired shortly before the first erosion events. The presented approach that uses coarse-resolution temporal data for determining erosion periods and medium-resolution data for effective erosion risk mapping is fast and straightforward. It shows good potential for successful application in other areas with high spatial and temporal variability of vegetation cover.

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[48]
Wang F, Wang C, 2009. Research for the influences of DEM resolution on topographical factors: A case study of Mengyin County.Research of Soil and Water Conservation, 16: 225-230. (in Chinese)The influnce of DEM resolution on four topographical factors including slope,slope length,aspect and catchment area was analyzed by taking the county of Mengyin as research area.Topographical factors mentioned above were derived from DEMs with resolution of 10,15,20,25,30,40,50,75,100,150,200,250,300,400,500,750,1 000 m based on the digital map(1 50 000) by ANUDEM.The result showed that with DEM resolution reduction the average of slope decreased following logarithmic function,slope tended to be lower,slope length tended to be longer and the accretion of slope length is faster in higher resolution range,the impact of DEM resolution within 200 m on aspect is lower than 10%,the average catchment area becom larger following linear fuction and the changes of area for different range of catchment area are different.

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[49]
Wang L H, Huang J L, Du Yet al., 2013. Dynamic assessment of soil erosion risk using landsat TM and HJ satellite data in Danjiangkou Reservoir area, China.Remote Sensing, 5: 3826-3848.Danjiangkou reservoir area is the main water source and the submerged area of the Middle Route South-to-North Water Transfer Project of China. Soil erosion is a factor that significantly influences the quality and transfer of water from the Danjiangkou reservoir. The objective of this study is to assess the water erosion (rill and sheet erosion) risk and dynamic change trend of spatial distribution in erosion status and intensity between 2004 and 2010 in the Danjiangkou reservoir area using a multicriteria evaluation method.The multicriteria evaluation method synthesizes the vegetation fraction cover, slope gradient, and land use. Based on the rules and erosion risk assessment results of the study area in 2004 and 2010, the research obtained the conservation priority map. This study result shows an improvement in erosion status of the study area, the eroded area decreased from 32.1% in 2004 to 25.43% in 2010. The unchanged regions dominated the study area and that the total area of improvement grade erosion was larger than that of deterioration grade erosion. The severe, more severe, and extremely severe areas decreased by 4.71%, 2.28%, and 0.61% of the total study area, respectively. The percentages of regions where erosion grade transformed from extremely severe to slight, light and moderate were 0.18%, 0.02%, and 0.30%, respectively. However, a deteriorated region with a 2,897.60 km2 area was still observed. This area cannot be ignored in the determination of a general governance scheme. The top two conservation priority levels cover almost all regions with severe erosion and prominent increase in erosion risk, accounting for 7.31% of the study area. The study results can assist government agencies in decision making for determining erosion control areas, starting regulation projects, and making soil conservation measures.

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[50]
Wischmeier W H, Smith D D, 1965. Predicting Rainfall Erosion Losses from Cropland East of the Rocky Mountains, Handbook No.282. US Department of Agriculture (USDA): Washington, DC, USA.react-text: 496 Managing a catchment for drinking water supply with a high proportion of agricultural land use is a difficult task if one has to maintain a reasonable balance between water quality demand and consequent restrictions for the farming industry. In this paper we present a neural net based method for finding good approximations to solutions of this problem. This method is capable of "inverting" a... /react-text react-text: 497 /react-text [Show full abstract]

[51]
Yang Q Y, Yuan B Y, 1991. Natural Environment of Loess Plateau and Its Evolution. Beijing, China: Science Press. (in Chinese)

[52]
Yang S T, Zhu Q, 2000. Effect of man-computer interactive interpretation method in soil erosion survey of large scale by remote sensing.Journal of Soil and Water Conservation, 14: 88-91. (in Chinese)Man computer interactive interpretation method means that the professional interpreters in the aspect of soil erosion and remote sensing identify the message of remote sensing using GIS software. On the basis of analyzing the features of soil erosion in the large area, it is displayed that the difficulties of getting the message of soil erosion in large area using computer automatic interpretation method,and it is appointed that the advantage of the man computer interactive interpretation method according to the theory of visual interpretation. Finally, the example of surveying soil erosion in Guizhou province using remote sensing is used to demonstrate the science and security of man computer interactive interpretation.

[53]
Zhang S G, Li Y P, Cheng Y D, 2002. Soil erosion and its improvement in Huangjiang Reservoir area in Guangdong province.Journal of Sediment Research, 5: 76-80. (in Chinese)The following issues are discussed in this paper, such as the influential factors of soil and water loss, its current distribution state and features, types of soil erosion and its improvement on Huangjinang Reservoir area. Based on investigation and experiments, the reasons of soil and water loss within the reservoir area were fully explained and a scientific theory is applied to guide the improvement technology.

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[54]
Zhang X W, Wu B F, Li X Set al., 2012. Soil erosion risk and its spatial pattern in upstream area of Guanting Reservoir.Environmental Earth Sciences, 65: 221-229.The spatial pattern of soil erosion can provide valuable insights into the soil erosion processes that require a rapid assessment in practical applications. Generally, quantitative technique is expensive and time-consuming. The objective of this paper is to reveal the spatial pattern of erosion with a rapid assessment method. The affecting factors such as land cover, vegetation fraction and slope gradient are integrated into this method using a qualitative means. Beijing-1 images in 2006 were used to produce land-cover and vegetation fraction, and 1:50,000 topographic maps were used to calculate slope gradient. The study area was classified into six grades. Results show that the upstream area of Guanting Reservoir, in general, is exposed to a moderate risk; there are 17,740.3302km 2 of land that suffered from water soil erosion in 2006, occupied 40.69% of the total area, and most of the soil erosion area is on the light and moderate risk, which occupied 25.05 and 62.83% of the eroded area, respectively. Eight elevation zones and six slope gradient zones were overlaid with the assessed risk. The analyzed results show that: (1) the areas above 2,00002m have the lowest erosion risk, which is only 0.75% of the eroded area; 1,250–1,50002m elevation zone has the highest erosion risk, which is 34.72% of the eroded area. (2) The slope gradient zone less than 502degrees and greater than 3502degrees have the lowest erosion risk, which is 0.02 and 0.75% of the eroded area, respectively; the slope gradient zone with 8–1502degrees has the highest erosion risk, which is 36.40% of the eroded area. These results will be useful for water and soil conservation management and the planning of mitigation measures.

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[55]
Zhang X W, Wu B F, Ling Fet al., 2010. Identification of priority areas for controlling soil erosion.Catena, 83; 76-86.Conservation prioritization is an important consideration for planning of natural resources management, allowing decison makers to implement management strategies that are more sustainable in the long-term. However, only a current erosion status or a relative index cannot exactly identify priority area for conservation. The objective of this paper is to identify conservation priorities by a specific multicriteria evaluation method. Trends in erosion risk indicate regions of increasing erosion risk and are also chosen as one of the evaluation criteria to identify the priorities based on the instability of soil erosion in the Yongding river basin. In this paper, vegetation cover, land-use, and slope gradient are used to assess erosion risk and trends in erosion risk are obtained by comparing the results of erosion risk between 2000 and 2006. Using this information, the priority conservation areas are graded into six levels. The two highest priority levels cover the regions with severe erosion or a substantial recent increase in erosion risk (4722.56 km 2, or 11.82% of the study area), and are recommended as erosion control regions with appropriate conservation strategies. The middle two levels cover the regions with stable erosion status or slight change, needing only minor measures. The method presented is fast and straightforward, showing good potential for successful application in other areas.

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[56]
Zhao X L, Zhang Z X, Liu Bet al., 2002. Method of monitoring soil erosion dynamic based on remote sensing and GIS.Bulletin of Soil and Water Conservation, 22: 29-32. (in Chinese)Dynamic monitoring soil erosion is one of important task in soil and water conservation management. The method and work proceedings of dynamic monitoring soil erosion are expounded supported by remote sensing and GIS through visual interpretation at scale of nation. Based on unchanged classification of second national soil erosion remote sensing investigation, the remote sensing monitor is carried out for national soil erosion at scale 1 100 000 from 1995 to 2000 and updated its database by comparing two different time images, employing the digital processing technique of remote sensing data, man computer interactive interpretation, vector map editing and area count.

[57]
Zhou W F, Wu B F, 2005. Overview of remote sensing approaches to soil erosion monitoring.Remote Sensing Technology and Application, 20(5): 537-542. (in Chinese)Soil erosion is the main reason for environment degradation globally.China is undergoing the severest erosion.Remote sensing is the only method that can monitor land exhaustively and directly by providing good coverage of the earth at a great range of scales.Since 1970s people have already utilized remote sensing to monitor soil erosion and do risk assessing.This paper tries to gather and review the currently remote sensing approaches in soil erosion monitoring.This paper describes several methods for effective erosion monitoring of large land areas,including: visual interpretation,vegetation index derivation,digital image classification,spectrum mixture modeling,digital elevation analysis obtained by orthorectified aerial photography.This paper discusses the dominances and limitations of these methods.We can choose a suitable method depending on the extent of erosion information obtained,application objectives,accessed data and other aspects.In China visual interpretation is the generalused method among these methods.Vegetation index derivation can be combined with soil erosion model well.Other methods were used less.With the development of remote sensing,it can be believed that remote sensing techniques would empower land managers to dynamically use satellite imagery for environmental management.The relationship of dynamic change detection and soil erosion mechanism,different resolution image integration use and microwave application in soil erosion monitoring should be paid more attention in coming research.

[58]
Zhou W F, Wu B F, Li Q Z, 2005. Spatial and temporary change analysis of soil erosion intensity in recent 20 years in the upper basin of Guanting reservoir.Research of Soil and Water Conservation, 12: 183-186. (in Chinese)Since 1982 water and soil conservation strategies and measures have been taken in the upper basin of Guangting reservoir.Further investigations on regional soil erosion were carried out.The results came to be data series on temporary scales.Based on the existing regional investigation results the spatial and temporary changes of soil erosion intensity in recent 20 years in the upper basin of Guangting reservoir were analyzed.It can be concluded that soil erosion intensity was decreased in recent 20 years,but was not steady-going in this period.The environment in some areas is becoming better,and some areas represent different change in different temporary intervals.

[59]
Zhou X, Yang S T, Liu X Yet al., 2015. Comprehensive analysis of changes to catchment slope properties in the high-sediment region of the Loess Plateau, 1978-2010.Journal of Geographical Sciences, 25: 437-450.中国科学院机构知识库(中国科学院机构知识库网格（CAS IR GRID）)以发展机构知识能力和知识管理能力为目标，快速实现对本机构知识资产的收集、长期保存、合理传播利用，积极建设对知识内容进行捕获、转化、传播、利用和审计的能力，逐步建设包括知识内容分析、关系分析和能力审计在内的知识服务能力，开展综合知识管理。

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[60]
Zhu M Y, 2012. Soil erosion risk assessment with CORINE model: Case study in the Danjiangkou Reservoir region, China.Stochastic Environmental Research and Risk Assessment, 26: 813-822.Soil erosion is one of the major threats to the conservation of soil and water resources in the Danjiangkou Reservoir region (DRR), China. In order to describe the areas with high soil erosion risk (SER) and to develop adequate erosion prevention measures, SER in the DRR was assessed by integrating the CORINE model with GIS and RS. The main factors of soil erosion including erosivitiy, soil erodibility, topography and vegetation cover were determined from daily meteorological data, field survey soil profile data and soil sample analysis, digital elevation model (DEM), and land use and land cover (LULC), respectively. Landsat 5 TM imagery was used to generate a LULC classification. The results indicate that 59.1%, 31.2%, and 2.3% of the study area were under low, moderate, and high actual erosion risks, respectively. The results also indicate the study area is in low to moderate erosion risk level on the whole. The areas with moderate to high erosion risk continuously distributed in the southwest of the study area, and sporadically distributed in the north of the study area. Low erosion risk areas mainly located in the east. Up till now, most of the semi-quantitative models have not been applied extensively. The semi-quantitative CORINE model was mostly applied in the European and Mediterranean countries, while spatial comparison of actual SER map and field investigation in this study indicates that the CORINE model can be applicable in the monsoon region of China.

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