spatiotemporal dynamics of soil erosion risk for anji county, china

ORIGINAL PAPER

Spatiotemporal dynamics of soil erosion risk for Anji County,China

Zhenlan Jiang • Shiliang Su • Changwei Jing •

Shengpan Lin • Xufeng Fei • Jiaping Wu

Published online: 24 April 2012

� Springer-Verlag 2012

Abstract Soil erosion, as a serious environmental prob-

lem worldwide, poses a great threat to human sustainabil-

ity. Spatiotemporal information on soil erosion is of vital

importance to finding a solution for this problem. A case

study was conducted to characterize the dynamics of soil

erosion risk in 1985, 1994, 2003 and 2008 for Anji County,

China, a region with seemingly high ecological quality.

Remote sensing and geographic information systems were

integrated to parameterize soil erosion-controlling factors.

By using the Revised Universal Soil Loss Equation, we

estimated annual soil loss, and generated categorical maps

of soil erosion risk in the County for the 4 years. Results

showed that, while appearing to improve in some areas,

soil erosion risk increased and eroded area expanded from

1985 to 2008. Spatial analysis revealed that the most vul-

nerable hotspots were erosion-free forests, where newly

eroded areas were most likely to occur. These results

implied that, similar to findings in many parts of the world,

soil erosion is an important issue in the study area, which

could be closely associated with local eutrophication and

algal blooms. Our research indicated that there should be

more focus on this issue. From a methodological point of

view, we believe that the approach used to estimate soil

loss in the study area has the potential to be applied in other

similar regions.

Keywords Soil erosion � RUSLE � Spatial variation �Temporal changes � Remote sensing � Geographic

information systems

1 Introduction

Soil erosion is one of the most serious environmental

problems in the world, given its negative impacts on agri-

cultural production, natural resources and the environment

(Onyando et al. 2005; Rahman et al. 2009). It is one of the

most significant forms of land degradation that is greatly

influenced by land use and management (Rey 2003; Jiang

et al. 2008). Soil erosion is closely associated with all three

major environmental challenges: water contamination, air

pollution and land degradation. Areas affected by soil ero-

sion account for 3.6 9 106 km2 in China, covering

approximately 37 % of the total national territory (Fu 2008).

The government has long been focused on erosion hotspots

suffering major soil-erosion problems, such as the middle

and upper reaches of the Yellow River (especially the Loess

Plateau) or the Yangtze River (Fan et al. 2004; Huang et al.

2007; Ouyang et al. 2010; Yang et al. 2011). Other areas,

however, such as the red soil region of Southeast China that

experiences severe soil and water losses due to high tem-

peratures and precipitation, mountainous and hilly land-

forms, and various large-scale construction activities (Deng

et al. 2010; Liang et al. 2010), usually attract scant attention

for management and conservation.

Selected from three nominated national eco-counties,

Anji, a typical red soil region, became the first, and thus far

the only, eco-county in East China. The term ‘‘eco-county’’,

highlighting harmony between economic development and

sustainable eco-environment, was proposed to facilitate the

implementation of China’s ‘‘new countryside’’ policy, one of

Z. Jiang � S. Su � C. Jing � S. Lin � X. Fei � J. Wu (&)

College of Environment and Natural Resources,

Zhejiang University, Hangzhou, China

e-mail: [email protected]

Z. Jiang

Department of Geographic Science, Minjiang University,

Fuzhou, China

123

Stoch Environ Res Risk Assess (2012) 26:751–763

DOI 10.1007/s00477-012-0590-0

the primary objectives of its 11th Five-Year (2006–2010)

Plan (Long et al. 2009). To meet this goal, many counties in

rural China have been adjusting their development plans to

the ‘‘Anji model’’. Therefore, characterizing spatiotemporal

dynamics of soil erosion in Anji can provide insight into

prospective soil and water erosion control in rural China, and

a case study of the County can serve as an example for the

scientific planning, management and development of new

countryside in China.

To draw public and governmental attention to soil ero-

sion, scientists should provide information about distribu-

tion patterns and dynamics of soil erosion, as well as its

ongoing processes (Tian et al. 2009; Wang et al. 2009). A

wide array of models can be applied for soil erosion esti-

mation, including the universal soil loss equation (USLE),

modified universal soil loss equation (MUSLE), revised

universal soil loss equation (RUSLE), European soil ero-

sion model (EUROSEM), etc. Among them, RUSLE, an

empirical soil erosion model designed on the basis of

USLE, has become the most frequently used model

worldwide, owing to its user-friendliness (Ismail and

Ravichandran 2008; Xu et al. 2008b). It can be used to

predict erosion rate of ungauged watersheds through

watershed characteristics and local hydro-climatic condi-

tions (Angima et al. 2003), and can also be applied to

obtain the spatial heterogeneity of soil erosion.

However, RUSLE is focused on estimating soil loss on

the basis of the water erosion ‘‘status quo’’. The prediction

of long-term soil erosion trend requires many years of

consistent and repeatable data (Gao and Liu 2010). This is

difficult in most cases, due to the dearth of such data from

the preceding decades (Masoudi et al. 2006). In these cases,

existing models cannot be directly applied in soil erosion

prediction (Van Rompaey and Govers 2002; Shrestha et al.

2004). In this study, soil erosion risk, which usually indi-

cates the relative incidence of erosion at a certain location

as compared to that of other locations in the mapped region

(Le Bissonnais et al. 2002), was used to provide information

about distribution patterns and dynamics of soil erosion and

its ongoing processes, because when planning land con-

servation practices, the spatial distribution of soil erosion

risk is more important than the absolute values of soil

erosion loss (Ma et al. 2003).

Soil erosion risk assessment involves comparing between

the present and past status to find out the incidence and rate

of degradation in the future. However, due to the lack of

quality geospatial data, an available model that fits locally,

and general public awareness, the task of soil erosion control

has long been delayed in the region. With the goal of

developing an approach for soil erosion risk assessment and

applying it in Anji County, we built up geospatial databases

and integrated RUSLE with remote sensing and geographic

information system (GIS) techniques. Specifically, remote

sensing was used to provide ground information because of

its regular revisiting capability, GIS was used to collect,

store, display, manipulate, and analyze various spatial data

and develop a spatial model, and RUSLE estimated soil

erosion risk. Thus, as a case study, Anji County, which has

great social and ecological significance in southeastern

China, was selected to characterize the spatiotemporal

dynamics of soil erosion risk.

2 Study area

With a total area of 1,886 km2 and a population of about

455,100, Anji County lies in the center of the Yangtze River

Delta, Zhejiang Province, China between 30�230 and

30�530N and 119�140–119�530E (Fig. 1). It covers more than

70 % of the West Tiaoxi watershed, the upper reach of the

Taihu Lake. Featuring a typical maritime monsoon climate

of the subtropical zone, the County has pleasant weather

with abundant sunshine and rainfall, as well as four distinct

seasons (Fig. 2). The yearly average temperature is 15.5�C

and the yearly average rainfall 1,400 mm. The dominant

soils are red soil, paddy soil, yellow soil, fluvo-aquic soil and

regosols (Zhejiang Office of Soil Survey 1985). The Coun-

ty’s topography is characterized by high elevation and steep

terrain in the southwest and relatively low and flat land in the

northeast. Its altitude ranges from 1,587 m to near 0 m

(above sea level). About 71 % of land surface has forest

coverage, with half being bamboo forests. Most of the for-

ested areas are located at the periphery of the county, while

the farmlands are primarily on a large plain in the middle.

During the 1950s and 1960s, the County fortunately

avoided the catastrophic impact of the ‘‘Steel Refinery

Movement’’, which prompted the felling of numerous trees

for firewood used in steel refining (Li et al. 2006). The

well-preserved ecological environment and tourist resour-

ces helped it become popularly known as ‘Bamboo Town’,

‘Land of White Tea’, ‘China’s First Ecological County’

and ‘Home of the Giant Panda’. It became more popular

when chosen as an important setting for the epic martial

arts movie ‘Crouching Tiger Hidden Dragon’, the 2001

Oscar winner for best foreign language film.

In 2000, Anji was designated to pilot a national program

on developing eco-friendly agriculture, industry and tour-

ism at the county level. On June 5, 2006, the 35th World

Environment Day, it was officially listed as a ‘‘National

Eco-County’’ by the Ministry of Environmental Protection,

China. Counties in rural China have followed its model of

planning and development, dubbed the ‘‘Anji model’’, to

facilitate their efforts in building ‘‘new countryside’’,

which were characterized with increased production, well-

off life, civilized rural customs, clean and tidy villages, and

democratic management (SCPRC 2006).

752 Stoch Environ Res Risk Assess (2012) 26:751–763

123

3 Methods

3.1 Data sets and preprocessing

Four satellite images were collected in the study, including

three Landsat Thematic Mapper (TM) images (30 m spatial

resolution) acquired in November 1985, May 1994 and

March 2003, and one Advanced Land Observation Satellite

(ALOS) image (10 m spatial resolution) acquired in

November 2008. They were used to derive vegetation frac-

tion cover (VFC) and prepare land use/land cover (LULC)

maps. Other data were digital topographical maps at a scale

of 1:10 000, a Digital Orthophoto Map (DOM) with 1 m

resolution, a soil survey geographic database originally from

soil field survey map at a scale of 1:50 000, and the digital

administration boundary of the study area. All the data were

geometrically rectified to the Universal Transverse Mercator

(UTM) projection system, zone 50 East.

Next, radiometric correction was performed on the

images in two steps. First, remotely sensed digital number

(DN) values were converted to at-sensor radiance (Lk) by

Eq. 1, which is based on the maximal and minimal spectral

radiance value for each band (Chander et al. 2009; Lim

et al. 2009).

Fig. 1 Location of Anji

County, Tiaoxi watershed,

China

Fig. 2 Monthly average rainfall

and temperature in Anji County

Stoch Environ Res Risk Assess (2012) 26:751–763 753

123

Lk ¼ Gaink � DNk þ Biask ð1Þ

where Lk is spectral radiance at the sensor’s aperture

(W m-2 sr-1 lm-1), Gaink is band-specific rescaling gain

factor (W m-2 sr-1 lm-1 DN-1), DNk is spectral digital

number, and Biask is band-specific rescaling bias factor

(W m-2 sr-1 lm-1). The gain and bias values are unique

for each spectral band acquired by a particular sensor.

Second, the at-sensor spectral radiance (Lk) was con-

verted to the top-of-atmosphere (TOA) reflectance, which

is required for quantitative analysis of multiple images

acquired on different dates. With regard to historical ima-

ges, in situ measurements of atmospheric parameters are

impossible to obtain for physically-based models. The

apparent reflectance model (Eq. 2) was selected to calcu-

late the TOA reflectance, because it does not call for any in

situ field measurements (Lu et al. 2002).

qk ¼pLkd2

ESUNk cos hSð2Þ

where qk is planetary TOA reflectance, p is a mathematical

constant equal to *3.141592, Lk is the same as Eq. 1, d is

Earth–Sun distance (astronomical units), ESUNk is the

mean exoatmospheric irradiance (W m-2 lm-1), and hS is

sun zenith angle (or 90-sun elevation angle) (degrees).

3.2 RUSLE

In the model of RUSLE, the mean annual soil loss is

expressed as a function of six erosion factors (Renard et al.

1997):

A ¼ R� K � LS� C � P ð3Þ

where A is the estimation of average annual soil loss

(t ha-1 year-1), R is the rainfall erosivity factor

(MJ mm ha-1 h-1 year-1), K is the soil erodibility factor

(t ha h ha-1 MJ-1 mm-1), LS is the combination of the

slope steepness and slope length measurements (unitless),

C is the cover-management factor (unitless), and P is the

support practices factor (unitless).

Of these factors, soil, topography and climate charac-

teristics are subject to natural conditions, while vegetation

and LULC are susceptible to human activities (Solaimani

et al. 2009); the latter are the most important factors,

because they have the greatest effect and can more easily

be changed to control soil loss and sediment yield (Ismail

and Ravichandran 2008; Meusburger et al. 2010).

RUSLE provides two options for erosion rate estimation

(Bahadur, 2009). The first, entitled ‘‘potential erosion’’, is

computed on the basis of only three factors, namely, R, K

and LS, while the second, entitled ‘‘actual erosion’’, is

computed with C and P factors taken into account in

addition to the three used for potential erosion. Therefore,

we treated the first three factors as the background factors,

i.e. RUSLE uses the same values of R, K, and LS for 1985,

1994, 2003 and 2008. The cover-management factor

(C) and the support practices factor (P) were taken as

dynamic factors, because they were driven by human

activities (Van Rompaey et al. 2001; Solaimani et al. 2009;

Di et al. 2010).

Average annual soil loss maps were generated using the

aforementioned RUSLE. Then, these maps were grouped

into different soil erosion categories followed the Stan-

dards of SL190-2007 (Table 1) (MWR 2007; Zhu et al.

2009). Figure 3 illustrates these steps.

3.3 Soil erosion risk assessment

3.3.1 Background factors

3.3.1.1 Rainfall erosivity factor (R) The R factor is an

index of rainfall erosivity, indicating the probability of

rainfall causing erosion. Renard and Freimund (1994)

developed a set of relationships for calculating the R factor

using mean annual rainfall data and the modified Fournier

index (MFI).

MFI ¼Xi¼12

i¼1

p2i

Pð4Þ

R ¼ 0:07397MFI1:847; when MFI\55 mm ð5Þ

R ¼ 95:77� 6:081MFI þ 0:4770MFI2;when MFI� 55 mm

ð6Þ

where MFI (mm) is the modified Fournier index value

(Arnoldus 1977), Pi (mm) is the mean monthly precipita-

tion of month i, P (mm) is the mean annual precipitation,

and R (MJ mm ha-1 h-1) is the RUSLE rainfall erosivity

factor. Equation 5 was applied to locations with modified

Fournier index values less than 55 mm, and Eq. 6 was used

for locations with modified Fournier index values above

55 mm.

Table 1 Standard for categorizing soil water erosion intensity in

China

Category Description Erosion modulusa Erosion depthb

1 Non-erosion \500 \0.74

2 Slight 500–2,500 0.74–1.9

3 Moderate 2,500–5,000 1.9–3.7

4 Severe 5,000–8,000 3.7–5.9

5 Very severe 8,000–15,000 5.9–11.1

6 Extreme [15,000 [11.1

SL190-2007 (Ministry of Water Resources, China, 2007)a Modulus in t km-2 year-1

b Depth in mm year-1


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Practically most current efforts for mapping rainfall

erosivity at large-scale rely on spatial interpolation

approaches, such as kriging, which is based on erosivity

values derived from rain gauge data. However, for data-

poor regions this is not an option (Vrieling et al. 2010).

There is only one long-term meteorological station in Anji

County, so spatial interpolation approach is not applicable

in the study. Moreover, elevation, latitude and longitude

are found to be related to precipitation in the region, so

regression of R-factor with elevation and latitude/longitude

was used to map rainfall erosivity in the study area. At first,

R-factor values of the 11 meteorological stations sur-

rounding Anji County were calculated by using average

monthly precipitation data from 1985 to 2008. Then, a

regression relationship, with R as the dependent variable,

and elevation, product of longitude and latitude as inde-

pendent variables, was established:

R ¼ 58825:535þ 4:090h� 14:091uk ð7Þ

(Determination coefficient = 0.80, P \ 0.01) where h is

elevation in meters, u is latitude in degrees, and k is

longitude in degrees.

By using Eq. 7, a 30 m grid map of R-factor was gen-

erated from a 30 m elevation map, as well as longitude and

latitude information of the County.

3.3.1.2 Soil erodibility factor (K) The K-factor is the rate

of soil erosion per unit erosivity of rainfall, and represents

the effect of the inherent soil properties in soil erosion,

especially those related to the surface layer (Wu et al.

1997; Mati and Veihe 2001). In the Erosion Productivity

Impact Calculator (EPIC) (Williams et al. 1984; Zhang

et al. 2008; Rahman et al. 2009), soil organic carbon and

soil particle size distribution are used to calculate the K

values:

K ¼(

0:2þ 0:3 exp �0:0256SAN 1� SIL

100

� �� )

� SIL

CLAþ SIL

� �0:3

1:0� 0:25C

C +exp(3:72�2:95CÞ

� �

� 1:0� 0:7SN1

SN1þ expð�5:51þ 22:9SN1Þ

� �ð8Þ

where SAN, SIL and CLA are sand fraction

(%, 0.05–2.00 mm diameter particles), silt fraction

(%, 0.002–0.05 mm diameter particles), and clay fraction

(%, \0.002 mm diameter particles), respectively, C is soil

organic carbon content (%), and SN1 equals 1-SAN/100.

The K value was calculated from the digital soil map.

Each soil type was associated with one K value, assuming

that the same soil type has the same soil properties

throughout the study area.

3.3.1.3 Slope length and steepness factor (LS) The LS

factor, accounting for the effect of topography on erosion

in RUSLE, is the expected ratio of soil loss per unit area on

a field slope to that from a 22.13 m length of uniform 9 %

slope. The slope length factor (L) represents the effect of

slope length on erosion, and the slope steepness factor (S)

reflects the influence of slope gradient on erosion. The

common equation used for calculating LS is an empirical

one by Wischmeier and Smith (1978):

LS ¼ k22:13

� �n

ð65:41 sin2 hþ 4:56 sin hþ 0:065Þ ð9Þ

where k is the slope length in meters, h is the angle of slope

in degrees, and n is a constant dependent on the value of

the slope gradient: n is 0.5 if the slope angle is greater than

2.86�, 0.4 on slopes of 1.72�–2.85�, 0.3 on slopes of 0.57�–

1.72�, and 0.2 on slopes less than 0.57�.

Fig. 3 Flow chart of soil

erosion risk assessment


123

The RUSLE-based ArcInfo Arc Macro Language

(AML) developed by Hickey et al. (1994) and Van Rem-

ortel et al. (2004) was downloaded from the website:

www.cwu.edu/*rhickey/slope/slope.html for computing

the LS factor. The basic input for generating an LS-factor

map in GIS is 30 m resolution DEM of Anji County, which

was derived from a 1:10 000 digital topographic map.

3.3.2 Dynamic factor

3.3.2.1 Cover-management factor (C) During recent

years, different approaches have been developed for C

factor mapping with the application of remotely sensed

data. Commonly used techniques in erosion assessment are

image classification, spectral indices and spectral unmixing

(Vezina et al. 2006; De Asis and Omasa 2007; Zhou et al.

2008). However, it is not possible to estimate C factor with

high accuracy through image classification alone, which

applies average C factor value to each vegetation type,

causing the smoothing of estimates and disappearance of

spatial heterogeneity and variability (Wang et al. 2002).

The most common spectral indices method is NDVI.

However, NDVI is only sensitive to fresh biomass, not

surface cover (Meusburger et al. 2010). Linear spectral

unmixing (LSU), which is applicable in deriving fractional

vegetation cover (FVC) (Vrieling 2006), has been proven

to be superior to NDVI in C factor mapping and was

successfully used to identify erosion features and areas

prone to soil erosion (Lu et al. 2004; De Asis and Omasa

2007; De Asis et al. 2008; Meusburger et al. 2010). Cau-

tion, however, should be taken in developing C factor.

While remotely sensed data capture the surface character-

istics only at the very moment of image acquisition, veg-

etation cover usually presents strong temporal dynamics

(Vezina et al. 2006; Vrieling 2006). The average C factor,

calculated from a multi-temporal evaluation of C factor,

can be used as input in the model (Vezina et al. 2006; De

Asis and Omasa 2007). In our study area, however, it is

even hard to find a cloud-free Landsat image in a year

because the region is cloudy most of the time (Jiang et al.

2012). There was even more difficulty in obtaining dif-

ferent seasonal images for calculating the average C.

To overcome this difficulty, we integrated image clas-

sification and LSU method in this study. For the LULC

types identified as built-up, water body, dryland, paddy

field and dense forest, the C-factor effects on soil erosion

often present high seasonal variability, but show similarity

within the type. These C-values were assigned following

the research of reported C values in the literature and local

conditions. For the remaining LULC types, i.e. sparse

forest, garden and bare soil, the C-factors show relatively

low seasonal variability, but have substantial annual

changes, and can vary by location. Therefore, they were

computed by LSU method. Detailed procedures for deter-

mining C-factor are as follows:

(1) LULC classification. Eight main LULC types, i.e.

dense forest, sparse forest, dryland, paddy field, built-

up, water body, garden, and bare soil, were classified

using remote sensing images in 1985, 1994, 2003 and

2008 by decision tree method (Quinlan 1993; Mahesh

and Mather 2003), with overall accuracy (OA) being

92.1, 93.3, 92.5 and 90.8 % in 1985, 1994, 2003 and

2008, respectively, with Kappa coefficients (Kappa)

of 0.89, 0.91, 0.91 and 0.88, respectively.

(2) The C-factor values of built-up, water body, dryland,

paddy field and dense forest were determined after a

study of the relevant reports and local conditions. The

C value for dense vegetation is 0, built-up 0.2, water

body 0, paddy field 0.05, and dryland 0.44 (Romken

1985; Shi et al. 2004; Fu et al. 2005; De Asis and

Omasa 2007; de Vente et al. 2009; Di et al. 2010).

(3) The C-factor value of sparse forest, garden and bare soil

were computed by LSU method. The spectral unmixing

approach assumes that the reflectance of each pixel is a

combination of spectral end-members, whose combi-

nations are usually green vegetation, non-photosyn-

thetic vegetation, bare soil, rock, and shadow (Adams

et al. 1995; Theseira et al. 2003). So the selection of

suitable end-members is the most important step in the

development of high quality fraction images.

In the study, a combination of automatic and supervised

end-member selections was performed on the Landsat/

ALOS images. At first, the minimum noise fraction (MNF)

algorithm was applied to the reflectance image. Then the

pixel purity index (PPI) obtained from MNF-transformed

data was used to find the spectrally purest pixels in the

images, i.e., the pixels with the highest PPI values were

selected as candidate end-members. At last, three end-

members, i.e., vegetation, water/shadow, and soil, were

determined by referring to the Landsat/ALOS images and

the results of field survey. The three end-members were

chosen because low correlation was found between the

fraction of the non-photosynthetic vegetation and the data

measured in the field, due to the confusion of reflectance

among different non-photosynthetic vegetation materials

(De Asis and Omasa 2007).

Then C-factor was obtained from Eq. 10 based on

vegetation, shadow, and soil fraction images derived from

LSU of remote sensing images (Lu et al. 2004; De Asis and

Omasa 2007).

C ¼ fsoil

1þ fgv þ fshadeð10Þ

where fsoil, fgv, and fshade are the three fraction values of

soil, green vegetation, and shadow. The values of fsoil, fgv,


123

http://www.cwu.edu/~rhickey/slope/slope.html

and fshade parameters range from 0 to 1, and they sum to 1

(Lu et al. 2007).

3.3.2.2 Support practices factor (P) Factor P in RUSLE

is the ratio of soil with support practices like contouring,

strip-cropping or terracing to soil loss with straight-row

farming up and down the slope (Renard et al. 1997).

P value ranges from 0 to 1, where value 0 represents good

man-made erosion resistance facility and value 1 represents

lack of such facility. P value for paddy field was fixed at

0.1 (Vezina et al. 2006) because of its good conservation

practices. For other LULCs, the value was 1.0, assuming

no significant soil conservation practice (Irvem et al. 2007).

3.4 The average annual soil loss (A)

Average annual soil loss in 1985, 1994, 2003 and 2008 for

Anji County were computed with RUSLE and the afore-

mentioned parameters. Although the model yields quanti-

tative values, the model outcomes were usually suggested

to be applied in a qualitative way (Van Remortel et al.

2001; Van Remortel et al. 2004; Xu et al. 2008a). In the

study, soil erosion risk maps were generated by grouping

the annual soil loss into six categories according to SL190-

2007 (Table 1), i.e., non-erosion, slight, moderate, severe,

very severe and extreme erosion risk (Fig. 4) (MWR 2007).

The SL190-2007 is a national standard and has been used

to assess soil erosion at regional and national levels in

China (Zhu et al. 2009).

3.5 Soil erosion model validation

The most common method of validating an erosion model is

erosion survey, in which a visual estimation of erosion risk

is conducted on the basis of observed features (Metternicht

and Zinck 1998; Cohen et al. 2005). It was impossible,

however, to obtain in situ erosion information to validate

the results from historical periods. In this study, 240 random

points (40 validation points for each erosion level) repre-

senting non-erosion, slight, moderate, severe, very severe

and extreme erosion risk, were selected from the erosion

images of 1985, 1994, 2003 and 2008 respectively to create

a confusion matrix, which was used to compute overall

accuracy (OA) and Kappa coefficient (Kappa). Erosion risk

of the points were then validated with expert interpretation

following the criteria of soil erosion intensity in China

Fig. 4 Maps of soil erosion risk

in 1985, 1994, 2003 and 2008

for Anji County, China


123

(MWR 2007) (judged by slope gradient, vegetation cover

rate and land cover) based on field soil erosion surveys,

high-resolution DOM, Google Earth, topographic infor-

mation and LULC data. For uncertain areas of the historical

images, we consulted local agents about the erosion status

during the related periods. And the overall accuracy (OA)

and Kappa coefficient (Kappa) were then computed on the

basis of the error matrix (Congalton 1991; Foody 2002). OA

of the four classification maps was 90.4, 92.5, 91.3 and

90.4 %, and Kappa was 0.89, 0.91, 0.90 and 0.89 in 1985,

1994, 2003 and 2008, respectively.

3.6 Spatiotemporal analysis of erosion risk

A spatiotemporal analysis of erosion risk was conducted by

using GIS. At first, extent and intensity of erosion risk and

transition matrices for erosion risks in various periods were

calculated from erosion risk maps from the 4 years. Then,

soil erosion tendency was classified into three groups, i.e.

unchanged areas (areas with erosion risk unchanged during

the period), deteriorated areas (areas with erosion risk

increased to a higher level) and improved areas (areas with

erosion risk decreased to a lower level). In addition, the

rate of change and the percentage of different erosion risk

changes were calculated to characterize temporal dynamic

of soil erosion risk.

4 Results

Figure 4 shows the extent and the intensity of soil erosion

in the County for the 4 years. Generally, eroded areas

expanded and erosion risk increased continuously during

the study period. Table 2 summarizes the changes. Ero-

sion-free areas continually decreased from 87.6 % of the

total county area in 1985, to 85.2 % in 1994, 83.2 % in

2003, and 81.7 % in 2008, indicating a continuous

expansion of eroded areas. Erosion risk of eroded areas

kept increasing (Fig. 5). For the categories of Moderate,

Severe, and Very Severe erosion risk, the percentage of the

areas in the County increased gradually from 1985 to 1994,

2003, and 2008 in sequence. For the category of Extreme

erosion risk, its area increased from 2.5 % in 1985, to

3.9 % in 1994, and 5.9 % in both 2003 and 2008.

Table 2 Transitional matrices

of soil erosion risk during the

study periods (unit: %)

Non-erosion Slight Moderate Severe Very severe Extreme Total

1994

1985

Non-erosion 81.87 1.92 0.55 0.42 0.53 2.28 87.6

Slight 0.64 3.72 0.34 0.24 0.18 0.09 5.2

Moderate 0.42 0.07 1.05 0.22 0.19 0.14 2.1

Severe 0.37 0.01 0.06 0.51 0.22 0.15 1.3

Very severe 0.46 0.00 0.02 0.06 0.50 0.32 1.4

Extreme 1.43 0.00 0.00 0.01 0.08 0.93 2.5

Total 85.2 5.7 2.0 1.5 1.7 3.9 100

2003

1994

Non-erosion 77.92 1.64 0.63 0.52 0.64 3.86 85.2

Slight 1.74 3.30 0.22 0.20 0.18 0.09 5.7

Moderate 0.63 0.17 0.98 0.07 0.06 0.11 2.0

Severe 0.50 0.12 0.14 0.55 0.08 0.08 1.5

Very severe 0.64 0.12 0.06 0.14 0.58 0.15 1.7

Extreme 1.78 0.07 0.11 0.09 0.25 1.61 3.9

Total 83.2 5.4 2.1 1.6 1.8 5.9 100

2008

2003

Non-erosion 76.40 1.45 0.73 0.58 0.98 3.08 83.2

Slight 1.65 3.07 0.40 0.21 0.08 0.00 5.4

Moderate 0.50 0.38 0.90 0.07 0.20 0.09 2.1

Severe 0.39 0.24 0.20 0.49 0.06 0.19 1.6

Very severe 0.48 0.11 0.31 0.21 0.49 0.17 1.8

Extreme 2.32 0.04 0.13 0.30 0.71 2.40 5.9

Total 81.7 5.3 2.7 1.9 2.5 5.9 100


123

Figure 6 shows the spatial distribution of erosion risk

change in Anji County, and Fig. 7 quantifies the area and

percentage of erosion risk change for the three periods.

While the improved area of soil erosion increased steadily

from the period of 1985–1994, 1994–2003, and 2003–2008,

more area became deteriorated in the same period (Fig. 7a).

In terms of percentage, the improved area of soil erosion

increased from 3.63 % in 1985–1994, 6.57 % in

1994–2003, to 7.98 % in 2003–2008, but the deteriorated

area remained about 8 % in the three periods (Fig. 7b).

Non-erosion risk and extreme risk were two main categories

leading to dynamics of eroded areas. These two erosion risk

categories were most likely to transform into each other’s

type over the period (Table 2).

5 Discussion and conclusions

5.1 Method for soil erosion estimation

By subdividing background factors (representative of spa-

tial variations) and dynamic factors (representative of both

spatial and temporal variations), the method developed in

the study took advantage of both remote sensing and GIS

techniques and reasonably parameterized all controlling

factors of soil erosion. In particular, a combination of

LULC classification and LSU of the remote sensing image

generated the C factors. In this way, we determined the C

factors for those LULCs with strong temporal dynamics

and high seasonal variability, and computed the C factors

for other LULCs with high spatial heterogeneity and

variability.

Fig. 5 Areal summary (%) of soil erosion risk in 1985, 1994, 2003,

and 2008 for Anji County, China

Fig. 6 Maps of erosion risk

change in Anji County


123

In this region, RUSLE had been used for estimating soil

erosion for two decades. However, limited by the avail-

ability of digital soil survey geospatial database, surface

geological data were used for estimating K factor. Given

the data availability, the method we reported clearly

showed some advantages, and could be applied to other

regions nearby. However, this reported method has limi-

tations: (1) accurate prediction of soil erosion depended on

the accuracy of C-factor, which was closely associated with

the accuracy of LULC classification and with end-member

selection in LSU. Classification errors of LULC will be

introduced to the C factor map. Incorrect selection of end-

members in a physical sense will cause errors in fractional

abundances and consequently the C factor; (2) the method

is suitable for predicting spatial distribution of soil erosion

risk, as opposed to absolute values of soil erosion loss,

since it takes R, K and LS as the background factors.

Nevertheless, new algorithms and techniques aimed at

increasing classification accuracy (Lu and Weng 2007) or

obtaining appropriate end-members (De Asis and Omasa

2007) have become available in recent years. They helped

in producing accurate classification maps and appropriate

LSU fractions for soil erosion analysis. In addition, soil

conservationists and environmental managers intended

more to identify areas that need immediate soil conserva-

tion supports, rather than absolute values of soil loss.

Therefore, the outcomes produced by using this method

could be helpful in soil and water conservation planning

and practices: (1) the generated erosion risk maps show the

relative incidence of erosion in a certain location as com-

pared to other locations in the region; (2) erosion change

maps delineate areas that are more likely to change into the

worst degradation, such as a change from moderate to

severe state of erosion, and measure risk of this adverse

change. Such soil erosion maps and analysis results are

certainly more helpful for regional planners and policy

makers to initiate controlling measures and prioritize

conservation areas (Ma et al. 2003; Masoudi et al. 2006;

Rahman et al. 2009).

5.2 Soil erosion in Anji County

Soil erosion risk in Anji has had a trend of deterioration,

with a significant increase in both extent and intensity. Soil

erosion in the study area featured two simultaneous pro-

cesses: increasing area where originally extreme risk areas

were controlled and reduced, and expanding new eroded

areas at an accelerating rate. This indicated that the real

threat of soil erosion in the study area came from new

eroded areas that expanded in the erosion-free forests. The

existing eroded areas, on the other hand, gradually recov-

ered with the implementation of conservation practices.

Therefore, top priority should be given to natural vegeta-

tion preservation, land-use management and other conser-

vation practices aimed at soil erosion control. The erosion-

free forests are the most important areas for taking con-

trolling measures due to their dominant deteriorating pro-

cess and high probability of erosion. It is crucial to prevent

further erosion and to restore destroyed vegetation in these

areas.

The case study of soil erosion risk in Anji gives insight

into the prospective of soil erosion risk in China. The

results of this study from a county with seemingly high

ecological quality were initially surprising to us. We sus-

pected that public and government did not give adequate

attention to the issue of soil erosion. The likely reasons

could be: (1) eroded areas in such regions were widely

scattered and almost imperceptible in the national soil

erosion map at a scale of 1:4000000 (MWR et al. 2010);

(2) the subtle process of soil erosion in these regions often

Fig. 7 Area (a) and percentage

(b) of erosion risk change for

the three periods


123

went on unnoticed, at least during the initial stage (Lu et al.

2007); (3) these regions boast a high forest coverage of

more than 52 % and are mainly covered with pure forests

of Masson pine, bamboo and plantations, while large areas

of ground surface under the forests were exposed, without

shrubs and grass. The high forest coverage, however, often

causes a neglect of the ‘‘erosion under forests’’ (MWR

et al. 2010; Liang et al. 2010); (4) policy makers and local

farmers failed to give due consideration to the impact of

soil erosion in achieving the short-term goal of economic

development.

5.3 Soil erosion and water environmental issue

in the region

Eutrophication and algal blooms have been considered the

biggest environmental issue in this region over last decade.

To address the issue, the government at local and national

levels plan to invest over one billion Yuan on a pilot

research and engineering project in the Tiaoxi watershed in

the coming decade. Besides, a huge investment will be

made on pollution control in other fresh water systems of

the region. But soil erosion has not been blamed and

connected specifically to water pollution.

It is commonly accepted that non-point sources are the

main contributors of nitrogen and phosphorous, the two

dominant nutrients causing eutrophication in this region

(Su et al. 2011, Liang et al. 2011). Immediate measures for

soil conservation must be taken to control the increased soil

erosion risk discovered over the study period. Otherwise

the discharge of nitrogen and phosphorus from farming

land will cause eutrophication and algal blooms on surface

water as well as reduce soil fertility (Eroglu et al. 2010;

Zhu 2011).

5.4 Conclusions

By selecting Anji County (China), an area with seemingly

high ecological quality as the study area, we integrated

remote sensing and GIS techniques and quantified soil

erosion controlling factors. Then, by using RSULE, we

estimated annual soil loss, and generated categorical maps

of soil erosion risk for the years of 1985, 1994, 2003 and

2008. Soil erosion risk increased and eroded area expanded

for the period. The most vulnerable hotspots were erosion-

free forests. Our results indicated that soil erosion was a

critical issue in the region, which was closely associated

with eutrophication and algal blooms of fresh water. Since

it is applicable to similar regions, the method developed in

this study can serve as an operational basis for environ-

mental managers and planners.

Acknowledgements We were very grateful to the Editor-in-Chief

and the two reviewers for their providing constructive comments and

suggestions. This work was partly supported by the Fundamental

Research Funds for the Central Universities, Fujian Education

Department Project Fund (No. JB11150), and Minjiang University

Project Fund (No. YKY1106).

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