ORIGINAL ARTICLE

Assessment of the effects of vegetation on soil erosion riskby water: a case of study of the Batta watershed in Tunisia

Mohamed Kefi • Kunihiko Yoshino •

Yudi Setiawan • Khemaies Zayani •

Mohamed Boufaroua

Received: 7 May 2010 / Accepted: 11 December 2010 / Published online: 30 December 2010

� Springer-Verlag 2010

Abstract Soil erosion by water is a serious environmental

problem which affects particularly the agriculture of devel-

oping countries. Due to specific factors, such as high rainfall

intensity, steep slopes and vegetation scarcity, Tunisia is

prone to soil erosion. Taking this into account, the main

objective of this study was to estimate the soil erosion risk in

the Batta watershed in Tunisia using qualitative and quanti-

tative erosion model with remote sensing data and geographic

information system (GIS). Moreover, a developed method

that deals with evaluating the impact of vegetation on soil

erosion by water is also applied. This method used multi-

temporal satellite images with seasonal variability and the

transformed soil adjusted vegetation index (TSAVI) which is

appropriate in arid and semi-arid areas. For both erosion

models, the results show that a large area of the Batta water-

shed is seriously affected by erosion. This potentially high risk

is due especially to severe slopes, poor vegetation coverage

and high soil erodibility in this watershed. Furthermore, the

use of multi-temporal satellite images and vegetation index

show that the effect of vegetation is a significant factor to

protect the soil against erosion. The risk is especially serious in

the summer season, but it decreases with the growth of veg-

etation cover in spring. Erosion model, combined with a GIS

and remote sensing, is an adequate method to evaluate the soil

erosion risk by water. The findings can be used by decision

makers as a guideline to plan appropriate strategies for soil and

water conservation practices.

Keywords Soil erosion risk � Erosion model �Remote sensing � GIS � Tunisia

Introduction

Soil erosion by water is a common environmental problem

which can affect the sustainable development and the agri-

culture of developing countries especially. It is a process

composed of three stages: detachment, transport and finally

deposition of soil (Lal 2001). According to Pimentel et al.

(1995), soil erosion is a major environmental threat to the

sustainability and productivity of agriculture due to the

damage it causes to arable land. Furthermore, this phenom-

enon can degrade not only soil productivity but also water

quality. It can cause sedimentation and increase the proba-

bility of floods (Zhou et al. 2008). Several reasons, such as

inappropriate agricultural techniques or deforestation,

increase its risk. This phenomenon will continue and even

increase during the twenty-first century, especially in

developing countries (Lal 2001). Because of its serious

impact, different methods and models have been developed

to assess and predict soil loss caused by soil erosion by water.

The universal soil loss equation (USLE) was widely used

(Wischmeier and Smith 1978) to predict the annual average

M. Kefi (&) � K. Yoshino

Department of Social Systems and Management,

Graduate School of Systems and Information Engineering,

University of Tsukuba, 1-1-1 Tennodai, Tsukuba,

Ibaraki 305-8572, Japan

e-mail: moh_kefi@yahoo.fr; sky@sk.tsukuba.ac.jp

Y. Setiawan

Graduate School of Life and Environmental Sciences,

University of Tsukuba, Tsukuba, Japan

K. Zayani

Borj Cedria Higher Institute of Environmental Sciences

and Technologies, Hammam Lif, Tunisia

M. Boufaroua

Soil and Water Conservation Directorate,

Ministry of Agriculture and Hydraulic Resources,

30, rue Alain Savary, 1002 Tunis, Tunisia

123

Environ Earth Sci (2011) 64:707–719

DOI 10.1007/s12665-010-0891-x

soil loss per hectare in agricultural lands due to rill and sheet

erosion. This model was revised by Renard et al. (1997) and

became the revised universal soil loss equation (RUSLE).

USLE and RUSLE are considered quantitative models

because they involve the measurement and quantification of

various parameters. Besides to these two models, qualitative

methods, which depend on the evaluation of specialists, are

also used. The objective of qualitative approaches is to assign

weights to spatial units expressing the erosion intensity

(Vrieling 2006). Hence, this kind of model is used to deter-

mine the spatial distribution of soil erosion risk. Moreover,

erosion models are obtained from several factors such as

topography, soil data and vegetation coverage, which is

considered as a significant factor in reducing soil erosion

(De Asis and Omasa 2007; Zheng 2006; Zhou et al. 2008).

Taking this into account, vegetation cover can be detected by

remote sensing tools such as satellite images or vegetation

indices. Among the large number of satellite images avail-

able, Landsat images are commonly employed, especially

for land use classification, which is helpful to map vegetation

types (Vrieling 2006). In addition, the employment of multi-

temporal satellite images is appropriate to assess vegetation

cover in different periods of the year (Cyr et al. 1995). In

order to identify areas threatened by soil erosion by water and

to increase the efficiency of erosion control, many

researchers have applied remote sensing and geographic

information systems (GIS) in their studies and combined

them with the USLE or RUSLE as qualitative models (De

Asis and Omasa 2007; Fistikoglu and Harmancioglu 2002;

Yoshino and Ishioka 2005; Yue-qing et al. 2009). However,

many others have dealt with qualitative models such as

ICONA (Bayramin et al. 2003; Sahin and Kurum 2002).

Therefore, the objectives of this study were to estimate the

soil erosion risk in the Batta watershed in Tunisia and to

assess the effect of vegetation on soil erosion. In order to

establish the soil erosion risk by water map, two approaches

based mainly on an erosion model, remote sensing and GIS

are used. The first approach uses the ICONA model as a

qualitative erosion model, and the second is combined with

the RUSLE, which is a quantitative erosion model. More-

over, a method is developed to assess the effects of vegeta-

tion on soil erosion. This method depends on multi-temporal

satellite images with seasonal variability and the trans-

formed soil adjusted vegetation index (TSAVI) which is

appropriate for arid and semi-arid areas. The result of this

research can be used as a baseline for suitable strategies for

erosion control in Tunisia.

Materials and methods

In this study, a method consists of a combination of soil

erosion models with remote sensing and GIS is applied in the

Batta watershed in Tunisia. Figure 1 shows the flowchart of

the methodology. As described in the flowchart, RUSLE and

ICONA models were applied to set up soil erosion risk map.

Furthermore, specific dataset such as rainfall, soil charac-

teristics, satellite images and topographic data were

employed to determine the factors of each model.

Study area

Mediterranean countries are prone to soil erosion (Terra-

nova et al. 2009; Van der Knijff et al. 2000); therefore,

Tunisia, with Mediterranean climate, is threatened by

erosion phenomena. In fact, 3 million ha of land is threa-

tened by erosion, and 50% is seriously affected. For this

reason, the Tunisian government has adopted two strate-

gies: the first during the period ranging from 1990 to 2001

and the second between 2002 and 2011. Their main

objectives are to preserve water and soil resources and

reduce erosion effects (DG/ACTA 2002). Table 1 shows

the main realizations of the Tunisian strategies.

In order to assess the erosion effect in Tunisia, the

watershed of the hillside dam Batta is chosen as a study

area. It is located in the Siliana prefecture between

36�2.6480 and 36�5.0480N latitude and 9�33.5190 and

9�35.0070E longitude (Fig. 2). The area of this watershed is

about 961 ha which is mainly composed of various land-

scapes with mountainous areas in the South and gentle

areas in the North. Elevation varies from 597 to 1,268 m

above the sea level. The highest elevation is in Jbel Bargou,

and the average altitude is about 820 m. This watershed is

mainly covered with rangeland and arable land suitable for

cereals. The annual average rainfall is around 495 mm

(Hydro-plante 2002). From a geological point of view,

Batta watershed is mainly composed of Eocene sedimen-

tary deposits consisting of limestone benches and yellow

marls with carbonated sandstone and coquina beds inter-

calations. The thickness of these marls can exceed 250 m

in that area. The Eocene outcrops are in contact with the

cretaceous deposits of Jebel Bargou and the coarse sand-

stone of the Oligocene. Cretaceous outcrops consist of

clay, limestone, marly limestone and marl. In Bargou area,

the continental conglomeratic deposit is assigned to the

Miocene. The quaternary sediments are deposited as hori-

zontal layers on the top of the pre-existing rocks. Around

Batta watershed, the quaternary sediments are mainly

composed of silt, alluvial deposit and screed slope (Mah-

joub and Dali 2001). The capacity of the hillside dam’s

reservoir is about 960,000 m3 (Hydro-plante 2002).

Data collection

Any model for computing potential soil loss from an area

must deal with a large number of variables such as

708 Environ Earth Sci (2011) 64:707–719

123

parameters concerning vegetation, management, soil,

topography and climate (Amore et al. 2004). That is why,

in this current study, several data are collected to determine

the different factors of soil erosion model.

Furthermore, the topographic factor was obtained using

the ASTER global digital elevation model (ASTER

GDEM) with a 30-m resolution produced by The Ministry

of Economy, Trade and Industry of Japan (METI) and the

National Aeronautics and Space Administration (NASA)

and downloaded freely from http://www.gdem.aster.ersdac.

or.jp/index.jsp. In order to generate the erodibility factor, a

FAO soil classification, soil map of Tunisia and informa-

tion of the study area were used. In addition, vegetation

cover factor is obtained from land use datasets. Down-

loaded Landsat images from global land cover facility

(GLCF) and ‘‘USGS’’ (http://glcf.umiacs.umd.edu/data/

landsat/) (http://glovis.usgs.gov/) were used. In this

research, multi-temporal imagery is employed taking into

account the seasonal variability in particular: specifically,

images from 1999 and 2007 that correspond to summer and

images from 2005 that correspond to spring. In addition,

the images of 1999 and 2007 are chosen to detect the land

use changes in this watershed. Table 2 describes the

characteristics of the satellite images.

Rainfall data are obtained from the M’Richet El Anse

weather station. It is the closest weather station to our study

area. These data are received from the Directorate of Water

and Soil Conservation (DG/ACTA) in Tunisia.

Data processing

The use of the different datasets of each factor required a

lot of pre-processing procedures. Indeed, a subset of

Soil Erosion Risk

ICONA Factors A = RxKxLSxCxP

K factor

LS factor

P factor = 1

Soil Erodibility map

Soil protection map

C factor

Slope map

Lithofacies Map

R factor

RU

SLE

Land use map

Vegetation cover map

Satellite image Landsat (1999,2005,2007)

Topography data ASTER GDEM30 m resolution

Data used

Soil data of the study area

Rainfall data

Atmospheric and geographic correction

Vegetation index « TSAVI »

Process and analysis

Digital Elevation Model (DEM)Supervised

classification

Digitizing

Geometric Transformation

Fig. 1 Flowchart of the methodology

Table 1 Realizations of Tunisian strategies

Erosion control measures First strategy

realizationsaSecond strategy

realizationsb

1990–2001 2002–2007

Watershed management (ha)

(contour ridges, terraces,…)

892,573 421,807

Cereal areas protection (ha) 70,494 2,137

Maintenance (ha) 338,496 169,532

Spreading and refill works (unit) 3,556 1,809

Hills dams (unit) 580 149

a DG/ACTA (2002)b MEDD (2007)

Environ Earth Sci (2011) 64:707–719 709

123

ASTER DEM 36-009 of the study area was used. Besides,

soil map of the watershed was digitalized, and various soil

type units were recognized.

Prior to image analysis, the atmospheric and radiometric

effects on the image have been corrected using the real

time of the sun elevation angle and the sun–earth distance.

Then supervised classification, using the maximum likeli-

hood algorithm, is used to determine different land use

classes in the study area (Lillesand and Kiefer 1994).

ERDAS imagine 9.1, as software for image processing and

analysis, is used for supervised classification. Additionally,

vegetation indices, which are used to measure biomass or

vegetative vigor, are used to get an accurate account of

vegetation coverage. These vegetation indices are com-

posed of a combination of several spectral bands. The

normalized difference vegetation index (NDVI) is widely

used to assess vegetation cover (Rouse et al. 1973).

Therefore, some researchers have acquired the vegetation

distribution factor for the erosion model using the NDVI as

the spectral vegetation indices which play an important role

in the reflection properties of vegetation (Jabbar et al.

2006). However, due to the sensitivity of the NDVI to soil

background and atmospheric effects, new indices were

developed (Rondeaux et al. 1996). In semi-arid and arid

areas where the vegetation cover is sparse, the NDVI can

have several problems of accuracy due to the influence of

the background soil (Huete 1988). For this reason, Huete

(1988) proposed the soil adjusted vegetation index (SAVI)

which minimizes spectral variance due to background soil

type. For more accuracy, Baret et al. (1989) suggested the

transformed soil adjusted vegetation index (TSAVI) which

is used in this research to obtain the vegetation coverage.

The application of the TSAVI leads to a significant

reduction of soil effects in areas with sparse vegetation

cover or bare soil (Purevdorj et al. 1998). The TSAVI is

based on soil line slope and intercept. It can be written as:

TSAVI ¼ aðNIR� aR� bÞaNIRþ R� ab

; ð1Þ

where a slope of the soil line, b intercept of the soil line,

NIR near infrared band or band 4 and R red band or band 3.

The soil line is a linear relationship between the bare

soil reflectance observed in two different wavebands (Baret

et al. 1993). It can be built by establishing a relationship

between the reflectance of the NIR band and red band. It is

written as follows:

NIRsoil ¼ aREDsoil þ b; ð2Þ

where NIRsoil is the soil reflectance in the near-infrared

band, REDsoil is the soil reflectance in the red band and

a and b represent the parameters of the soil line.

Fig. 2 Study area location

Table 2 Satellite images used

No. Path/row Sensor Acquisition data

1 191/35 Landsat 7 ETM? 14/08/1999

2 191/35 Landsat 7 ETM? 26/05/2005

3 191/35 Landsat 5 TM 12/08/2007

710 Environ Earth Sci (2011) 64:707–719

123

Then all these factors are integrated in a GIS. That is

why, during the analysis, the different layers are derived as

a raster with a cell resolution of 5 m 9 5 m and geo-ref-

erenced to Universal Transverse Mercator (WGS 84 Zone

32 N). Then they are computed using adapted datasets of

our study area and appropriate software such as ERDAS

imagine 9.1 and Arcgis 9.2.

Field assessment

There are different methods to validate erosion risk map in

large areas such as direct measurement of erosion volume

on the field (Bracken and Kirkby 2005), measurement of

sediment deposition in lakes and dams reservoirs (Van

Rompaey et al. 2003), and detection of spatial distribution

of radioactive tracer (Walling et al. 2003). The identifica-

tion of erosion features by remote sensing is also applied

(Vrieling et al. 2008). In addition, field survey is useful

approach to recognize erosion signs (PAP/RAC 1997; Bou

Kheir et al. 2001). In this current study, a field survey was

conducted to validate erosion risk map. In fact, sample

points from erosion risk map were compared with field

observations classified by erosion signs. According to the

soil erosion effects and signs, the ground points were

classified by local experts into three categories: low, it

represents slight erosion which shows the effect of sheet

erosion; moderate, it is recognized with the effect of sheet

and rill erosion and their impacts in loss of the topsoil;

severe, it is detected by removing of most of the topsoil by

sheet and rill erosion (PAP/RAC 1997). Additionally,

global positioning system (GPS) was used to localize each

selected observation points.

Erosion models

In this research, the qualitative method ICONA and the

quantitative method RUSLE are applied in order to map the

soil erosion risk by water. Both methods are mainly based

on topography, erodibility and vegetation cover factor.

In order to evaluate the effect of vegetation on soil

erosion risk, a vegetation factor of each model is modified

according to several multi-temporal satellite images and

TSAVI.

ICONA model

The ICONA model is a method developed by the General

Directorate for the Conservation of Nature, previously

known as the National Institute for the Conservation of

Nature in Spain. It is a qualitative model used to assess and

identify erosion risk locations which is particularly helpful

when establishing an erosion map. Slope, soil information,

land use and vegetation cover are required to create this

model. It consists of seven different steps as presented in

Fig. 1. For more accuracy, it was integrated in the

Guidelines for mapping and Measurement of erosion pre-

pared by the Priority Actions Program Regional Activity

Center ‘‘PAP/RAC’’(PAP/RAC 1997). In order to create a

soil erosion risk by water map, the steps of the guidelines

were followed.

RUSLE model

The revised universal soil loss equation ‘‘RUSLE’’ model

aims at predicting soil loss from agricultural lands due to

soil erosion by water. It is based on five factors related to

rainfall, soil characteristics, topography, land use and land

cover management. It can be written as

A ¼ R� K � C � LS� P; ð3Þ

where A is the annual land loss (t/ha year); R represents the

rainfall erosivity factor (MJ mm/ha h year); K is the

erodibility factor (Ton ha h/ha MJ mm); C is the crop

management factor; P is the supporting practices factor;

and LS is the slope length and slope inclination factor. C,

P and LS are dimensionless.

Factors of the models

The factors of the models are described below.

Topographic factor Using the DEM, slope layer of the

ICONA model can be classified into five groups: flat,

moderate, steep, very steep and extreme.

However, the topographic factor of the RUSLE is based

on two factors L and S where L is the slope length factor,

representing the effect of slope length on erosion and S is

the slope steepness, representing the effect of slope

steepness on erosion. In order to obtain the LS factor, some

approaches composed of the digital elevation model

(DEM) are especially used. In the current study, the LS

factor is produced using Topocrop terrain indices which are

an extension of Arcview 3.2 (Schmidt and Persson 2003).

Erodibility factor In the ICONA model, the erodibility

map is the result of overlaying the lithofacies map and the

slope map. The lithofacies map shows the kind of rock,

parent material or surface sediment or soil, with an

emphasis on their cohesiveness. It can be classified in five

classes related to their resistance to weathered. Class 1 is

composed of non-weathered compact rock such as massive

limestone. Class 2 is constituted by medium weathered

cohesive rocks or soil. Class 3 is represented by slightly to

medium compacted sedimentary rocks. Class 4 is com-

posed of low resistant weathered rock. Finally, class 5 is

represented by non-cohesive sediments or soils (PAP/RAC

Environ Earth Sci (2011) 64:707–719 711

123

1997). By applying the matrix presented in Table 3, the

erodibility layer is classified into five classes: class 1 refers

to ‘‘low’’, class 2 to ‘‘moderate’’, class 3 to ‘‘medium’’,

class 4 to ‘‘high’’ and class 5 to ‘‘extreme’’ erodibility.

In the RUSLE model, the erodibility factor K represents

the susceptibility of the soil to erosion. It is calculated

using the following relationship (Renard et al. 1997),

which is a combination of the soil’s texture, organic matter,

structure and permeability:

K ¼ 2:8� 10�7 � ð12� OMÞ �M1:14 þ 4:3� 10�3

� ðs� 2Þ þ 3:3� 10�3 � ðp� 3Þ; ð4Þ

where K is the soil erodibility factor; OM is the percentage

of organic matter content; p is the soil permeability code

which can have one of the 6-code values: 1 refers to ‘‘fast’’,

2 from ‘‘moderate to fast’’, 3 to ‘‘moderate’’, 4 from ‘‘slow

to moderate’’, 5 to ‘‘slow’’ and 6 to ‘‘very slow’’; s is the

soil structure code ranging from 1 to 4 (‘‘friable’’ is 1, ‘‘fine

polyhedral’’ is 2, ‘‘medium to coarse polyhedral’’ is 3 and

‘‘solid’’ is 4); M is the particle size parameter and can be

written as:

M ¼ ð% siltþ% very fine sandÞ � ð100�% clayÞ: ð5Þ

Vegetation factor The vegetation cover map of the

ICONA model and the crop management factor (C factor)

of RUSLE are estimated using statistical regressions.

In the ICONA model, the regression is based on the

relationship between the percentage of vegetation cover

and the TSAVI as the vegetation index. In this study, the

vegetation cover is classified into four groups: class 1 is a

class with less than 25% vegetation; class 2 is a class

between 25 and 50% vegetation; vegetation in class 3 is

between 50 and 75%; in class 4, it is more than 75% (PAP/

RAC 1997).

However, in case of the RUSLE model, vegetation

coverage is represented by the C factor. This factor is

mainly related to the vegetation cover. It is used to reflect

the effect of cropping and management practices on the

erosion rate. The C factor ranges from near 0 for high

density of vegetation to 1 for barren land. Several

approaches attempted to get the C factor and establish its

map. Some studies determined a land cover map from

supervised or unsupervised classification of satellite

images, and then the C factor value was assigned for each

class according to the literature (Folly et al. 1996; Kefi

et al. 2009). On the other hand, others used a regression

analysis between the C factor and spectral vegetation

indices such as NDVI (De Jong 1994; Van der Knijff et al.

2000) or the perpendicular vegetation index (PVI) (Yosh-

ino and Ishioka 2005). Furthermore, in the current study, a

regression which depends on the TSAVI was employed to

get the C factor value. Then a spatial distribution of the

C factor data layer was produced for each satellite image.

Specific factors of the RUSLE model The RUSLE model

has two additional factors, erosivity (R) and supporting

practices (P) factor.

The rainfall erosivity factor (R factor) represents the

erosive potential of rainfall. The R factor is the result of

the sum of the total kinetic energy (E) for a storm and the

storm’s maximum 30-min intensity (I30). The R factor

formula can be written as follows:

R ¼Xn

j¼1

Ek � I30ð Þj; ð6Þ

where R is the average rainfall erosivity factor (MJ mm/

ha h year), Ek is the total kinetic energy of the storm, I30 is

the storm’s maximum 30-min intensity, and n is the

number of storms in the series. The Kinetic energy is

expressed as follows (Foster et al. 1981):

Ek ¼ 0:119þ 0:0873Log10 Ið Þ: ð7Þ

Additionally, the supporting practices factor (P factor),

which refers to erosion practice factors, is the ratio of soil

loss for a specific practice to the soil loss with an up- and

downhill culture (Laflen and Moldenhauer 2003). This

factor represents the practices and measures used to control

the effect of soil erosion by water such as counter ridges or

terraces (Wischmeier and Smith 1978). Because of the

erosion control management is quite inexistent, P is

assumed to be 1 for each situation.

Erosion risk map In the case of ICONA, the erosion risk

map is the result of the combination of the soil protection

layer which is obtained from overlaying vegetation cover

and land use map and the soil erodibility layer. This

Table 3 Matrix of erodibility

processing

PAP/RAC (1997)

Slope class Lithofacies class

1 2 3 4 5

1 (flat) 1 (low) 1 (low) 1 (low) 1 (low) 2 (moderate)

2 (moderate) 1 (low) 1 (low) 2 (moderate) 3 (medium) 3 (medium)

3 (steep) 2 (moderate) 2 (moderate) 3 (medium) 4 (high) 4 (high)

4 (very steep) 3 (medium) 3 (medium) 4 (high) 5 (extreme) 5 (extreme)

5 (extreme) 4 (high) 4 (high) 5 (extreme) 5 (extreme) 5 (extreme)

712 Environ Earth Sci (2011) 64:707–719

123

combination is the result of the application of the matrix

presented in Table 4.

The total soil loss calculated using the RUSLE model is

the product of these five factors together.

Results and discussion

The results acquired using the ICONA and RUSLE models

are presented below.

Topography factor

According to ICONA model, approximately 54.69% of the

Batta watershed has slope varying from ‘‘steep’’ to

‘‘extreme’’. Therefore, this watershed is an area with steep

slopes where soil erosion by water can occur easily.

The LS factor classification also shows that 45.08% of

the study area has an LS value of less than 1. Furthermore,

10.62% of the Batta watershed has an LS factor superior to

10 which indicated high slope steepness and, consequently,

a high vulnerability to erosion. Table 5 shows that slope

factor of ICONA model and LS value of RUSLE.

Erodibility factor

The erodibility of the ICONA model is obtained by over-

laying different lithofacies classes and the slope map. The

results show that about 67.75% of the area is classified

from ‘‘medium’’ to ‘‘extreme’’ which indicates the soil

vulnerability in this watershed. This vulnerability is due to

the slope steepness and the low resistance of the soil par-

ticles to detachment and transport.

This result is confirmed by finding the K factor. The

classification shows that about 46.37% of the study area

has a K factor below 0.015 Ton ha h/ha MJ mm which can

be recognized as a low soil erodibility (Foster et al. 1981).

However, 20.90% of the area has a K factor ranging from

0.040 to more than 0.060 Ton ha h/ha MJ mm which is

considered as an area with high soil erodibility (Foster

et al. 1981). Furthermore, the erodibility factor of both

models is classified into five classes as presented in

Table 6.

Vegetation coverage

In order to estimate the vegetation factor of models, land

use map and vegetation index (TSAVI) were produced.

A supervised classification was applied for each Landsat

scenes (1999, 2005 and 2007) to generate land use map.

For the current study, only significant land use is consid-

ered, such as rangeland, olive trees and cereals. The result

of the classification was presented in Table 7.

The results indicate that the rangeland areas were

decreasing from 1999 to 2007 till they become bare land.

This land conversion can be due to the effects of over-

grazing and irregular distribution of precipitation. In

addition, the watershed is covered with cereals only in the

scene of 2005 because it was taken in spring which is the

growing period of cereals. However, the 1999 and 2007

images were taken in summer which is a dry season and

after the harvest of cereals. Therefore, a large area of the

watershed turned into bare soils which may decrease soil

protection.

Table 4 Matrix between level

of soil protection and level of

erodibility

PAP/RAC (1997)

Level of soil

protection

Level of erodibility

1 (low) 2 (moderate) 3 (medium) 4 (high) 5 (extreme)

1 (very high) 1 (very low) 1 (very low) 1 (very low) 2 (low) 2 (low)

2 (high) 1 (very low) 1 (very low) 2 (low) 3 (appreciable) 4 (high)

3 (medium) 1 (very low) 1 (very low) 2 (low) 4 (high) 4 (high)

4 (low) 2 (low) 3 (appreciable) 2 (low) 5 (very high) 5 (very high)

5 (very low) 2 (low) 3 (appreciable) 4 (high) 5 (very high) 5 (very high)

Table 5 Topographic factors

Classes Type of slope

ICONA

Area (%) LS factor

value

RUSLE

Area (%)

1 Flat to gentle (0–3%) 9.42 \1 45.08

2 Moderate (3–12%) 35.89 1–5 32.35

3 Steep (12–20%) 17.71 5–10 11.95

4 Very steep (20–35%) 17.71 10–20 7.50

5 Extreme ([35%) 19.27 [20 3.12

Table 6 Erodibility classes

Classes Classes of

erodibility

of ICONA

Area

(%)

K factor value of

RUSLE

(Ton ha h/ha MJ mm)

Area (%)

1 Low 19.19 \0.015 46.37

2 Moderate 13.06 0.015–0.030 15.55

3 Medium 36.61 0.030–0.040 17.18

4 High 26.81 0.040–0.060 18.60

5 Extreme 4.33 [0.060 2.30

Environ Earth Sci (2011) 64:707–719 713

123

Moreover, TSAVI comes out from the soil line. Table 8

indicates the different parameters for soil lines for each

Landsat scene.

Then the mean value of TSAVI’s pixels was correlated

with land use to evaluate vegetation cover of ICONA

model and C factor of RUSLE model.

For the ICONA model, a linear regression was applied

to estimate the percentage of vegetation cover from the

TSAVI. This relationship can be written as follows:

Vegetation cover (%) ¼ a � TSAVIþ b: ð8Þ

Table 9 shows the parameters of this relationship.

Through the relationship, the vegetation cover map was

classified into four classes which were presented in

Table 10. The findings depend especially on the TSAVI

value which is low in case of spare vegetation and high in

case of high vegetation density. According to ICONA, the

percentage of vegetation is considerable in 2005 with 18%

of the watershed covered with more than 75% of vegeta-

tion. However, in 1999 and 2007, more than 64% of the

area is covered with less than 25% of vegetation. There-

fore, seasonal variation has an impact on land use change.

Additionally, the relationship between C factor and

TSAVI is obtained using a statistical regression. This

regression is based on the correlation of the C factor value

of each land use and the mean value of the TSAVI’s pixels.

The best-fit regression equation is an exponential equation

which can be written as follows:

C ¼ a � e�b�TSAVI: ð9Þ

Table 11 shows the parameters of this relationship.

C factor depends on vegetation cover if C factor is close

to 0; it means a high density of vegetation but near 1; it

shows a low density of vegetation. Furthermore, in our

study, the findings show that areas with C factor\0.4 were

26.44, 33.95 and 16.76%, respectively, for 1999, 2005 and

2007. It indicated that C factor rose in 2005 due to the

increase of vegetation cover. However, the areas with

C factor ranging from 0.8 to 1 were 51.94 and 52.67%,

respectively, in 1999 and 2007. This signifies scarce veg-

etation. Furthermore, in 2005, C factor reduced to become

27.03% under the effect of vegetation. Due to the

improvement of vegetation density in 2005, the effect of

soil erosion may be attenuated. This classification is

described in Table 12.

Erosivity factor (R factor)

The R factor is considered as the most important factor for

soil erosion by water. Its value is obtained using Eq. 6

Table 7 Land occupation distribution

Land occupation Area (%)

1999 2005 2007

Rangeland 40.95 27.60 27.54

Olive trees 7.65 7.65 7.65

Cereals 0 15.52 0

Bare land 50.26 48.09 63.67

Water 1.14 1.14 1.14

Table 8 Soil line characteristics

No. Scene date Slope (a) Intercept (b) R2

1 1999 1.184 0.026 0.96

2 2005 1.390 0.035 0.91

3 2007 1.223 0.025 0.90

Table 9 Parameters of linear regression

No. Scene date a b R2

1 1999 295.11 -6.092 0.83

2 2005 214.73 17.825 0.73

3 2007 367.69 -1.246 0.84

Table 10 Distribution of vegetation cover classes per scene

Class Vegetation cover classes Area (%)

1999 2005 2007

1 Less than 25% 64.80 29.10 64.29

2 25–50% 19.20 31.20 9.88

3 50–75% 13.50 21.70 13.19

4 More than 75% 2.50 18 12.64

Table 11 Parameters of exponential regression

No. Scene date a b

1 1999 1.012 6.296

2 2005 1.002 4.309

3 2007 1.013 4.925

Table 12 C factor distribution

Class C factor value Area (%)

1999 2005 2007

1 0–0.20 4.17 7.07 0.08

2 0.20–0.40 22.27 26.88 16.68

3 0.40–0.60 12.58 19.21 19.57

4 0.60–0.80 9.04 19.81 11.00

5 0.80–1 51.94 27.03 52.67

714 Environ Earth Sci (2011) 64:707–719

123

which is based on the total kinetic energy (E) for a storm

and the maximum amount of rain falling within 30 con-

secutive minutes (I30). According to the available records, a

representative average of R factor is used for the entire

watershed. Therefore, the annual average for the erosivity

value is 740,080 MJ mm/ha h year which can be consid-

ered a moderate erosivity value (Foster et al. 1981).

Erosion risk map with the ICONA and the RUSLE

model

The soil erosion risk map of the ICONA model is obtained

by overlaying the erodibility map and the soil protection

map. The effect of vegetation on the soil erosion risk is

assessed by using the soil protection map for different

scenes: 1999, 2005 and 2007. The result of this combination

is presented in Table 13. Figure 3 shows the spatial distri-

bution of the soil erosion risk for the three periods. There

are five classes of erosion risk ranging from very low to very

high risk. Serious risk of erosion is shown especially in the

mountainous areas. This increased soil erosion risk from

1999 to 2007 is due to conversion of rangeland to bare land

and the high slope gradient. Indeed, a large area of the Batta

watershed is in very serious risk due to slope and land use

changes. Furthermore, the watershed has a wide area with

steep slopes which can increase the runoff velocity and,

consequently, the soil erosion risk. Besides the slope, the

effect of land use on soil erosion can be assessed. Areas

classified with severe risk of erosion increased from 44.75%

in 1999 to 48.91% in 2007. This high risk occured because

rangelands were converted to bare soil. Moreover, the 2005

scene shows that the arable lands changed into lands

growing cereals. Indeed, crops such as winter cereals pro-

vide a good protection against erosion (Boardman et al.

2003). For this reason, the risk of soil erosion diminished to

41.62% with the effect of vegetation. The vegetation cov-

erage is effective in preventing erosion to some extent. It

absorbs the kinetic energy of raindrops, covers a large

proportion of the soil during periods of the year when

rainfall is most aggressive, slows down runoff, and keeps

the soil surface porous (Roose 1996).

Therefore, the soil erosion risk increases when soils

remain bare and decreases with the presence of vegetation.

Table 13 Distribution of erosion risk classes of ICONA

Class Erosion risk classes Area (%)

1999 2005 2007

1 Very low 8.08 19.18 11.23

2 Low 23.35 20.55 24.19

3 Appreciable 23.82 18.65 15.67

4 High 30.95 22.84 27.97

5 Very high 13.80 18.78 20.94

Fig. 3 Spatial distribution of

soil erosion risk by water with

ICONA

Environ Earth Sci (2011) 64:707–719 715

123

According to the soil loss value, the spatial distribution

of annual soil loss can be classified into five classes ranging

from lower than 5 to more than 30 t/ha. In order to evaluate

the effect of vegetation on the soil erosion risk using the

RUSLE model, of the five factors only the C factor will be

altered. Indeed, the C factor map was derived using the

TSAVI from the images of 1999, 2005 and 2007. Table 14

shows the soil loss values found by the RUSLE model.

About 41.53 and 44.07% of the area, respectively, in 1999

and 2007 has a soil loss value of more than 30 t/ha. But, in

2005, it diminished to 37%. These results confirm that the

risk of soil erosion is serious in this watershed. The highest

soil loss occurs in 1999 and 2007 when a wide area of the

Batta watershed remained bare. However, the soil loss was

diminished in 2005 with the presence of vegetation. Fur-

thermore, the average of soil loss is 19.98, 17.55 and 22.07

t/ha, respectively, for 1999, 2005 and 2007. The spatial

distribution of the soil erosion risk is illustrated in Fig. 4.

Therefore, the vegetation cover provides protection for

the soil against the erosion process (Vrieling 2006). In

addition, the relationship between the vegetation factor and

the TSAVI is helpful to predict and delimitate vegetation

cover areas.

Comparative results of the erosion risk

Despite the distinction between the results of the parame-

ters of each model, the findings obtained from the two

methods are globally similar. Indeed, both models show

that erosion risk increases from south to north in particular

from mountainous areas to gentle areas. The result indi-

cates that this watershed is threatened by a high risk of

erosion which is corroborated by the study of the

bathymetry of the Batta hillside dam’s reservoir. It shows

that about 40% of the capacity of reservoir is reduced

(BUCET 2006). Indeed, the sedimentation in the reservoir

is caused by transport and deposition of eroded soil parti-

cles from the whole watershed. This problem will decrease

the water available for irrigation and can affect the farm-

ers’ revenues coming from agriculture.

In order to validate the findings, an accuracy assessment

of soil erosion risk map was conducted. The comparison

between soil erosion predictions obtained from RUSLE

and ICONA and ground observations shows that the

Table 14 Soil erosion by water distribution with RUSLE

Class Soil loss value

(t/ha year)

Erosion risk

classes

Area (%)

1999 2005 2007

1 \5 Very low 36.02 37.72 35.28

2 5–10 Low 4.13 5.55 3.61

3 10–20 Moderate 9.87 10.96 9.03

4 20–30 High 8.45 8.77 8.01

5 [30 Very high 41.53 37.00 44.07

Fig. 4 Spatial distribution of

soil erosion risk with by water

with RUSLE

716 Environ Earth Sci (2011) 64:707–719

123

classification accuracy is not significantly different

between the two methods. Indeed, the overall classification

accuracy of ICONA and RUSLE is, respectively, 71.43 and

85.71%.

The results were acceptable, but many potential sources

for errors remain. The accuracy of the GPS should be taken

into account in order to obtain the exact location. Fur-

thermore, the field observation was based on local experts’

judgements. For this reason, ground measurement of soil

erosion using appropriate materials should be taken in

order to guarantee accurate results.

Considerable erosion risk occurs in areas with steep

slope, poor vegetation and high soil erodibility. In fact,

severe slope increases the runoff velocity and, conse-

quently, intensifies the possibility of detachment and

transportation of soil particles endangered by the scarcity

of vegetation protection. These locations which are prone

to erosion require conservation measures such as counter

ridges or terraces. Furthermore, the comparison between

image of 1999 and 2007 shows that the rangeland con-

verted to bare land in mountainous areas. According to the

positive effect of vegetation coverage on reducing soil

erosion by water effects, a priority must be given to protect

vegetation coverage and afforestation of bare land. In

addition, the identification of areas at high risk of erosion is

of prime importance in soil conservation planning (Zhou

et al. 2008).

Further, the qualitative model is very useful for erosion

mapping in large areas and helps to plan appropriate ero-

sion control measures. In addition, it is possible to identify

the different kinds of erosion as sheet, rill and gully erosion

considering the different classes of soil erosion risk (PAP/

RAC 1997).

Additionally, information obtained from quantitative

methods is also significant, especially for the estimation of

the effects of soil loss on the productivity of crops. Indeed,

soil loss decreases soil fertility by reducing nutrients such

as nitrogen (N) and phosphorus (P). Consequently, the

potential crops’ yield will be reduced affecting the farmers’

incomes (Williams and Tanaka 1996).

Furthermore, qualitative and quantitative models pro-

vide appropriate information about the spatial distribution

of erosion risk areas in the watershed where suitable and

urgent measures and treatments will be required. Hence,

both methods can be complementary in regional risk

assessment of soil erosion by water.

The current study shows that the use of multi-temporal

satellite images which consider the seasonal variability is a

pertinent approach to distinguish land use changes. Vege-

tation coverage integrated in the erosion model is advan-

tageous to assess its impact in protecting soil against

erosion. The results show that vegetation coverage has a

positive impact on soil preservation and protection.

Therefore, vegetation restoration is the precondition eco-

system rehabilitation (Jiao et al. 2009). For this reason,

decision makers should encourage farmers to amplify

planting their lands with appropriate cultivation practices

because they will not only protect their parcels but also

improve their income. The abandoned land is more vul-

nerable to gully erosion than cultivated land (Lesschen

et al. 2007). In addition, the expenses for erosion control

management will be reduced. Finally, applying a vegeta-

tion index such as the TSAVI improves the accuracy of

vegetation cover identification in arid and semi-arid areas.

However, this method can be ameliorated by some field

experiments.

Conclusion

Environmental problems such as soil erosion by water

can cause soil damage and, consequently, affect agri-

culture in developing countries. Therefore, a suitable

assessment of this phenomenon can be useful and helpful

for decision makers in order to establish strategies and

programs for erosion control management. Using the

ICONA model as a method to evaluate soil erosion risk

with a GIS and remote sensing can provide a rapid

evaluation of the potential erosion risk in large areas

(Sahin and Kurum 2002). Additionally, mapping soil

erosion using the RUSLE model is interesting in order to

delimit the area threatened by erosion on the one hand

and to assess the effect of soil loss on crop’s produc-

tivity on the other. Furthermore, the detection of land

use changes using several satellite images in different

seasons shows the importance of vegetation in protecting

the soil against soil erosion by water. In addition, the

employment of remote sensing data such as satellite

images and vegetation index will be significant to get the

vegetation factor of erosion model. Further, in the cur-

rent study, the results show that in summer a high

potential soil erosion risk is detected, but it decreases in

spring. Regarding its positive impact, vegetation cover-

age should be preserved and increased. Using a GIS and

remote sensing tools can be suitable for planning mea-

sures and strategies for soil and water conservation.

However, the results of the ICONA model can be

improved by considering climatic data, and the results of

the RUSLE model can become more accurate by taking

into account the effect of the soil protection factor

(P factor).

Acknowledgments The authors gratefully acknowledge the CRDA

(Regional Office of the Ministry of Agriculture) of Siliana and the

General Directorate for Water and Soil Conservation in Tunisia (DG/

ACTA) for their help and data providing.

Environ Earth Sci (2011) 64:707–719 717

123

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