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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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