soil erosion hazard evaluation—an integrated use of remote...
TRANSCRIPT
Ssm
Ma
b
a
ARRA
KSZAWM
1
irpotc(ptstr
c
0d
Ecological Modelling 220 (2009) 1724–1734
Contents lists available at ScienceDirect
Ecological Modelling
journa l homepage: www.e lsev ier .com/ locate /eco lmodel
oil erosion hazard evaluation—An integrated use of remote sensing, GIS andtatistical approaches with biophysical parameters towardsanagement strategies
d. Rejaur Rahmana,b, Z.H. Shia, Cai Chongfaa,∗
College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, Hubei, ChinaDept. of Geography & Environmental Studies, University of Rajshahi, Rajshahi 6205, Bangladesh
r t i c l e i n f o
rticle history:eceived 4 December 2008eceived in revised form 30 March 2009ccepted 4 April 2009
eywords:oil erosion hazard index (SEHI)-Scorenalytical hierarchy process (AHP)eighted linear combination (WLC)anagement strategies
a b s t r a c t
Soil erosion hazard maps can be an essential tool in erosion prone areas as they explain and display thedistribution of hazards and areas likely to be affected to different magnitudes. Therefore, it is very usefulto planners and policy makers initiating remedial measures and for prioritizing areas. In this study, anumerical model was developed for soil erosion hazard assessment, in which Z-score analysis was com-bined with a geographical information system (GIS) to compute a synthetic soil erosion hazard index(SEHI). For this model, nine factors which have notable impact on soil erosion were selected. To generatethe selected factors remote sensing, analytical hierarchy process (AHP) and GIS techniques along withspatial models were applied. To standardize all of the factors and establish the factor weights, the AHPmethod was adopted. For Z-score analysis with selected standardized factors, the Integrated Land andWater Information System (ILWIS) software was used and nine individual layers with Z-scores were pre-pared. Afterwards, the layers were integrated with their factor weights by means of a weighted linear
combination to derive a SEHI value for each pixel. To classify the discrete SEHI map to represent a mean-ingful regionalization of soil erosion hazard, the equal distance cluster principle was used and graded intofour levels of hazard; very high, high, moderate and low. The results depicted that in general, a moderatehazardous condition of soil erosion was found in the study area and the proposed approach was alsoable to identify the areas under high and very high hazards that require urgent intervention on a priorityy, comt of p
basis. Based on this studthe efficient managemen
. Introduction
Soil erosion is one of the most serious environmental problemsn the world today, as it seriously threatens agriculture, naturalesources and the environment. Soil erosion is a natural geomor-hic process occurring persistently over the earth’s surface. Somef the problems associated with soil erosion include loss of fertileopsoil for agriculture, siltation of streams and lakes, eutrophi-ation of surface water bodies and loss of aquatic biodiversityOnyando et al., 2005). Management practices to minimize theseroblems can be effectively carried out if the magnitude and spa-
ial distribution of soil erosion are known. Soil erosion models canimulate erosion processes in the watershed and may be able toake into account many of the complex interactions that affectates of erosion. Soil erosion prediction and assessment has been a∗ Corresponding author.E-mail addresses: [email protected] (Md.R. Rahman),
[email protected] (C. Chongfa).
304-3800/$ – see front matter © 2009 Elsevier B.V. All rights reserved.oi:10.1016/j.ecolmodel.2009.04.004
prehensive erosion hazard management strategies were anticipated forresent and future erosion disaster in the area.
© 2009 Elsevier B.V. All rights reserved.
challenge to researchers since the 1930s and several models weredeveloped (Lal, 2001). These models are categorized as empiri-cal, semi-empirical and physical process-based models. The mostcommonly adopted empirical models are the Universal Soil LossEquation (USLE) (Wischmeier and Smith, 1965) and Revised Univer-sal Soil Loss Equation (RUSLE) (Renard et al., 1991). Other modelslike the Erosion Productivity Impact Calculator (EPIC) (Williams etal., 1990), European Soil Erosion Model (EUROSEM) (Morgan et al.,1992) and Water Erosion Prediction Project (WEPP) (Flanagan andNearing, 1995) are also used to estimate the status of soil loss.
These methods analyze soil erosion by attempting to estimatethe volumes or masses of soil loss. However, soil erosion by wateris one of the major causes of land degradation and, therefore, itis necessary to establish soil conservation measures to reducethe land degradation and ensure development of a sustainable
management of soil resources. The implementation of effective soilconservation measures has to be preceded by a spatially distributederosion hazard and risk assessment (Moussa et al., 2002; Souchèreet al., 2005). A soil erosion hazard map is essential and erosionhazard mapping can be a starting point of any regional intervention
al Mod
paiVr(((ovamafgsimEtcemplia
ntRsaaawftmeiiwstph
2
2
ati1wshmmT1As
Md.R. Rahman et al. / Ecologic
olicy for soil erosion control and conservation. In order to calculateregion’s erosion hazard, its soil conditions, climate character-
stics, vegetation, terrain, ground cover, etc. must be studied.arious papers involve methods of evaluating erosion hazard andisk, based on many parameters, such as morphometric variablesJozefaciuk and Jozefaciuk, 1993), sediment yield informationRooseboom and Annandale, 1981), and rainfall erosion indicesHundson, 1981). A simple erosion risk scoring system was previ-usly proposed by Stocking and Elwell (1973) using morphometricariables and rainfall indices. Using advanced remote sensingnd GIS techniques and modelling, investigators also developedethods for erosion hazard and risk evaluation, such as integrated
nd systematic approaches (Vezina et al., 2006; Tian et al., 2008),uzzy and artificial neural-network evaluation methods (He, 1999),eo-statistical multivariate approaches (Conoscenti et al., 2008),ensitivity analysis approaches (Mendicino, 1999), soft comput-ng method (Gournellos et al., 2004), analytical risk evaluation
ethods (Wu and Wang, 2007; Masoudi and Patwardhan, 2006).ven though most of these approaches were used for quantita-ive analysis, some of these were found very complex and timeonsuming, and the variables used in the models are not alwaysasy to be acquired and assessed. For example, the neural-networkethod requires a range of historical data, which especially is a
articular problem of using existing domain knowledge in theearning process. However, all this activity indicates an expandingnterest in the study of erosion and related processes for evaluationnd understanding of environmental changes.
The spatial technologies, such as remote sensing and GIS, andumerical modelling techniques have been developed as powerfulools for ecological and environmental assessment (Krivtsov, 2004;ahman and Saha, 2009). Combining these technologies not onlyupplies a platform to support multi-level and hierarchical resourcend environmental analysis, but also integrates the information incomparative theoretical framework (Li et al., 2006; Rahman et
l., 2009). It should be noted that soil erosion is a complex issueith many related factors, and investigators face great challenges
or quantifying the relationships between soil erosion and these fac-ors. Thus, an integrated and systematic approach should be imple-
ented. Therefore, in order to provide an effective result for soilrosion hazard assessment, remote sensing (RS) and geographicalnformation system (GIS) technologies were adopted, and a numer-cal model was developed using a Z-score method. Particularly, thisas an integrated approach to determine the spatial dynamics of
oil erosion vulnerability by water. In this context, the objective ofhis study was to provide a soil erosion hazard map by applying theroposed numerical model, from which a comprehensive erosionazard management strategy could be developed for the study area.
. Methodology
.1. Study area
The study area lies between 32◦14′19′′ to 32◦58′09′′N latitudend 110◦48′06′′to 111◦34′39′′E longitude. The selected area is withinhe Danjiangkou County, with an area of 3115.58 km2 and locatedn the north-western part of Hubei province of China. It consists of7 Town/Blocks, which are the second order administrative unitsithin the province (Fig. 1). This area has a subtropical with mon-
oon climate, an average annual precipitation 1116 mm with theighest intensity of rainfall during June to September. Due to theonsoon, the total amount of rainfall in these 4 months account for
ore than a half of the total amount of the yearly rainfall in this area.opography of the area represents elevations ranging from 150 to610 m, and 60.58% of the total area falls within 151–450 m altitude.bout 61.04% of the total area falls under moderate to moderatelyteep slope (11–30◦), indicating a potential zone for soil erosion. Soil
elling 220 (2009) 1724–1734 1725
in the area is classified as loam, sandy clay loam, sandy loam, siltloam and silty clay loam. Sandy clay loam, silt loam and sandy loamplay a dominant role in soil erosion by water. However, climate,slope, soils, vegetation cover and land management are the mostimportant factors influencing soil erosion, and population pressureleads to use of marginal lands and steep slopes which can accelerateerosion processes. The region has been suffering from soil erosion,and therefore should be considered as a priority area for soil conser-vation. A soil erosion hazard map can be incorporated to formulateeffective management strategies for soil conservation in the area.
2.2. Data acquisition and preparation
It is essential to prepare and analyze the different types of datain soil erosion prediction and hazard assessment as there are manyfactors that affect soil erosion status. Different sources and typesof data were used in this study. The basic data used in this studyinclude: (i) satellite image, IRS-P6 LISS III (19 September 2005); (ii)soil attribute data from the National Soil Survey Office (1:20,000);(iii) Digital Elevation Model (DEM), generated from a 10-m inter-val contour map of Danjiangkou County; (iv) monthly rainfall datafrom seven observation stations and (v) Town/Block wise popula-tion data, from the Annual Statistics of Hubei Province. The sourcesof data and preparation of layers are shown in Table 1. Finally, forfurther analysis, all data/layers were projected using the universaltransverse mercator (UTM) projection system, nearest neighbouralgorithm with polynomial transformation and 24 m pixel size.
2.3. Soil erosion estimation
Models are needed to predict soil erosion rates under differ-ent resource and land-use conditions. Several models and methodswere suggested to predict soil erosion. Empirical erosion predic-tion models continue to play an important role in soil conservationplanning and are widely used to predict soil erosion. In this study,the Revised Universal Soil Loss Equation (RUSLE) was adopted forthe assessment of soil erosion which is shown in Eq. (1) (Renard etal., 1997).
A = R × K × L × S × C × P (1)
where A is the soil loss in t ha−1 year−1; K is the soil erodibility fac-tor (t ha h ha−1 MJ−1 mm−1); R is the rainfall-runoff erosivity factorin MJ mm ha−1 h−1 year−1; L is the slope length factor; S is the slopesteepness factor; C is the cover and management factor and P is theconservation practices factor. The L, S, C and P values are dimen-sionless. These factors (RKLSCP) were combined via the RUSLE in aGIS environment for soil erosion prediction and, for these factors,individual maps were prepared in raster GIS. To generate individ-ual factor maps for R, K, L and S, the methods which were followedare shown in Eqs. (2)–(5). For the C factor, the classified land covermap was converted to the C factor layer through reclassification ofeach land cover type by its corresponding C value, which was esti-mated from the RUSLE guide table (Renard et al., 1997). While theP factor was assigned according to the conservation practice in thearea which ranges from 0.0 to 1.0, with the highest value assigned toareas with no conservation practices. Conservation information wasassessed from the survey based on the past and current manage-ment status. Finally, the above six factors were integrated in a GISenvironment and classified as four levels of erosion: low, moderate,high and very high.
Equation for R (Renard and Freimund, 1994):
R = 0.07397F1.847
1.72, when F < 55 mm (2-1)
R = 95.77 − 6.081F + 0.4770F2
17.2, when F ≥ 55 mm (2-2)
1726 Md.R. Rahman et al. / Ecological Modelling 220 (2009) 1724–1734
wing s
F
wc
K.0 − 01 + 2
w(
L
TD
T
N
L
S
SRSEFSP
Fig. 1. Sho
=∑12
i=1Pi2∑12i=1Pi
(2-3)
here Pi is the monthly rainfall in mm and F is modified Fournieroefficient.
Equation for K (Williams and Renard, 1983):
= {0.2 + 0.3 exp[−0.0256Sd(1 − Si/100)]} × [Si/(Cl + Si)]0.3 × {1{1 − Sd/100 + exp[−5.5
here Sd, Si, Cl and C represent sand (%), silt (%), clay (%) and carbon
%), respectively.Equation for L (McCool et al., 1987):
=(
�
22.13
)m
(4-1)
able 1ata preparation for this study.
hematic layer Description Data pre
DVI Vegetation cover over land surface NDVI waby the s
and use/land cover (LULC) Different land use/land cover (classified)over the land surface
InterpreFuzzy A
oil erodibility (K) Susceptibility of the soil Calculatcarbon d
oil depth Surface soil depth Prepareainfall Average annual rainfall Calculatlope Slope gradient in degree Calculatlevation Height above mean sea level Classifieallow land Bare soil and seasonal fallow Extracteoil erosion Existing soil erosion rate Calculatopulation Population density Populat
map wa
tudy area.
.25C/[C + exp(3.72 − 2.95C)]} × [1.0 − 0.7(1 − Sd/100)]2.9(1 − Sd/100)]} (3)
where � is the slope length in metre and m is the slope lengthcomponent expressed as
m = ˇ
(1 + ˇ)(4-2)
where ˇ represents
Sin �/0.0896
ˇ =[3.0(Sin �)0.8 + 0.56](4-3)
Equation for S (McCool et al., 1987; Yuan et al., 2002):
S = 10.8 Sin � + 0.03, if � ≤ 5◦ (5-1)
paration
s calculated as the difference between the NIR and red bands combination dividedum of the NIR and red bands combination of IRS-P6 LISS III (Rahman et al., 2004).ted from the geo-coded FCC (NIR, red and green band) of IRS-P6 LISS III image usingRTMAP neural-network approach (Mannan et al., 1998; Carpenter et al., 1991).ed using model given by Williams and Renard (1983) with sand, silt, clay andata of soil with GIS.
d from soil map with attribute of soil depth and GIS technique.ed from annual rainfall data from seven observation stations with GIS.ed from the DEM with GIS.d from the DEM with GIS.d from the classified LULC map.ed using the RUSLE model (Renard et al., 1997). Detail in Section 2.3.ion density is calculated by dividing the population by area. Population densitys generated using Town/Block wise population data with GIS technique.
al Modelling 220 (2009) 1724–1734 1727
S
S
w
2
dTccspissoc(il1aumTsssfocressdaatfav
soawb(mapHtinagiA(aMt
Md.R. Rahman et al. / Ecologic
= 16.8 Sin � − 0.5, if � > 5◦ (5-2)
= 21.91Sin� − 0.96 if � ≥ 10◦ (5-3)
here � is the slope gradient in degree.
.4. Hazard assessment
For soil erosion hazard assessment, an approach toecomposition–analysis–aggregation is presented in this paper.his approach depicted that the initial, ill-defined evaluationriteria can be decomposed to better defined sets, whose functionsan be identified both more easily and safely. After establishing aet of evaluation criteria with which to decompose the decisionroblem, the (spatial) criteria should be presented in the form of
nformation layers (maps). To be able to utilize criteria and theirpatial representation, they should be associated with a commoncale of measurement. The process of translating the various inputsf a decision problem to a common scale in order to allow foromparison, analysis and synthesis, is termed standardizationVoogd, 1983; Eastman, 1997). Various methods, from determin-stic and probabilistic to fuzzy approaches have been used in theiterature (Voogd, 1983; Massam, 1988; Thomas and Huggert,980; Eastman, 1997; Ross, 1995; Rahman and Saha, 2007). Annalytical hierarchy process (AHP) (Saaty, 1977) approach wassed for standardization in this case, which is the widely usedethod and could decompose the complex problem effectively.
o establish a rating score to determine the degree of hazard andtandardization of the factors, the impact of each factor on theoil erosion was assessed individually. However, in an area, theoil erosion status is a combined effect of several factors and theactors are interrelated. For example, rainfall has a great impactn soil erosion, but in an area where the density of vegetationover is high, the soil erosion rate will be less even though theainfall is high. Again, topography has a great impact on the soilrosion of an area. Therefore, several criteria need to be examinedimultaneously to decide on an inspection and prioritizationtrategy. Therefore, standardized factor maps (representing theegree of hazard) need to be normalized to minimize the bias ofny factor in the final evaluation, accordingly in this study a Z-scorenalysis was performed before aggregation. Therefore, we proposewo levels of standardization, i.e. one for the rating score and otheror minimizing the bias of factor(s). Thus, the final result would benumber of criteria (Z-score maps), linking the crisp and spatiallyariable data to the target complex set: vulnerability-to-erosion.
Moreover, all criteria that are taken into account in the deci-ion process are generally not equally important to the expectedutcome. This relative importance should therefore be identifiednd the criteria prioritized, typically through the application of aeighting scheme (Malczewski, 1999). Weights are considered toe an expression of the decision maker’s choices or experienceTkach and Simonovic, 1997) and can illustrate conflicts where
ultiple decision makers are involved. The process by which thelternatives are ordered, integrating data (criteria) and decisionreferences (weights) are termed the decision rules (Chankong andaimes, 1983). Depending on the nature of the alternatives and
he information (criteria), the rules can be applied in a determin-stic, probabilistic or fuzzy context. Finally, an aggregation processeeds to be employed to develop a prioritization scheme based onll criteria that are taken into account. There are three main cate-ories of operators that can be employed to perform aggregation:ntersection operators, union operators and averaging operators.
simple additive method, called the weighted linear combinationWLC), can be used to perform aggregation (due to their widespreadpplication in spatial decision-making). Eastman (1997) as well asalczewski (1999), emphasise that WLC implies the acceptance of
wo assumptions: namely linearity and additivity. Linearity implies
Fig. 2. Methodology for the soil erosion hazard assessment.
that the benefit from more input of a criterion is constant and inde-pendent of the characteristics of the problem, and additivity impliesindependency between the variables (criteria) (Makropoulos andButler, 2006). By following the approach discussed above, it allowsthe decision maker to: (a) break the problem down into a series ofelementary (easier to understand) problems, (b) analyze them (inthe broad sense of the word), and then (c) produce an answer tothe complex problem by aggregating the answers derived for theelementary problems. Hence, the methodology for the soil erosionhazard assessment using a combination of Z-scores and AHP withGIS is shown in Fig. 2 and the details are discussed in the followingsections.
2.4.1. Factors identificationThe soil erosion status of an area depends upon the regional
conditions of the area, such as climate, soil condition, landuse/land cover, topography, population density, etc. Therefore,to assess the erosion hazard of the area a range of evalua-tion criteria, objectives and attributes should be identified withrespect to the problem situation (Rahman and Saha, 2008).Based on field surveys, analysis of available information andcombined professional expert judgment; the dominating nine(9) factors were assessed and determined. The relevant fac-tors for soil erosion by water were soil erodibility, slope, soildepth, rainfall, elevation, vegetation, fallow land, population den-sity and presence of existing soil erosion. Methodologies forthe preparation of these selected factors were discussed inSection 2.2.
2.4.2. Rating score and standardizationThe assessment of the soil erosion hazard was first attempted
by selecting the dominant factors of soil erosion and then estab-lishing the threshold of severity of hazard for each factor. In thisstudy, to determine the degree of hazard for each factor, the AHPutilizing expert judgment was used (Saaty, 1977). From the view-point of decision makers, the AHP has several advantages and itcan also be used with different dimensions and distribution of dataincluding both qualitative and quantitative data. In this research,all factors were transformed to quantitative factor maps, whichshow the degree of hazard in each factor using the AHP. Detailsof the AHP method can be found in the literature (Saaty, 1977;Saaty, 1980; Malczewski, 1999, 2006). However, the AHP is a sys-tematic evaluation method able to treat complex and multi-indexsystems in a quantitative process, which decomposes the complexproblem and compares and calculates corresponding weights foreach factor. Beyond the decomposition principle, the AHP is basedon pairwise comparisons of elements in a decision hierarchy withrespect to the parent element at the next higher hierarchical level
(i.e. among criteria and lower level elements). Pairwise compar-isons are made on a scale of relative importance where the decisionmaker expresses preferences between two elements on a ratioscale. First, a decision-maker makes a comparison between eachelement under evaluation. Later, these are converted to quantitative
1728 Md.R. Rahman et al. / Ecological Mod
Table 2Continuous rating scale for pairwise comparison of Saaty’s method.
Scale Definition
9 Extremely
More important7 Very strongly5 Strongly3 Moderately1 Equally important1111
viest2
aeeadoufefAiis
S
wmvw
2
ematfGTioa
Z
wimomiztcf
and multiplied with corresponding weight value in each grid and
/3 Moderately
Less important/5 Strongly/7 Very strongly/9 Extremely
alues using a scale designed by Saaty (1977) which provides rat-ngs on a nine-point continuous scale (Table 2). Moreover, theigenvalue approach of the AHP provides an assessment of the con-istency of the judgments (consistency ratio), aiming to improvehe coherence among redundant judgments (Wolfslehner et al.,005).
In this study, a 0–1 scale and pairwise comparison techniquelong with the AHP was used to determine the degree of hazard forach category of each factor. If a category of a factor has almost noffect on soil erosion, the degree of hazard was set to 0, while if it hadgreat effect, the degree of hazard was assigned 1. Otherwise theegree of hazard was assigned a value between 0 and 1 dependingn the extent of its effect. Using this process and the WEIGHT mod-le in the IDRISI Andes software, the pairwise comparison matrixor the categories of each factor was prepared individually and theigenvectors for weights were calculated for each category of eachactor. The consistency ratio was accepted if it was less than 0.10.fterwards, each factor map was reclassified using the correspond-
ng eigenvector weight of each category to transform all factor mapsnto value maps (the degree of hazard). Finally, these maps weretandardized using the maximum value method, shown in Eq. (6).
ij = xij
xjmax× 10 (6)
here Sij is the standardized value of grid i of factor j, xij is theeasured value (weight) of grid i of factor j, xjmax is the maximum
alue of factor j. For convenience of processing, all transformed dataere magnified 10 times.
.4.3. Z-score analysisAs a complex system with multi-subjects and multi-levels of soil
rosion, a synthetic evaluation index for soil erosion hazard assess-ent is crucial and often difficult to solve to make the results more
ccurate and comparable. Therefore, in order to provide an objec-ive result for the soil erosion hazard assessment using selectedactors, a numerical model was proposed combining Z-score withIS. Z-Scores are a special application of the transformation rules.he Z-score for an item indicates how far and in what direction thattem deviates from the mean of the distribution, expressed in unitsf standard deviation of the distribution. Thus a Z-score is expresseds Eq. (7).
ij = xij − �i
�i(7)
here Zij is the Z-score value of grid i of factor j, xij is the standard-zed value (degree of hazard, raw score) of grid i of factor j, �i is the
ean value of grid i of the factors and �i is the standard deviationf grid i of the factors. The mathematics of the Z-score transfor-ation is such that if every item in a distribution is converted to
ts z-score, the transformed scores will necessarily have a mean ofero and a standard deviation of one. Therefore, Z-scores are some-imes called “standard scores”. Thus, raw scores of a data set can beonverted into standard scores using Z-score analysis. The z-scoreormula gives a way of normalizing the data to a common standard
elling 220 (2009) 1724–1734
based on how many standard deviations the value lies from themean. The Z-score transformation is especially useful when seek-ing to compare the relative standings of items from distributionswith different means and/or different standard deviations. There-fore, no matter what the data looks like, no matter what the meanvalue is, data can be reduced to one standard table by reformulat-ing the data using the z-score formula. It is possible to then take alltypes of experiments and build normalized tables using the z-scoreapproach. In this study, before aggregating the factors, the degreesof hazard were individually normalized to a standard score usingZ-score analysis to make the results more consistent and compa-rable. To do so, the mean and standard deviation maps were firstproduced with nine factors, and then using Eq. (7) the Z-score mapof each factor was prepared using GIS. Afterwards, for convenienceof further processing, all z-score maps were rescaled into the 0–10range using Eq. (6).
2.4.4. Weight assignment by AHPSince soil erosion is a complex effect of different biophysical
factors and the degree of effect depends on the behaviour of thespecific factors, it is necessary to establish a set of weights for eachfactor to place the importance/impact of each factor in the contextof the erosion hazard. Therefore, in order to evaluate those weightsmore objectively, the AHP technique with expert knowledge wasapplied. The same procedure was applied which was followed todetermine the degree of hazard for each factor (Section 2.4.2), andWEIGHT module of IDRISI Andes was also used. This step involvedthe design of a pairwise comparison matrix whereby each factorwas assigned a weight based on its significance relative to each ofthe other factors. For instance, vegetation cover, slope and rainfallare the most important factors for erosion in an area; therefore incomparison scale, importance was given more in these factors thanothers. In addition, the positive and negative impacts of the factorwere also taken under consideration. For example, a dense vegeta-tion cover reduces soil erosion even though the slope and rainfallare relatively high in an area (i.e. the area is covered by dense vege-tation and results in low soil erosion and vice versa), and in contrast,fallow land results in increased soil erosion compared to othertypes of land covers. So, a pairwise comparison scale was derivedbased on Table 2 and according to the relative importance/impacton erosion hazard. Based on the above analysis, Table 3 showsthe synthesizing judgment matrix formed by comparisons ofimportance levels, as well as the weights of the dominant factorsconsisting of the components of the eigenvector corresponding tothe maximum eigenvalues of the synthesizing judgment matrix.It should be pointed out that the consistency test of the matrixshould be performed by examining the consistency ratio. Theconsistency ratio was accepted as it was less then 0.10 (Table 3).
2.4.5. Evaluation: weighted linear combinationOnce all factor maps were in a form representing the degree of
erosion hazard for each pixel derived from the different aspects andperspectives, and their corresponding relative importance weightswere assigned, the evaluation of the soil erosion hazard index (SEHI)could be processed. For better confidence level and accuracy, theweighted linear combination (WLC) was adopted to derive the over-all SEHI. The WLC technique is a decision rule for deriving compositemaps using GIS. It is one of the most often used decision models inGIS (Malczewski, 2000). In WLC, the values of all factors are overlaid
the integrated value is used to determine the hazard condition. Theintegrated assessment value of each grid is the sum of the corre-sponding weight values of all the factors and can be express as Eq.(8). It is defined for a total effect of all factors on soil erosion usinga single term.
Md.R. Rahman et al. / Ecological Modelling 220 (2009) 1724–1734 1729
Table 3Pairwise comparison matrix by AHP for factors weight (Z-score maps).
Factors Z1 Z2 Z3 Z4 Z5 Z6 Z7 Z8 Z9 Eigenvector (weight)
NDVI (Z1) 1 0.19Slope (Z2) 1/2 1 0.16Rainfall (Z3) 1/2 1/3 1 0.14Fallow land (Z4) 1/3 2 2 1 0.14K-factor (Z5) 1 1/2 1/2 1 1 0.09Soil erosion (Z6) 1 1 1/3 1 1/2 1 0.09Elevation (Z7) 1/2 1/2 1/3 1 1 1 1 0.08Soil depth (Z8) 1/3 1/3 1/3 1/3 1 1 1/3 1 0.06Population (Z ) 1/3 1/4 1/4 1/4 1 1 1 1/3 1 0.05
tio = 0
H
wines
H
sftfatda
3
gofmaabtsftswsgTttefesat
9
Consistency ra
i =n∑
j=1
Zij × Wj (8)
here Hi is the synthetic soil erosion hazard index value of grid i, Zijs the z-score value of grid i of factor j, Wj is the weight of factor j andis the total number of factors. Therefore, using the eigenvector of
ach factor from Table 3 and Eq. (8), the final model for SEHI can betated as Eq. (9).
i = 0.19Z1 + 0.16Z2 + 0.14Z3 + 0.12Z4 + 0.09Z5 + 0.09Z6
+ 0.08Z7 + 0.06Z8 + 0.05Z9 (9)
In the computed index, high SEHI values represent a high ero-ion hazard, and in contrast, low SEHI values represent a low hazardor erosion. The result computed from the SEHI model was a con-inuous value and therefore, according to the equality distributionunction, the result of the synthetic evaluation index was gradeds four levels of hazard (low, moderate, high and very high) usinghe equal distance cluster principle. Each level presents the spatialistribution and regional difference of soil erosion hazard for therea.
. Results and discussion
Combining Z-score analysis with GIS, this study sets out an inte-rated approach to delineate the hazard condition of soil erosionf the area. In this model, determination of the degree of hazardor each dominant factor for soil erosion was an important assess-
ent process accomplished using the AHP. Before integration ofll factors in the model, Z-score analysis with GIS was performednd a Z-score map was prepared for each factor to normalize theias of any factor in the evaluation process, based on the principlehat the z-score gives a way of normalizing the data to a commontandard based on how many standard deviations each value liesrom the mean. Z-score map of each factor is shown in Fig. 3. Inhese maps, high Z-score values represent a high impact on soil ero-ion and vice versa. These Z-score maps, with their factor weights,ere used to compute the integrated SEHI, and the SEHI was clas-
ified to generate a soil erosion hazard map. The statistics of theraded/classified soil erosion hazard of the area are presented inable 4, which shows that half of the total area (52.09%) was foundo be under a moderate hazard of soil erosion. Another 23.09% of theotal area was under a high level of soil erosion hazard. It is inter-sting that the area under very high hazard status only accounted
or a very small proportion (2.00%) of the total area. The area of lowrosion hazard occupied 13.83% of the total area. Table 4 furtherhows that most of the soil-eroded area was in the low and moder-te hazard level, and occupied 65.92% of the total area. Therefore,he study area, in general, is exposed to a moderate hazard of soil.09
erosion. Moreover, in Fig. 4, the comparisons of the degree of soilerosion and erosion hazard of the area depicted that the major-ity of the eroded areas belong to the low (64.85%) and moderate(21.92%) level of erosion, while the maximum erosion hazardousareas were found in the moderate (52.09%) and high (23.09%) levelof hazard (Fig. 4). Therefore, it can be said that the present rate ofsoil erosion is denoted as a low to moderate level of erosion, butthere is a probability that the rate of erosion will increase in future,as hazard is the probability of occurrence of a potential damagingphenomenon, within a period of time and a given area (Varnes,1984).
The spatial distribution of soil erosion hazard (Fig. 5) depictedthat the hazard condition was very high in the distinct north-west, north-east and western parts of the county. These areas weremainly distributed in the uncultivated, fallow and sparsely vege-tated areas. High erosion hazard areas were widely distributed inthe north, north-west and central part of the County. With the spa-tial pattern, the areas under high level of soil erosion hazard weredistributed mainly in the cultivated and sparsely vegetated areas.The area under low hazardous condition of soil erosion was dis-tributed mainly in the central-southern part of the County, whichinclude areas mostly covered by dense vegetation. The areas undermoderate erosion hazard were distributed widely in the extremenorthern and southern areas and also in the western and easternparts of the County (Fig. 5). So, it may be said that hazardous con-ditions for soil erosion in the area have an east-west geographicaldistribution, and in general, the erosion hazard is comparativelyhigh in the northern and central parts and near the built-upareas of the County, indicating the most vulnerable areas for soilerosion.
In this study, the Town/Block wise distribution of soil ero-sion hazard was also summarized. To do so, the erosion hazardand Town/Block maps were overlayed and analyzed. The analysisshowed that the area under moderate erosion hazard was obviousin all the Towns/Blocks except Junxian. In these Towns/Blocks thearea under moderate hazard ranged from 40% to 85% of the totalarea (Fig. 6). On the other hand, in six Towns/Blocks (namely Junx-ian, Tutai, Shigu, Gaoping, Xijiadian and Dingjia Yin), the area underhigh erosion hazard ranged from 23% to 54% of the total area. Amongthese Towns/Blocks, Shigu, Xijiadian, Junxian and Gaoping werealso under very high erosion hazard. So, it can be said that theseTowns/Blocks were the most vulnerable within the investigatedarea with respect to the soil erosion hazard.
From the above findings, the soil erosion hazard of the Dan-jiangkou County exposed that in general, the degree of soil erosionhazard was at a moderate level. However, about one fourth of the
total area (25.09%) was identified as high and very high hazardareas. In the County, vegetation degradation, climate condition,unplanned cultivation of agricultural land and poor quality soilshad a serious influence on water erosion. In addition, to grow suffi-cient food for the increasing population, more and more forestland
1730 Md.R. Rahman et al. / Ecological Modelling 220 (2009) 1724–1734
p of th
wtdivpatoc
Fig. 3. Z-Score ma
as reclaimed into farmland over time, which introduced unsus-ainable land-use practices such as slope cultivation and vegetationevastation, resulting in the high level of soil erosion. Therefore,
n terms of soil erosion hazard zonation, the areas under high toery high hazard were recognized. Hence, it is recommended that
roper attention should be paid to control soil erosion and thatgricultural sustainable development is practiced in these partshe County. However, it was observed that in the area; the extentf mountains, slope conditions and elevation dependent climateause great differences in natural resources and conditions. Theree selected factors.
was a good vegetation cover in the mountainous areas with higherslope, which was distributed mainly in the northern and southernparts of the County. Due to the impact of the biophysical condi-tions of the area, the results strictly represent the regional features,and the low and moderate hazard areas were found mainly in the
extreme north, south east and southern parts of the County. There-fore, the findings of this study also highlighted that the vegetationis an important factor and soil erosion risk can be reduced to a satis-factory level by increasing vegetation cover in the area even thoughthe areas belong to higher slope gradients and high rates of rainfall.
Md.R. Rahman et al. / Ecological Modelling 220 (2009) 1724–1734 1731
Table 4Statistics of soil erosion hazard with rate of soil erosion.
Erosion hazard class (SEHI) Rate of soil erosion Area in hectare % of the total area Cumulative %
Low (≤2.5) Low (≤40 t h−1 year−1) 43101.80 13.83 13.83Moderate (2.5–5.0) Low (≤40 t h−1 year−1) 120724.12 38.75
Medium (40–100 t h−1 year−1) 41556.35 13.34Total 162280.47 52.09 65.92
High (5.0–7.5) Low (≤40 t h−1 year−1) 37481.73 12.03Medium (40–100 t h−1 year−1) 25865.04 8.30High (100–200 t h−1 year−1) 8606.36 2.76Total 71953.13 23.09 89.01
Very high (>7.5) Low (≤40 t h−1 year−1) 739.16 0.24Medium (40–100 t h−1 year−1) 867.41 0.28High (100–200 t h−1 year−1) 850.19 0.27Very high (>200 t h−1 year−1) 3778.98 1.21Total 6235.74 2.00 91.01
Water bodies 27987.26 8.98 100.00
T
hsm(ctltnhclrortretcfau
4. Erosion hazard management strategies
Soil erosion estimation and hazard assessment is essential forthe proper planning and management of future soil erosion dis-asters. Therefore, the developed soil erosion hazard map can be
otal
In order to test the effectiveness of the predictive soil erosionazard map in Fig. 5 it was spatially correlated with the NDVI,lope, elevation and rainfall maps, as the erosion is dependentainly on terrain, vegetation, rainfall and land cover of the area
Fan et al., 2004). Using image to image correlation, the coeffi-ient of correlation was computed with trend line (regression) usinghe IDRISI Andes image processing software (Fig. 7). Linear corre-ation coefficient analysis between SEHI and NDVI revealed thathe relationships between erosion hazard and vegetation cover wasegatively correlated (r = −0.41), meaning that the rate of erosionazard was decreasing in respect to the increasing of vegetationover of the area (Fig. 7a). It may be mentioned that in this case aow r value was found due to the impact on soil erosion of otherelated factors like slope, rainfall, land use/land cover, etc. On thether hand, the correlation coefficient between SEHI and slope,ainfall and altitude showed that the erosion hazard was posi-ively correlated with these factors (r = +0.59, r = +0.64 and r = +0.42,espectively, Fig. 7b–d). Thus, correlation analysis showed that therosion hazard was related particularly to the pattern of the vegeta-ion cover, rainfall, slope and elevation of the area. In other words,
orrelation between erosion hazard and the selected biophysicalactors supported the general ecological perception (hypothesis)nd indicated the validity of the erosion hazard map developedsing the model described here. However, though the correlationFig. 4. Degree of soil erosion and soil erosion hazard (based on Table 4).
311558.40 100.00
coefficient illustrated that the NDVI, slope, rainfall and altitude con-strained the spatial pattern of soil erosion hazard, it also signifiedthat the factors were interrelated. Hence, a very high correlationcoefficient (e.g. r = >±0.70) was not found in correlation analysis(Fig. 7). As a result, the soil erosion hazard is a combined effect ofseveral factors of the area, and therefore, in erosion conservationplanning, the impact of several factors needs to be considered.
Fig. 5. Spatial distribution of soil erosion hazard.
1732 Md.R. Rahman et al. / Ecological Modelling 220 (2009) 1724–1734
stribu
fomtphptgcemocj
scoatl
Fig. 6. Town/Block wise di
urther incorporated into land-use planning decisions. The resultsf the study can be used as basic data to assist conservationanagement and land-use planning, and the methods used in
his study are valid for generalized planning and assessment pur-oses to identify areas that are vulnerable to soil loss. This mayelp to reduce potential erosion damage in the study area. A com-rehensive plan addressing soil erosion hazard management isherefore, necessary. This plan should combine land-use strate-ies for each zone with careful consideration of certain structuralontrols. This can be achieved by minimal disruption of naturalnvironments. Table 5 presents an example of general manage-ent strategies based on this study, field investigations and expert
pinion. These strategies could serve as basic components in aomprehensive erosion management plan for the study area, Dan-iangkou County.
Soil erosion in the study area was a combination of natural ero-ion and accelerated erosion. The accelerated erosion arises from
ultivation, spoiled vegetation, uncontrolled infrastructure devel-pment, overgrazing, road construction, and from other humanctivities. It may also arise due to the lack of proper conserva-ion practices. Therefore, preservation of natural vegetation, properand-use planning and appropriate conservation processes shouldFig. 7. Correlation coefficient wi
tion of soil erosion hazard.
be the top priority when formulating policy for the management ofsoil erosion. High and very high hazard areas are the most importantareas to concentrate management effort due to their vulnerability.These areas are mainly distributed in low to moderate altitudes andthe low to moderate slope gradient belt, near built-up areas and inareas where the vegetation cover is low to very low. These areaswhere the rate of soil erosion is high to very high, and which arecore to economic development. These areas are also concentratedin the farmlands (cropland) areas. Therefore, in these areas prior-ity should be given to reduce or control the rate of soil erosion bymeans of conservation planning (Table 5). The moderate soil ero-sion hazard areas can be considered as focal regions for protectionand recovery. Thus, the first step should be taken to protect the areafrom further erosion, and then priority should be given to reducethe soil erosion and restoring destroyed vegetation. Low erosionhazard areas should be key regions for strict protection due to rea-sonable rates of soil erosion and low probability of further erosion.
This region is located in high altitude areas and it will be diffi-cult for the vegetation to recover if it is destroyed. Therefore, thisregion should be strictly protected from human activities, which areobviously a major cause of vegetation degradation and soil erosionacceleration.th regression (trend line).
Md.R. Rahman et al. / Ecological Modelling 220 (2009) 1724–1734 1733
Table 5General soil erosion hazard management strategies—Danjiangkou County.
Erosion hazard class Rate of soil erosion General management strategies
Low (≤2.5) Low (≤40 t h−1 year−1) Should be protected strictly. Lumbering and human activities not permitted.
Moderate (2.5–5.0) Low (≤40 t h−1 year−1) Should be protected form further erosion. Protected from vegetation degradationand removal. Stabilisation through plantations.
Moderate (40–100 t h−1 year−1) Process threatening to eco-environment. Protected from vegetation degradationand removal. Stabilisation through plantations. Lumbering and other humanactivities which cause degradation of the vegetation should be stopped.
High (5.0–7.5) Low (≤40 t h−1 year−1) There is a probability that the rate of erosion will increase in future in this area.Therefore, proper land-use planning is needed such as suitable cropping patternfor agricultural land. Low development densities may be allowed under certainconditions.
Moderate (40–100 t h−1 year−1) Carry out a scheme of forestation and establish a plan for proper resourceutilization. Crop rotation practice should be maintained. Environmental impactassessment (EIA) must be performed prior to allocation for development of anynew infrastructure. Lower cost erosion control techniques should be applied.
High (100–200 t h−1 year−1) Strictly maintain suitable cropping pattern and crop rotation practice. Planningshould be taken for restoring degraded vegetation and restoration. EIA must beperformed prior to allocation for the development of any new infrastructure.Community based soil erosion management program should be introduced andlower cost erosion control techniques should be applied.
Very high (>7.5) Low to moderate (≤40 and 40–100 t h−1 year−1) Should be rigorously protect from further erosion and preference should be givento the agronomic measures of soil conservation, such as conservation tillage, in theconservation planning. Lower cost erosion control techniques can also beimplemented.
High to very high (100–200 and >200 t h−1 year−1) Priority to be given for soil conservation on emergency basis. To control andprotect areas from severe soil erosion, preference should be given to agronomicmeasures of soil conservation, such as conservation tillage, in conservation
5
sopesdegTama
hcIwwrtsoeteetrtbas
. Conclusions
This research, supported by the Z-score analysis with GIS andelected parameters, provided a hazard assessment of soil erosionf the area. The methodology of combining the Z-score with GISrovided an improved method for the synthetic evaluation of soilrosion hazard, which extended the GIS capability of spatial analy-is and the Z-score capability of multi-layer analysis. This researchemonstrates that the model developed at the pixel level, was anffective tool for fast assessment of soil erosion hazard by the inte-ration of remotely sensed data, AHP, Z-score and GIS techniques.he approach described was able to determine soil erosion haz-rd quantitatively over a comparatively large area. Therefore, theethod could be very interesting to policy makers and planning
uthorities, particularly to soil scientists and conservationists.Previously, a variety of models and spatial analysis techniques
ave been used to assess erosion hazard. Many of them were con-erned with simple ranking or conventional multi-criteria analysis.n these methods, the degree of hazard for each factor and factoreights were the main steps for the evaluation of hazard and thereas no emphasis given to the combined effects of factors and inter-
elationship of the factors. In this study a framework is providedo effectively design and evaluate hazardous situations based oneveral factors, their severity and consideration of combined effectf factors on erosion, which normalize the bias of any factor in thevaluation process. The results obtained form the model depictedhat some areas were found under high and very high levels of soilrosion hazard even though the hazardous condition was at mod-rate levels in general in the area. Thus, the modelling exercise inhis study made it possible to identify hot-spot areas of erosion that
equire urgent intervention. In these areas the work for conserva-ion and protection should be carried out according to a priorityasis. The soil erosion hazard can be reduced by taking appropri-te management strategies for the conservation and protection ofoil erosion. Therefore, on the basis of this study, comprehensiveplanning. Agronomic measures are more suited to existing farming systems. Someother engineering structures for controlling soil erosion (terraces, contourbandings or contour hedgerows, etc.) should be implemented as appropriate.
management strategies for soil disaster were proposed (Table 5).It is anticipated that proposed strategies can contribute to effec-tive soil erosion hazard control and management. Finally, it couldbe concluded that soil erosion hazard maps along with existingsoil erosion maps can be effectively used to formulate appropri-ate management strategies and planning for the protection andconservation of soil erosion. By appropriate adjustment of somefactors with local relevance, the model (evaluation index system)could be applied to other regions. This model was applied at aregional scale, but if the data of determining factors were availableat a larger scale, it could be applied to evaluate the soil erosionhazard for small areas as well. Hence, the authors feel that thehazard-dependent aggregation method discussed in this paper, andthe concept of spatially variable decision rules advocated, consti-tute an interesting approach in spatial decision-making. Within thiscontext, SEHI is considered useful for spatial planning within thebroader environmental and risk management domains. The mainadvantage of the method proposed is the incorporation of spatiallyvariable factors into the decision-making process to determine ero-sion hazard. The mathematical background of the method and anexample of its application in soil erosion hazard assessment are pre-sented and discussed. The authors suggest that the method couldbe useful as an analytical and decision-making tool for the incor-poration of spatially variable risk perception in GIS-based decisionsupport systems. However, when natural resources, environmentaland ecological systems are modelled and mapped with the aid ofGIS and remotely sensed data, there is a need for validation stepfor the developed model. Steps for such validation are particularlydifficult when the size of the study area is comparatively large. Inthis study, regression analysis with correlation coefficient was per-
formed between the hazard map and some selected biophysicalparameters (vegetation, slope, elevation and rainfall) to determinethe effectiveness and validation of the result. The predicted resultsare related to the general ecological perception (hypothesis) anddenote the validity of the erosion hazard map using the model
1 al Mod
dfc
A
vtt4
R
C
C
C
E
F
F
G
H
HJ
K
LL
M
M
M
M
M
M
M
M
734 Md.R. Rahman et al. / Ecologic
escribed here. However, only a few factors were considered, there-ore, further application and validation of this method to moreomplicated landscapes may be needed.
cknowledgements
Authors are gratefully acknowledged the financial support pro-ided by the Major State Basic Research Development Program ofhe Peoples Republic of China (Project Number 2007CB407201) andhe National Natural Science Foundation of China (Project Number0671114).
eferences
arpenter, G.A., Grossberg, S., Reynolds, J.H., 1991. ARTMAP: supervised real timelearning and classification of nonstationary data by a self-organizing neuralnetwork. Neural Netw. 4, 565–588.
hankong, V., Haimes, Y.Y., 1983. Multi-Objective Decision-Making, Theory andMethodology. North-Holland, New York.
onoscenti, C., Maggio, C.D., Rotigliano, E., 2008. Soil erosion susceptibility assess-ment and validation using a geostatistical multivariate approach: a test inSouthern Sicily. Nat. Hazards 46, 287–305.
astman, J.R., 1997. IDRISI for Windows, Version 2.0, Tutorial Exercises. Clark Uni-versity.
an, J.R., Zhang, J.H., Zhong, X.H., Liu, S.Z., Tao, H.P., 2004. Monitoring of soil erosionand assessment for contribution of sediments to rivers in a typical watershed ofthe upper Yangtze river basin. Land Degrad. Dev. 15, 411–421.
lanagan, D.C., Nearing, M.A., 1995. USDA-Water erosion prediction project: hillslopeand watershed model documentation. NSERL Report No. 10. West Lafayette Ind.USDA-ARS National Soil Erosion Research Laboratory.
ournellos, Th., Evelpidou, N., Vassilopoulos, A., 2004. Developing an erosion riskmap using soft computing methods (case study at Sifnos Island). Nat. Hazards31, 63–83.
e, D., 1999. Modeling of dominating factors to soil and water conservation. FujianForest. Coll. Trans. 19 (1), 26–29.
undson, N.W., 1981. Soil Conservation. Batsford, London.ozefaciuk, C., Jozefaciuk, A., 1993. Gullies net density as a factor of water system
deformation in Vistula River basin. In: Banasik, K., Zbikowski, A. (Eds.), Runoffand Sediment Yield Modelling. Warsaw Agricultural University Press, Warsaw,pp. 169–174.
rivtsov, V., 2004. Investigations of indirect relationships in ecology and environ-mental sciences: a review and the implications for comparative theoreticalecosystem analysis. Ecol. Model. 174, 37–54.
al, R., 2001. Soil degradation by erosion. Land Degrad. Dev. 12, 519–539.i, A.N., Wang, A.S., Liang, S.L., 2006. Eco-environmental vulnerability evaluation in
mountainous region using remote sensing and GIS: a case study in the upperreaches of Mingjiang River, China. Ecol. Model. 192, 175–187.
akropoulos, C.K., Butler, D., 2006. Spatial ordered weighted averaging: incor-porating spatially variable attitude towards risk in spatial multi-criteriadecision-making. Environ. Model. Software 21, 69–84.
alczewski, J., 1999. GIS and Multi-criteria Decision Analysis. Wiley, New York, pp.392.
alczewski, J., 2000. On the use of weighted linear combination method in GIS:common and best practice approaches. Trans. GIS 4 (1), 5–22.
alczewski, J., 2006. GIS-based multicriteria decision analysis: a survey of the liter-ature. Int. J. Geogr. Informat. Sci. 20 (7), 249–268.
annan, B., Roy, J., Ray, A.K., 1998. Fuzzy ARTMAP supervised classification of multi-spectral remotely-sensed images. Int. J. Remote Sens. 19, 767–774.
asoudi, M., Patwardhan, A.M., 2006. Risk assessment of water erosion for the QarehAghaj subbasin, southern Iran. Stoch. Environ. Res. Risk Assess. 21, 15–24.
assam, B., 1988. Multi-criteria decision making techniques in planning. Progr.Plann. 30 (1), 1–84.
cCool, D.K., Brown, L.C., Foster, G.R., 1987. Revised slope steepness factor for theUniversal Soil Loss Equation. Trans. ASAE 30, 1387–1396.
elling 220 (2009) 1724–1734
Mendicino, G., 1999. Sensitivity analysis on GIS procedures for the estimate of soilerosion risk. Nat. Hazards 20, 231–253.
Morgan, R.P.C., Quinton, J.N., Rickson, J.R.J., 1992. Soil erosion prediction model forthe Europian Community. GB-ISCO-WASWC.
Moussa, R., Voltz, M., Andrieux, P., 2002. Effects of the spatial organization of agricul-tural management on the hydrological behaviour of a farmed catchment duringflood events. Hydrol. Process. 16, 393–412.
Onyando, J.O., Kisoyan, P., Chemelil, M.C., 2005. Estimation of potential soil erosionfor river perkerra catchment in Kenya. Water Resour. Manage. 19, 133–143.
Rahman, Md.R., Islam, A.H.M.H., Rahman, M.A., 2004. NDVI derived sugarcane areaidentification and crop condition assessment. Plan Plus 1 (2), 1–12.
Rahman, Md.R., Saha, S.K., 2007. Flood hazard zonation—a GIS aided multicriteriaevaluation approach (MCE) with remotely sensed data. Int. J. Geoinformat. 3 (3),25–37.
Rahman, Md.R., Saha, S.K., 2008. Remote sensing, spatial multi criteria evaluation(SMCE) and analytical hierarchy process (AHP) in optimal cropping pattern plan-ning for a flood prone area. J. Spatial Sci. 53 (2), 161–177.
Rahman, Md.R., Saha, S.K., 2009. Spatial dynamics of cropland and cropping patternchange analysis using Landsat TM and IRS P6 LISS III satellite images with GIS.Geo-Spatial Informat. Sci. 12 (2).
Rahman, Md.R., Shi, Z.H., Chongfa, C., 2009. Land use/land cover change analysisusing geo-information technology: two case studies in Bangladesh and China.Int. J. Geoinformat. 5 (2).
Renard, K.G., Foster, G.R., Weesies, G.A., McCool, D.K., Yoder, D.C., 1997. Predicting soilerosion by water: a guide to conservation planning with the Revised UniversalSoil Loss Equation (RUSLE). Agriculture Handbook, 703. USDA, Washington.
Renard, K.G., Foster, G.R., Weesies, G.A., Porter, J.P., 1991. RUSLE, revised universalsoil loss equation. J. Soil Water Conserv. 46 (1), 30–33.
Renard, K.G., Freimund, J.R., 1994. Using monthly precipitation data to estimate theR factor in the revised USLE. J. Hydrol. 157, 284–306.
Rooseboom, A., Annandale, G.W., 1981. Techniques applied in determining sedimentloads in South African rivers. Int. Assoc. Sci. Hydrol. Publ. 133, 219–224.
Ross, T., 1995. Fuzzy Logic with Engineering Applications. McGraw-Hill, New York.Saaty, T.L., 1980. The Analytic Hierarchy Process. McGraw-Hill, New York.Saaty, T.L., 1977. A scaling method for priorities in hierarchical structures. J. Math.
Psychol. 15, 234–281.Souchère, V., Cerdan, O., Dubreuil, N., Le Bissonnais, Y., King, C., 2005. Modelling
the impact of agro-environmental scenarios on overland flow in a cultivatedcatchment (Normandy, France). Catena 61, 229–240.
Stocking, M.A., Elwell, H.A., 1973. Soil erosion hazard in Rhodesia. Rhodesian Agric.J. 70, 93–101.
Thomas, R.H., Huggert, R.J., 1980. Modelling in Geography: A MathematicalApproach. Harper & Row, London.
Tian, Y.C., Zhou, Y.M., Wu, B.F., Zhou, W.F., 2008. Risk assessment of water soilerosion in upper basin of Miyun Reservoir, Beijing, China. Environ. Geol.,doi:10.1007/s00254-008-1376-z.
Tkach, R., Simonovic, S., 1997. A new approach to multicriteria decision mak-ing in water resources. J. Geogr. Informat. Decis. Anal. 1 (1), 25–43,http://publish.uwo.ca/∼jmalczew/gida.htm (4-2-2000).
Varnes, D.J., 1984. Landslide Hazard Zonation: A Reveiw of Principles and Practice.Unesco, Paris.
Vezina, K., Bonn, F., Van, C.P., 2006. Agricultural land-use patterns and soil erosionvulnerability of watershed units in Vietnam’s northern highlands. LandscapeEcol. 21, 1311–1325.
Voogd, H., 1983. Multicriteria Evaluation for Urban and Regional Planning. Pion,London.
Williams, J.R., Jones, C.A., Dyke, P.T., 1990. The EPIC model. United States Departmentof Agriculture (USDA) Technical Bulletin No. 1768.
Williams, J.R., Renard, K.G., 1983. A new method for assessing erosion’s effects onsoil productivity. J. Soil Water Conserv. 38, 381–383.
Wischmeier, W.H., Smith, D.D., 1965. Predicting Rainfall Erosion Losses from Crop-land East of the Rocky Mountains. Handbook no. 282. USDA, Washington, DC.
Wolfslehner, B., Vacik, H., Lexer, M., 2005. Application of the analytic network processin multi-criteria analysis of sustainable forest management. Forest Ecol. Manag.
207, 157–170.Wu, Q., Wang, M., 2007. A framework for risk assessment on soil erosion by waterusing an integrated and systematic approach. J. Hydrol. 337, 11–21.
Yuan, L.B., Keli, Z., Yun, X., 2002. An empirical soil loss equation. In: Proceedings ofthe 12th International Soil Conservation Organization Conference, Beijing, China,26–31 May, 2002.