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lable at ScienceDirect
Applied Geography 45 (2013) 1e9
Contents lists avai
Applied Geography
journal homepage: www.elsevier .com/locate/apgeog
Spatially-explicit sensitivity analysis for land suitability
evaluation
Erqi Xu a,b, Hongqi Zhang a,*a Institute of Geographic Sciences
and Natural Resources Research, Chinese Academy of Sciences,
Beijing 100101, PR ChinabGraduate University of Chinese Academy of
Sciences, Beijing 100049, PR China
Keywords:Land suitability evaluationMulti-criteria
decision-makingOne-dimensional sensitivitySpatially explicit
analysisEarth mover’s distance
* Corresponding author. Tel.: þ86 10 64889106.E-mail address:
[email protected] (H. Zhang).
0143-6228/$ e see front matter � 2013 Elsevier
Ltd.http://dx.doi.org/10.1016/j.apgeog.2013.08.005
a b s t r a c t
Land suitability evaluation (LSE) is an important step in
land-use planning. Using multi-criteria decision-making (MCDM)
techniques based on geographic information systems is a flexible
and effectiveapproach for this evaluation process. Implementation
of sensitivity analysis to validate and calibrate theMCDM can
enhance the understanding of the LSE results and assist in making
informed planning de-cisions. The main limitation of sensitivity
analysis in MCDM applications is a lack of insight into thespatial
dimensions. To address this issue, this paper presents a new
framework that incorporates thespatial configuration information
from sensitivity analysis for MCDM. The framework consists of a
landsuitability evaluation and a spatially explicit sensitivity
analysis. The sensitivity analysis couples spatialvisualization and
summary indicators, which include a traditional metric (i.e., the
mean of the absolutechange rate, MACR) and a novel spatially
explicit metric (the Earth Mover’s Distance, EMD). The
newlyreclaimed region of Yili in China was studied as the
representative area. We assumed that the weightswere the only
source of uncertainty and used a one-dimensional sensitivity
analysis. This experimentindicated that the expert LSE results for
wheat are robust but relatively sensitive in local areas to
changesin the weights. Our results confirm that the MACR and EMD
can effectively identify sensitive parametersbased on various
sensitivity aspects. The EMD explores the new information from the
spatial dimensions,which differs from traditional methods for
sensitivity analysis. This approach provides a suitableframework
based on a spatially explicit sensitivity analysis for the
effective implementation of MCDM forrobust LSE results.
� 2013 Elsevier Ltd. All rights reserved.
Introduction
Agricultural production activities are the foundation of
humansurvival and development. With the growth in the population
andthe reduction of arable lands, ensuring effective use of arable
landto meet the growing demand for food requires rational land
usemanagement and planning. Land suitability evaluation (LSE) is
animportant step in this planning. Because the Food and
AgriculturalOrganization (FAO) recommended an approach for LSE
based onclimatic, terrain, and soil properties data (FAO, 1976),
multi-criteriadecision-making (MCDM) techniques have been widely
applied tocombine information from different criteria for the LSE.
The inter-est of researchers in integrating geographic information
systems(GIS) with MCDM has grown steadily (Ceballos-Silva &
López-Blanco, 2003; Hossain & Das, 2010; Kumar, Patel, Sarkar,
&Dadhwal, 2013; Nisar Ahamed, Gopal Rao, & Murthy,
2000;Pereira & Duckstein, 1993; Tenerelli & Carver, 2012).
However,
All rights reserved.
GIS-based MCDM is a multi-disciplinary and multi-step
processthat can result in many sources of uncertainty (Burgman,
2005;Chen, Wood, Linstead, & Maltby, 2011; Wood, Beresford,
Barnett,Copplestone, & Leah, 2009), including criteria
selection, inputdata accuracy, standardization method, weight
calculation, andaggregation method (Elaalem, Comber, & Fisher,
2011; Reshmidevi,Eldho, & Jana, 2009).
The uncertainties can be classified as aleatory or
epistemic(Helton, 1993; Refsgaard, van der Sluijs, Højberg, &
Vanrolleghem,2007). Particularly, the weight assigned to each
criterion is one ofthe most sensitive parameters in MCDM and is a
potential source ofconsiderable uncertainty (Larichev &
Moshkovich, 1995). Forexample, the Analytical Hierarchy Process
(AHP) (Saaty, 2008) isone of the most popular methods for
calculating criteria weights inMCDM via an expert pair-wise
comparison matrix (Hossain & Das,2010; Marinoni, 2004; Ohta et
al., 2007; Vaidya & Kumar, 2006).Using their weights, the
criteria can be subsequently aggregatedinto a single imprecise MCDM
estimation point, which results inuncertainties with no confidence
(Benke, Pelizaro, & Lowell, 2009).Meanwhile, multiple
decision-makers are able to set differentweights and thus derive a
variety of MCDM results for various
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Fig. 1. Location of the newly reclaimed region Yili.
E. Xu, H. Zhang / Applied Geography 45 (2013) 1e92
policy targets (Al-Mashreki, Akhir, Rahim, Lihan, & Haider,
2011;Chen et al., 2011; Roura-Pascual, Krug, Richardson, & Hui,
2010).Therefore, the robustness of the LSE results should be
evaluated foreffective implementation in land-use planning (Fuller,
Gross, Duke-Sylvester, & Palmer, 2008; Ligmann-Zielinska &
Jankowski, 2008).For this purpose, use of the uncertainty and
sensitivity analysis ishelpful in the validation and calibration of
MCDM (Delgado &Sendra, 2004; Merritt, Croke, & Jakeman,
2005; Zoras,Triantafyllou, & Hurley, 2007).
Until now, sensitivity analysis has received only minimal
atten-tion in previous MCDM studies, although this situation is
changing(Chen, Yu, & Khan, 2010; Delgado & Sendra, 2004;
Ligmann-Zielinska, Jankowski, & Watkins, 2012; Lowell, Christy,
Benke, &Day, 2011). It should be noted that the most critical
shortcoming ofsensitivity analysis is a lack of insight into the
spatial dimensions(Chen et al., 2010; Feick & Hall, 2004). This
situation therefore re-quires spatial visualization techniques and
spatially explicitmethods applied in the sensitivity analysis to
create effective in-formation for the planning decision process,
i.e., GIS techniques andsimulation algorithms (Ligmann-Zielinska
& Jankowski, 2008;Mosadeghi, Warnken, Tomlinson, &
Mirfenderesk, 2012; Pannell,1997). Spatial visualization can
display the uncertainties of theevaluation results graphically
based on the uncertainty of the inputparameters and enhance the
experts’ and decision-makers’ under-standing of the possible risk
in identification of parameter sensi-tivity in MCDM (Blaser,
Sester, & Egenhofer, 2000; BojóRquez-Tapia,Cruz-Bello, &
Luna-González, 2012; Chen et al., 2011; Hallisey, 2005;Vitek,
Giardino, & Fitzgerald, 1996).
Few studies have attempted to develop a spatial
sensitivityanalysis for MCDM. Feick and Hall (2004) presented a
method forinvestigating the spatial dimension of the sensitivity of
multi-criteria weights. Chen et al. (2010) presented a
visualizedapproach for analyzing the dependency of MCDM output
onweightchanges and identifying those criteria that are especially
sensitiveto weight changes in a given spatial dimension. Chen et
al. (2011)used an indicator-based method to visually explore the
influenceof uncertainties on MCDM with the application of the
CatchmentEvaluation Decision Support System in the Tamar
catchment.Ligmann-Zielinska et al. (2012) employed a Monte Carlo
simulationand output variance decomposition to represent output
uncer-tainty in spatial form. Tenerelli and Carver (2012) set up a
landcapability model for assessing the potential of perennial
energycrops and performed an uncertainty analysis of the model with
aspatial distribution. Ligmann-Zielinska and Jankowski (2012)
pre-sented an approach for adjusting the criteria preferences based
ondistance measures using the explicit consideration of a
locationalstructure.
However, the aforementioned studies focused primarily onspatial
visualization of the sensitivity analysis and used traditional
statistical methods to summarize the sensitivity results.
Traditionalmethods for calculating the sensitivity indicators of
outputs underuncertainty simulation, i.e., change percentage
(Maguire,Goodchild, & Rhind, 1991), rank order (Benke, Steel,
& Weiss,2011; Butler, Jia, & Dyer, 1997), standard
deviation (Heumann,Walsh, & McDaniel, 2011; Lowell et al.,
2011; Pelizaro, Benke, &Sposito, 2011) and correlation
coefficient (Tenerelli & Carver,2012), consider the outputs of
MCDM as discrete and indepen-dent elements and ignore the spatial
configuration of the evalua-tion results. Evaluating spatially
explicit LSE results in sensitivityanalysis requires insight into
the spatial information of the sensi-tivity analysis. Fortunately,
the Earth Mover’s Distance (EMD),which is a spatial metric used in
image retrieval and histogramcomparison (Rubner, Tomasi, &
Guibas, 2000), provides an oppor-tunity to consider the spatial
dimension of sensitivity analysis.
The objective of this study is to present a new framework
thatincorporates the spatial configuration information of
sensitivityanalysis. We evaluated the LSE based on GIS-MCDM with
weightscalculated using the AHP. The framework examined the
sensitivityof different criteria with changes inweights via spatial
visualizationof the uncertainty outputs and summary sensitivity
indicatorsgenerated by traditional and spatially explicit
methods.
Materials and methods
Study area
The newly reclaimed region located in the valley of Yili
liesroughly between 80�2201400 and 83�305400E and 43�2203700
and44�802200N (Fig. 1) and is one of seven important land
resourcedevelopment regions established by the Ministry of Land and
Re-sources of the People’s Republic of China. Land resource
develop-ment engineers aim to achieve a balance of arable lands
andimprove the land productivity. The Yili River valley, with a
bettermatch of soil and water resources, is a limited potential
region forland resource development inWestern China. Therefore,
this regionrequires effective land-use planning to both combat
desertificationand improve the quality of newly cultivated
lands.
The study area belongs to Yining, Chabuchaer AutonomousCounty,
Huocheng County, andGongliu County in the administrativeregion. The
region covers an area of ca. 5000 km2, with elevationsranging from
661 m to 1572 m and lies within the temperate con-tinental
semi-arid climate zone with a mean annual temperature of8e9 �C, a
mean annual precipitation of 200e500 mm, a meanannual evaporation
of 1200e1900mm, andwater resources that arethe richest in Xinjiang.
Land-use types primarily include grasslandand farmlandwith a
partial distribution of sand and saline areas. Thesoil types
primarily consist of sierozemwith a partial distribution
ofkastanozem above an altitude of 850 m. Other soil types
include
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E. Xu, H. Zhang / Applied Geography 45 (2013) 1e9 3
marsh soil, meadow soil, aeolian sandy soil, and small amounts
ofsaline-alkali soil, damp soil, and chernozem soil.
Methods and data description
The flowchart (Fig. 2) of GIS-MCDM shows a series of basic
stepsfor implementing spatially explicit sensitivity analysis based
onMCDM for LSE.We assumed that theweightswere the only source
ofuncertainty and thus evaluated the sensitivity of the weights
alone.Application of our framework based on a raster consists
primarily oftwo stages: land suitability evaluation and sensitivity
analysis.
Land suitability evaluationThe first stage includes a sequence
of evaluated crop selection,
relevant criteria selection, criteria standardization, weight
calcula-tions, and final aggregation of criteria into the LSE
results. Becausewheat is the main food crop in Yili, it was chosen
for suitabilityevaluation in our study. Seven criteria related to
the wheat suit-ability evaluation were selected by the authors and
experts in Yili,who are familiar with the land-use characteristics
and agriculturaldevelopment of Yili. These criteria are soil
texture (ST), soil depth(SD), soil organic matter (SOM), sand dune
waviness (SDW), soilerosion (SE), water supply and drainage (WSD),
and groundwaterdepth (GD). The detailed data sources of the seven
criteria are listedin Table 1. Our study team excavated soil
profiles and collected soilsamples in the study area (Shi, Yang,
& Wang, 2009), and soil depthand soil organic matter maps were
generated using Kriging inter-polation based on the soil sample
properties in ArcGIS 9.3 (ESRI Inc.,USA). All data were processed
and converted to pixels with 300-mresolution in ArcGIS. Each
criterion was standardized to four suit-ability classes, i.e., high
suitability, moderate suitability, marginalsuitability and
unsuitability, based on the FAO system (FAO, 1976).The
classification threshold values of the criteria and the
standardscores for the corresponding classes given in Table 2 were
obtainedfrom a literature survey and expert opinions. Next, the
weights ofthe criteria were calculated in the AHP by constructing a
pair-wisecomparison matrix (Saaty, 2008) (Table 3). The scale for
the pair-wise comparison in our study ranges from 1 to 9,
indicating equalimportance to extreme importance (Saaty, 2008). The
final pair-wisecomparison matrix was determined using the mean
integer of everyexpert’s criteria pair-wise comparison.
Fig. 2. Flowchart of the spatially explicit sensitivit
The standardized criteria scores were aggregated linearly
byweight for the final land suitability evaluation result. The
equationused for this is given by:
R ¼Xni¼1
wi � ci (1)
where R is the result of the LSE with a high R indicating
highsuitability of the crop,wi is the weight of the i-th criterion
from theAHPwith
Pni¼1 wi ¼ 1, ci is the standard score of the i-th
criterion,
and n is the number of criteria.
One-at-a-time methodIn previous studies, two methods were used
for simulating the
uncertainty of the criteria weights (Feick & Hall, 2004).
Monte Carlosimulation is frequently used to generate random
criteria weights.However, the subjective assumptions for the
parameters of theprobability distributions and the normality of the
distribution areoften subject to bias (Crosetto, Tarantola, &
Saltelli, 2000). Incontrast, the One-At-a-Time (OAT) method
investigates the sensi-tivities of one-dimensional weights by
changing the relative in-fluence of each factor separately, without
assumptions. However,this method ignores the interactions caused by
modifying theweights of multiple factors simultaneously (Butler et
al., 1997). TheOAT method estimates the effect on the evaluation
results of vari-ation in a single input parameter while holding all
other parame-ters fixed at their nominal values (Daniel, 1958;
Rabitz, 1989;Saltelli, Chan, & Scott, 2000). In particular, the
weights are deter-mined by experts instead of random selection and
are constrainedwithin a certain range. Therefore, the OAT method
was used forsensitivity analysis in the second stage of our
framework.
The OAT method requires the setting of two parameters, i.e.,
therange and the step size of the particular weight changes.
Weassigned a step size of �5% and a range of �100%. To ensure that
allcriteria weights sum to one, the new adjusted weight used for
thesensitivity were calculated using the following equation:
wjðcrÞ ¼ ð1þ crÞ �wj (2)
where wjðcrÞ is the particular weight change from the OATmethod;
cr is the change rate of the weight, which can be set to
y analysis for crop-land suitability evaluation.
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Table 1Criteria data sources and processing.
Criteria Input dataset Data source Format Processing
Soil texture (ST) Soil type maps of Yiliregion (1:100000)
Institute of Geographical Sciencesand Natural Resources
Research
Polygon Format transformation
Soil depth (SD) Soil sampling points Fieldwork by the research
team Point Kriging interpolationSoil organic matter (SOM) Soil
sampling points Fieldwork by the research team Point Kriging
interpolationSand dune waviness (SDW) Topographic maps
of Yili region (1:50000)Institute of Geographical Sciencesand
Natural Resources Research
Raster Selection of the sand distributionfrom the land use map
andcalculation of the relative heightfrom the DEM digitized fromthe
topographic maps
Land use map of Yiliregion in 2008
Data Center for Resources and EnvironmentalSciences, Chinese
Academy of Sciences
Soil erosion (SE) Topographic mapsof Yili region (1:50000)
Institute of Geographical Sciencesand Natural Resources
Research
Raster Calculation of the gully densityfrom the above DEM
Water supply and drainage (WSD) Land resource mapof China
(1:1000000)
Institute of Geographical Sciencesand Natural Resources
Research
Raster Resampling
Groundwater depth (GD) Equi-potential line mapof the
groundwater
Department of Water Resources of XinjiangUygur Autonomous Region
in China
Poly-line Format transformation
E. Xu, H. Zhang / Applied Geography 45 (2013) 1e94
�5%, �10%, .. �100% in our study; and wj is the original
weightof the j-th criterion from the AHP. The equation used for the
otherweights is given as follows:
wiðcrÞ ¼ wi �1�wj1�wj
(3)
where wiðcrÞ is the other weight adjusted for wj, i.e., isj; and
wi isthe original weight of the i-th criterion from the AHP.
Next, 280 LSE results were generated for the sensitivity
analysis.The equation used for this calculation is given as:
R�wj; cr
� ¼ wj � cj þXnisj
wi � ci (4)
where R(wj,cr) is the simulated LSE result with wj as the
changerate, cj is the standard score of the i-th criterion, and ci
is the otherstandard score of the criteria, i.e., isj.
Local area uncertaintyThe uncertainty of the simulated results
was represented by the
change rate. Based on the GIS, the uncertainties of every pixel
in theregion using all step sizes for a particular weight were
spatiallyvisualized in the GIS. Thus, the local area difference
visualization of
Table 2Classification threshold values of criteria and standard
scores for suitability evalu-ation of wheat.
Highsuitability
Moderatesuitability
Marginalsuitability
Unsuitability
ST Criteria Loam Medium loam,sandy loam
Clay, heavyloam
Sand
Score 100 70 50 0SD Criteria (m) >0.8 0.5e0.8 0.3e0.5 4.0
2.0e4.0 0.5e2.0 6 3e6 1e3
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Fig. 3. Criteria used in the suitability evaluation of wheat.
Suitability classes are based on the threshold values in Table
2.
E. Xu, H. Zhang / Applied Geography 45 (2013) 1e9 5
s:t: fij �0X
fij � PiX
fij �QiX
fij ¼ min@XPi;XQjA
j i i;j
0i j
1
where ffijg represents the flows. Each fij represents the
amounttransported from the ith supply to the jth demand.We refer to
dij asthe ground distance between bin I and bin j in the
histograms.
Fig. 4. Land suitability evalu
The EMD has been used in image retrieval (Rubner, Guibas,
&Tomasi, 1997; Rubner et al., 2000), comparison of diffusion
tensormagnetic resonance images (Jiao et al., 2010), estimation
ofspatial rainfall distributions (van den Berg, Vandenberghe,
DeBaets, & Verhoest, 2011), and comparison of priority maps
formanaging woody invasive alien plants (Roura-Pascual et
al.,2010). The values of the original and simulated LSE results
are
ation result for wheat.
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Fig. 5. Mean absolute values of the change rate for the land
suitability evaluationunder simulations.
E. Xu, H. Zhang / Applied Geography 45 (2013) 1e96
similar to the gray values of ordinary images. Next, the
EMDcomputations of these two histograms of the LSE maps describethe
spatial differences in both, which are similar to the
imagecontrast. A higher EMD value indicates a greater change in the
LSEmaps. Therefore, we used the EMD as a spatially explicit
metricfor sensitivity analysis to compare the simulated LSE results
withthe changing weights and the original results. We used an
effi-cient algorithm (Pele & Werman, 2009) in MATLAB to
computethe EMD values.
Fig. 6. Change rate maps of the wheat suitability
Results
The standard criteria maps (Fig. 3) were aggregated usingweights
to create the final land suitability map for wheat in thenewly
reclaimed region (Fig. 4). The areas that are highly suitablefor
wheat are primarily located in the southeastern and north-eastern
regions. In contrast, relatively unsuitable land is located tothe
west of the region and along the banks of the Yili River
withdifferent restrictive suitability factors. The major
restrictions onwheat cropping in the northwest are the texture of
the sandy soiland low soil organic matter, whereas in the
southwestern region,these restrictions include thin soil and low
soil organic matter. Soilerosion, water supply and drainage
problems, and high ground-water depth restrict wheat cropping along
the banks of the YiliRiver.
Based on the OAT method, the simulated LSE maps for wheatwere
generated with the weight of each criterion changedfrom �100% to
100% with a step size of 5%. These simulated un-certainty maps can
be used for sensitivity analysis. The MACRssummarize themean of the
absolute change rates for the pixels’ LSEvalues based on the
changing weights (Fig. 5). These values show alinear increase with
an increase in the rate of change of the weightsbut with different
gradients for different criteria. The MACRs of thesame criteria are
almost equal using the same absolute rates ofchange but with
positive and negative values, which indicatessimilar sensitivities
for positive and negative weight changes. Ahigh gradient, which
indicates a greater change in the LSE valueswith the changing
weights, indicates high sensitivity of the crite-rion for LSE. The
ranking of the MACRs for all criteria is as follows:SD > ST >
SOM > SE > GD > WSD > SDW. The rankings of SD and
scores when criteria weights change by 75%.
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Fig. 7. Change rate maps of the wheat suitability scores when
the SOM weight changesby (a) þ50%, and (b) �50%.
Fig. 8. Earth Mover’s Distances for the land suitability
evaluation under simulations.
E. Xu, H. Zhang / Applied Geography 45 (2013) 1e9 7
ST follow the order of the criteria weights. Similarly, WSD and
SDW,which are assigned low weights, are low-sensitivity criteria.
Theweight of a 75% change is taken as an example. The SD, which is
thecriterion most sensitive to weight changes, has a MACR of 5.8%
ifthe weight changes by 75%, whereas SDW is the least
sensitivecriterion with a MACR of just 2.6%. The MACR of the
simulated re-sults is significantly lower than the rate of change
of the weights(Fig. 5), which indicates that the LSE result is
relatively robust.
The rates of change of the LSE values with a 75% change inweight
are displayed for spatial visualization in Fig. 6. This figureshows
a relatively large spatial variation in the LSE values,
whichindicates that sensitivity analysis should be carried out. In
thissituation, the uncertainty maps are displayed for local
comparison.For example, the rates of change of all pixels with a
75% change inthe SD range from �17.1% to 31.8% are displayed in
Fig. 6 andcompared with the MACR of 5.8%. Areas with high LSE
values arerelatively robust, which makes sense because these areas
have highvalues for all criteria scores and are not likely to be
affected by asingle parameter. However, the areas with relatively
low LSE valuesare more sensitive, especially when the changing
weight of thecriterion matches the corresponding restrained
criterion for theseareas. The distribution of sensitivities of
different changes inweights is associated with the distribution of
suitability classes forthe corresponding criterion (Figs. 3 and 6).
Taking the SOMmap of aweight changed by 50% as an example of a
locally visualizedcomparison (Fig. 7), the areas with highly
decreased LSE values aregenerally located where the SOM suitability
class is unsuitable. Incontrast, the areas with highly increased
LSE values have high SOMscores and vice versa (Fig. 7A). In
addition, the uncertainty maps ofthe same criterion with the same
absolute change rate but withpositive and negative values show a
highly similar distribution(Fig. 7A and B). The difference is that
the positive and negative LSEvalues change. This change indicates
similar sensitivities in thesame pixel for weights with the same
changing value, albeit posi-tive and negative.
The EMDwas used as a spatially explicit metric in the
sensitivityanalysis to identify new information from the spatial
dimension forLSE. The EMD is a metric that takes both the numerical
values andthe distribution into consideration. The EMDs show a
linear in-crease with little fluctuation as the rate of change of
the weights
increases. The change in EMD represents the difference in the
nu-merical LSE values for the same criterion. Therefore, for
differentcriteria, a high gradient indicates high sensitivity. The
ranking ofthe EMDs is SOM > SE > ST > SD > GD > WSD
> SDW (Fig. 8),which differs from the ranking of the MACRs. In
this situation, GD,WSD, and SDW are the same three criteria with
the lowest sensi-tivity, but the order of the other criteria has
changed. This result canbe attributed to the spatial distribution
of the corresponding cri-terion. According to Fig. 3, SOM, SE, and
ST display a more discretedistribution than that of the others,
which may result in greaterspatial variations in the LSE values as
the weights of the corre-sponding weights change. Unlike the other
criteria that include ahighly suitable class, the SOM criterion
contains four suitable classareas staggered across the region. This
result can explain why theEMD of SOM is the highest out of all
criteria, although the MACR ofSOM ranks third; it is also the
reason why the SE ranks secondaccording to the EMD but ranks fourth
according to the MACR.Furthermore, it appears that the SOM has a
more staggered spatialdistribution than the SE based on the local
area difference visuali-zation, which results in the sensitive
ranks of these two criteria.
In contrast, SD, which is considered to be the most
sensitiveparameter according to the MACR, exhibits less sensitivity
than theabove mentioned three criteria. This observation may be
attributedto the fact that the distribution of SD is similar to the
distribution inthe original evaluation map. However, SDW has the
lowest MACRvalue because the concentrated distribution in the areas
of the foursuitable classes have the least spatial variation; areas
with low-suitability SDW classes are concentrated in the northwest
of theregion, whereas the other largely contiguous area is highly
suitablefor wheat. Therefore, the differences in spatial
distribution betweenthe LSE maps (which are not easily detected by
the local area dif-ference visualization alone but are easily
ignored by the traditionalmetrics) can be explored using the
EMD.
Thus, LSE for wheat in our study area is robust yet relatively
andlocally sensitive to weight changes. This observation should be
awarning to experts and multiple decision-makers that the
sensi-tivities of the weights for SD and SOM should be taken
intoconsideration. Careful assignment of these two most
sensitivecriteria is beneficial for validation of the MCDM and
robustness ofthe LSE results.
Discussion
We used the MACR and EMD as different metrics to minedifferent
information from the sensitivity analysis in the LSE. The
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E. Xu, H. Zhang / Applied Geography 45 (2013) 1e98
MACRs provide information on the numerical changes in the
LSEvalues, indicating the sensitivity of criteria associated with
the sizeof the weights for different criteria. This result is
consistent withthat of a previous study (Chen et al., 2010).
However, the EMDsexplore the spatial information between maps,
which indicate thesensitivity differences of the criteria primarily
attributed to thespatial distribution of the corresponding
criteria. Although only asingle weight of the criterion is changed
and other weights changewith equal proportions using the OAT
method, the impact of thespatial difference of the criteria
propagated on the distinction offinal sensitivity is output via the
changing weights. This new in-formation from the spatial dimension
provided by the EMD cannotbe detected by traditional methods (Benke
et al., 2011; Butler et al.,1997; Lowell et al., 2011; Maguire et
al., 1991; Pelizaro et al., 2011;Tenerelli & Carver, 2012).
To make informed decisions, it is necessary to understand
therobustness of the evaluation and have confidence in the
decision-making (Chen et al., 2011). Sensitivity analysis can
provide usefulinformation for the LSE. In our study, the comparison
between theMACR of simulated results and the rate of change of the
weightsindicates that the LSE for wheat is relatively robust.
Spatial visu-alization shows that areas with high LSE values have
low un-certainties, which validates the application of the LSE
results forland allocation. The relative robustness can be partly
attributed tothe design of the MCDM because the evaluation results
are sensi-tive to different aggregation techniques (Zanakis,
Solomon,Wishart, & Dublish, 1998). The LSE focuses on selection
of suit-able areas for a given crop for potential land source
development(Shi, 1986). The additive model yields a more
comprehensive resultby combining each criterion and is suitable for
the LSE; therefore,this method is always chosen as the aggregation
technique in theLSE (Al-Mashreki et al., 2011; Chen et al., 2010;
Pelizaro et al., 2011).It makes sense that the additive model is
not susceptible to theinfluence of the individual factors,
especially because the weightsof the model (range from 0.1 to 0.2)
are relatively close in value.
The summary sensitivity analysis can provide a
straightforwardresult (i.e., an exact MACR and EMD value in our
studies), whichdecision-makers prefer to use in planning. The MACR
with a defi-nite statistical value can be easily understood
together with therobustness of the LSE results. However, the
question arises as tohow the EMD (as a new metric in our study) can
provide usefulinformation to assist planners in their
decision-making. Similar toother map comparison applications (van
den Berg et al., 2011; Jiaoet al., 2010; Roura-Pascual et al.,
2010), our results show that theEMD can effectively highlight the
differences between the simu-lated land suitability maps and the
original map for sensitivityanalysis. Different approaches for
cataloging uncertainties haveresulted in misunderstandings with
respect to which uncertaintiescan be resolved by MCDM (Mosadeghi et
al., 2012). Compared withthe MACR, the EMD presents a different
conclusion with respect tothe sensitivity results, which appears to
cause confusion for deci-sion-makers.
The EMD explores the spatial information of sensitivities for
theLSE from the results. We propose that our framework for
sensitivityanalysis should integrate these two metrics for the
MCDM.Consider the following example using SOM and SD. The SOM
wasidentified as the most sensitive factor by the EMD, which
meansthat it causes the largest spatial variation in the LSE values
undersimulation. These variations in different pixels indicate
relativepriority changes in the different pixel LSE values, which
results in alarge contrast in the histograms between the original
and simu-lated LSE maps. Next, these variations are similar to the
imagecontrast and can be detected by the EMD. Furthermore, the
prioritychanges between the pixels’ LSEs cause a decision change
for thearea selected for wheat cropping. Therefore, the EMD
exploration of
the spatial sensitivity of LSE with weight changes can assist in
landallocation planning. In contrast, SD was considered the most
sen-sitive factor according to the MACR, which indicates a
significantvalue variation in the LSE values under simulation.
These variationsrepresent a change in the pixels’ LSE values
compared withthemselves. However, the priorities of the pixels’ LSE
values remainrelatively stable when the SD weight changes, which is
why theEMD ranked fourth out of all criteria. Therefore, these two
metrics,which incorporate different aspects of the information, can
be usedto detect sensitive parameters in our framework for
validation ofMCDM.
Application of the EMD requires further study to confirm
itsvalidity in sensitivity analysis of the LSE and to investigate
its role insupporting decision-making. Although we evaluated only
thesensitivity of the weights, the EMD can be applied to other
sourcesof sensitivity (Burgman, 2005; Chen et al., 2011; Reshmidevi
et al.,2009; Wood et al., 2009). However, there is no rigorous
statisticalmethod for testing whether the LSE maps are
significantly differentusing the EMD. In our study, the EMD can
indicate the relativesensitivity of all criteria but cannot
determine whether a change inthe weight significantly influences
the spatial change in the results.Monte Carlo simulation, which is
used for significance tests inspatial analysis (Schabenberger &
Gotway, 2004), could be apromising approach for improving our
method.
Conclusion
A better understanding of the robustness of the LSE results
canprovide a better aid for its effective implementation in
land-useplanning. We proposed a framework based on spatially
explicitsensitivity analysis using the EMD as a new metric.
Application ofour framework in the newly reclaimed region of Yili
confirmed itseffectiveness in the validation of MCDM for LSE. This
result in-dicates that the LSE for wheat is robust according to the
MACR, butlocal areas are relatively sensitive to changing weights
according tothe spatial visualization. Based on our framework, the
MACRsummarizes the information in the numerical value
variations,whereas the EMD incorporates new information from the
spatialvariations for sensitivity analysis in the MCDM. In this
case, SD andSOM are the twomost sensitive criteria in terms of
weight changes.These two indicators explore different information
with respect tosensitivity and result in different sensitivity
orders for the sevencriteria. Integration of these two criteria was
proposed for valida-tion of MCDM. All the criteria provide a
spatially explicit approachfor the LSE and informed
decision-making. The improvementsmentioned above should be
incorporated in future studies.
Acknowledgments
This work was supported by the National Natural
ScienceFoundation of China (41071065). We thank Karen Bradshaw
inLiwen Bianji (Edanz Group China) for English editing. We are
mostgrateful for the excavation of soil profiles and the collection
of soilsamples performed by Wang Lixin, Yang Yang, Ma Hanqing,
andZhang Ying.
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Spatially-explicit sensitivity analysis for land suitability
evaluationIntroductionMaterials and methodsStudy areaMethods and
data descriptionLand suitability evaluationOne-at-a-time
methodLocal area uncertaintySummary sensitivity analysis
ResultsDiscussionConclusionAcknowledgmentsReferences