Research Article Impacts of Land Use Change on the ...downloads.hindawi.com/journals/amete/2015/563673.pdf · As a typical tropic and subtropics monsoon climate region, Southern China

Research ArticleImpacts of Land Use Change on the Regional ClimateA Structural Equation Modeling Study in Southern China

Zhanqi Wang Bingqing Li and Jun Yang

School of Public Administration China University of Geosciences Wuhan 430074 China

Correspondence should be addressed to Zhanqi Wang zhqwangcugeducn

Received 17 September 2014 Revised 17 February 2015 Accepted 23 February 2015

Academic Editor Jinwei Dong

Copyright copy 2015 Zhanqi Wang et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

With the frequent human activities operating on the earth the impacts of land use change on the regional climate are increasinglyperceptible Under the background of the rapid urbanization understanding the impacts of land use change on the regional climatechange is vital and significant In this study we investigated the relationships between land use change and regional climate changethrough a structural equationmodel SouthernChinawas selected as the study area for its rapid urbanization and different structureof land use among its counties The results indicate that the path coefficients of ldquovegetationrdquo ldquoUrban and surrounding areardquo andldquootherrdquo to ldquoclimaterdquo are minus042 020 and 046 respectively Adding vegetation area is the main method to mitigate regional climatechange Urban and surrounding area and other areas influence regional climate by increasing temperature and precipitation to acertain extent Adding grassland and forestry restraining sprawl of built-up area and making the most use of unused land areefficient ways to mitigate the regional climate change in Southern ChinaThe results can provide feasible recommendations to landuse policy maker

1 Introduction

Human activities have played a dominant role in land usechange especially in the recent decades [1]With accelerationof urbanization and industrialization the average incomeand living standard have been improved obviously Hencethe increasing material demands have led to rapid build-up area expansion and resources development all of whichhave largely influenced the land use structure [2] Firstly thephenomenon that a large number of rural population transferto the city continuously adds the demand for living andproduction room which has a lot of land surrounding citiesreplaced by asphalt metal buildings vegetative cover andother anthropogenic lands [2 3] Secondly in order to feedmore people forestry and grassland will be cultivated andnature land would be changed into cultivated and built-upland Consequently the land surface is changed significantlyto provide more resources and room for human beingsThe land use change caused by human activities mentionedabove has generated a series of ecological and climaticproblems and aroused widespread concern among scholarsSo far a great number of researches have documented

the relationship of land use change and regional climatechange For example Pielke Sr pointed out that land usechange played a first-order role in climate-forcing effect [1]Cai and Kalnay put forward the view that two-thirds ofwarming over the past four decades was caused by land usechange [4] The Intergovernmental Panel on Climate Changereported that the addition of artificial land could raise thegreenhouse gas in the air by fossil fuel burning which con-tributed to the change of average temperature [5] Deng et althought that land use and cover change could influence theecosystems at local regional and global scales and affectedclimate change in the direct or indirect way [6] Converselyclimate can influence the urban development growth ofvegetation and production of cultivated land among othersThe climate change is one of the most important agents in thechange of land use

Now it is widely accepted that the impacts of land usechange on the regional climate change include two aspectswhich are biogeophysics and biogeochemistry [7] The bio-geophysical effects refer to the change of physical parameterssuch as surface roughness albedo reflective and surfacehydrology and vegetation transpiration characteristicsThese

Hindawi Publishing CorporationAdvances in MeteorologyVolume 2015 Article ID 563673 10 pageshttpdxdoiorg1011552015563673

2 Advances in Meteorology

parameters are known for having close connection to theregional precipitation and temperature [8] Meanwhile thebiogeochemical effects mainly refer to altering the chemicalcomposition of atmosphere [9] There are a lot of literatureswhich had contributed to the research of carbon and nitrogenstorage during the process of land use change [10ndash13] whichhave significant impacts on the composition of atmosphere

For those complicated relations between land surfacersquosphysical chemical and biological characteristics the contri-butions of land use change to the regional climate system aredifferent and some uncertainties are still existing in the cur-rent stage of investigation [14ndash16]Massivemethods are beingapplied to identify the impacts For instance the regionalclimate model which has high resolution can simulate thecurrent climate and climate change by describing the changein boundary conditions of the land surface so as to analyzethe effects of land use change on regional climate change[15 17] Reliable land use information is necessary in theregional climate model Keeping the capacity of analyzing theradiation energy water and dynamics interaction betweenatmosphere and land surface the land surface model is usedto integrate with regional climate model [6] Furthermoreland use change evaluation models such as econometricmodel agent-based model scenario development and theirintegration are invited to assess and simulate the land usechange and regional climate in the future [18ndash21] Referringto previous researches it is obvious that most of them studythe impacts between land use change and regional climatethrough land surface parameters such as evapotranspirationroughness and leaf area index However the quantitativeanalysis by the data of land use and regional climate isinsufficient Hence we think that the relationship betweenpanel data of land use and regional climate can be revealedby empirical statistical method which is helpful to recognizethe impacts of land use change on regional climate

Taking advantage of abundant forestry and waterresources Southern China is experiencing the fast growthof population and rapid development of economy SinceSouthern China is facing the crucial period of acceleratingindustrialization and urbanization the urban developmentand land use structure are quite different among thecounties It will vary the regional climate environmentfurther Considering its typical climatic condition rapidurbanization and significant differences of land use structureamong counties Southern China was selected as the casestudy area Therefore in this study the impacts of land usechange on the regional climate in Southern China werequantitatively analyzed from the county level And based ona statistical model the effect significance and direction ofeach land use classification are assessed The results can beused to provide certain reference for the decisions making ofland use planning and policy so as to mitigate the regionalclimate change by land use change in the area

2 Data and Methodology

21 Study Area Southern China (located between 18∘101015840Nsim

26∘241015840N and 104∘261015840Esim117∘201015840E) is one of the seven geo-graphic partitions in China The study area has a total

land area of 451900 km2 covering three provinces whichare Guangdong Province the Guangxi Zhuang AutonomousRegion and Hainan Province (Figure 1) It is not just animportant grain production base but also an excellent baseof tropical crops in China

As a typical tropic and subtropics monsoon climateregion Southern China keeps the characteristics of rainhigh temperature and being evergreen Its average temper-ature and precipitation are about 165∘Csim259∘C and 1000sim2600mm respectively For the rich sunshine and waterresources its lush plants account for 6371of the total area in2005 most of which involve tropical rain forestry monsoonforestry and subtropical monsoon evergreen broad-leavedforestry In recent years the concerns of resourcesrsquo rationaluse and protection have widely spread Therefore afforesta-tion has been carried out frequently in Southern China

With the rapid development of economy the imbalanceddevelopment of agriculture industry and service industryresults in the remarkable land use change in Southern ChinaAdditionally the imbalanced regional development causesthe huge differences of regional land useThe irrational devel-opment and utilization of land resources have led to regionalclimate change to a certain extent According to the tempera-ture and precipitation data from meteorological observationstation it is clear that the temperature in Southern Chinacoastal area is rising and the annual precipitation in southernChina varies periodically In order to optimize land usestructure andmitigate the regional climate change a series ofplans were implemented for example ecological restorationprojects [22] Therefore the exploration of land use changersquosimpact mechanism on regional climate is significant andhelpful to the policy making and sustainable development

22 SEM (Structural Equation Model Analyses VariablesrsquoRelations by Covariance Matrix) Variables can be dividedinto four kinds exogenous latent variables endogenous latentvariables exogenous observed variables and endogenousobserved variables Observed variables generally refer toobjectives By contrast latent variables are difficult to beobserved directly but can be indicated by several relatedobserved variables This new multivariate statistical analysismethod integrated by traditional factor analysis and pathanalysis mainly describes two kinds of relationships therelationships between latent and related observed variablesand the relationships among latent variables [23 24] To solvequestions mentioned above SEM is divided into measure-ment component and structural component The measure-ment component deals with the measurement errors andrelationships between latent and related observed variables[25] It specifies latent variables as some linear functions oftheir observed variablesThe structural componentrsquos functionis to capture the impacts of exogenous latent variables onendogenous latent variables and the impacts of endogenouslatent variables upon one another [26] The based equationsare as follows

SEM[

[

[

measurement component [

119884 = ⋀

119884

120578 + 120576

119883 = ⋀

119883

120585 + 120575

structural component 120578 = 119861120578 + Γ120585 + 120577

(1)

Advances in Meteorology 3

117∘09984000998400998400E114∘09984000998400998400E111∘09984000998400998400E108∘09984000998400998400E105∘09984000998400998400E 117∘09984000998400998400E114∘09984000998400998400E111∘09984000998400998400E108∘09984000998400998400E105∘09984000998400998400E

114∘09984000998400998400E111∘09984000998400998400E108∘09984000998400998400E105∘09984000998400998400E 114∘09984000998400998400E111∘09984000998400998400E108∘09984000998400998400E105∘09984000998400998400E

27∘ 0

998400 0998400998400

N24

∘ 0998400 0

998400998400N

21∘ 0

998400 0998400998400

N18

∘ 0998400 0

998400998400N

27∘ 0

998400 0998400998400

N24

∘ 0998400 0

998400998400N

21∘ 0

998400 0998400998400

N18

∘ 0998400 0

998400998400N

24∘ 0

998400 0998400998400

N21

∘ 0998400 0

998400998400N

18∘ 0

998400 0998400998400

N

24∘ 0

998400 0998400998400

N21

∘ 0998400 0

998400998400N

18∘ 0

998400 0998400998400

N

0 40 80 160 240 320

(mi)0 40 80 160 240 320

(mi)

The year 1998

N

The year 2005

N

County boundaryCultivated landWater bodiesBuilt-up area

ForestryGrasslandUnused landOthers



Figure 1 Location and the land use structure of Southern China

where 119884 is endogenous observed variable 119883 is exogenousobserved variable 120578 are endogenous latent variables 120585 areexogenous latent variables and 120576 120575 are the measurementerrors of 119884 and 119883 respectively and

119884 and119883are the relationships

between latent and corresponding observed variables 119861 isthe relationship among endogenous latent variables Γ is theimpact of exogenous latent variable on endogenous latentvariable 120577 is the residual of 120578 reflecting the irresolubleparts Figure 2 is the sketch of SEM These ellipses representlatent variables the rectangles represent observed variablesthe lines (called paths) indicate that there are relationshipsbetween variables arrows show the effect direction ldquoa-brdquoare path coefficients and ldquocndashhrdquo are factor loadings Pathcoefficients and factor loadings are main research objectsused in measuring relationships Generally an SEM can haveany number of latent and observed variables

Compared with other traditional analysis methods theSEM has the advantages of [26 27] (1) estimating multipleand interrelated dependence relationships at the same time(2) allowing the measurement errors in the latent andobserved variables (3) the ability of dealing with unob-served variables making the model more flexible in appli-cation (4) setting the entire relationship and goodness-of-fit (5) handling nonnormal data (6) accounting for missingdata Although SEM is mainly used in psychometrics andquestionnaire analysis it is worth noting that the modelis being increasingly used in construction-related studieswhere latent variables and measurement errors exist in

Structural component

c d ge hf

a b

Measurement component

120577

1205851 1205852

X11 X12 Y1 Y2 X21 X22

120578

120575 11 120575 12 120575 21 120575 22120576 1 120576 2

Figure 2 The sketch of SEM

regressionmodel [28] Because there are measurement errorsand a series of interrelated unobserved variables in theassumptions the SEM is applied in this research

The implementation of SEM includes three steps [29]Due to the fact that SEM is obtained by the modificationof existing hypothesis the first step is to put forward ahypothesis SEM based on the advance verification of datastructure The verification estimates the relationships ofobserved variables and can be gotten by principal component


analysis Secondly confirmatory factor analysis (CFA) is usedto test the validity of hypothesisrsquo measurement componentAccording to goodness-of-fit indices and theoretical mean-ings of variables CFAwill modify paths and fit model repeat-edly until suitable corresponding relationships between latentand observed variables can be obtained Finally structuralcomponent is modified by the same method Therefore thefactor loadings and path coefficients are exploredMost of thetime measurement and structural component are adjustingat the same time to get a more scientific model

23 Data and Processing Thechanges of land use and climatein SouthernChinawere quantified by the differences betweenthe years 1988 and 2005The data used in the research mainlyinclude (1) land use data of Southern China in the years 1988and 2005 and (2)meteorological data in the years 1987 to 1989and 2004 to 2006

Firstly the land use data (with a spatial resolution of 1 kmtimes 1 km) were extracted from remote sensing digital images ofthe US landsat TMETM satellite which were obtained fromthe Resources and Environment Data Center of the ChineseAcademy of Science According to United States GeologicalSurvey Classification the land use was interpreted into sevenparts of cultivated land forestry grassland built-up areawater bodies unused land and others (most is the extensionfor coastline) The land use of each county was obtained byabove data and administrative area boundary of the countiesof Southern China

Secondly we used surface temperature and precipitationto reflect the regional meteorology The original data werecollected from meteorology stations Kriging interpolationalgorithm estimates unknown samples by original data andthe distribution and spatial structure of samples Comparedwith other methods it reflects samplesrsquo spatial structureand takes the chance to work with extreme data So it isgood in regional estimation of temperature and precipitationWith about 60 meteorological stations in the study area andmore than 30 around it (Figure 3) the original data wereinterpolated into 1 km resolution grid data by Kriging [30]The climates of the years 1988 and 2005 were indicated bythe average values of the years 1987 to 1989 and 2004 to2006 respectively Afterward we counted the annual averagetemperature and precipitation of each county by arithmeticaverage method

We can see from the above data that the land use ofSouthern China had changed greatly from 1988 to 2005Cultivated land and built-up area had reduced 3859 km2 andincreased 4369 km2 accounting for 357 and 3688of theirtotal respectively Their changes were significant Addition-ally unused land grassland water bodies and forestry hadreduced 489 km2 1490 km2 35 km2 and 349 km2 accountingfor 6059 478 028 and 012 respectively In additionto the built-up area they were all reduced to a certaindegreeThe proportion of unused land was the most obviousFrom 1988 to 2005 the annual average temperature andprecipitation of Southern China had changed 067∘C and

StationCounty

N

0 50 100 200(mi)

300 400

117∘09984000998400998400E114∘09984000998400998400E111∘09984000998400998400E108∘09984000998400998400E105∘09984000998400998400E

117∘09984000998400998400E114∘09984000998400998400E111∘09984000998400998400E108∘09984000998400998400E105∘09984000998400998400E

27∘ 0

998400 0998400998400

N24

∘ 0998400 0

998400998400N

21∘ 0

998400 0998400998400

N18

∘ 0998400 0

998400998400N

27∘ 0

998400 0998400998400

N24

∘ 0998400 0

998400998400N

21∘ 0

998400 0998400998400

N18

∘ 0998400 0

998400998400N

Figure 3 The distribution of meteorological station

4633mmThere was obviously upward trend in annual aver-age temperature However the annual average precipitationfluctuated frequently and showed no obvious trend

Principal component analysis is an important way to getthe basic SEM hypothesis which is put forward based on theadvance verification of data structure Principal componentanalysisrsquo dimensionality reduction function is to replace theoriginal variables by fewer variables The new variables con-tain most information of original variables Comparing thedifferent relationship coefficient between original and newvariables we can classify the original variables and concludethe new variablesrsquo meanings Therefore the data structure isclearly defined and SEM hypothesis can be constructed

The strong correlation among variables is necessary forprincipal component analysis Kaiser-Meyer-Olkin (KMO)determines the correlation through comparing the simpleand partial correlation coefficientThe greater value indicatesthe stronger correlation Bartlett test of sphericity is testingthe independent character of variables by correlation coef-ficients matrix The two tests are usually used to examinethe appropriateness of data for principal component analysisperformance It is often suggested that KMO value should beof 05 as aminimal level andBartlett test should be significantFor the land use data the KMO valuing 0594 and significantBartlett test result were proving that the data were suitable forprincipal component analysis

The principal component analysis of the land use datawas carried out by statistics software SPSS whose resultwas shown in Table 1 The ldquo1rdquo ldquo2rdquo and ldquo3rdquo representedthe three principal components The rotated componentmatrix which consisted of the numbers below the threeprincipal components was presenting how close correla-tions between variables and principal components wereThe number was called the factor loading of variable onprincipal component Considering the closest correlationeach variable was classified to a principal component Finally


Table 1 Rotated component matrix of principal component analy-sis

Principal component1 2 3

Cultivated land 0746 0025 0422Forestry minus0048 minus0071 0837Grassland minus0065 minus0026 0817Water bodies 0788 0265 minus0132Built-up area 0823 0015 minus0257Unused land 0158 0939 minus0050Others 0065 0953 minus0046Notes (1) principal component analysis was used (2) orthogonal rotationmethod with Kaiser standardized was used and converged after four timeiterations

Cultivated land

Others

Unused land

Built-up area

Others

Temperature

Precipitation

Climate

Grassland Vegetation

Urban and surrounding

Forestry

Water bodies

Figure 4 The basic hypothesis of structural equation model

the land use classifications were divided into three parts (1)vegetation area including forestry and grassland (2) urbanand surrounding area including cultivated land built-uparea and water bodies (3) other areas including unusedland and others The cumulative contribution rate of thethree principal components was 7703 which judged thepossession of original information Generally cultivated landis related to vegetation However from the perspective ofchange the cultivated land has closer relationship to built-up area This is because cultivated land keeps the capacityof feeding For the convenience of travelling built-up area issurrounded by cultivated land The production of cultivatedland also should satisfy the need of humanity Consequentlycultivated land and built-up were classified into one principalcomponentwhichwas called the urban and surrounding area

The basic SEM hypothesis of the research is shown inFigure 4 The research took each county as a sample so thesample size is 194 Based on above analysis we regardedcountyrsquos land use classifications as exogenous observed vari-ables regarded countyrsquos annual average temperature andprecipitation as endogenous observed variables regarded thethree principal components as exogenous latent variablesand regarded climate as endogenous latent variableThe SEMreflected the relationships among above observed and latentvariables

24 Reliability Analysis Reliability analysis is the foundationof this studyrsquos scientific benefit and properties Reliability is

used to test whether several variables can be used to explainthe question together So it is also called the consistency ofdata Before SEM analysis the index of Cronbachrsquos alpha isfrequently used to investigate the consistency and reliabilityof measurements When the Cronbachrsquos alpha is greater than07 these data are suitable for SEM analysis [23 29 31]Obtaining standard data by statistics software SPSS theCronbachrsquos alpha value of above data was calculated Thevalue of 0734 indicated that the data could be used for SEManalysis

3 Result and Discussion

The software LISREL 880 was used to confirm and modifythe basic hypothesis in our research As an important sta-tistical method the maximum likelihood estimation methodkeeps the optimal characters of unbiasedness consistencyand efficiencyThe result will not be influenced by the unit ofmeasurement [32] Taking advantage of these characters themaximum likelihood estimation method was selected as thefitting way After fitting model was estimated by goodness-of-fit indices Some modifications which included addingand deleting paths took place based on the path coefficients119879-values modification indices and the meaning of eachvariable Finally we got the suitable structural equationmodel which revealed the impacts of land use change on theregional climate in Southern China

31 Confirmatory Factor Analysis By the confirmatory factoranalysis we got the path coefficients between observed vari-ables and latent variables as well as the measurement errorsof the observed variables Considering that a large amount ofliterature had confirmed that precipitation and temperaturewere the key variables to reflect the area climate [16 33]this confirmatory factor analysis was mainly attributed to thepath between land use classifications and the three principalcomponentsThere were many indices involved in the assess-ment of overall goodness-of-fit of SEM of which we selectedthe following important seven indices as evaluation criteria[34 35] These indices measured the difference between thesamplersquos covariance matrix and model-implied covariancematrix Their meanings equations and criteria are shown inTable 2

During the process of model modification we deletedthe path whose 119879-value was less than 196 and added thepath bymodification indices Notably all of themodificationswere combined with proper theory analysis As the result thepaths of ldquovegetationrdquo rarr ldquocultivated landrdquo and ldquovegetationrdquo rarr

ldquowater bodiesrdquo were added to get a better fittingmeasurementmodel The expansion of built-up area is based on thedecrease of cultivated land due to the fact that the built-uparea is surrounded by cultivated land To satisfy the need offood grassland and forestry will be transformed to cultivatedlandTherefore the cultivated land has close relationshipwithldquovegetationrdquo and ldquourban and surroundingrdquo Consequently thepath of ldquovegetationrdquo rarr ldquocultivated landrdquo was addedThe landuse maps (Figure 1) showed that the built-up area was alwaysdeveloped with water bodies which kept the ability to reduce


Table 2 The meanings and equations of indices of the goodness-of-fit

Index Meaning Equation Criterion

120594

2df

120594

2 is impacted by the difference between sample andmodel-implied matrices The small 1205942 represents thegood fitting of model and samples Degree of freedom(df) is the number of independent variablesConsidering that 1205942 is too sensitive to sample size 1205942dfis selected as a relative chi-square value

mdash lt5

119875

The probability of error 119875 value and resultrsquos credibilitywill change in the opposite direction If 119875 lt 005 theresult is meaningful

mdash lt0005

GFIGoodness-of-fit presents the proportion of covariancein sample explained by implied model Greater GFImeans the good explanation of dependent variables byindependent variables

1 minus

119905119903[(119864

minus1

119878 minus 119868)

2

]

119905119903[(119864

minus1

119878)

2

]

gt09

CFIComparative fit index can avoid the underestimation offit in small samples It also tests the difference betweenthe worst (independence) model and model of interest

1 minus

max(1205942119879

minus df119879

0)

max(1205942119873

minus df119873

120594

2

119879

minus df119879

0)

gt09

RMSEARoot mean square error of approximation estimates thediscrepancy between model-implied and truepopulation covariance matrix per degree of freedom

Sqrtmax[(1205942 minus df)(119873 minus 1) 0]

df lt008

RMR Root mean square residual tests one kind of mean ofresidual Sqrt[

2ΣΣ(119904

119894119895

minus 119890

119894119895

)

2

119901(119901 + 1)

] lt005

NFINormed fit index reflects the proportion of worst(independence) model 1205942 explained by model ofinterest

120594

2

119873

minus 120594

2

119879

120594

2

119873

gt09

Notes119873 is samplersquos size 119901 is number of variables 119904119894119895is samplersquos relation coefficient 119890

119894119895is model-implied relation coefficient 119878 is samplersquos covariance matrix

119864 is model-implied covariance matrix 119868 is identity matrix 1205942119873

is chi-square of worst (independence) model 1205942119879

is chi-square of model of interest df119873is worst

(independence) modelrsquos degree of freedom and df119879is model of interestrsquos degree of freedom

temperature and influence precipitation in Southern ChinaHence water bodies have close relationship with ldquovegetationrdquoand ldquourban and surroundingrdquo So the path of ldquovegetationrdquo rarr

ldquowater bodiesrdquo was also added

32 Structural Equation Modeling As a good measurementmodel was obtained in the confirmatory factor analysisthe relationships among four latent variables in hypothesiswere tested in structural model There was no need for pathmodification Therefore we got the final structural equationmodel of the impacts of land use change on the regionalclimate (Figure 5) The numbers ranging from 0 to 1 on thelines of latent variables refer to observed variables presentingthe standardized factor loading which could reflect therelated direction and significance The numbers next to thearrows pointed to observed variables presenting the errors ofobserved variables The numbers on the line between latentvariables which made up the main part of structural modelwere called the standardized path coefficient

Table 3 shows the measurement and structural compo-nentrsquos estimate models errors 119879-values and correspondingsignificance Different from standardized factor loading andpath coefficient in Figure 5 the coefficient of estimate modelsreflected the quantitative relationship between data If 119879-value was larger than 196 and 119875 was below 0001 the pathis significant The SEM was composed of nine measurement

equations and one structural equation Accordingly in addi-tion to the path ldquoUrban and surroundingrdquo rarr ldquoClimaterdquo allthe path coefficients hadpassed the significant test It iswidelyknown that the development of urban leads to climate changeso we did not delete this pathTherefore we thought the SEMwas valid Table 3 is helpful to deepen the understanding ofvariablesrsquo relationships and test the significance of each pathHowever because the standard coefficient is comparable theanalysis of the result is based on Figure 5

Table 4 presents the results of goodness-of-fit of confir-matory factor analysis and structural equation modeling Allpathswere based on theoretical analysis For the confirmatoryfactor analysis and structural equation modeling it showedthat their 120594

2df 119875 value GFI CFI RMR and NFI fell wellwithin their acceptable ranges but RMSEA was slightly outof its limit This might be caused by weaker linkage betweenldquourban and surrounding areardquo with ldquoclimaterdquo On the onehand the goodness-of-fit measure indices are sensitive tosample size and easy to be underestimated when sample sizeis relatively small [26]On the other hand like other statisticalanalyses the statistical relationship or association cannotprove themust-be relationship but simply provides support tothe logical and intuitive belief influence between the data [35]So the best model should be established based on the propertheory and the result of goodness-of-fit According to aboveanalysis we considered that this SEM whose indices were


Cultivated land

Others

Unused land

Built-up area

Others

084087

046

020

Temperature

096

029

081

Precipitation

Climate

033



076086

Forestry

Water bodies

074019

090

091

008

043

027

023

039

018

035

088

minus042

Figure 5 The structural equation model of the impacts of land use change on climate

Table 3 The measurement and structural equations estimated by LISREL

Component Estimate model Error119905-value Significance


Forestry = 076 times vegetation 043 1152 119875 lt 0001

Grassland = 086 times vegetation 027 1349 119875 lt 0001

Cultivated land = 074 times vegetation + 087 times urban andsurrounding 023 1026

1190 119875 lt 0001

Water bodies = 019 times vegetation + 084 times urban andsurrounding 039 297

1196 119875 lt 0001

Built-up area = 091 times urban and surrounding 018 1536 119875 lt 0001

Unused land = 081 times other 035 624 119875 lt 0001

Others = 033 times other 088 392 119875 lt 0001

Precipitation = 029 times climate 091 mdash mdashTemperature = 095 times climate 008 307 119875 lt 0001

Structural component Climate = minus042 times vegetation + 020 times urban andsurrounding + 046 times other 038

minus274 119875 lt 0001

146 119875 lt 0005

234 119875 lt 0001

Table 4 The result of goodness-of-fit

goodness-of-fit measure 120594

2df 119875 GFI CFI RMSEA RMR NFIIndices of confirmatory factor analysis 243 00007 095 096 0086 0045 094Indices of structural equation modeling 240 00006 095 096 0085 0044 093

below but close to the criteria was acceptable Consequentlythe SEM was valid

The standard factor loading and path coefficient revealedthe significance and direction of relationships among thenine observed variables and four latent variables The pathcoefficient of ldquovegetationrdquo to ldquoclimaterdquo (minus042) proved thatvegetation area had dramatically negative impact on theregional climate changeThat is to say the addition of forestrygrassland cultivated land and water bodies could reduce thetemperature and precipitation effectivelyThe path coefficientof ldquourban and surroundingrdquo to ldquoclimaterdquo (020) proved that

the urban and surrounding area had positive impact on theclimate change Accordingly the addition of cultivated landwater bodies and built-up area could rise the temperatureand precipitation to a certain extent The path coefficientof ldquootherrdquo to ldquoclimaterdquo (046) showed that other areas hadpositive impact on climate change and the significance ofeffects was almost the same as ldquourban and surroundingrdquo Wethought this path coefficient was abnormal

33 Analysis of SEM The objective of this research is tocarry out the quantitative impacts of land use change on


the regional climate by a statistical model so as to providescientific understanding of the relationship between landuse change and regional climate change to policy makersAccording to the factor loadings path coefficients of mea-surement and structural models we summed the opinionsas follows

(1)The structural model displays that all the three parts ofvegetation area urban and surrounding area and other areascan impact regional climate to a certain extent Comparedwith other two parts vegetation area has negative relationshipwith regional climate change First of all adding vegetationarea in low latitude area can mitigate temperature riseby affecting planetary energetic atmospheric compositionand hydrologic cycle [36] Additionally in coastal provincecities with high temperature increase their precipitation byevaporation and atmospheric circulation thus the changeof temperature and precipitation in Southern China hasthe same direction Above analyses and the result of pre-vious researches clearly indicate that decrease of vegetationresulting from deforestation and urbanization would makethe temperature and precipitation increase in low latitudearea [37 38] Urban and surrounding area has positiverelationshipwith climate changeUrban has a large number ofheaters motor vehicle and other energy consuming deviceswhich transmit a lot of heat to air Furthermore a largenumber of buildings and roads increase heat capacity andconductivity of land Therefore adding urban and surround-ing area leads to higher temperature This result can be usedto explain the urban heat island effect The structural modelshows that other areas whichmainly include unused land andthe extension for coastline have positive effects on regionalclimate change Because the change of unused landwas highlydifferent among counties the coefficient between ldquootherrdquo andldquoclimaterdquo calculated by SEM is relatively big Referring to theland use change of Southern China in 1988 and 2005 unusedland had reduced 489 km2 accounting for 6059 which wasthe most significant change A large proportion of unusedland was changed to built-up area Consequently we thinkwhat this path indicates is that the rapid development of built-up area leads to the great climate change

(2) The relationships between seven exogenous observedvariables and three exogenous latent variables can be revealedin measurement model Firstly ldquoforestryrdquo ldquograsslandrdquo andldquocultivated landrdquo have strong and positive linkages withldquovegetationrdquo but the linkage between ldquowater bodiesrdquo andldquovegetationrdquo is weaker relatively Forestry grassland andcultivated land belong to vegetation area which keeps thecharacteristic of affecting a series of parameters of earthrsquossurface However water bodiesmitigate regional temperatureand precipitation mainly by evaporation Compared with thesea the effect of evaporation of inland water bodies is lessobvious which results in the weaker link of water bodiesin Southern China Secondly ldquobuilt-up areardquo ldquocultivatedlandrdquo and ldquowater bodiesrdquo have strong and positive linkageswith ldquourban and surroundingrdquo Generally the urban andsurrounding area almost refers to the built-up area In orderto satisfy humanrsquos needs for food and water resources theareas of cultivated land and water bodies have the trend

to keep the same change direction with built-up area Thiscan be used to explain why the three variables are allrelated to ldquourban and surroundingrdquo and ldquocultivated landrdquo andldquowater bodiesrdquo have positive and slightly weaker relationshipswith ldquourban and surroundingrdquo in SEM Both unused landand others have positive linkages with other areas Becauseunused landrsquos distribution is much wider and larger than theextension of coastline the SEM displays that ldquounused landrdquohas closer linkage with ldquoothersrdquo

(3) The urbanization of Southern China had changedthe land use structure to a great extent which had causedthe obviously regional climate change In the procession ofslowing down the regional climate change addition of veg-etation is playing the most important role For the majorityof study area has been occupied by forestry which is muchlarger than grassland the regional climate change is slightlyless sensitive to deforestation than grassland developmentwhen they change the same area The cultivated land andwater bodies have the functions ofmitigating regional climatechange and feeding Due to their weaker comprehensiveimpacts the reclamation of grassland and forestry is againstthe mitigation of regional temperature and precipitationchange During the urbanization the policy makers had putforward a series of efficient measures to protect the totalamount of cultivated land such as land consolidation landrequisition-compensation balance and land increase anddecrease linked We think the reasonable scales of cultivatedland and water bodies should be judged by requirementThe change of unused land to vegetation can mitigate theregional climate change obviously However the change ofunused land to built-up area can slightlymitigate the regionalclimate This may be because the utilization of unused landcan decrease the occupancy of vegetation Hence under thebackground of land intensive and sustainable use taking fulluse of unused land plays an important role in mitigating theincreasing temperature and precipitation

4 Conclusion

In this paper we applied SEM to analyze the impactsmechanism of land use change on the regional climate fromthe county level Southern China was selected as case studyarea due to its typical climatic conditions obvious land useconversion and unbalanced regional development amongcounties The contribution of this paper is to explore thepotential relationship and effects between land use changeand regional climate change by the application of SEM Thismethod is worth spreading Besides the identification of therelationship is helpful to generate land use policy optionsfor authorities in Southern China The conclusions of thisresearch were summarized as follows

Firstly it is demonstrated that adding vegetation areais a significant effective way to mitigate regional climatechange by reducing temperature and precipitation Addingurban and surrounding area which enhances temperatureand precipitation can affect regional climate to a certainextent It seems that rapid urbanization contributes to theregional climate change of Southern China in recent decades


The influence of city expansion is relatively weaker From theanalysis of statistical data other areas which mainly includeunused land and extension of coastline show the significantand positive effect on the regional climate change This pathalso reveals the positive relationship between city expansionand climate change

Secondly forestry and grassland both have strong link-ages with vegetation area which indicates the climate changecan be mitigated by afforestation and adding grassland areaThere is a significant and positive relationship betweenbuilt-up area and urban and surrounding area Accordinglyrestraining sprawl of built-up area and adopting the strategyof sustainable development are important methods to mit-igate regional climate change Possessing the characteristicsof mitigating regional climate change and feeding cultivatedland andwater bodies both have relationshipswith vegetationarea and urban and surrounding area However the suitablescales of them depend on the demand of humanity Otherlands mainly point to unused land in the study area whichcan be used to ease the reduction of vegetation area caused byurbanizationThis is a good way to solve the conflict betweenurban development and regional climate change

It needs to be mentioned however that there are stillsome limitations in the research For example the resultof good-of-fit is defective In particular it is because therelationship revealed by panel data is only a ldquoprobable andperformancerdquo relationship That is to say the structuralequation model should be analyzed and applied based ontheories and practices To solve the problem more data andvariables are needed for the sample to improve model fitFurthermore the comparison among multitemporal data ishelpful to get more credible results

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The research presented in this paper was supported bythe National Basic Research Program of China (973 Pro-gram) (no 2010CB950904) The data were collected by theResources and Environment Data Center of the ChineseAcademy of Science The authors are grateful to XiangzhengDeng for helpful comments on the early draft of the paper andto the editor and reviewers for their valuable comments andsuggestions

References

[1] R A Pielke Sr ldquoLand use and climate changerdquo Science vol 310no 5754 pp 1625ndash1626 2005

[2] J Zhan J Huang T Zhao XGeng andY Xiong ldquoModeling theimpacts of urbanization on regional climate change a case studyin the beijing-tianjin-tangshan metropolitan areardquo Advances inMeteorology vol 2013 Article ID 849479 8 pages 2013

[3] QWeng ldquoFractal analysis of satellite-detected urban heat islandeffectrdquo Photogrammetric Engineering and Remote Sensing vol69 no 5 pp 555ndash566 2003

[4] M Cai and E Kalnay ldquoClimate (communication arising)Impact of land-use change on climaterdquoNature vol 427 no 6971pp 213ndash214 2004

[5] R Pachauri and A Reisinger IPCC Fourth Assessment ReportIntergovernmental Panel on Climate Change(IPCC) GenevaSwitzerland 2007

[6] X Deng C Zhao and H Yan ldquoSystematic modeling of impactsof land use and land cover changes on regional climate areviewrdquo Advances in Meteorology vol 2013 Article ID 31767811 pages 2013

[7] J J Feddema K W Oleson G B Bonan et al ldquoAtmosphericscience the importance of land-cover change in simulatingfuture climatesrdquo Science vol 310 no 5754 pp 1674ndash1678 2005

[8] R A Betts P D Falloon K K Goldewijk and N RamankuttyldquoBiogeophysical effects of land use on climate model simula-tions of radiative forcing and large-scale temperature changerdquoAgricultural and Forest Meteorology vol 142 no 2ndash4 pp 216ndash233 2007

[9] J Liu and X Deng ldquoImpacts and mitigation of climate changeon Chinese citiesrdquo Current Opinion in Environmental Sustain-ability vol 3 no 3 pp 188ndash192 2011

[10] J D Aber and C T Driscoll ldquoEffects of land use climatevariation and N deposition on N cycling and C storage innorthern hardwood forestsrdquo Global Biogeochemical Cycles vol11 no 4 pp 639ndash648 1997

[11] L P Rosa S K Ribeiro M S Muylaert and C P de CamposldquoComments on the Brazilian proposal and contributions toglobal temperature increase with different climate responses-CO2

emissions due to fossil fuels CO2

emissions due to landuse changerdquo Energy Policy vol 32 no 13 pp 1499ndash1510 2004

[12] S Queguiner E Martin S Lafont J-C Calvet S Faroux andP Quintana-Seguı ldquoImpact of the use of a CO

2

responsive landsurface model in simulating the effect of climate change on thehydrology of French Mediterranean basinsrdquo Natural Hazardsand Earth System Science vol 11 no 10 pp 2803ndash2816 2011

[13] J Dong X Xiao S Sheldon et al ldquoA 50-m forest cover mapin Southeast Asia from ALOSPALSAR and its applicationon forest fragmentation assessmentrdquo PLoS ONE vol 9 no 1Article ID e85801 2014

[14] V K Arora and A Montenegro ldquoSmall temperature benefitsprovided by realistic afforestation effortsrdquo Nature Geosciencevol 4 no 8 pp 514ndash518 2011

[15] A Kattenberg F Giorgi H Grassl et al ldquoClimate models-projections of future climaterdquo in Climate Change 1995 TheScience of Climate Change Contribution of Working Group I tothe Second Assessment Report of the Intergovernmental Panel onClimate Change pp 285ndash357 1996

[16] X Gao Y Luo W Lin Z Zhao and F Giorgi ldquoSimulation ofeffects of land use change on climate in China by a regionalclimate modelrdquo Advances in Atmospheric Sciences vol 20 no4 pp 583ndash592 2003

[17] H Von Storch E Zorita and U Cubasch ldquoDownscaling ofglobal climate change estimates to regional scales an applica-tion to Iberian rainfall in wintertimerdquo Journal of Climate vol 6no 6 pp 1161ndash1171 1993

[18] R B Matthews N G Gilbert A Roach J G Polhill and N MGotts ldquoAgent-based land-use models a review of applicationsrdquoLandscape Ecology vol 22 no 10 pp 1447ndash1459 2007


[19] W D Solecki and C Oliveri ldquoDownscaling climate changescenarios in an urban land use change modelrdquo Journal ofEnvironmental Management vol 72 no 1-2 pp 105ndash115 2004

[20] G B Bonan ldquoEffects of land use on the climate of the UnitedStatesrdquo Climatic Change vol 37 no 3 pp 449ndash486 1997

[21] R N Lubowskia A J Plantingab and R N Stavins ldquoLand-usechange and carbon sinks econometric estimation of the carbonsequestration supply functionrdquo Journal of Environmental Eco-nomics and Management vol 51 no 2 pp 135ndash152 2006

[22] Y Qin H Yan J Liu J Dong J Chen and X Xiao ldquoImpactsof ecological restoration projects on agricultural productivity inChinardquo Journal of Geographical Sciences vol 23 no 3 pp 404ndash416 2013

[23] S-C Huang S-L Lo and Y-C Lin ldquoApplication of a fuzzycognitive map based on a structural equation model for theidentification of limitations to the development of wind powerrdquoEnergy Policy vol 63 pp 851ndash861 2013

[24] X-Y Song F Chen and Z-H Lu ldquoA Bayesian semiparametricdynamic two-level structural equation model for analyzingnon-normal longitudinal datardquo Journal ofMultivariate Analysisvol 121 pp 87ndash108 2013

[25] S P Washington M G Karlaftis and F L Mannering Statis-tical and econometric methods for transportation data analysisChapman and Hall Boca Raton Fla USA 2nd edition 2011

[26] T F Golob ldquoStructural equation modeling for travel behaviorresearchrdquo Transportation Research Part B Methodological vol37 no 1 pp 1ndash25 2003

[27] R B Keline Principles and Practice of Structural EquationModeling The Guilford Press New York NY USA 2005

[28] S Rabe-Hesketh A Skrondal and A Pickles ldquoGeneralizedmultilevel structural equation modelingrdquo Psychometrika vol69 no 2 pp 167ndash190 2004

[29] H L Dang E Li I Nuberg and J Bruwer ldquoUnderstandingfarmersrsquo adaptation intention to climate change a structuralequation modelling study in the Mekong Delta VietnamrdquoEnvironmental Science amp Policy vol 41 pp 11ndash22 2014

[30] M R Holdaway ldquoSpatial modeling and interpolation ofmonthly temperature using krigingrdquo Climate Research vol 6no 3 pp 215ndash225 1996

[31] J-H Wu S-C Wang and L-M Lin ldquoMobile computingacceptance factors in the healthcare industry a structuralequation modelrdquo International Journal of Medical Informaticsvol 76 no 1 pp 66ndash77 2007

[32] S-Y Lee and H-T Zhu ldquoMaximum likelihood estimation ofnonlinear structural equation modelsrdquo Psychometrika vol 67no 2 pp 189ndash210 2002

[33] J Al-Bakri A Suleiman F Abdulla and J Ayad ldquoPotentialimpact of climate change on rainfed agriculture of a semi-aridbasin in Jordanrdquo Physics and Chemistry of the Earth vol 36 no5-6 pp 125ndash134 2011

[34] XCao P LMokhtarian and S LHandy ldquoDo changes in neigh-borhood characteristics lead to changes in travel behavior Astructural equations modeling approachrdquo Transportation vol34 no 5 pp 535ndash556 2007

[35] B Xiong M Skitmore B Xia M A Masrom K Ye and ABridge ldquoExamining the influence of participant performancefactors on contractor satisfaction a structural equation modelrdquoInternational Journal of Project Management vol 32 no 3 pp482ndash491 2014

[36] G B Bonan ldquoForests and climate change forcings feedbacksand the climate benefits of forestsrdquo Science vol 320 no 5882pp 1444ndash1449 2008

[37] S A Changnon Jr ldquoRainfall changes in summer caused by StLouisrdquo Science vol 205 no 4404 pp 402ndash404 1979

[38] S D Chow and C Chang ldquoShanghai urban influences onhumidity and precipitation distributionrdquoGeoJournal vol 8 no3 pp 201ndash204 1997

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ClimatologyJournal of

EcologyInternational Journal of


EarthquakesJournal of


Hindawi Publishing Corporationhttpwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2014

Mining


Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal of

Geophysics

OceanographyInternational Journal of


Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering


GeochemistryHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Atmospheric SciencesInternational Journal of


OceanographyHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in


MineralogyInternational Journal of


MeteorologyAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Paleontology JournalHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Geological ResearchJournal of


Geology Advances in


parameters are known for having close connection to theregional precipitation and temperature [8] Meanwhile thebiogeochemical effects mainly refer to altering the chemicalcomposition of atmosphere [9] There are a lot of literatureswhich had contributed to the research of carbon and nitrogenstorage during the process of land use change [10ndash13] whichhave significant impacts on the composition of atmosphere

For those complicated relations between land surfacersquosphysical chemical and biological characteristics the contri-butions of land use change to the regional climate system aredifferent and some uncertainties are still existing in the cur-rent stage of investigation [14ndash16]Massivemethods are beingapplied to identify the impacts For instance the regionalclimate model which has high resolution can simulate thecurrent climate and climate change by describing the changein boundary conditions of the land surface so as to analyzethe effects of land use change on regional climate change[15 17] Reliable land use information is necessary in theregional climate model Keeping the capacity of analyzing theradiation energy water and dynamics interaction betweenatmosphere and land surface the land surface model is usedto integrate with regional climate model [6] Furthermoreland use change evaluation models such as econometricmodel agent-based model scenario development and theirintegration are invited to assess and simulate the land usechange and regional climate in the future [18ndash21] Referringto previous researches it is obvious that most of them studythe impacts between land use change and regional climatethrough land surface parameters such as evapotranspirationroughness and leaf area index However the quantitativeanalysis by the data of land use and regional climate isinsufficient Hence we think that the relationship betweenpanel data of land use and regional climate can be revealedby empirical statistical method which is helpful to recognizethe impacts of land use change on regional climate

Taking advantage of abundant forestry and waterresources Southern China is experiencing the fast growthof population and rapid development of economy SinceSouthern China is facing the crucial period of acceleratingindustrialization and urbanization the urban developmentand land use structure are quite different among thecounties It will vary the regional climate environmentfurther Considering its typical climatic condition rapidurbanization and significant differences of land use structureamong counties Southern China was selected as the casestudy area Therefore in this study the impacts of land usechange on the regional climate in Southern China werequantitatively analyzed from the county level And based ona statistical model the effect significance and direction ofeach land use classification are assessed The results can beused to provide certain reference for the decisions making ofland use planning and policy so as to mitigate the regionalclimate change by land use change in the area

2 Data and Methodology

21 Study Area Southern China (located between 18∘101015840Nsim

26∘241015840N and 104∘261015840Esim117∘201015840E) is one of the seven geo-graphic partitions in China The study area has a total

land area of 451900 km2 covering three provinces whichare Guangdong Province the Guangxi Zhuang AutonomousRegion and Hainan Province (Figure 1) It is not just animportant grain production base but also an excellent baseof tropical crops in China

As a typical tropic and subtropics monsoon climateregion Southern China keeps the characteristics of rainhigh temperature and being evergreen Its average temper-ature and precipitation are about 165∘Csim259∘C and 1000sim2600mm respectively For the rich sunshine and waterresources its lush plants account for 6371of the total area in2005 most of which involve tropical rain forestry monsoonforestry and subtropical monsoon evergreen broad-leavedforestry In recent years the concerns of resourcesrsquo rationaluse and protection have widely spread Therefore afforesta-tion has been carried out frequently in Southern China

With the rapid development of economy the imbalanceddevelopment of agriculture industry and service industryresults in the remarkable land use change in Southern ChinaAdditionally the imbalanced regional development causesthe huge differences of regional land useThe irrational devel-opment and utilization of land resources have led to regionalclimate change to a certain extent According to the tempera-ture and precipitation data from meteorological observationstation it is clear that the temperature in Southern Chinacoastal area is rising and the annual precipitation in southernChina varies periodically In order to optimize land usestructure andmitigate the regional climate change a series ofplans were implemented for example ecological restorationprojects [22] Therefore the exploration of land use changersquosimpact mechanism on regional climate is significant andhelpful to the policy making and sustainable development

22 SEM (Structural Equation Model Analyses VariablesrsquoRelations by Covariance Matrix) Variables can be dividedinto four kinds exogenous latent variables endogenous latentvariables exogenous observed variables and endogenousobserved variables Observed variables generally refer toobjectives By contrast latent variables are difficult to beobserved directly but can be indicated by several relatedobserved variables This new multivariate statistical analysismethod integrated by traditional factor analysis and pathanalysis mainly describes two kinds of relationships therelationships between latent and related observed variablesand the relationships among latent variables [23 24] To solvequestions mentioned above SEM is divided into measure-ment component and structural component The measure-ment component deals with the measurement errors andrelationships between latent and related observed variables[25] It specifies latent variables as some linear functions oftheir observed variablesThe structural componentrsquos functionis to capture the impacts of exogenous latent variables onendogenous latent variables and the impacts of endogenouslatent variables upon one another [26] The based equationsare as follows

SEM[

[

[

measurement component [

119884 = ⋀

119884

120578 + 120576

119883 = ⋀

119883

120585 + 120575

structural component 120578 = 119861120578 + Γ120585 + 120577

(1)


117∘09984000998400998400E114∘09984000998400998400E111∘09984000998400998400E108∘09984000998400998400E105∘09984000998400998400E 117∘09984000998400998400E114∘09984000998400998400E111∘09984000998400998400E108∘09984000998400998400E105∘09984000998400998400E

114∘09984000998400998400E111∘09984000998400998400E108∘09984000998400998400E105∘09984000998400998400E 114∘09984000998400998400E111∘09984000998400998400E108∘09984000998400998400E105∘09984000998400998400E

27∘ 0

998400 0998400998400

N24

∘ 0998400 0

998400998400N

21∘ 0

998400 0998400998400

N18

∘ 0998400 0

998400998400N

27∘ 0

998400 0998400998400

N24

∘ 0998400 0

998400998400N

21∘ 0

998400 0998400998400

N18

∘ 0998400 0

998400998400N

24∘ 0

998400 0998400998400

N21

∘ 0998400 0

998400998400N

18∘ 0

998400 0998400998400

N

24∘ 0

998400 0998400998400

N21

∘ 0998400 0

998400998400N

18∘ 0

998400 0998400998400

N

0 40 80 160 240 320

(mi)0 40 80 160 240 320

(mi)

The year 1998

N

The year 2005

N











c d ge hf

a b


120577

1205851 1205852

X11 X12 Y1 Y2 X21 X22

120578

120575 11 120575 12 120575 21 120575 22120576 1 120576 2










StationCounty

N

0 50 100 200(mi)

300 400

117∘09984000998400998400E114∘09984000998400998400E111∘09984000998400998400E108∘09984000998400998400E105∘09984000998400998400E

117∘09984000998400998400E114∘09984000998400998400E111∘09984000998400998400E108∘09984000998400998400E105∘09984000998400998400E

27∘ 0

998400 0998400998400

N24

∘ 0998400 0

998400998400N

21∘ 0

998400 0998400998400

N18

∘ 0998400 0

998400998400N

27∘ 0

998400 0998400998400

N24

∘ 0998400 0

998400998400N

21∘ 0

998400 0998400998400

N18

∘ 0998400 0

998400998400N










Cultivated land

Others

Unused land

Built-up area

Others

Temperature

Precipitation

Climate



Forestry

Water bodies














120594

2df

120594


mdash lt5

119875


mdash lt0005


1 minus

119905119903[(119864

minus1

119878 minus 119868)

2

]

119905119903[(119864

minus1

119878)

2

]

gt09


1 minus

max(1205942119879

minus df119879

0)

max(1205942119873

minus df119873

120594

2

119879

minus df119879

0)

gt09



df lt008


2ΣΣ(119904

119894119895

minus 119890

119894119895

)

2

119901(119901 + 1)

] lt005


120594

2

119873

minus 120594

2

119879

120594

2

119873

gt09















Cultivated land

Others

Unused land

Built-up area

Others

084087

046

020

Temperature

096

029

081

Precipitation

Climate

033



076086

Forestry

Water bodies

074019

090

091

008

043

027

023

039

018

035

088

minus042








1190 119875 lt 0001


1196 119875 lt 0001






minus274 119875 lt 0001

146 119875 lt 0005

234 119875 lt 0001














4 Conclusion









Acknowledgments


References















2






































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


117∘09984000998400998400E114∘09984000998400998400E111∘09984000998400998400E108∘09984000998400998400E105∘09984000998400998400E 117∘09984000998400998400E114∘09984000998400998400E111∘09984000998400998400E108∘09984000998400998400E105∘09984000998400998400E

114∘09984000998400998400E111∘09984000998400998400E108∘09984000998400998400E105∘09984000998400998400E 114∘09984000998400998400E111∘09984000998400998400E108∘09984000998400998400E105∘09984000998400998400E

27∘ 0

998400 0998400998400

N24

∘ 0998400 0

998400998400N

21∘ 0

998400 0998400998400

N18

∘ 0998400 0

998400998400N

27∘ 0

998400 0998400998400

N24

∘ 0998400 0

998400998400N

21∘ 0

998400 0998400998400

N18

∘ 0998400 0

998400998400N

24∘ 0

998400 0998400998400

N21

∘ 0998400 0

998400998400N

18∘ 0

998400 0998400998400

N

24∘ 0

998400 0998400998400

N21

∘ 0998400 0

998400998400N

18∘ 0

998400 0998400998400

N

0 40 80 160 240 320

(mi)0 40 80 160 240 320

(mi)

The year 1998

N

The year 2005

N











c d ge hf

a b


120577

1205851 1205852

X11 X12 Y1 Y2 X21 X22

120578

120575 11 120575 12 120575 21 120575 22120576 1 120576 2










StationCounty

N

0 50 100 200(mi)

300 400

117∘09984000998400998400E114∘09984000998400998400E111∘09984000998400998400E108∘09984000998400998400E105∘09984000998400998400E

117∘09984000998400998400E114∘09984000998400998400E111∘09984000998400998400E108∘09984000998400998400E105∘09984000998400998400E

27∘ 0

998400 0998400998400

N24

∘ 0998400 0

998400998400N

21∘ 0

998400 0998400998400

N18

∘ 0998400 0

998400998400N

27∘ 0

998400 0998400998400

N24

∘ 0998400 0

998400998400N

21∘ 0

998400 0998400998400

N18

∘ 0998400 0

998400998400N










Cultivated land

Others

Unused land

Built-up area

Others

Temperature

Precipitation

Climate



Forestry

Water bodies














120594

2df

120594


mdash lt5

119875


mdash lt0005


1 minus

119905119903[(119864

minus1

119878 minus 119868)

2

]

119905119903[(119864

minus1

119878)

2

]

gt09


1 minus

max(1205942119879

minus df119879

0)

max(1205942119873

minus df119873

120594

2

119879

minus df119879

0)

gt09



df lt008


2ΣΣ(119904

119894119895

minus 119890

119894119895

)

2

119901(119901 + 1)

] lt005


120594

2

119873

minus 120594

2

119879

120594

2

119873

gt09















Cultivated land

Others

Unused land

Built-up area

Others

084087

046

020

Temperature

096

029

081

Precipitation

Climate

033



076086

Forestry

Water bodies

074019

090

091

008

043

027

023

039

018

035

088

minus042








1190 119875 lt 0001


1196 119875 lt 0001






minus274 119875 lt 0001

146 119875 lt 0005

234 119875 lt 0001














4 Conclusion









Acknowledgments


References















2






































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in







StationCounty

N

0 50 100 200(mi)

300 400

117∘09984000998400998400E114∘09984000998400998400E111∘09984000998400998400E108∘09984000998400998400E105∘09984000998400998400E

117∘09984000998400998400E114∘09984000998400998400E111∘09984000998400998400E108∘09984000998400998400E105∘09984000998400998400E

27∘ 0

998400 0998400998400

N24

∘ 0998400 0

998400998400N

21∘ 0

998400 0998400998400

N18

∘ 0998400 0

998400998400N

27∘ 0

998400 0998400998400

N24

∘ 0998400 0

998400998400N

21∘ 0

998400 0998400998400

N18

∘ 0998400 0

998400998400N










Cultivated land

Others

Unused land

Built-up area

Others

Temperature

Precipitation

Climate



Forestry

Water bodies














120594

2df

120594


mdash lt5

119875


mdash lt0005


1 minus

119905119903[(119864

minus1

119878 minus 119868)

2

]

119905119903[(119864

minus1

119878)

2

]

gt09


1 minus

max(1205942119879

minus df119879

0)

max(1205942119873

minus df119873

120594

2

119879

minus df119879

0)

gt09



df lt008


2ΣΣ(119904

119894119895

minus 119890

119894119895

)

2

119901(119901 + 1)

] lt005


120594

2

119873

minus 120594

2

119879

120594

2

119873

gt09















Cultivated land

Others

Unused land

Built-up area

Others

084087

046

020

Temperature

096

029

081

Precipitation

Climate

033



076086

Forestry

Water bodies

074019

090

091

008

043

027

023

039

018

035

088

minus042








1190 119875 lt 0001


1196 119875 lt 0001






minus274 119875 lt 0001

146 119875 lt 0005

234 119875 lt 0001














4 Conclusion









Acknowledgments


References















2






































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in





Cultivated land

Others

Unused land

Built-up area

Others

Temperature

Precipitation

Climate



Forestry

Water bodies














120594

2df

120594


mdash lt5

119875


mdash lt0005


1 minus

119905119903[(119864

minus1

119878 minus 119868)

2

]

119905119903[(119864

minus1

119878)

2

]

gt09


1 minus

max(1205942119879

minus df119879

0)

max(1205942119873

minus df119873

120594

2

119879

minus df119879

0)

gt09



df lt008


2ΣΣ(119904

119894119895

minus 119890

119894119895

)

2

119901(119901 + 1)

] lt005


120594

2

119873

minus 120594

2

119879

120594

2

119873

gt09















Cultivated land

Others

Unused land

Built-up area

Others

084087

046

020

Temperature

096

029

081

Precipitation

Climate

033



076086

Forestry

Water bodies

074019

090

091

008

043

027

023

039

018

035

088

minus042








1190 119875 lt 0001


1196 119875 lt 0001






minus274 119875 lt 0001

146 119875 lt 0005

234 119875 lt 0001














4 Conclusion









Acknowledgments


References















2






































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in




120594

2df

120594


mdash lt5

119875


mdash lt0005


1 minus

119905119903[(119864

minus1

119878 minus 119868)

2

]

119905119903[(119864

minus1

119878)

2

]

gt09


1 minus

max(1205942119879

minus df119879

0)

max(1205942119873

minus df119873

120594

2

119879

minus df119879

0)

gt09



df lt008


2ΣΣ(119904

119894119895

minus 119890

119894119895

)

2

119901(119901 + 1)

] lt005


120594

2

119873

minus 120594

2

119879

120594

2

119873

gt09















Cultivated land

Others

Unused land

Built-up area

Others

084087

046

020

Temperature

096

029

081

Precipitation

Climate

033



076086

Forestry

Water bodies

074019

090

091

008

043

027

023

039

018

035

088

minus042








1190 119875 lt 0001


1196 119875 lt 0001






minus274 119875 lt 0001

146 119875 lt 0005

234 119875 lt 0001














4 Conclusion









Acknowledgments


References















2






































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in


Cultivated land

Others

Unused land

Built-up area

Others

084087

046

020

Temperature

096

029

081

Precipitation

Climate

033



076086

Forestry

Water bodies

074019

090

091

008

043

027

023

039

018

035

088

minus042








1190 119875 lt 0001


1196 119875 lt 0001






minus274 119875 lt 0001

146 119875 lt 0005

234 119875 lt 0001














4 Conclusion









Acknowledgments


References















2






































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in







4 Conclusion









Acknowledgments


References















2






































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in







Acknowledgments


References















2






































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in










Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in

Documents

Research Article Impacts of Land Use Change on the ...downloads.hindawi.com/journals/amete/2015/563673.pdf · As a typical tropic and subtropics monsoon climate region, Southern China