基于tm影像的沱江中游土壤重金屬預(yù)測(cè)研究

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1、萬方數(shù)據(jù)AbstractAimingatfindingoutaneasyandquickmonitoringmethodonquantifyingtheheavymetalpollutionofsoil，thisstudyestablishesalinearregressionpredictionmodelbyusingeachbandspectralreflectanceofTMremote—sensingimage，9vegetationindexes，4groundcofactors，andthemeasuredva

2、lueofheavymetalcontentof80％samplesoil．ThismodeltakesthefactorswhicharenotinvolvedinthemodelingbuthavesignificantcorrelationwithheavymetalcontentofsoilascorrectionfactorsandinnsMonteCarlosimulation．Therest20％samplesoilbecomescheckingpointtOdothetrendanalysisanderro

3、rstatisticstestforallkindsofmodels．Intheend．thisstudyinvertsthespatialdistributionofmeasuredvalueandpredictedvalueandcontraststheirdifferenceswithordinarykrigingmethod．Themainconelusionsareasfollows．UsingtheTMimagespectruminformationandotherfactorscanpredictthecon

4、tentofAs，Hg，Cr,Cd,Cu，Pb，AvailableMn，AvailableFeandAvailableZn，modelofAvailableFeisbetter．ModelingfactorsareextractedfromTMremotesensingimagesanddigitalelevationmodel，whichiseasytoget，calleffectivelysavecostandtime．Allthethreekindsofmodeling，separatemodelingforeach

5、bandspectralreflectivityofremote．sensingimage，commonmodelingwithgroundfactors，separatemodelingfordifferentlandforms，canpredictheavymetalcontent(P

6、nhasaprogressiveincrease．Therefore，itisnecessarytoestablishpredictionmodelbasedondifferentlandformswhenusingremotesensingtostudysoilheavymetalincomplex—landformareas．MonteCarlosimulationcancorrectthepredictionmodels．Comparedwiththedataoflinearregressionpredictionm

7、odel，theroot·mean—square-errorofarsenic，mercBry,chromium，cadmium，copper，lead,activemanganese，activeferric，andactivezincarerespectivelyreduced22．7％，18．O％，37．7％，36．2％，13．5％，30．5％，5．4％，25．2％，and1．1．9％aftercorrectedbyMonteCarlosimulation，whichindicatesthatMonteCarlosi

8、mulationcanimprovethemodelprecision，butthedifferencesbetweencorrectionfactorsandtheII萬方數(shù)據(jù)degreeofcorrelationoftargetvaluecanmakedifferentcorrectionfacto