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113()Vol.11No.320106JOURNALOFBEIHUAUNIVERSITY(NaturalScience)Jun.2010:100924822(2010)0320251204GIS1,1,2,1,1,1(1.,350002;2.,365000):530,GIS(NDVI,VARIg),,,,.26.:80.96%,.:GIS;;;:S771.4:A:2010201214:(2009GJC40032);(2006BAD23B05).:(1981-),,,3S;(1963-),,,,3S.EstimatingModelofVegetationFractionBasedonGISandRSTechnologiesCHENGuo2rong1,LIUJian1,2,SHICong2zhi1,YUKun2yong1,LAIGuang2hui1(1.ForestryCollegeofFujianAgricultureandForestryUniversity,Fuzhou350002,China;2.SanmingUniversity,Sanming365000,China)Abstract:Thisresearchextracted30samplesofvegetationfractioninZhangpuCountyfrom5thforestresourcecontinuousinvestigationdataofFujianProvince,establishedanestimatingmodelofvegetationfractionwiththeelementsofthevegetationindex(NDVI,VARIg)andterrainwhichextracteddirectlyobtainedfromtheGISandRStechnologiesandflitteredbythethricestandarddeviationmethodandlinerregressionmethod.Theprecisionofthemodelweretestedwithdatathatwereextractedfromanother26samples.Theresultsshowthattheaverageprecisionofthemodelis80.96%,whichshowedthatthemodelisusefulforvegetationfractionestimate.Keywords:GIS;RS;vegetationfraction;linerregression,,,[1].,,,[2].,,,.,,[3].,.,,,,[4].,,GIS,,,,.1,233224061173511758.,,.,21,2119h,15001800mm,,.2153.3km2,,3.85hm2,12.1hm2,3.41hm2.16411.80.4,,72.1,35.1.22003LANDSATTM(119243,120243)150000DEM.15000020035.33.1ERDAS8.7,150000,,0.33.AOI,.,.3.2.()[5].,(NDVI),,.LANDSAT25,NDVINDVI=(TM4-TM3)/(TM4+TM3),,TM3TM4LANDSAT253()4(),NDVI[-1,1].Gitelson[6],NDVI,VIg=(Rg-Rr)/(Rg+Rr)VARIg=(Rg-Rr)/(Rg+Rr-Rb),,,.ERDAS8.7,,NDVIVARIg,,30GPSNDVIVARIg.3.3,,(),GIS,,.150000DEM,ERDAS,.3.43.4.1,,I[7].,1.252()111Tab.1Classesofqualityvariableandrecodes12345610m()1050m()50500m()500m()5()615()1625()2635()3645()46()1(1,0),,2.2Tab.2Recodesofeachlevelofdifferentsitefactorsinvariousplots1010010000012010001001003100000101004100010001005100001001006010000110003.4.2.,.4SPSS,,,,F,.3,4.3Tab.3VariablesfromlinerregressionmodelFFP1VARIg0.5833.8010.0010.5831.0001.0002VARIg0.4983.5460.0010.5640.9541.0484-0.398-2.8290.009-0.4780.9541.0484Tab.4ModelsummaryRR2R2R2Fdf1df2FP10.583a0.3400.3170.272390.34014.4501280.00120.701b0.4910.4540.243610.1518.0061270.0094,,R2,R2,,[8].,VARIg2.4.1F,F00.5,000,.3523,:GIS5Tab.5VarianceanalysisdfFP11.07211.07214.4500.001a2.078280.0743.1502921.54720.77413.0360b1.602270.0593.150294.26,0,1,,,.,y=0.413+0.993VARIg-0.3054.6Tab.6AnalysisofregressioncoefficientstP95%10.3230.0605.35000.1990.447VARIg1.1620.3060.5833.8010.0010.5361.7891.0001.00020.4130.0636.59000.2850.542VARIg0.9930.2800.4983.5460.0010.4181.5670.9541.0484-0.3050.108-0.398-2.8290.009-0.525-0.0840.9541.0487Tab.7Testresultsofregressionmodelaccuracy/%90809070806070506050796400/%26.9234.6223.0815.380080.964.3,26,,,,(=1--/).7,80.96%,70%22,84.62%.51)NDVI,VARIg,,,.,80.96%.2);,.3)NDVI,VARIg,,,,,.:[1],,,.[J].,2004,26(4):1532159.[2],,,.TM[J].,2006,31(1):43245.[3],,,.[J].,2001,5(6):4162422.[4].3S[D].:,2006.[5].3S[D].:,2003.[6]AnatolyAGitelson,YoramJKaufman,RobertStark,etal.NovelAlgorithmsforRemoteEstimationofVegetationFraction[J].RemoteSensingofEnvironment,2002,80(1):76287.[7],,,.[J].:,2000,1(1):77281.[8],,,.RS[J].,2006,33(1):16220.:452()11