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1、第17卷第6期草地学报2009年11月Vol.17No.6ACTAAGRESTIASINICANov.2009偏最小二乘在遥感监测西藏草地生物量上的应用*张正健,刘志红,郭艳芬,韩建宁,李扬(成都信息工程学院资源环境学院,成都610225)摘要:在多年平均年最大归一化植被指数(NDVI)的基础上,结合西藏地区年降雨量、年积温等气象资料,利用偏最小二乘(partialleastsquares,PLS)回归方法对数据进行分析并建立西藏地区草地生物量与归一化植被指数、降雨量等解释变量的回归估测模型
2、。并和一般最小二乘法(ordinaryleastsquares,OLS)中的逐步回归法(Stepwise)相比较。结果表明:草地生物量与年最大NDVI值和年降雨量有很强的相关性,偏最小二乘回归在拟合及估测效果上均优于一般最小二乘的逐步回归法,回归方程的相关系数为0.895,取得了较为可靠的结果。偏最小二乘回归在解释变量多、样本个数少、变量间存在多重共线性时尤为有效,为遥感监测植被生物量时的数据处理提供了新的途径。关键词:偏最小二乘回归;一般最小二乘回归;生物量;NDVI;降雨量中图分类号:S812文献标识码:A
3、文章编号:10070435(2009)06073505TheApplicationofPartialLeastSquarestoTibetsGrasslandBiomassMonitoringbyRemoteSensing*ZHANGZhengjian,LIUZhihong,GUOYanfen,HANJianning,LIYang(CollegeofResourcesandEnvironment,ChengduUniversityofInformationTechnology,Chengdu,Si
4、chuanProvince610225,China)Abstract:Remotesensingisaveryfastandeffectivewaytomonitorthegrasslandbiomass,thepreviousstudiesaremostlybasedonthecorrelationofvegetationindex(VI)andbiomass.Inthispaper,themethodofpartialleastsquaresregression(PLSR)wasusedtosetuptheregr
5、essionandpredictionmodelsbetweengrasslandbiomassandnormalizeddifferencevegetationindex(NDVI)basedonthemultiyearaverageannualmaximumnormalizeddifferencevegetationindex(NDVI)combinedwithannualrainfall,annualaccumulatedtemperature,andothermeteorologicalmaterialsi
6、nTibet.Inaddition,thismethodwasalsocomparedwiththestepwiseregressionofordinaryleastsquares(OLS)method.TheresultsshowthattherewasastrongcorrelationbetweengrassbiomassandtheannualmaximumNDVIvalueandtheannualrainfall.PLSRachievedbettereffectsoffittingandpredictionth
7、anthestepwiseregressionofOLSandthecorrelationcoefficientwas0.895,meanwhilereliableresultswereobtained.PLSRwouldprovideanewwayfordataprocessinginvegetationbiomassmonitoringbyremotesensingbecauseitisparticularlyeffectiveinthecaseofmorepredictorvariables,lesssample
8、s,andexistentmulticollinearityamongvariables.Keywords:Partialleastsquaresregression(PLSR);Ordinaryleastsquaresregression(OLSR);Biomass;Normalizeddifferencevege
