生态环境学报
生態環境學報
생태배경학보
ECOLOGY AND ENVIRONMENT
2009年
6期
2294-2299
,共6页
辐射校正%植被指数%绿量%模型
輻射校正%植被指數%綠量%模型
복사교정%식피지수%록량%모형
radiometric correction%vegetation index%living vegetation volume%model
选用南京市SPOT5图像的灰度值(DN)、星上辐射率(SR)、表观反射率(TOA)和地物反射率(PAC)数据,提取了两种植被指数(VI),即归一化植被指数(NDVI)和比值植被指数(RVI),并与地面实测的绿量(LVV)进行相关分析,建立了165个关系模型.结果表明,LVV与VI呈极显著的相关关系,其相关系数多以相对均质植被高于植被总体,基于灰度值高于常用的地物反射率为主.LVV-VI关系模型的R~2均值以多元线性回归模型最高(0.821),指数模型最低(0.536),而1～3次多项式模型均接近0.7.每种植被样方优选出一个模型,即阔叶林LVV-7.802 RVI_(PAC)-2.455(R~2=0.827,RMSE=0.498);针阔叶混交林LVV=-15.421 RVI_(TOA)+26.971 RVI_(DN)-8.261(R~2=0.918,RMSE=0.356);灌木LVV=-342.591 NDVI_(DN)~3-20.553 NDVI_(DN)~2+14.013 NDVI_(DN)+1.509(R~2=0.764,RMSE=0.689);草地LVV=2.934 RVI_(PAC)+2.147 RVI_(TOA)-3.193(R2=0.903,RMSE=0.464);总体植被LVV=1.789RVI_(PAC)-6.814NDVIs+4.258NDVI_(PAC)+12.854 NDVI_(DN)-0.342(R~2=0.810,RMSE=0.638).这些优选模犁的自变量包括了4种辐射校正水平下提取的两种植被指数,显示基于不同辐射校正水平的植被指数在植被LVV遥感反演中具有一定的应用潜力.
選用南京市SPOT5圖像的灰度值(DN)、星上輻射率(SR)、錶觀反射率(TOA)和地物反射率(PAC)數據,提取瞭兩種植被指數(VI),即歸一化植被指數(NDVI)和比值植被指數(RVI),併與地麵實測的綠量(LVV)進行相關分析,建立瞭165箇關繫模型.結果錶明,LVV與VI呈極顯著的相關關繫,其相關繫數多以相對均質植被高于植被總體,基于灰度值高于常用的地物反射率為主.LVV-VI關繫模型的R~2均值以多元線性迴歸模型最高(0.821),指數模型最低(0.536),而1～3次多項式模型均接近0.7.每種植被樣方優選齣一箇模型,即闊葉林LVV-7.802 RVI_(PAC)-2.455(R~2=0.827,RMSE=0.498);針闊葉混交林LVV=-15.421 RVI_(TOA)+26.971 RVI_(DN)-8.261(R~2=0.918,RMSE=0.356);灌木LVV=-342.591 NDVI_(DN)~3-20.553 NDVI_(DN)~2+14.013 NDVI_(DN)+1.509(R~2=0.764,RMSE=0.689);草地LVV=2.934 RVI_(PAC)+2.147 RVI_(TOA)-3.193(R2=0.903,RMSE=0.464);總體植被LVV=1.789RVI_(PAC)-6.814NDVIs+4.258NDVI_(PAC)+12.854 NDVI_(DN)-0.342(R~2=0.810,RMSE=0.638).這些優選模犛的自變量包括瞭4種輻射校正水平下提取的兩種植被指數,顯示基于不同輻射校正水平的植被指數在植被LVV遙感反縯中具有一定的應用潛力.
선용남경시SPOT5도상적회도치(DN)、성상복사솔(SR)、표관반사솔(TOA)화지물반사솔(PAC)수거,제취료량충식피지수(VI),즉귀일화식피지수(NDVI)화비치식피지수(RVI),병여지면실측적록량(LVV)진행상관분석,건립료165개관계모형.결과표명,LVV여VI정겁현저적상관관계,기상관계수다이상대균질식피고우식피총체,기우회도치고우상용적지물반사솔위주.LVV-VI관계모형적R~2균치이다원선성회귀모형최고(0.821),지수모형최저(0.536),이1～3차다항식모형균접근0.7.매충식피양방우선출일개모형,즉활협림LVV-7.802 RVI_(PAC)-2.455(R~2=0.827,RMSE=0.498);침활협혼교림LVV=-15.421 RVI_(TOA)+26.971 RVI_(DN)-8.261(R~2=0.918,RMSE=0.356);관목LVV=-342.591 NDVI_(DN)~3-20.553 NDVI_(DN)~2+14.013 NDVI_(DN)+1.509(R~2=0.764,RMSE=0.689);초지LVV=2.934 RVI_(PAC)+2.147 RVI_(TOA)-3.193(R2=0.903,RMSE=0.464);총체식피LVV=1.789RVI_(PAC)-6.814NDVIs+4.258NDVI_(PAC)+12.854 NDVI_(DN)-0.342(R~2=0.810,RMSE=0.638).저사우선모리적자변량포괄료4충복사교정수평하제취적량충식피지수,현시기우불동복사교정수평적식피지수재식피LVV요감반연중구유일정적응용잠력.
The images of post atmospheric correction reflectance(PAC), top of atmosphere reflectance(TOA) , satellite radi-ance(SR), and digital number(ZW) of a SPOT5 HRG image of Nanjing were used to derive two vegetation indices(VI), i.e., normalized difference vegetation index(NDVI), and ratio vegetation index(RVI). Between these Vis and living vegetation volume(LW) data which obtained from ground measurement,correlations were analyzed and then 165 relationship models were established. The results showed that LVV was significantly correlated with VI. LVV-VI correlation coefficients of relatively 'pure' vegetation are higher than those of total vegetation, and of digital number(DN) higher than those of post atmospheric correction reflectance(PAC) which is universally used.The average R~2 of multi-variable linear regression LVV-VI models was the highest(0.821),of exponential models the lowest(0.536),and of all polynomial models(linear,quadratic,and cubic) near 0.7.One 'best' model was selected for each of the vegetation quadrats.i.e., broad-leaf forest: LVV=7.802RVI_(PAC) -2.455(R~2= 0.827, RMSE=0.498),broad-conifer leaf mixed forest: LVV=-15.421RVI_(TOA)+26.971RVI_(DN)-8.261(R~2=0.918, RMSE= 0.356),shrub:LVV=-342.591 NDVI_(DN)~3 -20.553NDVI_(DN)~2+ 14.013NDVI_(DN) +1.509(R~2=0.764, RMSE=0.689) .grass:LVV=2.934RVI_(PAC)+2.147RVI_(TOA) -3.193(R~2=0.903, RMSE = 0.464),and total vegetation: LVV=1.789RVI_(PAC)-6.814NDVI_(SR)+ 4.258NDVI_(PAC)+12.854 NDVI_(DN) -0.342(R~2=0.810, RMSE = 0.638) .The independent variables of these selected models include two vegetation indices from 4 radiometric correction lev-els,indicating the potentials of spectral vegetation indices from different radiometric correction levels in LW estimating.
