河南科技学院学报（自然科学版）
河南科技學院學報（自然科學版）
하남과기학원학보（자연과학판）
JOURNAL OF HENAN INSTITUTE OF SCIENCE AND TECHNOLOGY(NATURAL SCIENCES EDITION)
2014年
3期
41-44
,共4页
遥感技术%植被指数%模型%新乡市
遙感技術%植被指數%模型%新鄉市
요감기술%식피지수%모형%신향시
remote sensing technique%vegetation index%model%Xinxiang City
对2011年8月覆盖新乡市人民公园的SPOT5遥感影像进行预处理提取植被信息,再用代表性样地法采集植被信息,选定归一化植被指数（NDVI）和比值植被指数（RVI）作为自变量,以实测样本数据（LVV）作为应变量,采用多元回归分析法建立基于遥感影像的新乡市人民公园植被遥感模型.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.每种植被样方优化出一个模型,即针阔混交林：LVV=16.216RVITOA+19.698RVIDN-9.112（R2=0.866,RMSE=0.289）；阔叶林：LVV=8.111RVIPAC-3.142（R2=0.795, RMSE=0.512）；灌木：LVV=313.621NDVIDN3-19.118NDVIDN2+2.612（R2=0.812, RMSE=0.714）；草地：LVV=3.121RVITOA+1.992RVIDN-4.002（ R2=0.892, RMSE=0.547）；总体植被：LVV=2.231RVIPAC-7.112NDVISR+5.122NDVIPAC+9.982NDVIDN-1.417（R2=0.796,RMSE=0.712）.这些优选模型在新乡市人民公园的植被调查中具有一定的应用价值.
對2011年8月覆蓋新鄉市人民公園的SPOT5遙感影像進行預處理提取植被信息,再用代錶性樣地法採集植被信息,選定歸一化植被指數（NDVI）和比值植被指數（RVI）作為自變量,以實測樣本數據（LVV）作為應變量,採用多元迴歸分析法建立基于遙感影像的新鄉市人民公園植被遙感模型.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.每種植被樣方優化齣一箇模型,即針闊混交林：LVV=16.216RVITOA+19.698RVIDN-9.112（R2=0.866,RMSE=0.289）；闊葉林：LVV=8.111RVIPAC-3.142（R2=0.795, RMSE=0.512）；灌木：LVV=313.621NDVIDN3-19.118NDVIDN2+2.612（R2=0.812, RMSE=0.714）；草地：LVV=3.121RVITOA+1.992RVIDN-4.002（ R2=0.892, RMSE=0.547）；總體植被：LVV=2.231RVIPAC-7.112NDVISR+5.122NDVIPAC+9.982NDVIDN-1.417（R2=0.796,RMSE=0.712）.這些優選模型在新鄉市人民公園的植被調查中具有一定的應用價值.
대2011년8월복개신향시인민공완적SPOT5요감영상진행예처리제취식피신식,재용대표성양지법채집식피신식,선정귀일화식피지수（NDVI）화비치식피지수（RVI）작위자변량,이실측양본수거（LVV）작위응변량,채용다원회귀분석법건립기우요감영상적신향시인민공완식피요감모형.결과표명：LVV여VI정겁현저적상관관계,기상관계수다이상대균질식피고우식피총체.매충식피양방우화출일개모형,즉침활혼교림：LVV=16.216RVITOA+19.698RVIDN-9.112（R2=0.866,RMSE=0.289）；활협림：LVV=8.111RVIPAC-3.142（R2=0.795, RMSE=0.512）；관목：LVV=313.621NDVIDN3-19.118NDVIDN2+2.612（R2=0.812, RMSE=0.714）；초지：LVV=3.121RVITOA+1.992RVIDN-4.002（ R2=0.892, RMSE=0.547）；총체식피：LVV=2.231RVIPAC-7.112NDVISR+5.122NDVIPAC+9.982NDVIDN-1.417（R2=0.796,RMSE=0.712）.저사우선모형재신향시인민공완적식피조사중구유일정적응용개치.
Extracting vegetation information from SPOT5 remote sensing image and representative sample method to collect the vegetation information of the People’s Park in Xinxiang City were used to derive two vegetation indices,i. e.,normalized difference vegetation index(NDVI),and ratio vegetation index (RVI),to establish the vegetation remote sensing investigation model using multiple regression analysis.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.One ‘best’ model was selected for each of the vegetation quadrates,i.e.,broad-conifer leaf mixed forest:LVV=16.216RVITOA+19.698 RVIDN-9.112 (R2=0.866,RMSE=0.289),broad-leaf forest:LVV=8.111RVIPAC-3.142 (R2=0.795,RMSE=0.512),shrub:LVV=313.621NDVIDN3-19.118NDVIDN2+2.612(R2=0.812,RMSE=0.714),grass:LVV=3.121RVITOA+1.992RVIDN-4.002 (R2=0.892,RMSE=0.547),and total vegetation:LVV=2.231RVIPAC-7.112NDVISR+5.122NDVIPAC+9.982NDVIDN-1.417(R2=0.796,RMSE=0.712).The optimization model has certain application value in the People's Park vegetation survey in Xinxiang City.
