%0 Journal Article
%T Land Cover Classification based on MODIS NDVI & LST Time Series Data in Northeast China<br>基于MODIS LST修正NDVI时序数列的土地覆盖分类
%A GONG Pan
%A TANG Hua-jun
%A CHEN Zhong-xin
%A ZHANG Feng-rong
%A <br>宫攀
%A 唐华俊
%A 
%A 陈仲新
%A 张凤荣
%J 资源科学
%D 2006
%I 
%X MODIS was built based on seven bands specifically designed for land cover monitoring,wherein an improved spectral/spatial response compared to AVHRR.this allows for greater accuracy in distinguishing different land cover categories.The paper investigated the regional land cover classification with MODIS time-series data.Northeast China is the ideal area of research on MODIS land cover classification for its pure and diversified land cover types.The normalized difference vegetation index(NDVI) increased with the growth of the vegetation,and gradually decreased after reaching the maximum at some growth stage.This characteristic is similar to hyper-spectral data,whose pixels having similar NDVI profiles are the same land cover classes;however,MODIS with high spectral and temporal resolution is more sensitive to land cover.The important factor to the accuracy classification is the quality of the NDVI.So the first step of classification is to find a method to produce the high-quality NDVI data.The maximum value composite(MVC) can eliminate the contaminated data and abnormal data in the NDVI multi-temporal image at some degree.But the profile of NDVI after compositing cannot effectively reflect the change of vegetation growth in a year.SavizkyGolay filter was used to smooth the 10-day compositing data.The result proved that the time-series data was more correlational with the vegetation growth.Through analyzing the profiles,NDVI time-series data can distinguish the perennial woody and herbaceous vegetation and non-vegetation categories depending on the seasonal differences.Grassland and cropland,needle-leaf-deciduous forest and broadleaf-deciduous forest have similar characteristics easy to be confused.We add the LST(land surface temperature) data to resolve this problem and select five principal components for classification.Five principal components are selected as the data source to join classification processing after principal analysis.Validating results with 363 filed samples,the overall classification accuracy of new time series data is 69.15% and kappa index is 0.6499.The accuracy of grassland and cropland,needle-leaf-deciduous forest and broadleaf-deciduous forest are all above 70%.From the result,we concluded that LST is correlated to altitude,latitude and other natural factors.So LST & NDVI is more sensitive to land cover than NDVI;MODIS data is good at updating the regional land cover classification.If the model of remote sensing time-series data and the key plant phenophase were established,the accuracy of land cover classification would be improved at a large degree.
%K MODIS
%K LST
%K NDVI
%K Land cover classification<br>MODIS
%K LST
%K NDVI
%K 土地覆盖分类
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=B5EDD921F3D863E289B22F36E70174A7007B5F5E43D63598017D41BB67247657&cid=B47B31F6349F979B&jid=9DEEAF23637E6E9539AD99BE6ABAB2B3&aid=F541E270F3BB7842&yid=37904DC365DD7266&vid=D3E34374A0D77D7F&iid=E158A972A605785F&sid=DBF54A8E2A721A6D&eid=4DB1E72614E68564&journal_id=1007-7588&journal_name=资源科学&referenced_num=1&reference_num=14