INTEGRATED VEGETATION CLASSIFICATION AND MAPPINGUSING REMOTE SENSING AND GIS TECHNIQUES  

作　　者：庄大方 凌扬荣 

出　　处：《Chinese Geographical Science》1999年第1期49-56,共8页中国地理科学（英文版）

摘　　要：NOAA-AVHRR data have been more and more used by scientists because of its short temporal resolution,large scope, inexpensive cost and broad wave bands. On macro and middle scale of vegetation remote sensing, NOAAAVHRR possesses an advantage when compared with other satellites. However, because NOAA-AVHRR also problem of low resolution, data distortion and geometrical distortion, in the area of application of NOAA-AVHRR in largescale vegetation - mapping, the accuracy of vegetation classification should be improved. This paper discuss the feasibilityof integrating the geographic data in GIS(Geographical Information System) and remotely sensed data in GIS. Under theenvironment of GIS, temperature, precipitation and elevation, which serve as main factors affecting vegetation growth,were processed by a mathematical model and qualified into geographic image under a certain grid system. The geographicimage were overlaid to the NOAA-AVHRR data which had been compressed and processed. In order to evaluate the usefulness of geographic data for vegetation classification, the area under study was digitally classified by two groups of interpreter: the proposed methodology using maximum likelihood classification assisted by the geographic database and a conventional maximum likelihood classification only. Both result were compared using Kappa statistics. The indices to both theproposed and the conventional digital classification methodology were 0. 668(yew good) and 0. 563(good), respetively.The geographic database rendered an improvement over the conventional digital classification. Furthermore, in this study,some problems related to multi-sources data integration are also discussed.NOAA-AVHRR data have been more and more used by scientists because of its short temporal resolution, large scope, inexpensive cost and broad wave bands. On macro and middle scale of vegetation remote sensing, NOAA-AVHRR possesses an advantage when compared with other satellites. However, because NOAA-AVHRR also possesses problem of low resolution, data distortion and geometrical distortion, in the area of application of NOAA-AVHRR in large scale vegetation-mapping, the accuracy of vegetation classification should be improved. This paper discuss the feasibility of integrating the geographic data in GIS (Geographical Information System) and remotely sensed data in GIS. Under the environment of GIS, temperature, precipitation and elevation, which serve as main factors affecting vegetation growth, were processed by a mathematical model and qualified into geographic image under a certain grid system. The geographic image were overlaid to the NOAA-AVHRR data which had been compressed and processed. In order to evaluate the usefulness of geographic data for vegetation classification, the area under study was digitally classified by two groups of interpreter: the proposed methodology using maximum likelihood classification assisted by the geographic database and a conventional maximum likelihood classification only. Both result were compared using Kappa statistics. The indices to both the proposed and the conventional digital classification methodology were 0.668 (very good) and 0.563 (good), respectively. The geographic database rendered an improvement over the conventional digital classification. Furthermore, in this study, some problems related to multi-sources data integration are also discussed.

关 键 词：NOAA-AVHRR NDVI(Normal DIVISION VEGETATION Index) GEOGRAPHIC IMAGE INTEGRATED IMAGE remote sensing supervised classification GIS 

分 类 号：P208[天文地球—地图制图学与地理信息工程]