As the satellite remote sensing data have been available since early 1990s

these data are being employed towards the improvement of vegetation classification. On macro and middle scale of vegetation remote sensing

NOAA AVHRR possesses an advantage when compared to other satellite data On the other hand

because the scanning width of NOAA AVHRR is so large （2800km）

the earth’s curvature

characteristics

the angle of reflection from earth’s object and atmosphere as well as the angle of scanner and deviation of sun’s height cause a serious effect on the data. Therefore

NOAA AVHRR also has problems of low resolution

data distortion and geometrical distortion. AS a result

applying NOAA AVHRR to large scale vegetation-mapping

the accuracy of vegetation classification should be increased. This paper discusses the feasibility of integrating the geographic and remotely sensed data in GIS. Under the GIS environment

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 interpreter methods. A maximum likelihood classification assisted by the geographic database

and a conventional maximum likelihood classification only.Both results 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 discussed.