Vegetation Integrated Classification and Mapping Using Remote Sensingand GIS Techniques in Northeast China
基于GIS的中国东北植被综合分类研究

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.