基于GIS的中国东北植被综合分类研究

ＮＯＡＡ／ＡＶＨＲＲ由于运行周期短、覆盖范围大、成本低、波段宽等特点，目前正越来越广泛地受到人们的普遍关注。在大尺度、中尺度植被遥感上，ＮＯＡＡ／ＡＶＨＲＲ具有陆地卫星无法比拟的优势，但在另一方面，ＮＯＡＡＡＶＨＲＲ也存在分辨率低、数据变形较大和几何畸变较严重等问题。这样，在应用ＮＯＡＡＡＶＨＲＲ数据进行大区域植被制图时，植被分类的精度仍待提高。本文从理论上探讨了将地理信息系统提供的地理数据与遥感数据复合的可行性；尝试在ＧＩＳ环境下，将气温、降水、高程３个影响区域植被覆盖的主要指标，按一定的地面网格系统和数学模式进行量化，生成数字地学影像，并使之与经过优化、压缩处理的ＮＯＡＡＡＶＨＲＲ数据进行复合，对复合后的综合影像进行监督分类。分类结果显示，与传统的应用最大似然分类方法对单一遥感图像分类相比，该综合分类方法分类精度提高了１８．３％，该研究方法改变了遥感影像的单一信息结构；丰富了图像的信息含量；完成了地理数据的数字传输、处理、存储及影像化显示。

Vegetation Integrated Classification and Mapping Using Remote Sensingand GIS Techniques in Northeast China

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.