Assessing Land Cover Performance in the Grand Canal of China using Spot Data - A Case Study of Qingkou Region

Traditional land cover performance from remote sensing imagery using the statistical characteristics of the pixel have encountered a lot of difficulties in dealing with the issue of classification of high-resolution images. Object-oriented classification techniques based on image segmentation are a good solution to this problem. Qingkou region, the Beijing-Hangzhou Grand Canal and other eight natural rivers junctions, has take break dramatic changes in the last 50 years, therefore, research on land cover performance in this region have great significance. In this paper, object-oriented classification method was used to extract land cover information from SPOT5 imagery in Qingkou region. The imagery was segmented in two scale parameters to create homogeneous objects for different classes. And the overall classification stability is up to more than 87%. It is available to provide research data for land cover performance evaluation in Beijing-Hangzhou Grand Canal and development research on cities along the canal.