Abstract:Land use/cover change (LUCC) is an important field of global and local environmental change research. LUCC and its corresponding effects have a direct impact on the environment and ecological processes, and in turn natural resources management and related decisions. In China, LUCC research is at the core of all issues related to sustainable development, with tremendous practical significance. Remote sensing imagery change detection has great application value in many areas, such as the survey of land-use change, urban expansion, and vegetation resource monitoring. The choice of an effective change detection method for a given study area is a central issue of such detection. In research conducted in various countries, remote sensing imagery change detection methods have transitioned from pixel level to feature level and knowledge level. Currently used LUCC detection methods are in two categories, direct spectrum comparison and comparison of classification results. The spectrum feature vector of a segment in a Landsat Thematic Mapper (TM) image can be regarded as a vector of six-dimensional feature space. If the angle between two vectors is smaller and vector mode closer, the more similar are the two vectors. Thus, we used the cosine of the angle between two vectors and the ratio of vector mode to establish a vector similarity index for measuring vector similarity. Combined with the "National Change of Ecological Environment Decade (2000-2010) Remote Sensing Survey and Assessment" project, jointly organized and implemented by the Ministry of Environmental Protection and Chinese Academy of Sciences, this paper focuses on application of a change detection method. This method uses segment similarity of a spectrum vector based on a knowledge base in the northwest region. There is also an evaluation of method accuracy. The area covered by TM path 134 row 33, representative of the northwest region, was chosen as a change detection method validation test site. We used 2005 and 2010 two-phase Landsat TM imagery to detect land-cover change, using spectrum vector similarity based on a segment with support from eCognition Developer 8.64 software. We used 2010 land-cover data as a priori knowledge base to classify regions of change. The results showed the following. 1) Segment similarity of the spectrum vector method is appropriate for change detection in the northwest region, and accuracy of the 2005 land-cover database established using the 2010 land-cover database as a priori knowledge was relatively high. 2) The method of segment similarity of the spectrum vector has many advantages, such as less computation, fast operation, and suitability for large-scale rapid change detection. 3) The method is more effective for farmland, water, built-up land and vegetation cover change. 4) Accuracy of the land-cover database established by change detection depends on accuracy of the baseline land-cover database. Compared with the latter database, accuracy of the former database declined slightly. The main cause of this decline is change that was difficult to detect and change area misclassification, attributable to land-cover types with similar spectral features such as lakes and reservoirs, bare land, and sparse vegetation. 5) Because of the effects of imagery phase and cloud coverage, the land-cover database established by change detection requires additional manual modifications to improve its accuracy.