Assessing Different Remote Sensing Techniques to Detect Land Use/Cover Changes in the Northwestern Yunnan province, China

摘要

Timely and accurate change detection of Earth's surface features is extremely important for understanding relationships and interactions between human and natural phenomena, in order to promote better decision making. The Purpose of this paper to assess application of the common change detection techniques; focusing on the techniques, that were likely to help alleviate existing problems associated with change detection in the Yunnan region environment. It was demonstrated that, for change detection from Landsat TM and ETM+ imageries. It describes the different change detection techniques, including image regression and change vector analysis (CVA)to assess their effectiveness for detecting land use/cover change in Dali city and surrounding area. Methodology: Using 30m*30m spatial resolution Landsat (Enhanced)Thematic Mapper (TM/ETM+)subset data (Path:131/Row:42)covered the study area, which recorded on 2nd January 1989, 3rd January 2001 and 11th December 2006 to minimize change detection error introduced by seasonal differences. Three scenes of Landsat Images were geometrically and radiometrically corrected, and the impact factors of LUCC (Land Use/Cover Change)are systematically identified by integrating remote sensing as well as statistical data. The two change detection techniques were applied and was used as a cross classification to determine change which enabled assessment of the two techniques; and five land cover classes can be discriminated. Results: The change vector analysis resulted in the largest overall accuracy of 79.25 and 79.65% for the 1989-2001 and 2001-2006 image pairs, respectively. The image regression technique yielded the least accurate results with an overall accuracy of 68.37 and 69.28% for the 1989-2001 and 2001-2006 image pairs, respectively. Different change detection algorithms have their own merits and advantages. However, the change vector analysis change detection technique was the most accurate model for handling the variability present of land cove/land use in study area.