Land-Use/Land-Cover Change Detection Using Improved Change-Vector Analysis
Digital Object Identifier (DOI)
Change-vector analysis (CVA) is a valuable technique for land-use/land-cover change detection. However, how to reasonably determine thresholds of change magnitude and change direction is a bottleneck to its proper application. In this paper, a new method is proposed to improve CVA. The method (the improved CVA) consists of two stages, Double-Window Flexible Pace Search (DFPS), which aims at determining the threshold of change magnitude, and direction cosines of change vectors for determining change direction (category) that combines single-date image classification with a minimum-distance categorizing technique. When the improved CVA was applied to the detection of the land-use/land-cover changes in the Haidian District, Beijing, China, Kappa coefficients of “change/no-change” detection and “from-to” types of change detection were 0.87 and greater than 0.7, respectively, for all kinds of land-use changes. The experimental results indicate that the improved CVA has good potential in land-use/land-cover change detection.
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Citation / Publisher Attribution
Photogrammetric Engineering & Remote Sensing, v. 69, issue 4, p. 369-379
Scholar Commons Citation
Chen, Jing; Gong, Peng; He, Chunyang; Pu, Ruiliang; and Shi, Peijun, "Land-Use/Land-Cover Change Detection Using Improved Change-Vector Analysis" (2003). School of Geosciences Faculty and Staff Publications. 397.