Land-Use/Land-Cover Change Detection Using Improved Change-Vector Analysis

Change-vector analysis (CVA) is a valuable technique for land-use/land-cover change detection. This article proposes a new method that can improve CVA. The improved method has two components: 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. The authors report on the use of the improved CVA for the detection of the land-use/land-cover changes in the Haidian District, Beijing, China. The authors conclude that good data quality (similar acquisition dates in different years and cloud-free) and image radiometric normalization have a strong impact on the final change-detection result, because the proposed method is based on an assumption of radiometric similarity among multi-temporal remotely sensed data. The experimental results indicate that the improved CVA has good potential in land-use/land-cover change detection, particularly when ancillary information for classification is only available for one date.

  • Availability:
  • Authors:
    • Chen, Jin
    • Gong, Peng
    • He, Chunyang
    • Pu, Ruiliang
    • Shi, Peijun
  • Publication Date: 2003-4

Language

  • English

Media Info

  • Media Type: Print
  • Features: Figures; Maps; References; Tables;
  • Pagination: pp 369-379
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 01001832
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jul 14 2005 10:11AM