Land-use/land-cover change detection using improved change-vector analysis

By J. Chen, Peng Gong, Chunyang He, Ruiliang Pu, P. Shi. 2003

373 citations recorded by the source corpus.

Change-vector analysis (CVA) is a valuable technique for landuse/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.

Subfields: Atmospheric Science, Global and Planetary Change, Media Technology

View Paper: https://www.semanticscholar.org/paper/ec631c3b85bb4202fb1e84ffc2a78032cd678975

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Citations in the corpus

Change detection from remotely sensed images: From pixel-based to object-based approaches (2013, background): “…from the same phonological period (Chen et al., 2003) Land cover monitoring (Johnson and Kasischke, 1998) Land-Use/ Land-Cover Change Detection (Chen et al., 2003) Forest change detection (Nackaerts et al., 2005) Disaster assessment (Bayarjargal et al., 2006) Principal component analysis…”
Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects (2009, neutral): “In addition, continuousvariable methods that simultaneously track several variables often must be collapsed into categorical variables to simply make sense of the change (Chen et al., 2003), potentially diminishing the advantage over strictly discrete methods.”
A Framework for Automatic and Unsupervised Detection of Multiple Changes in Multitemporal Images (2012, methodology): “, neglecting B − 2 spectral bands) and to use this suboptimal representation as the solution to the initial BD CD problem [22], [23].”
A Theoretical Framework for Unsupervised Change Detection Based on Change Vector Analysis in the Polar Domain (2007, methodology): “Direction cosines were used also in [11] and [12].”
Deep learning for change detection in remote sensing: a review (2022, methodology): “…2005), 3) Direct Classification Methods (DCMs) (Bovolo, Bruzzone, and Marconcini 2008), 4) Transformation methods (Gong 1993), 5) Change Vector Analysis (CVA) meth-ods (Chen et al. 2003; Johnson, and Kasischke 1998), and 6) Hybrid change detection methods (Healey et al. 2018; McDermid et al. 2008).”
Advances in fusion of optical imagery and LiDAR point cloud applied to photogrammetry and remote sensing (2017, background): “Change detection LULC change detection is an important field in both regional and global change research (Chen et al. 2003).”
Assessing the accuracy of land cover change with imperfect ground reference data (2010, methodology): “This approach has been widely used in a range of remote sensing studies (e.g. Woodcock et al., 2001; Chen et al., 2003; Stehman, 2005; Lunetta et al., 2006) and is the focus of this article.”