A Novel Method for Mapping Land Cover Changes: Incorporating Time and Space With Geostatistics

• Published in: IEEE transactions on geoscience and remote sensing Vol. 44; no. 11; pp. 3427 - 3435
• Main Authors: Boucher, A., Seto, K.C., Journel, A.G.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-11-2006; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Applied geophysics; Change detection; Context modeling; Earth; Earth sciences; Earth, ocean, space; Exact sciences and technology; geostatistics; Internal geophysics; Land cover; Mathematical models; Maximum likelihood detection; Monitoring; Pixel; Pixels; probability; Remote sensing; Satellites; Studies; Temporal logic; Time domain analysis; Time series; Time series analysis; time series analysis; geostatistics; models; Change detection; land cover maps; kriging; probability; Space remote sensing; Landsat; variograms; accuracy; remote sensing; time series; indicators; quality; land use; imagery; maximum likelihood; prediction; land cover; Pixel; high resolution
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2006.879113

Landsat data are now available for more than 30 years, providing the longest high-resolution record of Earth monitoring. This unprecedented time series of satellite imagery allows for extensive temporal observation of terrestrial processes such as land cover and land use change. However, despite this unique opportunity, most existing change detection techniques do not fully capitalize on this long time series. In this paper, a method that exploits both the temporal and spatial domains of time series satellite data to map land cover changes is presented. The time series of each pixel in the image is modeled with a combination of: 1) pixel-specific remotely sensed data; 2) neighboring pixels derived from ground observation data; and 3) time series transition probabilities. The spatial information is modeled with variograms and integrated using indicator kriging; time series transition probabilities are combined using an information-based cascade approach. This results in a map that is significantly more accurate in identifying when, where, and what land cover changes occurred. For the six images used in this paper, the prediction accuracy of the time series improves significantly, increasing from 31% to 61%, when both space and time are considered with the maximum likelihood. The consideration of spatial continuity also reduced unwanted speckles in the classified images, removing the need for any postprocessing. These results indicate that combining space and time domains significantly improves the accuracy of temporal change detection analyses and can produce high-quality time series land cover maps