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Review ArticleDigital change detection methods in ecosystem monitoring: a review

International Journal of Remote Sensing · 2004 · Vol. 25(9) · pp. 1565–1596
Pol CoppinInge JonckheereKristiaan NackaertsBart MuysÉric F. Lambin

Abstract

Techniques based on multi-temporal, multi-spectral, satellite-sensor-acquired data have demonstrated potential as a means to detect, identify, map and monitor ecosystem changes, irrespective of their causal agents. This review paper, which summarizes the methods and the results of digital change detection in the optical/infrared domain, has as its primary objective a synthesis of the state of the art today. It approaches digital change detection from three angles. First, the different perspectives from which the variability in ecosystems and the change events have been dealt with are summarized. Change detection between pairs of images (bi-temporal) as well as between time profiles of imagery derived indicators (temporal trajectories), and, where relevant, the appropriate choices for digital imagery acquisition timing and change interval length definition, are discussed. Second, pre-processing routines either to establish a more direct linkage between remote sensing data and biophysical phenomena, or to temporally mosaic imagery and extract time profiles, are reviewed. Third, the actual change detection methods themselves are categorized in an analytical framework and critically evaluated. Ultimately, the paper highlights how some of these methodological aspects are being fine-tuned as this review is being written, and we summarize the new developments that can be expected in the near future. The review highlights the high complementarity between different change detection methods.

Remote-Sensing Image ClassificationRemote Sensing in AgricultureRemote Sensing and Land UseChange detectionComputer scienceRemote sensingComplementarity (molecular biology)Data miningData scienceArtificial intelligenceGeography
Citations
2,054
FWCI
94.43
field-weighted impact
References
120
Percentile
100%
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Citations per year
References
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