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Review ArticleDigital change detection methods in ecosystem monitoring: a review
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.
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Cited by
Ghamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., Rasti, B., & Plaza, A. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762087 · Cited by 909
Monitoring 25 years of land cover change dynamics in Africa: A sample based remote sensing approach
Brink, A. B., & Eva, H. D. (2009) · Applied Geography · doi:10.1016/j.apgeog.2008.10.004 · Cited by 417
Application of Remote Sensing in Water Resource Management: The Case Study of Lake Trasimeno, Italy
Giardino, C., Bresciani, M., Villa, P., & Martinelli, A. (2010) · Water Resources Management · doi:10.1007/s11269-010-9639-3 · Cited by 187
Analyzing the agricultural transition in Mato Grosso, Brazil, using satellite-derived indices
Arvor, D., Meirelles, M., Dubreuil, V., Bégué, A., & Shimabukuro, Y. E. (2012) · Applied Geography · doi:10.1016/j.apgeog.2011.08.007 · Cited by 172
Gbanie, S., Griffin, A., & Thornton, A. (2018) · Remote Sensing · doi:10.3390/rs10010129 · Cited by 87
Metadata
- Authors
- Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., & Lambin, E.
- Venue
- International Journal of Remote Sensing
- Publisher
- Informa UK Limited
- Published
- 2004-05
- Volume
- 25
- Issue
- 9
- Pages
- 1565-1596
- ISSN
- 0143-1161 (ISSN-L)
- 1366-5901
- OpenAlex
- W2140023211
- Cited by
- 2,100
- References
- 0
- FWCI
- 153.70
- Type
- journal article
- Language
- en
- Open access
- closed
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- Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., & Lambin, E. (2004). Review ArticleDigital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing. https://doi.org/10.1080/0143116031000101675
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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