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Analysis of the dynamics of African vegetation using the normalized difference vegetation index
Abstract
Images at a resolution of 8 km are currently being generated for the whole of Africa, displaying the normalized difference vegetation index (NDVI). These images have undergone a process of temporal compositing to reduce the effects of cloud cover and atmospheric variation. When the NDVI is plotted against time, different cover types are shown to have characteristic profiles corresponding closely with their phenology. The resultant pattern of NDVI values displayed on the images is analysed in terms of the cover types present and local variations in rainfall. Comparison between images for 1983 and 1984 overall showed considerable similarities, but significant differences were observed in the northward extent of the greening wave in the Sahel, the greening up of the Kalahari Desert and East African communities. It is concluded that vegetation monitoring using NDVI images needs to be associated with scene stratification according to cover type
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Cited by
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Land-cover change detection using multi-temporal MODIS NDVI data
Lunetta, R. S., Knight, J. F., Ediriwickrema, J., Lyon, J. G., & Worthy, L. D. (2006) · Remote Sensing of Environment · doi:10.1016/j.rse.2006.06.018 · Cited by 830
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Metadata
- Authors
- TOWNSHEND, J. R. G., & JUSTICE, C. O.
- Venue
- International Journal of Remote Sensing
- Publisher
- Informa UK Limited
- Published
- 1986-11
- Volume
- 7
- Issue
- 11
- Pages
- 1435-1445
- ISSN
- 0143-1161 (ISSN-L)
- 1366-5901
- OpenAlex
- W1998945640
- Cited by
- 538
- References
- 0
- FWCI
- 8.27
- Type
- journal article
- Language
- en
- Open access
- closed
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- TOWNSHEND, J. R. G., & JUSTICE, C. O. (1986). Analysis of the dynamics of African vegetation using the normalized difference vegetation index. International Journal of Remote Sensing. https://doi.org/10.1080/01431168608948946
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.