Articles | Volume 16, issue 11
https://doi.org/10.5194/essd-16-5375-2024
https://doi.org/10.5194/essd-16-5375-2024
Data description paper
 | 
26 Nov 2024
Data description paper |  | 26 Nov 2024

Mapping rangeland health indicators in eastern Africa from 2000 to 2022

Gerardo E. Soto, Steven W. Wilcox, Patrick E. Clark, Francesco P. Fava, Nathaniel D. Jensen, Njoki Kahiu, Chuan Liao, Benjamin Porter, Ying Sun, and Christopher B. Barrett

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Cited articles

Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., and Hegewisch, K. C.: TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015, Sci. Data, 5, 1–12, https://doi.org/10.1038/sdata.2017.191, 2018. 
Adams, E. C., Parache, H. B., Cherrington, E., Ellenburg, W. L., Mishra, V., Lucey, R., and Nakalembe, C.: Limitations of remote sensing in assessing vegetation damage due to the 2019–2021 desert locust upsurge, Front. Climate, 3, 714273, https://doi.org/10.3389/fclim.2021.714273, 2021. 
AghaKouchak, A., Farahmand, A., Melton, F. S., Teixeira, J., Anderson, M. C., Wardlow, B. D., and Hain, C. R.: Remote sensing of drought: Progress, challenges and opportunities, Rev. Geophys., 53, 452-480, https://doi.org/10.1002/2014RG000456, 2015. 
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This paper uses machine learning and linear unmixing to produce rangeland health indicators: Landsat time series of land cover classes and vegetation fractional cover of photosynthetic vegetation, non-photosynthetic vegetation, and bare ground in arid and semi-arid Kenya, Ethiopia, and Somalia. This represents the first multi-decadal Landsat-resolution dataset specifically designed for mapping and monitoring rangeland health in the arid and semi-arid rangelands of this portion of eastern Africa.
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