Mapping rangeland health indicators in eastern Africa from 2000 to 2022
| Autor: | G. E. Soto, S. W. Wilcox, P. E. Clark, F. P. Fava, N. D. Jensen, N. Kahiu, C. Liao, B. Porter, Y. Sun, C. B. Barrett |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Earth System Science Data, Vol 16, Pp 5375-5404 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1866-3508 1866-3516 |
| DOI: | 10.5194/essd-16-5375-2024 |
| Popis: | Tracking environmental change is important to ensure efficient and sustainable natural resources management. Eastern Africa is dominated by arid and semi-arid rangeland systems, where extensive grazing of livestock represents the primary livelihood for most people. Despite several mapping efforts, eastern Africa lacks accurate and reliable high-resolution maps of rangeland health necessary for many management, policy, and research purposes. Earth observation data offer the opportunity to assess spatiotemporal dynamics in rangeland health conditions at much higher spatial and temporal coverage than conventional approaches, which rely on in situ methods, while also complementing their accuracy. Using machine learning classification and linear unmixing, we produced rangeland health indicators – Landsat-based time series from 2000 to 2022 at 30 m spatial resolution for mapping land cover classes (LCCs) and vegetation fractional cover (VFC; including photosynthetic vegetation, non-photosynthetic vegetation, and bare ground) – two important data assets for deriving metrics of rangeland health in eastern Africa. Due to the scarcity of in situ measurements in the large, remote, and highly heterogeneous landscape, an algorithm was developed to combine high-resolution WorldView-2 and WorldView-3 satellite imagery at < 2 m resolutions with a limited set of ground observations to generate reference labels across the study region using visual photo-interpretation. The LCC algorithm yielded an overall accuracy of 0.856 when comparing predictions to our validation dataset comprised of a mixture of in situ observations and visual photo-interpretation from high-resolution imagery, with a kappa of 0.832; the VFC returned a R2=0.795, p < 2.2×10-16, and normalized root mean squared error (nRMSE) = 0.123 when comparing predicted bare-ground fractions to visual photo-interpreted high-resolution imagery. Our products represent the first multi-decadal Landsat-resolution dataset specifically designed for mapping and monitoring rangelands health in eastern Africa including Kenya, Ethiopia, and Somalia, covering a total area of 745 840 km2. These data can be valuable to a wide range of development, humanitarian, and ecological conservation efforts and are available at https://doi.org/10.5281/zenodo.7106166 (Soto et al., 2023) and Google Earth Engine (GEE; details in the “Data availability” section). |
| Databáze: | Directory of Open Access Journals |
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