Preprints
https://doi.org/10.5194/essd-2023-217
https://doi.org/10.5194/essd-2023-217
20 Nov 2023
 | 20 Nov 2023
Status: a revised version of this preprint is currently under review for the journal ESSD.

Mapping Rangeland Health Indicators in East Africa from 2000 to 2022

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

Abstract. Tracking environmental change is important to ensure efficient and sustainable natural resources management. East Africa is dominated by arid and semi-arid rangeland systems, where extensive grazing of livestock represents the primary livelihood for most of the human population. Despite several mapping efforts, East Africa lacks accurate and reliable high-resolution rangeland health maps necessary for management, policy, and research purposes. Earth Observations offer the opportunity to assess spatiotemporal dynamics in rangeland health conditions at much higher spatial and temporal coverage than conventional approaches that rely on in-situ methods, while complimenting their certainty. Using machine learning-based classification and linear unmixing, this paper produced Landsat-based time series at 30 m spatial resolution for mapping of land cover classes (LCC) and vegetation fractional cover (VFC, including photosynthetic vegetation PV, non-photosynthetic vegetation NPV, and bare ground BG), two major data assets to derive metrics for rangeland health in East Africa. Due to scarcity of in-situ measurements in a large, remote and highly heterogeneous landscape, an algorithm was developed to combine very high-resolution WorldView-2 and -3 satellite imagery at < 2 m resolutions with a limited set of ground observations to generate reference labels across the study region. The LCC analysis yielded an overall accuracy of 0.856 using our validation dataset, with Kappa of 0.832; VFC, yielded R2 = 0.801, p < 2.2e-16, normalized root mean squared error (nRMSE) = 0.123. Our products represent the first multi-decadal high-resolution dataset specifically designed for mapping and monitoring rangelands health in East Africa including Kenya, Ethiopia and Somalia, covering a total area of 745,840 km2, dominated by arid and semi-arid extensive rangeland systems. 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 data availability section).

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Gerardo E. Soto, Steven Wilcox, Patrick E. Clark, Francesco P. Fava, Nathan M. Jensen, Njoki Kahiu, Chuan Liao, Benjamin Porter, Ying Sun, and Christopher Barrett

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-217', Anonymous Referee #1, 03 Apr 2024
  • RC2: 'Comment on essd-2023-217', Anonymous Referee #2, 11 Apr 2024
  • RC3: 'Comment on essd-2023-217', Anonymous Referee #3, 23 Apr 2024
  • AC1: 'Comment on essd-2023-217', Gerardo Soto, 11 Jun 2024
Gerardo E. Soto, Steven Wilcox, Patrick E. Clark, Francesco P. Fava, Nathan M. Jensen, Njoki Kahiu, Chuan Liao, Benjamin Porter, Ying Sun, and Christopher Barrett

Data sets

Landsat-derived rangeland condition indicators in East Africa from 2000 to 2022 Gerardo E. Soto, Steven Wilcox, Patrick E. Clark, Francesco P. Fava, Nathan M. Jensen, Njoki Kahiu, Chuan Liao, Benjamin Porter, Ying Sun, and Christopher Barrett https://doi.org/10.5281/zenodo.7106165

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

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Short summary
Using machine learning classification and linear unmixing, this paper produced Landsat-based time series of land cover classes and vegetation fractional cover of photosynthetic vegetation, non-photosynthetic vegetation, and bare ground. This dataset represents a first multi-decadal high-resolution dataset specifically designed for mapping and monitoring rangelands health in East Africa including Kenya, Ethiopia, and Somalia, which are dominated by arid and semi-arid extensive rangeland systems.
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