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Spatio-temporal modeling of rangeland degradation in response to changing environment in the Upper Ewaso Ngiro River Basin, Kenya

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Abstract

Rangelands primarily provide forage for grazing and browsing animals, yet their ecosystems are degraded due to natural causes and anthropogenic activities such as pastoralism, tourism, and ranching. Increased rangeland detrimental effects led the present research to model the severity of rangeland degradation in the Upper Ewaso Ngiro River Basin (UENRB) in Kenya between 1986 and 2021 and predict the future scenario for 2031. The severity of rangeland degradation was analysed using the multi-criteria analytic hierarchical process and principal component analysis, while the cellular automata Markov chain-analysis model was used for prediction. The models utilized datasets including land-use land cover, surface albedo, bareness index, vegetation health index, soil moisture index, topographic wetness index, reconnaissance drought index, k-factor, slope, and population density. The findings indicated that rangeland degradation varied sporadically, with the reconnaissance drought index being the significant influencing parameter, contributing to about 19.2% of the total degradation. In average, between the years under study, non-rangeland zones covered 10.4%, while low, moderate, high, and very high degradability severity covered 15.3%, 49.1%, 25.2%, and 0%, respectively. Prediction results for the year 2031 revealed that non-rangeland zones will cover 5.3%, whereas low, moderate, high and very high will cover 18.1%, 39.2%, 37.4%, and 0%, respectively. The hybrid model proved to be effective in modeling rangeland degradation. The study recommends the county and national governments to propose and adopt by-laws on legislation to regulate the exploitation of natural resources in the study area in order to restore the rangelands.

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Availability of data and materials

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

CA:

Cellular automata

USGS:

United States Geological Surveys

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Acknowledgements

I wish to thank the data providers USGS Earth Explorer, Alaska Satellite Facility, FAO Digital Soil Map of the World (DSMW), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Moderate Resolution Imaging Spectroradiometer (MODIS), the Kenya National Bureau of Statistics, and the Kenya National Research Fund through the collaboration between Kenya Forestry Research Institute (KeFRI) and Dedan Kimathi University of Technology (DeKUT) for their support during the field work.

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  1. Institute of Geomatics, GIS and Remote Sensing, Dedan Kimathi University of Technology, P.O Box Private Bag-10143 Dedan Kimathi, Nyeri, Kenya

    Obed Mogare Kiana, Charles Ndegwa Mundia, Moses Karoki Gachari & Duncan Maina Kimwatu

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  1. Obed Mogare Kiana
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Obed Mogare Kiana: conceptualization, methodology, writing of the original draft, and submission of the research article for publication. Charles Ndegwa Mundia: conceptualization, methodology, writing of the original draft, supervision, and reviewing and editing. Moses Karoki Gachari: conceptualization, methodology, writing of the original draft, supervision, and reviewing and editing. Duncan Maina Kimwatu: methodology, writing of the original draft and reviewing and editing.

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Kiana, O.M., Mundia, C.N., Gachari, M.K. et al. Spatio-temporal modeling of rangeland degradation in response to changing environment in the Upper Ewaso Ngiro River Basin, Kenya. Environ Monit Assess 195, 1311 (2023). https://doi.org/10.1007/s10661-023-11898-z

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