Evaluation of Land Use/Land Cover Change with Time in Assessing Soil Erosion Risk in Isiukhu River Catchment, Kakamega County, Kenya

Saidi Fwamba Wekulo


An evaluation of land use/land cover (LULC) change with time in assessing soil erosion risk is essential in soil conservation and environmental management. Land use/land cover management factor (C) plays crucial role in determination of soil loss and thus affects agricultural production. Land use/land cover is influenced by anthropogenic activities. Isiukhu river catchment and its environs have experienced fatal landslides leading to loss of lives and property. Land use/land cover change between 1990 and 2015 was determined in ArcGIS 10.3 environment. Soil erosion risk was determined by applying revised universal soil loss equation (RUSLE) model in ArcGIS 10.3. The LULC changed with time, in 1990 weighted mean of C factor was 0.051 and in 2015 was 0.344. The soil erosion risk was influenced by change in LULC, in 1990 weighted mean (RUSLEweightedmean) was 7.2 t/ha/y and 85% of the catchment was within soil loss tolerance limit (12t/ha/y), and in 2015 weighted mean (RUSLEweighted mean) was 32 t/ha/y and only 3% of the catchment was within tolerance limit. This could be due to degradation of natural cover within the catchment. Deforestation as a result of farming activities and settlement in the catchment forest could have led to exposure of ground to surface run-off. The high rate of soil erosion could be reduced by controlling encroachment on the forest, proper land use/land cover through multiple-cropping and implementation of soil erosion control support practices.


Land use/land cover change; soil erosion risk; years; RUSLE; GIS; Isiukhu river catchment; Kenya.

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W. Drzewiecki, P Wezyk, M Pierzchlski and B. Szafranska. “Quantitative and Qualitative Assessment of Soil Erosion Risk in Małopolska (Poland), Supported by an Object-Based Analysis of High-Resolution Satellite Images.” Pure and Applied Geophysics, open access at Springerlink.com, DOI 10.1007/s00024-013-0669-7 published online Apr. 2013.

T. Brieby. “Assessment of Soil Erosion Risk within a Sub-watershed using GIS and RUSLE with a comparative analysis of the use of STATSGO and SSURGO Soil Databases.” Saint Mary’s University of Minnesota Central Services Press. Winona, MN. Volume 8, Papers in Resource Analysis, pp 22, 2006.

H.A Rabia. “Mapping Soil Erosion Risk Using Rusle, Gis and Remote Sensing Techniques.” in the 4th International Congress of ECSSS, EUROSOIL, Bari, Italy, 2012, pp. 1-15.

E.K Biamah, K. Fahlstrom, C.K.K. Gachene, J.M. Gachingiri, J.K.Kiara, M. Mbegera et al. Soil and water conservation manual for Kenya. Republic of Kenya: Soil and Water Conservation Branch, Ministry of Agriculture, Livestock Development and Marketing, 1997, pp. 1-18.

C S. Renschler and J Harbor. “Soil ersion assessment tools from point to regional scales – the role of geomorphologists in land management research and implementation.” Elsevier Science B.V., Vol. 47, pp. 189-209, 2002.

R Luis, A Carlos, V. Simone, R Coen and S. Juan. “Harmonization of risk assessment methods of soil erosion by water in the European Union.” Land Use Planning Department. Centro de Investigation ions – CIDE – (CSIC, University de Valencia, GV), Spain. 2012.

B.K Rop. “Landslide disaster vulnerability in Western Kenya and Mitigation options: A synopsis of evidence and issues of Kuvasali landslide.” Journal of environmental Science & engineering Vol.5, Issue 1, pp. 110, Jan. 2011.

United Nations. “Kenya Humanitarian Update.” Office of the United Nations Resident Coordinator in Kenya. Vol. 3, Aug. 2016.

N. D. Thuweba, A. Folkard, B. Mathias and M. Frank. “Characterizing farming systems around Kakamega Forest, Western Kenya, for targeting soil fertility–enhancing technologies.” ResearchGate Journal of Plant Nutrition and Soil Science, Vol. 176, pp. 585-594, Aug. 2013.

K.G Renard,., G.R Foster, , G.A Weesiies,., D.K MCCool,. and D.C Yoder. Handbook 703, Predicting Rainfall Erosion Losses – A Guide to Conservation Planning with Revised Universal Soil Loss Equation (RUSLE). Washington, DC: US Department of Agriculture, 1997.

G.R Foste,r, D.C, Yoder, G.A. Weesies, D.K. Mccool, K.C. Mcgregor, and R.L Bingner,. User’s Guide, Revised Universal Soil Loss Equation, Version 2 RUSLE 2, USDA. Washington, D.C: Agricultural Research Service, 2003, pp. 77.

ESJWQC. “Sediment and Erosion Assessment Report.” East San Joaquin Water Quality Coalition, Order R5-2012-0116-R1, Jan. 2014.

A. Dewangan. “Modeling Surface Runoff Path and Soil Erosion in catchment Area of Hanp River of District Kabeerdham, CG, India, using GIS.” International Journal of Scientific and Research Publication Vol. 6, pp.645-649, May. 2016

M Reusing, T Schneider and U Ammer. “Modelling soil loss rates in the Ethiopian Highlands by integration of high resolution MOMS-02/D2-stereo-data in a GIS.” Journal of Remote Sensing, Vol. 21 pp. 1885-1896, 2000.

H.M.J Arnoldus,. Predicting soil losses due to sheet and rill erosion. FAO, Rome, Land and water development division in Guidelines for watershed management, 1977, pp. 99-124.

W.H. Wischmeier and D.D Smith. Predicting Rainfall erosion losses – a Guide to Conservation Planning.USDA, Handbook 537. Washington DC, 1978..

S.D. Angima, D.E. Stott, M.K. O’Neil, C.K. Ongc, G.A. Weesies. “Soil erosion prediction using RUSLE for central Kenyan highland conditions.” Elsevier, Agriculture, Ecosystems and Environment Vol. 97, pp. 295–308, 2003.


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