Greener Journal of Environment Management and Public Safety




Open Access

Katana et al

Greener Journal of  Environment Management and Public Safety Vol. 2 (4), pp. 146-157, August 2013.  

ISSN: 2354-2276 © 2011 Greener Journals

Research Paper

Manuscript Number: 052113625

 

Detection and Prediction of Land Cover Changes in Upper Athi River Catchment, Kenya: A Strategy towards Monitoring Environmental Changes

 

*1Katana S.J.S., 2Ucakuwun E.K. and 3Munyao T.M.

 

School of Environmental Studies, University of Eldoret, Box 1125-30100, Eldoret, Kenya.

 

Emails: 2ucakuwun@gmail.com3munyaothomas@gmail.com

 

*Corresponding Author’s Email:  samuelsirya @ yahoo. com


Abstract:

The Upper Athi River Catchment is one of the major catchment areas in Kenya which have experienced land cover changes due to changes in land uses and population pressure. The main objective of the study was to determine past spatial and temporal land cover changes and predict future land cover changes in Upper Athi River Catchment as a means of monitoring environmental changes. Landsat TM images of the years 1984, 2000 and 2010 were used to determine spatial and temporal land cover changes in the period 1984-2010 while the Cellular Automat-Markov (CA-Markov) model was used to predict land cover changes between 2010 and 2030 based on 1984-2010 trends. Change detection between 1984 and 2010 revealed that agricultural and built-up lands increased by 8.67% and 23.70%, while closed/open woody vegetation, broadleaved evergreen forest and rangeland decreased by 9.98%, 2.52% and 19.88%, respectively. Between 2010 and 2030, it was predicted that built-up and agricultural lands would increase by 7.66% and 5.61%, while rangeland; closed/open woody vegetation and broadleaved evergreen forest would decrease by 6.42%, 6.62 % and 0.22 %, respectively. The results showed that agricultural expansion and urbanization will be the main causes of land cover and environmental changes within the catchment. 

Keywords: Land cover, CA-Markov model, monitoring, prediction, change detection.

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