GIS-BASED MODELING OF LAND USE DYNAMICS IN RIVER NZOIA BASIN, KENYA

Abstract

River Nzoia basin in Kenya is predisposed to degradation associated with land use change. The change is attributable to natural and socio-economic factors and their spatio-temporal utilization due to anthropogenic activities. Therefore, it is important to establish land use change within a specific period of time and spatially project patterns of future changes. Hence, the main objective of this study was to characterize, simulate, and predict spatio-temporal land use change in river Nzoia basin. Examining land use dynamics is valuable for environmental management purposes. It supports the process of decision making in planning for future changes as well as policy formulation to mitigate extreme events associated with the changes. Spatial datasets including: 90m resolution Digital Elevation Model (DEM) and Landsat satellite images (ranging from 1990 to 2010) that depict land use patterns were used to provide spatio-temporal details. Image analysis was carried out by the use of ENVI 4.7 software. Land use change projection integrated Cellular Automata-Markov methods in Remote Sensing (RS) and Geographic Information System (GIS). During for the period 1990, 2000 and 2010, shrubland, annual cropland, and grassland increased by 13.28%, 5.59% and 0.06% while forestland, waterbody and wetlands experienced decrease in percentage coverage by 10.97, 3.78 and 4.18, respectively. The Chi-square test revealed a highly insignificant difference between predicted and reference land use (calculated 2.5356 2 4,0.05   where p>0.01). Pearson correlation analysis revealed a highly significant (p<0.01) positive correlation r = 0.999(0.000) between the predicted and the reference land use categories for the year 2010. Cellular Automata (CA)-Markov model projected land use scenario for 2020. Nash Sutcliffe ( 2 NS R = 99.92%) indicated very satisfactory model performance for land use projection. The study revealed that agricultural expansion is the main driving force for loss of forest in river Nzoia basin and has the potential to continue in future. The projected land use scenario for the year 2020 would provide useful inputs to the land use planners for effective management of river Nzoia basin. Cellular Automata-Markov model predicted a decline in forestland and an increase in annual cropland by 2020. Annual cropland and forestland are the key drivers of land use change in river Nzoia basin. The existing forest cover in river Nzoia basin should be protected and not be released for agriculture or subjected to untenable extraction of forest resources. There is need to embrace afforestation programs to enhance forest cover in the basin.