Assessing land cover change in Kenya's Mau Forest region using remotely sensed data

Kenya's Rift Valley has been undergoing rapid land cover change for the past two decades, which has resulted in ecological and hydrological changes. An effort is under way to quantify the timing and rate of these changes in and around the River Njoro watershed located near the towns of Njoro and Nakuru using remote sensing and geographic information system (GIS) methods. Three Landsat TM images, representing a 17-year period from 1986 to 2003 in which the area underwent a significant land cover transition, were classified and compared with one another. Vegetation diversity and temporal variability, common to tropical and sub-tropical areas, posed several challenges in disaggregating classified data into sub-classes. An iterative approach for the resolving challenges is presented that incorporates unsupervised and supervised classification routines in coordination with knowledge-based spatial analyses. Changes are analysed at three spatial scales ranging from the highly impacted and deforested uplands to the watershed and landscape scales. Land cover transitions primarily occurred after 1995, and included large forest losses coupled with increases in mixed small-scale agriculture and managed pastures and degraded areas. These changes in cover type are highly spatially variable and are theorized to have significant impacts on ecological and hydrologic systems with implications for environmental sustainability.