| dc.description.abstract | Landsat series multispectral remote sensing imagery has gained increasing attention in providing solutions to environmental problems such as land degradation which exacerbate soil erosion and landslide
disasters in the case of rainfall events. Multispectral data has facilitated the mapping of soils, land-cover
and structural geology, all of which are factors affecting landslide occurrence. The main aim of this
research was to develop a methodology to visualize and map past landslides as well as identify land
degradation effects through soil erosion and land-use using remote sensing techniques in the central
region of Kenya. The study area has rugged terrain and rainfall has been the main source of landslide
trigger. The methodology comprised visualizing landslide scars using a False Colour Composite (FCC) and
mapping soil erodibility using FCC components applying expert based classification. The components of
the FCC were: the first independent component (IC1), Principal Component (PC) with most geological
information, and a Normalised Difference Index (NDI) involving Landsat TM/ETMþ band 7 and 3.
The FCC components formed the inputs for knowledge-based classification with the following 13
classes: runoff, extreme erosions, other erosions, landslide areas, highly erodible, stable, exposed volcanic rocks, agriculture, green forest, new forest regrowth areas, clear, turbid and salty water. Validation
of the mapped landslide areas with field GPS locations of landslide affected areas showed that 66% of the
points coincided well with landslide areas mapped in the year 2000. The classification maps showed
landslide areas on the steep ridge faces, other erosions in agricultural areas, highly erodible zones being
already weathered rocks, while runoff were mainly fluvial deposits. Thus, landuse and rainfall processes
play a major role in inducing landslides in the study area. | en_US |
