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Advances in classification using multispectral remote sensing imagery have gained increasing attention in solving environmental problems, and the management of disasters such as floods and landslides, due to their wide coverage and enabling ease of access in times of calamities. 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 map landslide-affected areas using remote sensing techniques for the central region of Kenya, where landslide disasters are common occurrences. The study area has a highly rugged terrain, and rainfall has been the main trigger of recent landslide events. False colour composite (FCC), Principal Component Analysis (PCA), Independent Component Analysis (ICA), spectral indices in the form of Normalised Difference Index (NDI), and knowledge-based classification formed the methodology. PCA and ICA were performed on Landsat data sets, and the components with the most geologic information after factor loading analysis were chosen to be used in a colour composite. The blue component of the colour composite was a spectral index involving bands 7 and 3 for Landsat ETM+, or bands 7 and 4 for Landsat OLI. The FCC formed the inputs for knowledge based classification with the following 13 classes: runoff, extreme erosions, other erosions, landslide areas, highly erodible, stable, weathering rocks, agriculture, green, new forest regrowth areas, as well as clear, turbid, and salty water. Validation of the mapped landslide areas with field GPS locations of landslide affected areas showed that 66% and 62% of the points coincided well with landslide areas mapped in the years 2000 and 2014, respectively. The classification maps showed extreme erosions taking place along drainage channels and other erosions in agricultural areas; with highly eroble zones charchaterised by already weathered rocks or deposit area, while fluvial deposits mainly characterised runoff areas. Thus, landuse and rainfall processes play a major role in landslide processes in the study area.

Landslide Inventory Using Knowledge Based Multi-sources Classification Time Series Mapping: A Case Study of Central Region of Kenya