Abstract:
Recently land-use change has been the main concern for worldwide environment change and is being used by city and
regional planners to design sustainable cities. Nakuru in the central Rift Valley of Kenya has undergone rapid urban
growth in last decade. This paper focused on urban growth using multi-sensor satellite imageries and explored the po-
tential benefits of combining data from optical sensors (Landsat, Worldview-2) with Radar sensor data from Advanced
Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data for urban
land-use mapping. Landsat has sufficient spectral bands allowing for better delineation of urban green and impervious
surface, Worldview-2 has a higher spatial resolution and facilitates urban growth mapping while PALSAR has higher
temporal resolution compared to other operational sensors and has the capability of penetrating clouds irrespective of
weather conditions and time of day, a condition prevalent in Nakuru, because it lies in a tropical area. Several classical
and modern classifiers namely maximum likelihood (ML) and support vector machine (SVM) were applied for image
classification and their performance assessed. The land-use data of the years 1986, 2000 and 2010 were compiled and
analyzed using post classification comparison (PCC). The value of combining multi-temporal Landsat imagery and
PALSAR was explored and achieved in this research. Our research illustrated that SVM algorithm yielded better results
compared to ML. The integration of Landsat and ALOS PALSAR gave good results compared to when ALOS PAL-
SAR was classified alone. 19.70 km
of land changed to urban land-use from non-urban land-use between the years
2000 to 2010 indicating rapid urban growth has taken place. Land-use information is useful for the comprehensive
land-use planning and an integrated management of resources to ensure sustainability of land and to achieve social Eq-
uity, economic efficiency and environmental sustainability.
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