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Abstract

The advent of the new political dispensation in South Africa has seen an exponential growth in the rate of land transformation and encroachment by other land uses into valuable agro-ecological zones. Due to the socio-economic value of the often limited high-potential agricultural land in the country, a reliable determination of encroachment and transformation is necessary for effective monitoring and management of such agro-ecological resources. Using the robust support vector machine classification algorithm, this study adopted multi-temporal, remotely sensed datasets to assess the extent to which the physical development footprint in the uMngeni Local Municipality affected the existing agro-ecological zones from 1993 to 2003 and from 2003 to 2013. The results show a steady increase in built-up areas during the period under investigation. The study demonstrates the value of multi-temporal, remotely sensed datasets and techniques in mapping the vulnerability of existing agricultural land to urbanisation in the study area.

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