Despite the increased availability of high resolution satellite image data,their operational use for mapping
urban land cover in Sub-Saharan Africa continues to be limited by lack of computational resources and
technical expertise. As such, there is need for simple and efficient image classification techniques. Using
Bamenda in North West Cameroon as a test case, we investigated two completely unsupervised pixel
based approaches to extract tree/shrub (TS) and ground vegetation (GV) cover from an IKONOS derived
soil adjusted vegetation index. These included: (1) a simple Jenks Natural Breaks classification and (2) a
two-step technique that combined the Jenks algorithm with agglomerative hierarchical clustering. Both
techniques were compared with each other and with a non-linear support vector machine (SVM) for
classification performance. While overall classification accuracy was generally high for all techniques
(>90%), One-Way Analysis of Variance tests revealed the two step technique to outperform the simple
Jenks classification in terms of predicting the GV class. It also outperformed the SVM in predicting the
TS class. We conclude that the unsupervised methods are technically as good and practically superior for
efficient urban vegetation mapping in budget and technically constrained regions such as Sub-Saharan
Africa.