Abstract:
Recent development has identified the benefits of using hyper-temporal satellite time series data for land
cover change detection and classification in South Africa. In particular, the monitoring of human settlement
expansion in the Limpopo province is of relevance as it is the one of the most pervasive forms of
land-cover change in this province which covers an area of roughly 125 000km2. In this paper, a spatiotemporal
autocorrelation change detection (STACD) method is developed to improve the performance
of a pixel based temporal Autocorrelation change detection (TACD) method previously proposed. The
objective is to apply the algorithm to large areas to detect the conversion of natural vegetation to settlement
which is then validated by an operator using additional data (such as high resolution imagery).
Importantly, as the objective of the method is to indicate areas of potential change to operators for further
analysis, a low false alarm rate is required while achieving an acceptable probability of detection. Results
indicate that detection accuracies of 70% of new settlement instances are achievable at a false alarm rate
of less than 1% with the STACD method, an improvement of up to 17% compared to the original TACD
formulation.
