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 125 000 km². In this paper, a spatio-temporal 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.

Item Type:	Refereed Article
Keywords:	change detection, autocorrelation, time-series, hyper-temporal, settlements
Research Division:	Engineering
Research Group:	Environmental engineering
Research Field:	Air pollution modelling and control
Objective Division:	Expanding Knowledge
Objective Group:	Expanding knowledge
Objective Field:	Expanding knowledge in the environmental sciences
UTAS Author:	Salmon, BP (Dr Brian Salmon)
UTAS Author:	Olivier, JC (Professor JC Olivier)
ID Code:	100006
Year Published:	2015
Web of Science® Times Cited:	6
Deposited By:	Engineering
Deposited On:	2015-04-22
Last Modified:	2017-11-06
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