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Abstract

The well-being of the environment is one of the major factors that contributes to sustainability. Sustainable human settlements require local governance to plan, implement, develop, and manage human settlements expansions. This isimportant as the number anthropogenic activities is directly correlated to the increase in human population within a geographical region. Regional mapping of land cover conversion of natural vegetation to new human settlements is essential. In this paper we explore the effect which the length of a temporal sliding window has on the success of detecting land cover change. It is shown using a short Fourier transform as a feature extraction method provides meaningful robust input toa machine learning method. In theory, the performance is increased by improving the estimates on the features by increasing the length of the sliding window. Experiments were conducted in the Limpopo province of South Africa and werefound that increasing the length of the sliding window beyond 12 months yield minor improves due to other seasonal and external factors.

Item Type:	Conference Publication
Authors/Creators:	Salmon, BP and Kleynhans, W and Olivier, JC and Schwegmann, CP
Keywords:	change detection, fourier transform, satellite, time series
Journal or Publication Title:	Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium
Publisher:	IEEE
Copyright Information:	Copyright 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Related URLs:	Google Scholar: Salmon, BP UTAS Profile: Salmon, BP UTAS Profile: Olivier, JC
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