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A novel framework for parameter selection of the autocorrelation change detection method using 250m MODIS time-series data in the Gauteng Province of South Africa

Citation

Kleynhans, W and Salmon, BP and Wessels, KJ, A novel framework for parameter selection of the autocorrelation change detection method using 250m MODIS time-series data in the Gauteng Province of South Africa, South African Journal of Geomatics, 6, (3) pp. 407-415. ISSN 2225-8531 (2017) [Refereed Article]


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Copyright Statement

Copyright 2017 the authors

DOI: doi:10.4314/sajg.v6i3.10

Abstract

Human settlement expansion is one of the most prominent types of land cover change in South Africa. These changes typically occur in areas that are covered by natural vegetation. Methods that can rapidly indicate areas having a high probability of change are very valuable to analysts as this can be used to direct their attention to high probability change areas for further evaluation. MODIS time-series data (8-daily composite) at a resolution of 500 m has been proven to be an effective data source for detecting human settlements in South Africa and it was proposed in Kleynhans et al., 2012 that a Temporal Autocorrelation Change detection method (TACD) be used to detect the formation of new settlements in the Gauteng province of South Africa. In this paper, the TACD that was proposed by Kleynhans et al., 2012 is adapted to be usable with variable sampled temporal resolutions for 250m MODIS data by using a novel framework for parameter selection. The proposed method is applied to variably sampled 250m MODIS time-series data ranging from daily to semi-annually and a comparison of change detection accuracy vs. false alarm rate is done in each instance. Key results indicate that there is little difference in performance between daily sampled and 2-monthly sampled 250m MODIS time-series data for the use case evaluated in this paper.

Item Details

Item Type:Refereed Article
Keywords:remote sensing, time series, change detection
Research Division:Engineering
Research Group:Geomatic Engineering
Research Field:Photogrammetry and Remote Sensing
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in Engineering
UTAS Author:Salmon, BP (Dr Brian Salmon)
ID Code:123035
Year Published:2017
Deposited By:Engineering
Deposited On:2017-12-12
Last Modified:2018-06-18
Downloads:70 View Download Statistics

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