Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/12140
Title: Binary images in seasonal land-cover change identification: a comparative study in parts of New South Wales, Australia
Contributor(s): Sinha, Priyakant (author); Kumar, Lalit (author)orcid 
Publication Date: 2013
DOI: 10.1080/01431161.2012.742214
Handle Link: https://hdl.handle.net/1959.11/12140
Abstract: Numerous land-cover change detection techniques have been developed with varying opinions about their appropriateness and success. Decisions on the selection of the most suitable change detection method is often influenced by the study region landscape complexity and the type of data used for analysis. For different climatic areas, the method that suits best the seasonal land-cover change identification remains uncertain. In this study, 11 different binary change detection methods were tested and compared with respect to their capability in detecting land-cover change/no-change information in different seasons. The methods include image differencing (I_Diff), Improved image differencing (Imp_Diff), principal component image differencing (PC_Diff), vegetation index differencing (VI_Diff), change vector analysis (CVA), image ratioing (IR), improved image ratioing (Imp_IR), vegetation index image ratioing (VI_R), multi-date principal component analysis (PCA) using all bands (M_PCA), two-date bands PCA (B_PCA), and two-date vegetation index images PCA (VI_PCA). Multi Date Thematic Mapper (TM) data were used for a wide set of change image generation. A relatively new approach was applied for optimal threshold value determination for the separation of change/no-change areas. Research results indicated that any methods involving TM Band 4 performed better than those using TM Band 3 or 5 on each of the change periods. However, irrespective of the method used, the accuracy assessment and change/no-change validation results from normalized difference vegetation index (NDVI)-based techniques outperformed all other tested techniques in the change detection process (overall accuracy >90% and kappa value >0.85 for all six change periods). The image differencing technique was found to be marginally better than PCA and IR in most cases and any of these techniques can be used for change detection. However, because of the simplistic nature and relative ease in identifying both negative and positive changes from difference images, the NDVI differencing technique is recommended for seasonal land-cover change identification in the study region.
Publication Type: Journal Article
Source of Publication: International Journal of Remote Sensing, 34(6), p. 2162-2186
Publisher: Taylor & Francis Ltd
Place of Publication: United Kingdom
ISSN: 0143-1161
1366-5901
Field of Research (FOR): 090905 Photogrammetry and Remote Sensing
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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School of Environmental and Rural Science

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