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Sustainability 2017, 9(3), 479; doi:10.3390/su9030479

A Bi-Band Binary Mask Based Land-Use Change Detection Using Landsat 8 OLI Imagery

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,
1,2,3,* , 4,5,* , 1,2
and
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1
School of Geographic and Oceanographic Sciences, Nanjing University, 163 Xianlin Ave, Qixia District, Nanjing 210023, China
2
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, 163 Xianlin Ave, Qixia District, Nanjing 210023, China
3
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
4
Norwegian Institute of Bioeconomy Research (NIBIO), Postboks 115, 1431 Ås, Norway
5
CEES, Department of Biosciences, University of Oslo, Blindern, 0316 Oslo, Norway
*
Authors to whom correspondence should be addressed.
Academic Editor: Marc A. Rosen
Received: 5 January 2017 / Revised: 23 February 2017 / Accepted: 17 March 2017 / Published: 22 March 2017
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Abstract

Land use and cover change (LUCC) is important for the global biogeochemical cycle and ecosystem. This paper introduced a change detection method based on a bi-band binary mask and an improved fuzzy c-means algorithm to research the LUCC. First, the bi-band binary mask approach with the core concept being the correlation coefficients between bands from different images are used to locate target areas with a likelihood of having changed areas. Second, the improved fuzzy c-means (FCM) algorithm was used to execute classification on the target areas. This improved algorithm used distances to the Voronoi cell of the cluster instead of the Euclidean distance to the cluster center in the calculation of membership, and some other improvements were also used to decrease the loops and save time. Third, the post classification comparison was executed to get more accurate change information. As references, change detection using univariate band binary mask and NDVI binary mask were executed. The change detection methods were applied to Landsat 8 OLI images acquired in 2013 and 2015 to map LUCC in Chengwu, north China. The accuracy assessment was executed on classification results and change detection results. The overall accuracy of classification results of the improved FCM is 95.70% and the standard FCM is 84.40%. The average accuracy of change detection results using bi-band mask is 88.92%, using NDVI mask is 81.95%, and using univariate band binary mask is 56.01%. The result of the bi-band mask change detection shows that the change from farmland to built land is the main change type in the study area: total area is 9.03 km2. The developed method in the current study can be an effective approach to evaluate the LUCC and the results helpful for the land policy makers. View Full-Text
Keywords: change detection; correlation coefficient; binary mask; Voronoi distance; fuzzy c-means; Landsat 8 OLI change detection; correlation coefficient; binary mask; Voronoi distance; fuzzy c-means; Landsat 8 OLI
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Li, X.; Zhao, S.; Yang, H.; Cong, D.; Zhang, Z. A Bi-Band Binary Mask Based Land-Use Change Detection Using Landsat 8 OLI Imagery. Sustainability 2017, 9, 479.

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