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

Xian Li, Shuhe Zhao, Hong Yang, Dianmin Cong and Zhaohua Zhang
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Xian Li: School of Geographic and Oceanographic Sciences, Nanjing University, 163 Xianlin Ave, Qixia District, Nanjing 210023, China
Shuhe Zhao: School of Geographic and Oceanographic Sciences, Nanjing University, 163 Xianlin Ave, Qixia District, Nanjing 210023, China
Hong Yang: Norwegian Institute of Bioeconomy Research (NIBIO), Postboks 115, 1431 Ås, Norway
Dianmin Cong: School of Geographic and Oceanographic Sciences, Nanjing University, 163 Xianlin Ave, Qixia District, Nanjing 210023, China
Zhaohua Zhang: School of Geographic and Oceanographic Sciences, Nanjing University, 163 Xianlin Ave, Qixia District, Nanjing 210023, China

Sustainability, 2017, vol. 9, issue 3, 1-17

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 km 2 . 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.

Keywords: change detection; correlation coefficient; binary mask; Voronoi distance; fuzzy c-means; Landsat 8 OLI (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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