Next Article in Journal
Detection and Characterization of Ship Targets Using CryoSat-2 Altimeter Waveforms
Next Article in Special Issue
Change Detection of Submerged Seagrass Biomass in Shallow Coastal Water
Previous Article in Journal
0-Norm Sparse Hyperspectral Unmixing Using Arctan Smoothing
Previous Article in Special Issue
Automatic Sun Glint Removal of Multispectral High-Resolution Worldview-2 Imagery for Retrieving Coastal Shallow Water Parameters
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Remote Sens. 2016, 8(3), 190;

Fuzzy Classification for Shoreline Change Monitoring in a Part of the Northern Coastal Area of Java, Indonesia

Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
Faculty of Geography, Universitas Gadjah Mada, Bulaksumur, Yogyakarta 55281, Indonesia
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Academic Editors: Deepak R. Mishra, Richard W. Gould, Xiaofeng Li and Prasad S. Thenkabail
Received: 10 December 2015 / Revised: 25 January 2016 / Accepted: 18 February 2016 / Published: 27 February 2016
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
Full-Text   |   PDF [7882 KB, uploaded 27 February 2016]   |  


This study presents an unsupervised fuzzy c-means classification (FCM) to observe the shoreline positions. We combined crisp and fuzzy methods for change detection. We addressed two perspectives of uncertainty: (1) uncertainty that is inherent to shoreline positions as observed from remote sensing images due to its continuous variation over time; and (2) the uncertainty of the change results propagating from object extraction and implementation of shoreline change detection method. Unsupervised FCM achieved the highest kappa (κ) value when threshold (t) was set at 0.5. The highest κ values were 0.96 for the 1994 image. For images in 2013, 2014 and 2015, the κ values were 0.95. Further, images in 2003, 2002 and 2000 obtained 0.93, 0.90 and 0.86, respectively. Gradual and abrupt changes were observed, as well as a measure of change uncertainty for the observed objects at the pixel level. These could be associated with inundations from 1994 to 2015 at the northern coastal area of Java, Indonesia. The largest coastal inundations in terms of area occurred between 1994 and 2000, when 739 ha changed from non-water and shoreline to water and in 2003–2013 for 200 ha. Changes from water and shoreline to non-water occurred between 2000 and 2002 (186 ha) and in 2013–2014 (65 ha). Urban development in flood-prone areas resulted in an increase of flood hazards including inundation and erosion leading to the changes of shoreline position. The proposed methods provided an effective way to present shoreline as a line and as a margin with fuzzy boundary and its associated change uncertainty. Shoreline mapping and monitoring is crucial to understand the spatial distribution of coastal inundation including its trend. View Full-Text
Keywords: shoreline change; fuzzy classification; coastal inundation; uncertainty; Indonesia shoreline change; fuzzy classification; coastal inundation; uncertainty; Indonesia

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Dewi, R.S.; Bijker, W.; Stein, A.; Marfai, M.A. Fuzzy Classification for Shoreline Change Monitoring in a Part of the Northern Coastal Area of Java, Indonesia. Remote Sens. 2016, 8, 190.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top