Transferability and Upscaling of Fuzzy Classification for Shoreline Change over 30 Years
Local authorities require information on shoreline change for land use decision making. Monitoring shoreline changes is useful for updating shoreline maps used in coastal planning and management. By analysing data over a period of time, where and how fast the coast has changed can be determined. The...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-08-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/10/9/1377 |
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| author | Ratna Sari Dewi Wietske Bijker Alfred Stein Muh Aris Marfai |
| author_facet | Ratna Sari Dewi Wietske Bijker Alfred Stein Muh Aris Marfai |
| author_sort | Ratna Sari Dewi |
| collection | DOAJ |
| description | Local authorities require information on shoreline change for land use decision making. Monitoring shoreline changes is useful for updating shoreline maps used in coastal planning and management. By analysing data over a period of time, where and how fast the coast has changed can be determined. Thereby, we can prevent any development in high risk areas. This study investigated the transferability of a fuzzy classification of shoreline changes and to upscale towards a larger area. Using six sub areas, three strategies were used: (i) Optimizing two FCM (fuzzy c-means) parameters based on the predominant land use/cover of the reference subset, (ii) adopting the class mean and number of classes resulting from the classification of reference subsets to perform FCM on target subsets, and (iii) estimating the optimal level of fuzziness of target subsets. This approach was applied to a series of images to identify shoreline positions in a section of the northern Central Java Province, Indonesia which experienced a severe change of shoreline position over three decades. The extent of shoreline changes was estimated by overlaying shoreline images. Shoreline positions were highlighted to infer the erosion and accretion area along the coast, and the shoreline changes were calculated. From the experimental results, we obtained m (level of fuzziness) values in the range from 1.3 to 1.9 for the seven land use/cover classes that were analysed. Furthermore, for ten images used in this research, we obtained the optimal m = 1.8. For a similar coastal characteristic, this m value can be adopted and the relation between land use/cover and two FCM parameters can shorten the time required to optimise parameters. The proposed method for upscaling and transferring the classification method to a larger, or different, areas is promising showing κ (kappa) values > 0.80. The results also show an agreement of water membership values between the reference and target subsets indicated by κ > 0.82. Over the study period, the area exhibited both erosion and accretion. The erosion was indicated by changes into water and changes from non-water into shoreline were observed for approximately 78 km2. Accretion was due to changes into non-water and changes from water into shoreline for 19.5 km2. Erosion was severe in the eastern section of the study area, whereas the middle section gained land through reclamation activities. These erosion and accretion processes played an active role in the changes of the shoreline. We conclude that the method is applicable to the current study area. The relation between land use/cover classes and the value of FCM parameters produced in this study can be adopted. |
| first_indexed | 2024-04-11T19:57:21Z |
| format | Article |
| id | doaj.art-ba8874b00671438985998edad157ffcd |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T19:57:21Z |
| publishDate | 2018-08-01 |
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| spelling | doaj.art-ba8874b00671438985998edad157ffcd2022-12-22T04:05:57ZengMDPI AGRemote Sensing2072-42922018-08-01109137710.3390/rs10091377rs10091377Transferability and Upscaling of Fuzzy Classification for Shoreline Change over 30 YearsRatna Sari Dewi0Wietske Bijker1Alfred Stein2Muh Aris Marfai3Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The NetherlandsFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The NetherlandsFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The NetherlandsFaculty of Geography, Universitas Gadjah Mada, Bulaksumur, Yogyakarta 55281, IndonesiaLocal authorities require information on shoreline change for land use decision making. Monitoring shoreline changes is useful for updating shoreline maps used in coastal planning and management. By analysing data over a period of time, where and how fast the coast has changed can be determined. Thereby, we can prevent any development in high risk areas. This study investigated the transferability of a fuzzy classification of shoreline changes and to upscale towards a larger area. Using six sub areas, three strategies were used: (i) Optimizing two FCM (fuzzy c-means) parameters based on the predominant land use/cover of the reference subset, (ii) adopting the class mean and number of classes resulting from the classification of reference subsets to perform FCM on target subsets, and (iii) estimating the optimal level of fuzziness of target subsets. This approach was applied to a series of images to identify shoreline positions in a section of the northern Central Java Province, Indonesia which experienced a severe change of shoreline position over three decades. The extent of shoreline changes was estimated by overlaying shoreline images. Shoreline positions were highlighted to infer the erosion and accretion area along the coast, and the shoreline changes were calculated. From the experimental results, we obtained m (level of fuzziness) values in the range from 1.3 to 1.9 for the seven land use/cover classes that were analysed. Furthermore, for ten images used in this research, we obtained the optimal m = 1.8. For a similar coastal characteristic, this m value can be adopted and the relation between land use/cover and two FCM parameters can shorten the time required to optimise parameters. The proposed method for upscaling and transferring the classification method to a larger, or different, areas is promising showing κ (kappa) values > 0.80. The results also show an agreement of water membership values between the reference and target subsets indicated by κ > 0.82. Over the study period, the area exhibited both erosion and accretion. The erosion was indicated by changes into water and changes from non-water into shoreline were observed for approximately 78 km2. Accretion was due to changes into non-water and changes from water into shoreline for 19.5 km2. Erosion was severe in the eastern section of the study area, whereas the middle section gained land through reclamation activities. These erosion and accretion processes played an active role in the changes of the shoreline. We conclude that the method is applicable to the current study area. The relation between land use/cover classes and the value of FCM parameters produced in this study can be adopted.http://www.mdpi.com/2072-4292/10/9/1377fuzzy classificationtransferabilityupscalingshoreline change |
| spellingShingle | Ratna Sari Dewi Wietske Bijker Alfred Stein Muh Aris Marfai Transferability and Upscaling of Fuzzy Classification for Shoreline Change over 30 Years Remote Sensing fuzzy classification transferability upscaling shoreline change |
| title | Transferability and Upscaling of Fuzzy Classification for Shoreline Change over 30 Years |
| title_full | Transferability and Upscaling of Fuzzy Classification for Shoreline Change over 30 Years |
| title_fullStr | Transferability and Upscaling of Fuzzy Classification for Shoreline Change over 30 Years |
| title_full_unstemmed | Transferability and Upscaling of Fuzzy Classification for Shoreline Change over 30 Years |
| title_short | Transferability and Upscaling of Fuzzy Classification for Shoreline Change over 30 Years |
| title_sort | transferability and upscaling of fuzzy classification for shoreline change over 30 years |
| topic | fuzzy classification transferability upscaling shoreline change |
| url | http://www.mdpi.com/2072-4292/10/9/1377 |
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