A Novel Method for River Bank Detection from Landsat Satellite Data: A Case Study in the Vietnamese Mekong Delta
River bank (RB) erosion is a global issue affecting livelihoods and properties of millions of people. However, it has not received enough attention in the Vietnamese Mekong Delta (VMD), i.e., the world’s third largest delta, compared to salinity intrusion and flooding. There have been several studie...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-10-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/20/3298 |
| _version_ | 1797551366984433664 |
|---|---|
| author | Doan Van Binh Basil Wietlisbach Sameh Kantoush Ho Huu Loc Edward Park Giovanni de Cesare Do Huy Cuong Nguyen Xuan Tung Tetsuya Sumi |
| author_facet | Doan Van Binh Basil Wietlisbach Sameh Kantoush Ho Huu Loc Edward Park Giovanni de Cesare Do Huy Cuong Nguyen Xuan Tung Tetsuya Sumi |
| author_sort | Doan Van Binh |
| collection | DOAJ |
| description | River bank (RB) erosion is a global issue affecting livelihoods and properties of millions of people. However, it has not received enough attention in the Vietnamese Mekong Delta (VMD), i.e., the world’s third largest delta, compared to salinity intrusion and flooding. There have been several studies examining RB and coastal erosion in the VMD using remotely sensed satellite data, but the applied methodology was not adequately validated. Therefore, we developed a novel SRBED (Spectral RB Erosion Detection) method, in which the M-AMERL (Modified Automated Method for Extracting Rivers and Lakes) is proposed, and a new RB change detection algorithm using Landsat data. The results show that NDWI (Normalized Difference Water Index) and MNDWI (Modified Normalized Difference Water Index) using the M-AMERL algorithm (i.e., NDWI<sub>M-AMERL</sub>, MNDWI<sub>M-AMERL</sub>) perform better than other indices. Furthermore, the NDWI<sub>M-AMERL; SMA</sub> (i.e., NDWI<sub>M-AMERL</sub> using the SMA (Spectral Mixture Analysis) algorithm) is the best RB extraction method in the VMD. The NDWI<sub>M-AMERL; SMA</sub> performs better than the MNDWI, NDVI (Normalized Difference Vegetation Index), and WNDWI (Weighted Normalized Difference Water Index) indices by 35–41%, 70% and 30%, respectively. Moreover, the NDVI index is not recommended for assessing RB changes in the delta. Applying the developed SRBED method and RB change detection algorithm, we estimated a net erosion area of the RB of –1.5 km<sup>2</sup> from 2008 to 2014 in the Tien River from Tan Chau to My Thuan, with a mean erosion width of –2.64 m and maximum erosion widths exceeding 60 m in places. Our advanced method can be applied in other river deltas having similar characteristics, and the results from our study are helpful in future studies in the VMD. |
| first_indexed | 2024-03-10T15:43:42Z |
| format | Article |
| id | doaj.art-b25a6422b479408298d3ab800958847e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T15:43:42Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b25a6422b479408298d3ab800958847e2023-11-20T16:36:24ZengMDPI AGRemote Sensing2072-42922020-10-011220329810.3390/rs12203298A Novel Method for River Bank Detection from Landsat Satellite Data: A Case Study in the Vietnamese Mekong DeltaDoan Van Binh0Basil Wietlisbach1Sameh Kantoush2Ho Huu Loc3Edward Park4Giovanni de Cesare5Do Huy Cuong6Nguyen Xuan Tung7Tetsuya Sumi8Water Resources Center, Disaster Prevention Research Institute, Kyoto University, Goka-sho, Uji City, Kyoto 611-0011, JapanStaubli, Kurath & Partner AG, Bachmattstrasse 53, Zürich 8084, SwitzerlandWater Resources Center, Disaster Prevention Research Institute, Kyoto University, Goka-sho, Uji City, Kyoto 611-0011, JapanNational Institute of Education (NIE), Nanyang Technological University (NTU), Singapore 639798, SingaporeNational Institute of Education (NIE), Nanyang Technological University (NTU), Singapore 639798, SingaporePlatform of Hydraulic Constructions (PL-LCH), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1000, SwitzerlandInstitute of Marine Geology and Geophysics, VAST, 18 Hoang Quoc Viet, Hanoi 100000, VietnamInstitute of Marine Geology and Geophysics, VAST, 18 Hoang Quoc Viet, Hanoi 100000, VietnamWater Resources Center, Disaster Prevention Research Institute, Kyoto University, Goka-sho, Uji City, Kyoto 611-0011, JapanRiver bank (RB) erosion is a global issue affecting livelihoods and properties of millions of people. However, it has not received enough attention in the Vietnamese Mekong Delta (VMD), i.e., the world’s third largest delta, compared to salinity intrusion and flooding. There have been several studies examining RB and coastal erosion in the VMD using remotely sensed satellite data, but the applied methodology was not adequately validated. Therefore, we developed a novel SRBED (Spectral RB Erosion Detection) method, in which the M-AMERL (Modified Automated Method for Extracting Rivers and Lakes) is proposed, and a new RB change detection algorithm using Landsat data. The results show that NDWI (Normalized Difference Water Index) and MNDWI (Modified Normalized Difference Water Index) using the M-AMERL algorithm (i.e., NDWI<sub>M-AMERL</sub>, MNDWI<sub>M-AMERL</sub>) perform better than other indices. Furthermore, the NDWI<sub>M-AMERL; SMA</sub> (i.e., NDWI<sub>M-AMERL</sub> using the SMA (Spectral Mixture Analysis) algorithm) is the best RB extraction method in the VMD. The NDWI<sub>M-AMERL; SMA</sub> performs better than the MNDWI, NDVI (Normalized Difference Vegetation Index), and WNDWI (Weighted Normalized Difference Water Index) indices by 35–41%, 70% and 30%, respectively. Moreover, the NDVI index is not recommended for assessing RB changes in the delta. Applying the developed SRBED method and RB change detection algorithm, we estimated a net erosion area of the RB of –1.5 km<sup>2</sup> from 2008 to 2014 in the Tien River from Tan Chau to My Thuan, with a mean erosion width of –2.64 m and maximum erosion widths exceeding 60 m in places. Our advanced method can be applied in other river deltas having similar characteristics, and the results from our study are helpful in future studies in the VMD.https://www.mdpi.com/2072-4292/12/20/3298remote sensingLandsatsatellitespectral indexriver bank detection/extractionVietnamese Mekong Delta |
| spellingShingle | Doan Van Binh Basil Wietlisbach Sameh Kantoush Ho Huu Loc Edward Park Giovanni de Cesare Do Huy Cuong Nguyen Xuan Tung Tetsuya Sumi A Novel Method for River Bank Detection from Landsat Satellite Data: A Case Study in the Vietnamese Mekong Delta Remote Sensing remote sensing Landsat satellite spectral index river bank detection/extraction Vietnamese Mekong Delta |
| title | A Novel Method for River Bank Detection from Landsat Satellite Data: A Case Study in the Vietnamese Mekong Delta |
| title_full | A Novel Method for River Bank Detection from Landsat Satellite Data: A Case Study in the Vietnamese Mekong Delta |
| title_fullStr | A Novel Method for River Bank Detection from Landsat Satellite Data: A Case Study in the Vietnamese Mekong Delta |
| title_full_unstemmed | A Novel Method for River Bank Detection from Landsat Satellite Data: A Case Study in the Vietnamese Mekong Delta |
| title_short | A Novel Method for River Bank Detection from Landsat Satellite Data: A Case Study in the Vietnamese Mekong Delta |
| title_sort | novel method for river bank detection from landsat satellite data a case study in the vietnamese mekong delta |
| topic | remote sensing Landsat satellite spectral index river bank detection/extraction Vietnamese Mekong Delta |
| url | https://www.mdpi.com/2072-4292/12/20/3298 |
| work_keys_str_mv | AT doanvanbinh anovelmethodforriverbankdetectionfromlandsatsatellitedataacasestudyinthevietnamesemekongdelta AT basilwietlisbach anovelmethodforriverbankdetectionfromlandsatsatellitedataacasestudyinthevietnamesemekongdelta AT samehkantoush anovelmethodforriverbankdetectionfromlandsatsatellitedataacasestudyinthevietnamesemekongdelta AT hohuuloc anovelmethodforriverbankdetectionfromlandsatsatellitedataacasestudyinthevietnamesemekongdelta AT edwardpark anovelmethodforriverbankdetectionfromlandsatsatellitedataacasestudyinthevietnamesemekongdelta AT giovannidecesare anovelmethodforriverbankdetectionfromlandsatsatellitedataacasestudyinthevietnamesemekongdelta AT dohuycuong anovelmethodforriverbankdetectionfromlandsatsatellitedataacasestudyinthevietnamesemekongdelta AT nguyenxuantung anovelmethodforriverbankdetectionfromlandsatsatellitedataacasestudyinthevietnamesemekongdelta AT tetsuyasumi anovelmethodforriverbankdetectionfromlandsatsatellitedataacasestudyinthevietnamesemekongdelta AT doanvanbinh novelmethodforriverbankdetectionfromlandsatsatellitedataacasestudyinthevietnamesemekongdelta AT basilwietlisbach novelmethodforriverbankdetectionfromlandsatsatellitedataacasestudyinthevietnamesemekongdelta AT samehkantoush novelmethodforriverbankdetectionfromlandsatsatellitedataacasestudyinthevietnamesemekongdelta AT hohuuloc novelmethodforriverbankdetectionfromlandsatsatellitedataacasestudyinthevietnamesemekongdelta AT edwardpark novelmethodforriverbankdetectionfromlandsatsatellitedataacasestudyinthevietnamesemekongdelta AT giovannidecesare novelmethodforriverbankdetectionfromlandsatsatellitedataacasestudyinthevietnamesemekongdelta AT dohuycuong novelmethodforriverbankdetectionfromlandsatsatellitedataacasestudyinthevietnamesemekongdelta AT nguyenxuantung novelmethodforriverbankdetectionfromlandsatsatellitedataacasestudyinthevietnamesemekongdelta AT tetsuyasumi novelmethodforriverbankdetectionfromlandsatsatellitedataacasestudyinthevietnamesemekongdelta |