Water feature extraction and change detection using multitemporal landsat imagery
Lake Urmia is the 20th largest lake and the second largest hyper saline lake (before September 2010) in the world. It is also the largest inland body of salt water in the Middle East. Nevertheless, the lake has been in a critical situation in recent years due to decreasing surface water and increasi...
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MDPI AG
2014
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| _version_ | 1796861422971387904 |
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| author | Rokni, Komeil Ahmad, Anuar Selamat, Ali Hazini, Sharifeh |
| author_facet | Rokni, Komeil Ahmad, Anuar Selamat, Ali Hazini, Sharifeh |
| author_sort | Rokni, Komeil |
| collection | ePrints |
| description | Lake Urmia is the 20th largest lake and the second largest hyper saline lake (before September 2010) in the world. It is also the largest inland body of salt water in the Middle East. Nevertheless, the lake has been in a critical situation in recent years due to decreasing surface water and increasing salinity. This study modeled the spatiotemporal changes of Lake Urmia in the period 2000-2013 using the multi-temporal Landsat 5-TM, 7-ETM+ and 8-OLI images. In doing so, the applicability of different satellite-derived indexes including Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalized Difference Moisture Index (NDMI), Water Ratio Index (WRI), Normalized Difference Vegetation Index (NDVI), and Automated Water Extraction Index (AWEI) were investigated for the extraction of surface water from Landsat data. Overall, the NDWI was found superior to other indexes and hence it was used to model the spatiotemporal changes of the lake. In addition, a new approach based on Principal Components of multi-temporal NDWI (NDWI-PCs) was proposed and evaluated for surface water change detection. The results indicate an intense decreasing trend in Lake Urmia surface area in the period 2000-2013, especially between 2010 and 2013 when the lake lost about one third of its surface area compared to the year 2000. The results illustrate the effectiveness of the NDWI-PCs approach for surface water change detection, especially in detecting the changes between two and three different times, simultaneously. |
| first_indexed | 2024-03-05T19:56:09Z |
| format | Article |
| id | utm.eprints-63227 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:56:09Z |
| publishDate | 2014 |
| publisher | MDPI AG |
| record_format | dspace |
| spelling | utm.eprints-632272017-06-18T06:58:17Z http://eprints.utm.my/63227/ Water feature extraction and change detection using multitemporal landsat imagery Rokni, Komeil Ahmad, Anuar Selamat, Ali Hazini, Sharifeh HD Industries. Land use. Labor Lake Urmia is the 20th largest lake and the second largest hyper saline lake (before September 2010) in the world. It is also the largest inland body of salt water in the Middle East. Nevertheless, the lake has been in a critical situation in recent years due to decreasing surface water and increasing salinity. This study modeled the spatiotemporal changes of Lake Urmia in the period 2000-2013 using the multi-temporal Landsat 5-TM, 7-ETM+ and 8-OLI images. In doing so, the applicability of different satellite-derived indexes including Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalized Difference Moisture Index (NDMI), Water Ratio Index (WRI), Normalized Difference Vegetation Index (NDVI), and Automated Water Extraction Index (AWEI) were investigated for the extraction of surface water from Landsat data. Overall, the NDWI was found superior to other indexes and hence it was used to model the spatiotemporal changes of the lake. In addition, a new approach based on Principal Components of multi-temporal NDWI (NDWI-PCs) was proposed and evaluated for surface water change detection. The results indicate an intense decreasing trend in Lake Urmia surface area in the period 2000-2013, especially between 2010 and 2013 when the lake lost about one third of its surface area compared to the year 2000. The results illustrate the effectiveness of the NDWI-PCs approach for surface water change detection, especially in detecting the changes between two and three different times, simultaneously. MDPI AG 2014 Article PeerReviewed Rokni, Komeil and Ahmad, Anuar and Selamat, Ali and Hazini, Sharifeh (2014) Water feature extraction and change detection using multitemporal landsat imagery. Remote Sensing, 6 (5). pp. 4173-4189. ISSN 2072-4292 http://dx.doi.org/10.3390/rs6054173 DOI :10.3390/rs6054173 |
| spellingShingle | HD Industries. Land use. Labor Rokni, Komeil Ahmad, Anuar Selamat, Ali Hazini, Sharifeh Water feature extraction and change detection using multitemporal landsat imagery |
| title | Water feature extraction and change detection using multitemporal landsat imagery |
| title_full | Water feature extraction and change detection using multitemporal landsat imagery |
| title_fullStr | Water feature extraction and change detection using multitemporal landsat imagery |
| title_full_unstemmed | Water feature extraction and change detection using multitemporal landsat imagery |
| title_short | Water feature extraction and change detection using multitemporal landsat imagery |
| title_sort | water feature extraction and change detection using multitemporal landsat imagery |
| topic | HD Industries. Land use. Labor |
| work_keys_str_mv | AT roknikomeil waterfeatureextractionandchangedetectionusingmultitemporallandsatimagery AT ahmadanuar waterfeatureextractionandchangedetectionusingmultitemporallandsatimagery AT selamatali waterfeatureextractionandchangedetectionusingmultitemporallandsatimagery AT hazinisharifeh waterfeatureextractionandchangedetectionusingmultitemporallandsatimagery |