Next Article in Journal
On Line Validation Exercise (OLIVE): A Web Based Service for the Validation of Medium Resolution Land Products. Application to FAPAR Products
Previous Article in Journal
Determination of Carbonate Rock Chemistry Using Laboratory-Based Hyperspectral Imagery
Remote Sens. 2014, 6(5), 4173-4189; doi:10.3390/rs6054173
Article

Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery

1,* , 1,* , 2
 and 3
Received: 21 January 2014; in revised form: 21 April 2014 / Accepted: 22 April 2014 / Published: 5 May 2014
View Full-Text   |   Download PDF [1167 KB, updated 19 June 2014; original version uploaded 19 June 2014]   |   Browse Figures
Abstract: 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.
Keywords: NDWI; Landsat; surface water; change detection NDWI; Landsat; surface water; change detection
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.

Export to BibTeX |
EndNote


MDPI and ACS Style

Rokni, K.; Ahmad, A.; Selamat, A.; Hazini, S. Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery. Remote Sens. 2014, 6, 4173-4189.

AMA Style

Rokni K, Ahmad A, Selamat A, Hazini S. Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery. Remote Sensing. 2014; 6(5):4173-4189.

Chicago/Turabian Style

Rokni, Komeil; Ahmad, Anuar; Selamat, Ali; Hazini, Sharifeh. 2014. "Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery." Remote Sens. 6, no. 5: 4173-4189.


Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert