@article{MAKHILLJEAS2018131416598,
    title = {Water Feature Extraction, Enhancement and Change Detection Using
Landsat-5 TM Multi Temporal Images by Image Fusion},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {14},
    pages = {5868-5872},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.5868.5872},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.5868.5872},
    author = {M. and},
    keywords = {NDWI,MNDWI,spatiotemporal,NDVI,AWEI,WRI},
    abstract = {In this research letter, Urima lake satellite portrait is extracted and enhanced. Urima is 20th second
largest lake and second largest saline lake in the world. There are several reasons that lead to shrinkage of this
lake. The main cause is increasing salinity and decreasing surface water of the lake. Remote sensing derived
indices such as Normalized Difference Water Index (NDWI)-method 1, Normalized Difference Vegetation Index
(NDVI)-method 2, Normalized Difference Moisture Index (NDMI)-method 3, Modified Normalized Difference
Water Index (MNDWI)-method 4, Water Ratio Index (WRI)-method 5 and Automated Water Extraction Index
(AWEI )-method 6 were tested on surface water of Landsat-5 TM data. Out of all these remote sensing
Indices, NDWI was found superior to model the spatiotemporal changes of Urima lake. Normalized
Difference Water Index-Principle Component (NDWI-PC) and Modified Normalized Difference Water
Index-Principle Component (MNDWI-PC) fusion is proposed for multi temporal surface water change
detection. The surface water area of Urima lake is reduced from 2000-2010. The temporal changes of Lake
Urima are implemented in this study. Accuracy assessment analysis is performed to show that proposed method
is better than existing methods.}
    }