Water Body Detection Analysis Using NDWI Indices Derived from Landsat-8 OLI


POLISH JOURNAL OF ENVIRONMENTAL STUDIES, vol.29, no.2, pp.1759-1769, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 29 Issue: 2
  • Publication Date: 2020
  • Doi Number: 10.15244/pjoes/110447
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, Central & Eastern European Academic Source (CEEAS), Environment Index, Greenfile, Public Affairs Index, Veterinary Science Database
  • Page Numbers: pp.1759-1769
  • Keywords: water body detection, remote sensing, NDWI, Landsat-8 OLI, ATIKHISAR RESERVOIR, KIZILIRMAK RIVER, CLASSIFICATION, QUALITY
  • Çanakkale Onsekiz Mart University Affiliated: Yes


Normalized different water indices (NDWIs) derived from satellite images are commonly and successfully utilized in surface water body detection and mapping. In this study, the water body detection capability of three NDWI models (NDWI(Green, NIR), NDWI(Green, SWIR1) and NDWI(Green, SWIR2)) generated using 28 multitemporal Landsat-8 OLI multispectral satellite images was analyzed for Atikhisar Dam Lake, the only water source of Canakkalecity's central district in Turkey between 2013 and 2017. This study focused on two important open research questions: (i) Which NDWI model produces the most superior results? and (ii) How much does accuracy change in the use of 15 m and 30 m spatial resolution satellite data? For the accuracy analysis, area values extracted from the NDWI models were compared with in-situ lake area values as measured by the General Directorate of State Hydraulic Works (DSI). The results of this study show that as the lake area grows, discrimination of water from other classes is better with NIR region, and that the performance of NDWI(Green, NIR) is relatively better in terms of lake expansion effect. Results also indicate that hydrometeorological factors such as precipitation and evaporation and anthropogenic factors such as irrigation and daily consumption are decisive in lake area variations. The order of accuracy from high to low was found to be NDWI(Green, NIR), NDWI(Green, SWIR1) and NDWI(Green, SWIR2), and 15 m spatial resolution data generated better results than 30 m resolution.