Determination of flood risks in the yeniciftlik stream basin by using remote sensing and GIS techniques

Akar I., Maktav D., Uysal C.

28th European-Association-of-Remote-Sensing-Laboratories (EARSeL) Symposium and Workshops on Remote Sensing for a Changing Europe, İstanbul, Turkey, 2 - 05 June 2008, pp.40-45 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.3233/978-1-58603-986-8-40
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.40-45
  • Istanbul Technical University Affiliated: Yes


Floods are among the most devastating natural hazards in Turkey and worldwide, causing the largest amount of casualties and property damage. GIS and remote sensing methods are very attractive, fast, and reliable tools for various flood applications and management. In this study, we investigated floods which occurred and are likely to occur in a study area in Istanbul, Turkey, to determine the potential use of these tools with respect to these floods. Floods which caused loss of life and property in the Yeniciftlik stream basin located within the boundaries of Beykoz, a suburb of Istanbul, attracted our attention due to their negative impact on human life and activities, and this was selected as the study area. Many geographical parameters such as vegetation, topographic and geologic features, precipitation, and land use features play a significant role in the occurrence of flood related disasters. Data used were topographic, soil, vegetation, and geological maps at scale 1: 25000, IKONOS pan-sharpened imaging (02.03.2008), as well as aerial photographs taken in 2006. Using the Arcinfo 9.2 Spatial Analyst module, flood risk maps were created, assigning different weights to vegetation, geologic and land use features, and other morphometric features such as slope, aspect, and so on. Land use and vegetation features were determined by applying a supervised classification technique to IKONOS data. All data were processed using HEC-GeoRAS (in ArcGIS) and HEC-RAS software. The results indicate that the precision and diversity of the data used greatly affects the precision of these risk maps.