FLOOD ANALYSIS WITH REMOTE SENSING DATA – A CASE STUDY: MARITSA RIVER, EDIRNE


SUNAR A. F. , YAĞMUR N. , Dervişoğlu A.

Gi4DM 2019, 3 - 06 September 2019, vol.38, pp.497-502 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 38
  • Doi Number: 10.5194/isprs-archives-xlii-3-w8-497-2019
  • Page Numbers: pp.497-502
  • Keywords: Maritsa-Evros Basin, Flood, Remote Sensing, Sentinel 1/ 2 Data, GEE

Abstract

A flood, one of the most devastating natural disasters in the world, occurs when water inundates land that's normally dry. Although floods can develop in many ways, river floods (i.e. overflow by rivers or river banks) are the most common. Turkey is one of the flood-affected countries with its 20 main basins in 8 regions. One of the most aggrieved basins in Turkey is the Maritsa river basin in in Eastern Balkans, which also contains the natural border regions with Greece and Bulgaria. 65% of the Maritsa River basin, which originates from the Rila Mountains and joins the Arda and Tundzha rivers, is located in Bulgaria. When the melting snow flow or precipitation in the basin increases, the Maritsa River overflows from the slopes to the Edirne Plain and from time to time exceeds the capacity of the bed, causing floods. On the other hand, since the water level in the dams and reservoirs was kept at the highest level for production purposes, the flood repeat interval increased in the region, since 2000s. Today, it is possible to monitor and evaluate the damages of flood by obtaining very reliable information with space technology. Especially, microwave SAR images that can penetrate clouds, are of great importance in flood mapping because they provide immediate information on the extent of inundation and support the evaluation of property and environmental damages. In this study, rapid flood risk assessment in the region was performed using Landsat 8 and Sentinel 2 Normalized Difference Water Index (NDWI) time series images, and calibrated Sentinel 1 SAR images produced on Google Earth Engine (GEE) platform for 2015-2018 period. GEE is a cloud-based platform that facilitates access to high-performance computing resources to handle very large geographic data sets. The results were compared and verified using meteorological data, riverbed flow data, and digital media news. The results showed that the most affected areas were consistent with the highest measured flow rates and the magnitude of flood damages caused by two main causes in the basin (i.e. opening of shutters in Bulgarian dams or local excessive rainfall) was very different (approximately 8 times larger) from each other.