Volume XLII-3/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W4, 109-113, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-W4-109-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W4, 109-113, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-W4-109-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  06 Mar 2018

06 Mar 2018

EXPLOITING MULTI-TEMPORAL SENTINEL-1 SAR DATA FOR FLOOD EXTEND MAPPING

C. Bayik1, S. Abdikan1, G. Ozbulak2, T. Alasag2, S. Aydemir2, and F. Balik Sanli3 C. Bayik et al.
  • 1BEU, Engineering Faculty, Dept. of Geomatics Engineering, 67100 Zonguldak, Turkey
  • 2TUBITAK-BILGEM, 41470 Kocaeli, Turkey
  • 3YTU, Civil Engineering Faculty, Dept. of Geomatics Engineering, 34220 İstanbul, Turkey

Keywords: Flood, Disaster, Sentinel-1, Threshold, Change detection, Deep learning

Abstract. Recently, global climate change is one of the biggest challenges in the world. Dense downfall and following catastrophic floods are one of the most destructive natural hazards among all. Consequences do not only risk human life but also cause economical damage. It is critical rapid mapping of flooding for decision making and emergency services in river management. In this study, we apply a multi-temporal change detection analysis to investigate the flooded areas occurred in Edirne province of Turkey. The study area is located at the lower course of Meric River (Evros in Greece or Maritsa in Bulgarian) which is the border between Turkey and Greece. The river basin is dominated by cropland which suffers from strong catastrophic precipitation. This situation cause overflow of capacity of the dams located along the river and serious flooding occur. Due to its dynamic structure the region exposed to heavy flooding in the past. One of the biggest inundations was occurred at 2nd February 2015 which resulted severe devastation in both urban and rural areas. For the analyses of the temporal and spatial dynamics of the disaster we use Sentinel-1 Synthetic Aperture Radar (SAR) data due to its systematic frequent acquisition. A dataset of pre-event and post-event Sentinel-1 images within the January and February of 2015 period was acquired. Flooded areas were extracted with threshold, random forest and deep learning approaches.