Flood Mapping in a Complex Environment Using Bistatic TanDEM-X/TerraSAR-X InSAR Coherence

Chayma Chaabani,Marco Chini,Riadh Abdelfattah,Karem Chokmani,Renaud Hostache

doi:10.3390/rs10121873

Abstract

In this paper, we assess the flood mapping capabilities of the X-band Synthetic Aperture Radar (SAR) imagery acquired by the bistatic pair TanDEM-X/TerraSAR-X (TDX/TSX). The main objective is to investigate the added value of the bistatic TDX/TSX Interferometric Synthetic Aperture Radar (InSAR) coherence in addition to the SAR backscatter in the context of inundation mapping. As a classifier, we consider a Random Forest (RF) classification scheme using TDX/TSX SAR intensities and their bistatic InSAR coherence to extract the flood extent map. To evaluate the classification results and as no “ground truth” was available at the SAR data acquisition time, we set up a LISFLOOD-FP hydraulic model for simulating the temporal evolution of the flood water. The flood map simulated by the model shows good performances with an Overall Accuracy (OA) of 97.92 % and a Critical Success Index (CSI) of 94 . 01 % . The SAR-derived flood map is then compared to the LISFLOOD-FP extent map simulated at the SAR data acquisition time. As a test case, we consider the flooding event of the Richelieu River that occurred in the Montérégie region of Quebec (Canada) from April to June 2011. Experimental results highlight the potential of the bistatic InSAR coherence for more accurate flood mapping in a complex landscape with urban and vegetation areas. The classification results of the SAR-derived flood map with respect to the LISFLOOD-FP flood map reach an OA of 78.65 % and a Precision of 82.08 % when integrating the bistatic InSAR coherence. These classification OA and Precision values are 69.63 % and 64.52 % , respectively, using only the TDX/TSX SAR intensity.

Highlights

Over the past decade, inundations have accounted for 56% of all climate-related disasters that have had severe environmental and social impacts worldwide [1]
The training process is performed based on the decision-tree concept and training samples are trained using the “feature bagging” method where the algorithm selects a random subset of the features
The flood mapping capacity of bistatic TanDEM-X/TerraSAR-X (X-band) data was investigated in the presence of different landcover types

Summary

Introduction

Inundations have accounted for 56% of all climate-related disasters that have had severe environmental and social impacts worldwide [1]. During the period 1970–2014, the estimated flood costs in Canada represented about 78% of the total amount provided by the Disaster Financial Assistance Arrangements (DFAA) [2]. The heavy spring snowmelt and the intense rainfall were the main causes of this unprecedented inundation. This unexpected natural disaster affected approximately 3927 people and 11 municipalities, and Montérégie declared a local state of emergency [3]. This shows that appropriate management approaches are necessary to minimize the social and economic losses due to floods. A relevant step in this process is to have access to reliable and precise information on the flood extent to execute the rescue activities effectively

Results

Discussion

Conclusion