Flood extent mapping for Namibia using change detection and thresholding with SAR

Abstract

A new method for flood detection change detection and thresholding (CDAT) was used with synthetic aperture radar (SAR) imagery to delineate the extent of flooding for the Chobe floodplain in the Caprivi region of Namibia. This region experiences annual seasonal flooding and has seen a recent renewal of severe flooding after a long dry period in the 1990s. Flooding in this area has caused loss of life and livelihoods for the surrounding communities and has caught the attention of disaster relief agencies. There is a need for flood extent mapping techniques that can be used to process images quickly, providing near real-time flooding information to relief agencies. ENVISAT/ASAR and Radarsat-2 images were acquired for several flooding seasons from February 2008 to March 2013. The CDAT method was used to determine flooding from these images and includes the use of image subtraction, decision-based classification with threshold values, and segmentation of SAR images. The total extent of flooding determined for 2009, 2011 and 2012 was about 542 km 2 , 720 km 2 , and 673 km 2 respectively. Pixels determined to be flooded in vegetation were typically <0.5% of the entire scene, with the exception of 2009 where the detection of flooding in vegetation was much greater (almost one third of the total flooded area). The time to maximum flooding for the 2013 flood season was determined to be about 27 days. Landsat water classification was used to compare the results from the new CDAT with SAR method; the results show good spatial agreement with Landsat scenes.

A review of applications of satellite SAR, optical, altimetry and DEM data for surface water modelling, mapping and parameter estimation

“…Schumann et al, 2007;Horritt, 2006;Di Baldassarre et al, 2009;Vermeulen et al, 2005;Long et al, 2014). The variation of radar backscatter from different targets enables flood extent mapping.…”

“…Several methods have been used to delineate the flooding extent from SAR data; e.g. utilization of multi-polarized advanced SAR images, application of a statistical active control model, multi-temporal image enhancement and differencing, histogram thresholding/clustering, radiometric thresholding, pixel-based segmentation, and use of artificial neural networks (Long et al, 2014). Multi-temporal image flood mapping involves acquiring flooding and non-flood images of the same area and combing them to get an image which indicates change by colours appearing in the image.…”

“…Flood extent maps were created over 4 years of seasonal flooding in the Chobe floodplain, Namibia (Long et al, 2014). Eleven scenes of SAR data were enhanced using adaptive Gamma filtering (to remove speckles) and difference images created by subtracting from the reference nonflood season image.…”

A Comparison of Terrain Indices toward Their Ability in Assisting Surface Water Mapping from Sentinel-1 Data

“…Synthetic Aperture Radar (SAR) sensors have been widely applied in detecting and monitoring water bodies due to their suitable spatial resolution, as well as the ability to penetrate cloud and vegetation cover [5,6]. Until recently, SAR sensors had a relatively low temporal frequency (about 24-35 days for acquisitions in the same geometric configuration) [7], which hinders the intensive monitoring of surface water variation.…”

An evaluation of flood inundation mapping from MODIS and ALOS satellites for Pakistan

“…For example, the extent of flooding in the various river basins has been studied using quick-look mosaics provided by the SPOT satellite (Yan et al 2010;Gaurav et al 2011;Long et al 2014). Additionally, Xiao et al (2002) used the temporal normalized difference vegetation index and the normalized difference water index derived from the SPOT VEGETATION sensor to detect the inundation and transplantation of rice.…”