Flood Detection and Mapping Using Microwave Remote Sensing; A Case Study on Lake Koka Cachment, Awash River Basin, Ethiopia
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Date
2017-07
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Addis Ababa Universty
Abstract
Sentinel-1 is a microwave remote sensing mission providing continuous all-weather and day-night time radar data. The main goal of the present study is to evaluate microwave remote sensing data for flood detection and to develop the flood extent map from a series of radar SAR images. The study area is on Lake Koka catchment, Awash River basin which has an increased agricultural investment interests. This area was frequently affected by flood during the “belg” and summer seasons in 2016 caused by the over flow of Awash River and the flash flood of the surrounding tributary streams. For the present study, Sentinel-1 SAR time series images, covering the same scene but at different times were utilized in order to achieve the research objectives. These images were: i) before flooding i.e. acquired on 22 March, 2016 and ii) after the flood event; acquired on 15 April and 09 May, 2016. The images were de-speckled using various filtering algorisms. After comparison of the image quality based on the algorisms, the gamma map 77 kernel size speckle filtering method was selected and used as speckle removal for the study. The backscatter properties of five different feature classes in the context of flood extent extraction were derived from time series SAR images. These feature classes were open water, flooded area, agriculture, vegetation and bare soil. From such backscatter properties of test class features on the SAR image, appropriate change detection threshold value was set by visual interpretation and image histogram analysis. Based on the threshold value the changed and unchanged areas were identified for inundated area delineation. Change detection algorithms were applied to extract the flood extent from the processed SAR images. Of all other change detection methods, the band subtraction, band ratioing and principal component differencing (PCD) techniques were utilized. The results of each technique was compared with one another. The band subtraction and band rationg algorithms showed similar flood extent map. The flood extent extracted from band subtraction method was 24.12 km2 for 15 April, 2016 and 17.63 km2 for 09 May, 2016 flood events. The band ratio method has resulted 23.22 km2 and 17.3 km2 flood extent of 15 April, 2016 and 09 May, 2016 respectively. The other method, the PCD accounted for 20.67 km2 area of 15 April, 2016 and 15.7 km2 of 09 May, 2016 flood extent. The flood extent maps were presented separately for each flood detection method. The SAR images was also used for land-use/land-cover classification with Landsat 8 optical sensor image. Based on these stacked different sensor images, the land-use/land-cover of the study area was classified in to six classes. Theses six land-cover classes were; 1) agriculture field, 2) bare land, 3) irrigated land, 4) water body, 5) settlement and 6) vegetation. The overall classification accuracy was 90% with 0.86 kappa statistics value. Generally, this research has observed that space-born SAR satellite data is an outstanding technology for near real time flood detection and mapping. It provided promising flood extent map that could help in the preparation of flood monitoring and management processes.
Keywords: Microwave remote sensing, Sentinel-1, SAR, Awash River, Change detection, Flood, Backscatter analysis, Speckle filtering
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Keywords
Microwave remote sensing, Sentinel-1, SAR, Awash River, Change detection, Flood, Backscatter analysis, Speckle filtering