A Multi-Scale Flood Monitoring System Based on Fully Automatic MODIS and TerraSAR-X Processing Chains

Abstract

A two-component fully automated flood monitoring system is described and evaluated. This is a result of combining two individual flood services that are currently under development at DLR's (German Aerospace Center) Center for Satellite based Crisis Information (ZKI) to rapidly support disaster management activities. A first-phase monitoring component of the system systematically detects potential flood events on a continental scale using daily-acquired medium spatial resolution optical data from the Moderate Resolution Imaging Spectroradiometer (MODIS). A threshold set controls the activation of the second-phase crisis component of the system, which derives flood information at higher spatial detail using a Synthetic Aperture Radar (SAR) based satellite mission (TerraSAR-X). The proposed activation procedure finds use in the identification of flood situations in different spatial resolutions and in the time-critical and on demand programming of SAR satellite acquisitions at an early stage of an evolving flood situation. The automated processing chains of the MODIS (MFS) and the TerraSAR-X Flood Service (TFS) include data pre-processing, the computation and adaptation of global auxiliary data, thematic classification, and the subsequent dissemination of flood maps using an interactive web-client. The system is operationally demonstrated and evaluated via the monitoring two recent flood events in Russia 2013 and Albania/Montenegro 2013.

MODIS-based multi-parametric platform for mapping of flood affected areas. Case study: 2006 Danube extreme flood in Romania

“…Overall, the use of coarse resolution MODIS data is suitable to map relatively large floods in plain areas and not recommended for small floods in complex landscapes (built-up areas, small agriculture parcels with different crops, forested areas, hilly or mountain areas, etc.). These results are consistent with the ones obtained in other studies (Martinis et al, 2013;Nigro et al, 2014;Ticehurst et al, 2015). Higher resolution, optical and SAR (Synthetic Aperture Radar) satellite images are able to provide more accurate information for flood delineation (Schuman et al, 2009).…”

“…MODIS sensors are also able to provide useful information for hydrodynamic modelling at large regional/basin scales (Ticehurst et al, 2013). The German Aerospace Center (DLR) developed an automated flood monitoring system that is using MODIS data to detect potential flood events on a continental scale and derives flood information in combination with the high resolution Synthetic Aperture Radar (SAR) data provided by TerraSAR-X satellite (Martinis et al, 2013). A more recent study (Coltin et al, 2016) demonstrated the feasibility of an effective automatic flood mapping system using MODIS imagery and machine learning algorithms.…”

Review article: Detection of inundation areas due to the 2015 Kanto and Tohoku torrential rain in Japan based on multi-temporal ALOS-2 imagery

“…SAR images are effective for extracting inundation areas. Several methods, both pixel-and object-based, have been proposed to extract inundation zones from SAR images (Martinis et al, 2009(Martinis et al, , 2013Hoque et al, 2011;Manjusree et al, 2012;Pulvirenti et al, 2014;Kundu et al, 2015;Nakmuenwai et al, 2017). Thresholding is a common and effective pixel-based approach.…”

“…It is difficult to judge the most suitable value objectively without additional information. Automated thresholding methods using the graylevel histogram have been introduced to overcome this issue (Fan and Lei, 2012;Martinis et al, 2009Martinis et al, , 2013Pulvirenti et al, 2011;Nakmuenwai et al, 2017). The global threshold value was merged from several local threshold values, which were obtained from the multimodal histograms of sub-areas.…”

A Fuzzy Logic-Based Approach for the Detection of Flooded Vegetation by Means of Synthetic Aperture Radar Data

“…The update for the land cover map is performed during the five-year utilization period, whereby the state for data-topicality is two years in relation to the download time on January 2015. Flooded open water areas were provided by a fully automated TerraSAR-X based flood service for reliable delimitation to other land cover areas (Martinis et al 2013). This service can be activated for emergency response in case of a flood event and is based on an unsupervised thresholding approach using TerraSAR-X data, suitable for rapid mapping of inundated areas (Martinis et al 2009).…”