A multi-scale flood monitoring system based on fully automatic MODIS and TerraSAR-X processing chains

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

Synergistic use of optical and radar data for rapid mapping of forest fires in the European Mediterranean

In a classical approach, optical data are being used for forest fire detection in a rush mode. Difficulties arise due to persistent cloud coverage, haze layers and smoke plumes. In contrast, radar measurements offer high acquisition rates because of their ability to penetrate clouds and their independence of sun illumination. However, a visual interpretation of radar data is generally less intuitive than optical imagery for an untrained image analyst. Thus the main focus of our work was to combine the advantages of both data types and to develop a robust and fast but at the same time precise and transferable algorithm for burned area detection in the European Mediterranean region. Object-based change detection approaches and a synergistic use of optical and radar data can improve detection capabilities. The optical part of the algorithm covers very high resolution satellite images (like SPOT 5) including index calculation such as MSAVI, BAI and NDSWIR in single-temporal approaches and their temporal differences in multi-temporal approaches. Within the scope of both methodologies the burned area can be detected with an accuracy higher than 90%. In line with other authors (Libonati et al., 2011; Pereira et al., 1999) our work confirms the middle infrared band as crucial for burned area detection. The radar algorithm was based on TerraSAR-X StripMap data acquired before and after the forest fires. Different polarisations (VV and HH) have been used to improve the forest fire mapping capability. In addition, a comparison of burned and unburned areas was performed using different backscatter coefficients. These change detection techniques were based on image differences, image ratios and index calculation. The image segmentation was performed by using the new calculated layers. The burned area was then classified via a threshold given by the pre- and post- disaster differences. The classification result achieved an accuracy of 78%. This result shows the limitations of burned area mapping with microwaves. Therefore a combination of the optical and the radar technique, which takes advantage of both the optical accuracy and the ability of microwaves to penetrate clouds, led to the design and implementation of a single- and multi-temporal, object-based and semi-operational tool for burned area mapping

Strategies for the automatic mapping of flooded areas and other water bodies from high resolution TerraSAR-X data

Medium resolution SAR satellite data have been widely used for water and flood mapping in recent years. Since the beginning of 2008 high resolution radar data with up to one meter pixel spacing of the TerraSAR-X satellite are operationally available. The improved ground resolution of the system offers a high potential for water detection. However, image analysis gets more challenging due to the large amount of image objects that are visible in the data. Water body detection methods are reviewed with regard to their applicability for TerraSAR-X data. The concept for the water body detection in the scope of the TanDEM-X mission is shown. Finally flood detection approaches for rapid disaster mapping are presented in this paper along with a flood map example

Extraction of water and flood areas from SAR data

Medium resolution SAR satellite data have been widely used for water and flood mapping in recent years. Since 2007 high resolution radar data with up to 1 m pixel spacing of the TerraSAR-X satellite are operationally avail-able. The improved ground resolution of the system offers enormous potential for water detection. However, im-age analysis gets more challenging due to the large amount of image objects that are visible in the data. Water body detection methods are reviewed with regard to their applicability for TerraSAR-X data. Flood detection approaches for rapid disaster mapping are presented in this paper

SAR-HQ - Methoden zur Erfassung und Analyse von großflächigen Hochwasserereignissen mittels hochauflösender Radardaten - Schlussbericht

Ziel von SAR-HQ war es, dedizierte Methoden zur Hochwasserdetektion und Schadensabschätzung zu entwickeln und hierbei die Anwendbarkeit von hochauflösenden X-Band Radardaten zu untersuchen und zu verbessern. Da Überflutungsereignisse in der Regel von starker Wolkenbedeckung begleitet werden, sind wetterunabhängige SAR (Synthetic Aperture Radar)-Fernerkundungsplattformen besonders geeignet um schnell, zuverlässig/wiederholbar und kostengünstig Informationen von Überschwemmungsgebieten zu erlangen. Obwohl bestehende C-Band gestützte Radarplattformen (ERS-2, ENVISAT ASAR, RADARSAT) ihre Nützlichkeit für die Kartierung großflächiger Hochwasserereignisse bereits bewiesen haben, weisen sie für die Extraktion von Hochwassermasken in komplexen bzw. kleinräumigen Szenarien, insbesondere urbanen Gebieten, deutliche Einschränkungen auf. Erst die neuen europäischen X-Band Radarsatelliten TerraSAR-X und Cosmo-SkyMed ermöglichen es, wetterunabhängig, räumlich flächendeckend und zeitlich wiederholbar Überschwemmungsflächen und Schäden in sehr hoher räumlicher Auflösung zu erfassen. Da eine effiziente Hochwasserkartierung Datenaufnahmen erfordert, die möglichst nahe am Zeitpunkt des maximalen Pegelstandes liegen, kann gerade die synergetische Nutzung mehrerer Satellitenplattformen die zeitliche Auflösung und Reaktionsfähigkeit entscheidend verbessern. Um den Erfordernissen und Möglichkeiten der neuen Radarsatelliten gerecht zu werden, wurden im Rahmen des Projektes angepasste Prozessierungs- und Analysetechniken für diese neue Gattung von Radarsatellitendaten entwickelt. Das Projekt wurde auf eine Einbindung der erstellten Methoden in operationelle Arbeitsabläufe der Datenprozessierung und Datenauswertung ausgerichtet, mit dem Ziel, eine schnelle und zuverlässige Bereitstellung von hochgenauen Kriseninformationen zu gewährleisten. Durch die Kombination von aus Radardaten abgeleiten Hochwassermasken mit zusätzlichen Datenquellen wie topographischen Karten oder digitalen Geländemodellen wurden Informationsprodukte generiert, die für Krisenmanagement, Risikoabschätzung und Wiederaufbau von zentraler Bedeutung sein können

Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data

In this paper, an automatic near-real time (NRT) flood detection approach is presented, which combines histogram thresholding and segmentation based classification, specifically oriented to the analysis of single-polarized very high resolution SAR satellite data. The challenge of SAR-based flood detection is addressed in a completely unsupervised way, which assumes no training data and therefore no prior information about the class statistics to be available concerning the area of investigation. This is usually the case in NRT-disaster management, where the collection of ground truth information is not feasible due to time-constraints. A simple thresholding algorithm can be used in the bulk of the cases to distinguish between the classes ‘flood’ and ‘non-flood’ in a high resolution SAR image to detect the largest part of an inundation area. Due to the fact that local gray-level changes may not be distinguished by global thresholding techniques in large satellite scenes the thresholding algorithm is integrated into a split-based approach for the derivation of a global threshold by the analysis and combination of the split inherent information. The derived global threshold is then integrated into a multi-scale segmentation step combining the advantages of small-, medium-and large-scale per parcel segmentation. Experimental investigations performed on a TerraSAR-X Stripmap scene from southwest England during heavy inundations in the year 2007 confirm the effectiveness of the proposed split-based approach in combination with image segmentation and optional integration of digital elevation models

Landslide Mapping in Vegetated Areas Using Change Detection Based on Optical and Polarimetric SAR Data

Mapping of landslides, quickly providing information about the extent of the affected area and type and grade of damage, is crucial to enable fast crisis response, i.e., to support rescue and humanitarian operations. Most synthetic aperture radar (SAR) data-based landslide detection approaches reported in the literature use change detection techniques, requiring very high resolution (VHR) SAR imagery acquired shortly before the landslide event, which is commonly not available. Modern VHR SAR missions, e.g., Radarsat-2, TerraSAR-X, or COSMO-SkyMed, do not systematically cover the entire world, due to limitations in onboard disk space and downlink transmission rates. Here, we present a fast and transferable procedure for mapping of landslides, based on change detection between pre-event optical imagery and the polarimetric entropy derived from post-event VHR polarimetric SAR data. Pre-event information is derived from high resolution optical imagery of Landsat-8 or Sentinel-2, which are freely available and systematically acquired over the entire Earth’s landmass. The landslide mapping is refined by slope information from a digital elevation model generated from bi-static TanDEM-X imagery. The methodology was successfully applied to two landslide events of different characteristics: A rotational slide near Charleston, West Virginia, USA and a mining waste earthflow near Bolshaya Talda, Russia

Rapid Damage Assessment and Situation Mapping: Learning from the 2010 Haiti Earthquake

The paper reports on the activity of the Center for Satellite based Crisis Information (ZKI) of the German Aerospace Center (DLR) in the aftermath of the devastating earthquake in Port-au-Prince, Haiti on 12 January 2010. DLR/ZKI closely coordinated with the European Global Monitoring for Environment and Security (GMES) program and the International Charter Space and Major Disasters. All DLR/ZKI damage maps are based on a specific analysis approach, including pre-processing procedures and visual interpretation on a grid-basis. As the satellite-based mapping response globally was so extensive for this event, problems resulting from the large number and inconsistency of satellite maps generated internationally are addressed. In order to avoid this kind of "mapping challenge" in future the setting-up of an international working group to elaborate global guidelines and cooperation procedures for better coherence of international satellite rapid mapping efforts for extreme events such as the Haiti earthquake is suggested

Unsupervised flood detection in X-band SAR data using multi-contextual Markov image modeling on irregular graphs

The worldwide increasing occurrence of flooding and the short-time monitoring capability of the new generation of high resolution synthetic aperture radar (SAR) sensors require accurate and automatic methods for the detection of inundations. This is especially important for operational rapid mapping purposes where the fast provision of precise information about the extent of a disaster and its spatio-temporal evolution is of key importance for decision makers and humanitarian relief organizations. In this contribution, a hybrid multi-contextual Markov model for unsupervised near real-time flood detection in multi-temporal X-band synthetic aperture radar (SAR) data is presented. It incorporates scale-dependent as well as spatio-temporal contextual information into the classification process by combining causal with noncausal Markov image modeling related to hierarchical directed and planar un-directed graphs, respectively. Hierarchical Markov modeling is accomplished by hierarchical marginal posterior mode (HMPM) estimation using Markov Chains in scale. This model is initialized by an automatic tile-based thresholding algorithm, to differentiate between open water, flooded vegetation and dry land areas in a completely unsupervised manner. In order to increase computational performance, marginal posterior-based entropies are used for restricting the iterative bi-directional exchange of spatio-temporal information between consecutive images of a time sequence to image elements, exhibiting a low probability to be classified correctly according to the HMPM estimation. The Markov image models, originally developed for inference on regular graph structures of quadtrees and planar lattices, are adapted to the variable nature of irregular graphs, which are related to information driven image segmentation. With respect to accuracy and computational effort, experiments performed on a bi-temporal TerraSAR-X ScanSAR data-set from Lake Liambezi in Namibia during flooding in 2009 and 2010 confirm the effectiveness of integrating hierarchical as well as spatio-temporal context into the labeling process, and of adapting the models to irregular graph structures