Enrico Stein

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.

Can the Future EnMAP Mission Contribute to Urban Applications? A Literature Survey

With urban populations and their footprints growing globally, the need to assess the dynamics of the urban environment increases. Remote sensing is one approach that can analyze these developments quantitatively with respect to spatially and temporally large scale changes. With the 2015 launch of the spaceborne EnMAP mission, a new hyperspectral sensor with high signal-to-noise ratio at medium spatial resolution, and a 21 day global revisit capability will become available. This paper presents the results of a literature survey on existing applications and image analysis techniques in the context of urban remote sensing in order to identify and outline potential contributions of the future EnMAP mission. Regarding urban applications, four frequently addressed topics have been identified: urban development and planning, urban growth assessment, risk and vulnerability assessment and urban climate. The requirements of four application fields and associated image processing techniques used to retrieve desired parameters and create geo-information products have been reviewed. As a result, we identified promising research directions enabling the use of EnMAP for urban studies. First and foremost, research is required to analyze the spectral information content of an EnMAP pixel used to support material-based land cover mapping approaches. This information can subsequently be used to improve urban indicators, such as imperviousness. Second, we identified the global monitoring of urban areas as a promising field of investigation taking advantage of EnMAP’s spatial coverage and revisit capability. However, owing to the limitations of EnMAPs spatial resolution for urban applications, research should also focus on hyperspectral resolution enhancement to enable retrieving material information on sub-pixel level.

A Method for Detecting Buildings Destroyed by the 2011 Tohoku Earthquake and Tsunami Using Multitemporal TerraSAR-X Data

In this letter, a new approach is proposed to classify tsunami-induced building damage into multiple classes using pre- and post-event high-resolution radar (TerraSAR-X) data. Buildings affected by the 2011 Tohoku earthquake and tsunami were the focus in developing this method. In synthetic aperture radar (SAR) data, buildings exhibit high backscattering caused by double-bounce reflection and layover. However, if the buildings are completely washed away or structurally destroyed by the tsunami, then this high backscattering might be reduced, and the post-event SAR data will show a lower sigma nought value than the pre-event SAR data. To exploit these relationships, a rapid method for classifying tsunami-induced building damage into multiple classes was developed by analyzing the statistical relationship between the change ratios in areas with high backscattering and in areas with building damage. The method was developed for the affected city of Sendai, Japan, based on the decision tree application of a machine learning algorithm. The results provided an overall accuracy of 67.4% and a kappa statistic of 0.47. To validate its transferability, the method was applied to the town of Watari, and an overall accuracy of 58.7% and a kappa statistic of 0.38 were obtained.

Contribution of earth observation and modelling to disaster response management: Methodological developments and recent examples

The paper outlines new research findings and hereof generated products in the field of earth observation and modeling technologies to support emergency response measures. Based on the recent earthquake and tsunami disaster in Japan (March 2011) examples will be given for new methodological developments and products to support emergency response strategies more effectively.

The Integration of an Operational Fire Hot Spots Processing Chain in a Multi-Hazard Emergency Management Service Platform (PHAROS)

The project PHAROS (Project on a Multi-Hazard Open Platform for Satellite Based Downstream Services) designs and implements a multi-hazard open service platform which integrates space-based earth observation, satellite communications and navigation (Galileo/GNSS) assets to provide sustainable (pre-operational) services for a wide variety of users in multi-application domains, such as prediction/early detection of emergencies, population alerting, environmental monitoring and crisis management. While the service platform is designed to be multi-hazard, the specific developments for the pre-operational system and pilot demonstration will be focused on the forest fire scenario. The platform will integrate data from EO satellites and in-situ sensors process it and provide the results to a series of key services for disaster management in its different phases. One of the main concerns is to provide fire hot spots as an input for the PHAROS Simulation Service. These fire hot spots (thermal anomalies) are derived automatically and in near real time (NRT) from MODIS data. The MODIS data are available in a high (1d) temporal and in a medium (250m – 1000m) spatial resolution. For the detection of high temperature events (HTE) the MOD14 algorithm is used. The algorithm is based on the shift of the radiances/reflectance to shorter wavelengths (middle infrared) with an increasing surface temperature. MOD14 is well documented and tested in operational services and guarantees comparability and reproducibility as well as a standardized international acknowledged product. The thermal information is collected at 1000 m spatial resolution twice daily by each sensor (Terra and Aqua) providing up to four thermal observations daily. The MODIS images used for fire detection are acquired from two direct broadcast receiving stations from DLR located in Oberpfaffenhofen and Neustrelitz (Germany). This Poster will give an overview of the processing chain from the reception, the processing and derivation of the fire hot spots to the dissemination in the Pharos system.

Detecting building damage caused by the 2011 Tohoku earthquake tsunami using TerraSAR-X data

In this study, a semi-automated method to estimate building damage in a tsunami affected area is developed using pre- and post-event high-resolution synthetic aperture radar (TerraSAR-X) data. For development, some coastal areas affected by the 2011 Tohoku earthquake tsunami were focused. The method for estimating building damage consists of three steps, 1) To detect flooded areas by the tsunami, 2) To detect built-up areas, 3) To estimate building damage inside the flooded built-up areas. The previously proposed methods using high-resolution SAR data needs building footprint data for estimating building damage[1]. However, this problem was improved by developing a new method which does not need building footprint data to estimate building damage caused by the tsunami. The developed method was validated on the other test sites and the estimated results showed good consistency with the ground truth data.