Juliane Huth

Flood Mapping and Flood Dynamics of the Mekong Delta: ENVISAT-ASAR-WSM Based Time Series Analyses

Abstract : Satellite remote sensing is a valuable tool for monitoring flooding. Microwave sensors are especially appropriate instruments, as they allow the differentiation of inundated from non-inundated areas, regardless of levels of solar illumination or frequency of cloud cover in regions experiencing substantial rainy seasons. In the current study we present the longest synthetic aperture radar-based time series of flood and inundation information derived for the Mekong Delta that has been analyzed for this region so far. We employed overall 60 Envisat ASAR Wide Swath Mode data sets at a spatial resolution of 150 meters acquired during the years 2007–2011 to facilitate a thorough understanding of the flood regime in the Mekong Delta. The Mekong Delta in southern Vietnam comprises 13 provinces and is home to 18 million inhabitants. Extreme dry seasons from late December to May and wet seasons from June to December characterize people’s rural life. In this study, we show which areas of the delta are frequently affected by floods and which regions remain dry all year round. Furthermore, we present which areas are flooded at which frequency and elucidate the patterns of flood progression over the course of the rainy season. In this context, we also examine the impact of dykes on floodwater emergence and assess the relationship between retrieved flood occurrence patterns and land use. In addition, the advantages and shortcomings of ENVISAT ASAR-WSM based flood mapping are discussed. The results contribute to a comprehensive understanding of Mekong Delta flood

Land Cover and Land Use Classification with TWOPAC: towards Automated Processing for Pixel- and Object-Based Image Classification

We present a novel and innovative automated processing environment for the derivation of land cover (LC) and land use (LU) information. This processing framework named TWOPAC (TWinned Object and Pixel based Automated classification Chain) enables the standardized, independent, user-friendly, and comparable derivation of LC and LU information, with minimized manual classification labor. TWOPAC allows classification of multi-spectral and multi-temporal remote sensing imagery from different sensor types. TWOPAC enables not only pixel-based classification, but also allows classification based on object-based characteristics. Classification is based on a Decision Tree approach (DT) for which the well-known C5.0 code has been implemented, which builds decision trees based on the concept of information entropy. TWOPAC enables automatic generation of the decision tree classifier based on a C5.0-retrieved ascii-file, as well as fully automatic validation of the classification output via sample based accuracy assessment.Envisaging the automated generation of standardized land cover products, as well as area-wide classification of large amounts of data in preferably a short processing time, standardized interfaces for process control, Web Processing Services (WPS), as introduced by the Open Geospatial Consortium (OGC), are utilized. TWOPAC’s functionality to process geospatial raster or vector data via web resources (server, network) enables TWOPAC’s usability independent of any commercial client or desktop software and allows for large scale data processing on servers. Furthermore, the components of TWOPAC were built-up using open source code components and are implemented as a plug-in for Quantum GIS software for easy handling of the classification process from the user’s perspective.

Multi-sensoral and automated derivation of inundated areas using TerraSAR-X and ENVISAT ASAR data

During recent years, synthetic aperture radar (SAR) data have been increasingly used for flood mapping. New radar satellites especially, such as TerraSAR-X, Radarsat-2 and COSMO-SkyMed, provide high-resolution data with high potential for fast and reliable detection of inundated areas. This article compares three simple approaches to derive water areas from SAR data in relation to the German–Vietnamese project, Water-related Information System for the Sustainable Development of the Mekong Delta (WISDOM). Two methods are pixel based and use histogram-based grey-level thresholds, as well as a homogeneity criterion for classification. The third approach is object based and applies characteristic attributes of water objects such as grey value, texture and relations to neighbouring objects. Further discussed are the influence of a variation of the thresholds and the challenges to validate water masks derived from active remote-sensing data. We implemented one of the introduced approaches for surface water derivation in a water mask processor for automatic water mask calculation from radar satellite imagery (WaMaPro). This fully automatic processing chain was developed to process TerraSAR-X and Environmental Satellite Advanced Synthetic Aperture Radar (ENVISAT ASAR) imagery in order to meet the demands for automatic flood monitoring.

Comparing four operational SAR-based water and flood detection approaches

In recent years, the German Remote Sensing Data Center (DFD) of the German Aerospace Center (DLR) has gained a lot of experience in water surface extraction from synthetic aperture radar (SAR) data for various application domains. In this context, four approaches have been developed, which jointly form the so-called DFD Water Suite: The Water Mask Processor (WaMaPro) is based on a simple and high-performance algorithm that processes multi-sensor SAR data in order to provide decision-makers with information about the location of water surfaces. The Rapid Mapping of Flooding tool (RaMaFlood) has been developed for flood extent mapping using an interactive object-based classification algorithm. The TerraSAR-X Flood Service (TFS) is used for rapid mapping activities and provides satellite-derived information about the extent of floods in order to support emergency management authorities and decision-makers. It is based on a fully automated processing chain. The last approach is the TanDEM-X Water Indication Mask processor (TDX WAM). It is part of the processing chain for the generation of the seamless, accurate, and high-resolution global digital elevation model (DEM) produced based on data of the TanDEM-X mission. Its purpose is to support the subsequent DEM editing process by the generation of a global reference water mask. In this study, the design of the four approaches and their methodological backgrounds are explained in detail, while simultaneously elaborating on the preferred application domains for the different algorithms. The advantages and disadvantages of the four approaches are identified by qualitatively as well as quantitatively evaluating the water masks derived from data of the TanDEM-X mission for five test sites located in Vietnam, China, Germany, Mali, and the Netherlands.

A comparison of TerraSAR-X Quadpol backscattering with RapidEye multispectral vegetation indices over rice fields in the Mekong Delta, Vietnam

Satellite-based multispectral imagery and/or synthetic aperture radar (SAR) data have been widely used for vegetation characterization, plant physiological parameter estimation, crop monitoring or even yield prediction. However, the potential use of satellite-based X-band SAR data for these purposes is not fully understood. A new generation of X-band radar satellite sensors offers high spatial resolution images with different polarizations and, therefore, constitutes a valuable information source. In this study, we utilized a TerraSAR-X satellite scene recorded during a short experimental phase when the sensor was running in full polarimetric ‘Quadpol’ mode. The radar backscatter signals were compared with a RapidEye reference data set to investigate the potential relationship of TerraSAR-X backscatter signals to multispectral vegetation indices and to quantify the benefits of TerraSAR-X Quadpol data over standard dual- or single-polarization modes. The satellite scenes used cover parts of the Mekong Delta, the rice bowl of Vietnam, one of the major rice exporters in the world and one of the regions most vulnerable to climate change. The use of radar imagery is especially advantageous over optical data in tropical regions because the availability of cloudless optical data sets may be limited to only a few days per year. We found no significant correlations between radar backscatter and optical vegetation indices in pixel-based comparisons. VV and cross-polarized images showed significant correlations with combined spectral indices, the modified chlorophyll absorption ratio index/second modified triangular vegetation index (MCARI/MTVI2) and transformed chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index (TCARI/OSAVI), when compared on an object basis. No correlations between radar backscattering at any polarization and the normalized difference vegetation index (NDVI) were observed.

SAR Time Series for the Analysis of Inundation Patterns in the Yellow River Delta, China

Earth Observation using radar remote sensing is a valuable tool for the monitoring large scale inundation over time. This study performs a time series analysis using 18 ENVISAT/ASAR Wide Swath Mode data sets for the year 2008 and 13 TerraSAR-X Stripmap data sets for the year 2013/2014 to characterize inundation patterns in the Yellow River Delta, located in Shandong Province of China. Water surfaces are automatically derived using the software package WaMaPro, developed at the German Remote Sensing Data Center (DFD), of the German Aerospace Center (DLR), which allows an automatic classification using empirical thresholding. The temporal analysis allows the separation of different types of water bodies such as rivers, water storage basins, aquaculture, brine ponds, and agricultural fields based on inundation frequencies. This supports the understanding of the water dynamics in this highly variable study region. As ENVISAT data is not available anymore since April 2012, and as access to TerraSAR-X data is limited, Sentinel-1 data of the European Space Agency, ESA, are eagerly expected for the region. The good spatial resolution between 40 up to 5 m, as well as a dense temporal coverage, which allow to generate “true” SAR time series, and will help to lift annual analyses to the next level.

Research Centre for Environmental Information Science (RCEIS)

The Research Centre for Environmental Information Science (RCEIS) was established in March 2014. In the long-term vision RCEIS shall become a Sino-German competence centre and research platform for Earth systems observation and modelling by combining expertise in the fields of environmental and information sciences using modern information technology. As a scientific goal, RCEIS will develop novel concepts by using infrastructures for Earth system observation and analysis in order to better understand the evolution and dynamics of environmental systems under global change and globalization (anthropogenic pressure). An important methodology is the further development and combination of different infrastructures for Earth system observation, e.g. combining information from remote sensing (satellites and airborne) and “ground-truth” measurements. Moreover, monitoring (remote sensing and monitoring) and modelling platforms (multi-sphere/compartment, cycle-oriented) need to be combined.

A Water Related Information System for the Sustainable Development of the Mekong Delta: Experiences of the German-Vietnamese WISDOM Project

This chapter presents the evolvement of an environmental information system, built for the Mekong Delta, in the context of the German-Vietnamese research project WISDOM (Water related Information System for the Sustainable Development of the Mekong Delta). The WISDOM project (2007–2014) belonged to a group of Integrated Water Resources Management, IWRM, projects, funded by the German Ministry of Education and Science, BMBF, on the German side, and the Vietnamese Ministry of Science and Technology, MOST, on the Vietnamese side. Goal of the multi-disciplinary project has been to contribute to numerous knowledge gaps existing for the Mekong Delta. Applied research questions from the fields of hydrology, hydro-morphology, chemistry, geography, ecology, biology, socio-economy, as well as administration and law were addressed by a large group of PhD students and post-doctoral researchers active in the project. One goal and also one—but not the—central element of the project has been the design of a water related information system, which can serve as a planning aid for decision makers and stakeholders in the delta. At the same time the freely and online available, bilingual (English and Vietnamese) WISDOM Information System, serves as a central project hub, which ensures that the majority of project findings that come in the form of geodata, in situ measurement collections, maps, statistics, reports, or scientific publications is available to the public. In this paper, geographic background and challenges of the focus area, project set-up, Information System design, components realized, training measures undertaken, as well as general experiences when realizing large projects in emerging countries are elucidated and discussed.