@article{UllmannSchmittRothetal.2014,
  author    = {Ullmann, Tobias and Schmitt, Andreas and Roth, Achim and Duffe, Jason and Dech, Stefan and Hubberten, Hans-Wolfgang and Baumhauer, Roland},
  title     = {Land Cover Characterization and Classification of Arctic Tundra Environments by Means of Polarized Synthetic Aperture X- and C-Band Radar (PolSAR) and Landsat 8 Multispectral Imagery — Richards Island, Canada},
  doi       = {10.3390/rs6098565},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-113303},
  year      = {2014},
  abstract  = {In this work the potential of polarimetric Synthetic Aperture Radar (PolSAR) data of dual-polarized TerraSAR-X (HH/VV) and quad-polarized Radarsat-2 was examined in combination with multispectral Landsat 8 data for unsupervised and supervised classification of tundra land cover types of Richards Island, Canada. The classification accuracies as well as the backscatter and reflectance characteristics were analyzed using reference data collected during three field work campaigns and include in situ data and high resolution airborne photography. The optical data offered an acceptable initial accuracy for the land cover classification. The overall accuracy was increased by the combination of PolSAR and optical data and was up to 71\% for unsupervised (Landsat 8 and TerraSAR-X) and up to 87\% for supervised classification (Landsat 8 and Radarsat-2) for five tundra land cover types. The decomposition features of the dual and quad-polarized data showed a high sensitivity for the non-vegetated substrate (dominant surface scattering) and wetland vegetation (dominant double bounce and volume scattering). These classes had high potential to be automatically detected with unsupervised classification techniques.},
  language  = {en}
}
@article{UllmannBuedelBaumhaueretal.2016,
  author    = {Ullmann, Tobias and B{\"u}del, Christian and Baumhauer, Roland and Padashi, Majid},
  title     = {Sentinel-1 SAR Data Revealing Fluvial Morphodynamics in Damghan (Iran): Amplitude and Coherence Change Detection},
  series = {International Journal of Earth Science and Geophysics},
  volume    = {2},
  journal   = {International Journal of Earth Science and Geophysics},
  number    = {1},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-147863},
  pages     = {007},
  year      = {2016},
  abstract  = {The Sentinel-1 Satellite (S-1) of ESA's Copernicus Mission delivers freely available C-Band Synthetic Aperture Radar (SAR) data that are suited for interferometric applications (InSAR). The high geometric resolution of less than fifteen meter and the large coverage offered by the Interferometric Wide Swath mode (IW) point to new perspectives on the comprehension and understanding of surface changes, the quantification and monitoring of dynamic processes, especially in arid regions. The contribution shows the application of S-1 intensities and InSAR coherences in time series analysis for the delineation of changes related to fluvial morphodynamics in Damghan, Iran. The investigations were carried out for the period from April to October 2015 and exhibit the potential of the S-1 data for the identification of surface disturbances, mass movements and fluvial channel activity in the surroundings of the Damghan Playa. The Amplitude Change Detection highlighted extensive material movement and accumulation - up to sizes of more than 4,000 m in width - in the east of the Playa via changes in intensity. Further, the Coherence Change Detection technique was capable to indicate small-scale channel activity of the drainage system that was neither recognizable in the S-1 intensity nor the multispectral Landsat-8 data. The run off caused a decorrelation of the SAR signals and a drop in coherence. Seen from a morphodynamic point of view, the results indicated a highly dynamic system and complex tempo-spatial patterns were observed that will be subject of future analysis. Additionally, the study revealed the necessity to collect independent reference data on fluvial activity in order to train and adjust the change detector.},
  language  = {en}
}
@article{TaubenboeckWurmNetzbandetal.2011,
  author    = {Taubenb{\"o}ck, H and Wurm, M and Netzband, M and Zwenzner, H and Roth, A and Rahman, A and Dech, S},
  title     = {Flood risks in urbanized areas - multi-sensoral approaches using remotely sensed data for risk assessment},
  series = {NATURAL HAZARDS AND EARTH SYSTEM SCIENCES},
  volume    = {11},
  journal   = {NATURAL HAZARDS AND EARTH SYSTEM SCIENCES},
  number    = {2},
  doi       = {10.5194/nhess-11-431-2011},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-139605},
  pages     = {431-444},
  year      = {2011},
  abstract  = {Estimating flood risks and managing disasters combines knowledge in climatology, meteorology, hydrology, hydraulic engineering, statistics, planning and geography - thus a complex multi-faceted problem. This study focuses on the capabilities of multi-source remote sensing data to support decision-making before, during and after a flood event. With our focus on urbanized areas, sample methods and applications show multi-scale products from the hazard and vulnerability perspective of the risk framework. From the hazard side, we present capabilities with which to assess flood-prone areas before an expected disaster. Then we map the spatial impact during or after a flood and finally, we analyze damage grades after a flood disaster. From the vulnerability side, we monitor urbanization over time on an urban footprint level, classify urban structures on an individual building level, assess building stability and quantify probably affected people. The results show a large database for sustainable development and for developing mitigation strategies, ad-hoc coordination of relief measures and organizing rehabilitation.},
  language  = {en}
}
@article{NguyenKerstenSenmaoetal.2015,
  author    = {Nguyen, Duy Ba and Kersten, Clauss and Senmao, Cao and Vahid, Naeimi and Kuenzer, Claudia and Wagner, Wolfgang},
  title     = {Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data},
  series = {Remote Sensing},
  volume    = {7},
  journal   = {Remote Sensing},
  number    = {12},
  doi       = {10.3390/rs71215808},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-137554},
  pages     = {15868-15893},
  year      = {2015},
  abstract  = {Rice is the most important food crop in Asia, and the timely mapping and monitoring of paddy rice fields subsequently emerged as an important task in the context of food security and modelling of greenhouse gas emissions. Rice growth has a distinct influence on Synthetic Aperture Radar (SAR) backscatter images, and time-series analysis of C-band images has been successfully employed to map rice fields. The poor data availability on regional scales is a major drawback of this method. We devised an approach to classify paddy rice with the use of all available Envisat ASAR WSM (Advanced Synthetic Aperture Radar Wide Swath Mode) data for our study area, the Mekong Delta in Vietnam. We used regression-based incidence angle normalization and temporal averaging to combine acquisitions from multiple tracks and years. A crop phenology-based classifier has been applied to this time series to detect single-, double- and triple-cropped rice areas (one to three harvests per year), as well as dates and lengths of growing seasons. Our classification has an overall accuracy of 85.3\% and a kappa coefficient of 0.74 compared to a reference dataset and correlates highly with official rice area statistics at the provincial level (R-2 of 0.98). SAR-based time-series analysis allows accurate mapping and monitoring of rice areas even under adverse atmospheric conditions.},
  language  = {en}
}