@misc{MalinowskiGroomSchwanghartetal.2017,
  author    = {Malinowski, Radosław and Groom, Geoff and Schwanghart, Wolfgang and Heckrath, Goswin},
  title     = {Detection and delineation of localized flooding from WorldView-2 multispectral data},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-400149},
  pages     = {23},
  year      = {2017},
  abstract  = {Remote sensing technology serves as a powerful tool for analyzing geospatial characteristics of flood inundation events at various scales. However, the performance of remote sensing methods depends heavily on the flood characteristics and landscape settings. Difficulties might be encountered in mapping the extent of localized flooding with shallow water on riverine floodplain areas, where patches of herbaceous vegetation are interspersed with open water surfaces. To address the difficulties in mapping inundation on areas with complex water and vegetation compositions, a high spatial resolution dataset has to be used to reduce the problem of mixed pixels. The main objective of our study was to investigate the possibilities of using a single date WorldView-2 image of very high spatial resolution and supporting data to analyze spatial patterns of localized flooding on a riverine floodplain. We used a decision tree algorithm with various combinations of input variables including spectral bands of the WorldView-2 image, selected spectral indices dedicated to mapping water surfaces and vegetation, and topographic data. The overall accuracies of the twelve flood extent maps derived with the decision tree method and performed on both pixels and image objects ranged between 77\% and 95\%. The highest mapping overall accuracy was achieved with a method that utilized all available input data and the object-based image analysis. Our study demonstrates the possibility of using single date WorldView-2 data for analyzing flooding events at high spatial detail despite the absence of spectral bands from the short-waveform region that are frequently used in water related studies. Our study also highlights the importance of topographic data in inundation analyses. The greatest difficulties were met in mapping water surfaces under dense canopy herbaceous vegetation, due to limited water surface exposure and the dominance of vegetation reflectance.},
  language  = {en}
}
@article{MalinowskiGroomSchwanghartetal.2015,
  author    = {Malinowski, Radoslaw and Groom, Geoff and Schwanghart, Wolfgang and Heckrath, Goswin},
  title     = {Detection and Delineation of Localized Flooding from World View-2 Multispectral Data},
  series = {Remote sensing},
  volume    = {7},
  journal   = {Remote sensing},
  number    = {11},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2072-4292},
  doi       = {10.3390/rs71114853},
  pages     = {14853 -- 14875},
  year      = {2015},
  abstract  = {Remote sensing technology serves as a powerful tool for analyzing geospatial characteristics of flood inundation events at various scales. However, the performance of remote sensing methods depends heavily on the flood characteristics and landscape settings. Difficulties might be encountered in mapping the extent of localized flooding with shallow water on riverine floodplain areas, where patches of herbaceous vegetation are interspersed with open water surfaces. To address the difficulties in mapping inundation on areas with complex water and vegetation compositions, a high spatial resolution dataset has to be used to reduce the problem of mixed pixels. The main objective of our study was to investigate the possibilities of using a single date WorldView-2 image of very high spatial resolution and supporting data to analyze spatial patterns of localized flooding on a riverine floodplain. We used a decision tree algorithm with various combinations of input variables including spectral bands of the WorldView-2 image, selected spectral indices dedicated to mapping water surfaces and vegetation, and topographic data. The overall accuracies of the twelve flood extent maps derived with the decision tree method and performed on both pixels and image objects ranged between 77\% and 95\%. The highest mapping overall accuracy was achieved with a method that utilized all available input data and the object-based image analysis. Our study demonstrates the possibility of using single date WorldView-2 data for analyzing flooding events at high spatial detail despite the absence of spectral bands from the short-waveform region that are frequently used in water related studies. Our study also highlights the importance of topographic data in inundation analyses. The greatest difficulties were met in mapping water surfaces under dense canopy herbaceous vegetation, due to limited water surface exposure and the dominance of vegetation reflectance.},
  language  = {en}
}
@article{MoehligFloeterSprangeretal.2006,
  author    = {Moehlig, M. and Floeter, A. and Spranger, Joachim and Weickert, Martin O. and Schill, T. and Schloesser, H. W. and Brabant, G. and Pfeiffer, Andreas F. H. and Selbig, Joachim and Schoefl, C.},
  title     = {Predicting impaired glucose metabolism in women with polycystic ovary syndrome by decision tree modelling},
  series = {Diabetologia : journal of the European Association for the Study of Diabetes (EASD)},
  volume    = {49},
  journal   = {Diabetologia : journal of the European Association for the Study of Diabetes (EASD)},
  publisher = {Springer},
  address   = {Berlin},
  issn      = {0012-186X},
  doi       = {10.1007/s00125-006-0395-0},
  pages     = {2572 -- 2579},
  year      = {2006},
  abstract  = {Aims/hypothesis Polycystic ovary syndrome (PCOS) is a risk factor of type 2 diabetes. Screening for impaired glucose metabolism (IGM) with an OGTT has been recommended, but this is relatively time-consuming and inconvenient. Thus, a strategy that could minimise the need for an OGTT would be beneficial. Materials and methods Consecutive PCOS patients (n=118) with fasting glucose < 6.1 mmol/l were included in the study. Parameters derived from medical history, clinical examination and fasting blood samples were assessed by decision tree modelling for their ability to discriminate women with IGM (2-h OGTT value >= 7.8 mmol/l) from those with NGT. Results According to the OGTT results, 93 PCOS women had NGT and 25 had IGM. The best decision tree consisted of HOMA-IR, the proinsulin:insulin ratio, proinsulin, 17-OH progesterone and the ratio of luteinising hormone:follicle-stimulating hormone. This tree identified 69 women with NGT. The remaining 49 women included all women with IGM (100\% sensitivity, 74\% specificity to detect IGM). Pruning this tree to three levels still identified 53 women with NGT (100\% sensitivity, 57\% specificity to detect IGM). Restricting the data matrix used for tree modelling to medical history and clinical parameters produced a tree using BMI, waist circumference and WHR. Pruning this tree to two levels separated 27 women with NGT (100\% sensitivity, 29\% specificity to detect IGM). The validity of both trees was tested by a leave-10\%-out cross-validation. Conclusions/interpretation Decision trees are useful tools for separating PCOS women with NGT from those with IGM. They can be used for stratifying the metabolic screening of PCOS women, whereby the number of OGTTs can be markedly reduced.},
  language  = {en}
}