@article{GonschorekLangerBernhardtetal.2016,
  author    = {Gonschorek, Julia and Langer, Anja and Bernhardt, Benjamin and Raebiger, Caroline},
  title     = {Big Data in the Field of Civil Security Research: Approaches for the Visual Preprocessing of Fire Brigade Operations},
  series = {Science},
  volume    = {7},
  journal   = {Science},
  publisher = {IGI Global},
  address   = {Hershey},
  issn      = {1947-3192},
  doi       = {10.4018/IJAEIS.2016010104},
  pages     = {54 -- 64},
  year      = {2016},
  abstract  = {This article gives insight in a running dissertation at the University in Potsdam. Point of discussion is the spatial and temporal distribution of emergencies of German fire brigades that have not sufficiently been scientifically examined. The challenge is seen in Big Data: enormous amounts of data that exist now (or can be collected in the future) and whose variables are linked to one another. These analyses and visualizations can form a basis for strategic, operational and tactical planning, as well as prevention measures. The user-centered (geo-) visualization of fire brigade data accessible to the general public is a scientific contribution to the research topic 'geovisual analytics and geographical profiling'. It may supplement antiquated methods such as the so-called pinmaps as well as the areas of engagement that are freehand constructions in GIS. Considering police work, there are already numerous scientific projects, publications, and software solutions designed to meet the specific requirements of Crime Analysis and Crime Mapping. By adapting and extending these methods and techniques, civil security research can be tailored to the needs of fire departments. In this paper, a selection of appropriate visualization methods will be presented and discussed.},
  language  = {en}
}
@article{NitzeGrosse2016,
  author    = {Nitze, Ingmar and Grosse, Guido},
  title     = {Detection of landscape dynamics in the Arctic Lena Delta with temporally dense Landsat time-series stacks},
  series = {Remote sensing of environment : an interdisciplinary journal},
  volume    = {181},
  journal   = {Remote sensing of environment : an interdisciplinary journal},
  publisher = {Elsevier},
  address   = {New York},
  issn      = {0034-4257},
  doi       = {10.1016/j.rse.2016.03.038},
  pages     = {27 -- 41},
  year      = {2016},
  abstract  = {Arctic permafrost landscapes are among the most vulnerable and dynamic landscapes globally, but due to their extent and remoteness most of the landscape changes remain unnoticed. In order to detect disturbances in these areas we developed an automated processing chain for the calculation and analysis of robust trends of key land surface indicators based on the full record of available Landsat TM, ETM +, and OLI data. The methodology was applied to the similar to 29,000 km(2) Lena Delta in Northeast Siberia, where robust trend parameters (slope, confidence intervals of the slope, and intercept) were calculated for Tasseled Cap Greenness, Wetness and Brightness, NDVI, and NDWI, and NDMI based on 204 Landsat scenes for the observation period between 1999 and 2014. The resulting datasets revealed regional greening trends within the Lena Delta with several localized hot-spots of change, particularly in the vicinity of the main river channels. With a 30-m spatial resolution various permafrost-thaw related processes and disturbances, such as thermokarst lake expansion and drainage, fluvial erosion, and coastal changes were detected within the Lena Delta region, many of which have not been noticed or described before. Such hotspots of permafrost change exhibit significantly different trend parameters compared to non-disturbed areas. The processed dataset, which is made freely available through the data archive PANGAEA, will be a useful resource for further process specific analysis by researchers and land managers. With the high level of automation and the use of the freely available Landsat archive data, the workflow is scalable and transferrable to other regions, which should enable the comparison of land surface changes in different permafrost affected regions and help to understand and quantify permafrost landscape dynamics. (C) 2016 Elsevier Inc. All rights reserved.},
  language  = {en}
}