@article{RichardAbdelRahmanSubramanianetal.2017,
  author    = {Richard, Kyalo and Abdel-Rahman, Elfatih M. and Subramanian, Sevgan and Nyasani, Johnson O. and Thiel, Michael and Jozani, Hosein and Borgemeister, Christian and Landmann, Tobias},
  title     = {Maize cropping systems mapping using RapidEye observations in agro-ecological landscapes in Kenya},
  series = {Sensors},
  volume    = {17},
  journal   = {Sensors},
  number    = {11},
  doi       = {10.3390/s17112537},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-173285},
  year      = {2017},
  abstract  = {Cropping systems information on explicit scales is an important but rarely available variable in many crops modeling routines and of utmost importance for understanding pests and disease propagation mechanisms in agro-ecological landscapes. In this study, high spatial and temporal resolution RapidEye bio-temporal data were utilized within a novel 2-step hierarchical random forest (RF) classification approach to map areas of mono- and mixed maize cropping systems. A small-scale maize farming site in Machakos County, Kenya was used as a study site. Within the study site, field data was collected during the satellite acquisition period on general land use/land cover (LULC) and the two cropping systems. Firstly, non-cropland areas were masked out from other land use/land cover using the LULC mapping result. Subsequently an optimized RF model was applied to the cropland layer to map the two cropping systems (2nd classification step). An overall accuracy of 93\% was attained for the LULC classification, while the class accuracies (PA: producer's accuracy and UA: user's accuracy) for the two cropping systems were consistently above 85\%. We concluded that explicit mapping of different cropping systems is feasible in complex and highly fragmented agro-ecological landscapes if high resolution and multi-temporal satellite data such as 5 m RapidEye data is employed. Further research is needed on the feasibility of using freely available 10-20 m Sentinel-2 data for wide-area assessment of cropping systems as an important variable in numerous crop productivity models.},
  language  = {en}
}
@article{GrossAmuzudeCimanetal.2011,
  author    = {Groß, Uwe and Amuzu, Sylvarius K. and de Ciman, Ring and Kassimova, Iparkhan and Groß, Lisa and Rabsch, Wolfgang and Rosenberg, Ulrike and Schulze, Marco and Stich, August and Zimmermann, Ortrud},
  title     = {Bacteremia and Antimicrobial Drug Resistance over Time, Ghana},
  series = {Emerging Infectious Diseases},
  volume    = {17},
  journal   = {Emerging Infectious Diseases},
  number    = {10},
  doi       = {10.3201/edi1710.110327},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-133805},
  pages     = {1879-1882},
  year      = {2011},
  abstract  = {Bacterial distribution and antimicrobial drug resistance were monitored in patients with bacterial bloodstream infections in rural hospitals in Ghana. In 2001-2002 and in 2009, Salmonella enterica serovar Typhi was the most prevalent pathogen. Although most S. enterica serovar Typhi isolates were chloramphenicol resistant, all isolates tested were susceptible to ciprofloxacin.},
  language  = {en}
}