TY  - JOUR
A1  - Groß, Uwe
A1  - Amuzu, Sylvarius K.
A1  - de Ciman, Ring
A1  - Kassimova, Iparkhan
A1  - Groß, Lisa
A1  - Rabsch, Wolfgang
A1  - Rosenberg, Ulrike
A1  - Schulze, Marco
A1  - Stich, August
A1  - Zimmermann, Ortrud
T1  - Bacteremia and Antimicrobial Drug Resistance over Time, Ghana
JF  - Emerging Infectious Diseases
N2  - 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.
KW  - Typhoid-fever
KW  - Children
KW  - Surveillance
KW  - Kenya
Y1  - 2011
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-133805
N1  - All material published in Emerging Infectious Diseases is in the public domain and may be used and reprinted without special permission; proper citation, however, is required.
VL  - 17
IS  - 10
ER  - 
TY  - JOUR
A1  - Richard, Kyalo
A1  - Abdel-Rahman, Elfatih M.
A1  - Subramanian, Sevgan
A1  - Nyasani, Johnson O.
A1  - Thiel, Michael
A1  - Jozani, Hosein
A1  - Borgemeister, Christian
A1  - Landmann, Tobias
T1  - Maize cropping systems mapping using RapidEye observations in agro-ecological landscapes in Kenya
JF  - Sensors
N2  - 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.
KW  - remote sensing
KW  - RapidEye
KW  - bi-temporal
KW  - cropping systems
KW  - random forest
KW  - Kenya
Y1  - 2017
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-173285
VL  - 17
IS  - 11
ER  -