title: Multisensor earth observations to characterize wetlands and malaria epidemiology in Ethiopia.
abstract: Malaria is a major global public health problem, particularly in Sub-Saharan Africa. The spatial heterogeneity of malaria can be affected by factors such as hydrological processes, physiography, and land cover patterns. Tropical wetlands, for example, are important hydrological features that can serve as mosquito breeding habitats. Mapping and monitoring of wetlands using satellite remote sensing can thus help to target interventions aimed at reducing malaria transmission. The objective of this study was to map wetlands and other major land cover types in the Amhara region of Ethiopia and to analyze district-level associations of malaria and wetlands across the region. We evaluated three random forests classification models using remotely sensed topographic and spectral data based on Shuttle Radar Topographic Mission (SRTM) and Landsat TM/ETM+ imagery, respectively. The model that integrated data from both sensors yielded more accurate land cover classification than single-sensor models. The resulting map of wetlands and other major land cover classes had an overall accuracy of 93.5%. Topographic indices and subpixel level fractional cover indices contributed most strongly to the land cover classification. Further, we found strong spatial associations of percent area of wetlands with malaria cases at the district level across the dry, wet, and fall seasons. Overall, our study provided the most extensive map of wetlands for the Amhara region and documented spatiotemporal associations of wetlands and malaria risk at a broad regional level. These findings can assist public health personnel in developing strategies to effectively control and eliminate malaria in the region.
  
  
  CONCLUSIONS
  Remote sensing produced an accurate wetland map for the Ethiopian highlandsWetlands were associated with spatial variability in malaria riskMapping and monitoring wetlands can improve malaria spatial decision support.
authors: Geospatial Sciences Center of Excellence, South Dakota State University Brookings, South Dakota, USA.
authors: Alemayehu
authors: A
authors: Midekisa
authors: U.S. Geological Survey Earth Resources Observation and Science Center Sioux Falls, South Dakota, USA.
authors: Gabriel B
authors: GB
authors: Senay
authors: Geospatial Sciences Center of Excellence, South Dakota State University Brookings, South Dakota, USA.
authors: Michael C
authors: MC
authors: Wimberly
keywords: early warning; hydrology; malaria; public health; remote sensing; wetlands
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