FLOE EDGE
WATER QUALITY
ARCTIC TOURISM
VEGETATION DENSITY

MAST
LEAR
AUTO TRACKER
IMAGE INSIGHT
ICE TUTOR
DADS

WETLANDS CLASSIFICATION
LAKE/RIVER ICE
MALARIA
INTERFEROMETRY
CCIN

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DEVELOPMENT AND DEMONSTRATION OF EARTH OBSERVATION TECHNOLOGY FOR IDENTIFYING NATURAL MOSQUITO HABITATS AND PREDICTING MALARIA RISK IN AFRICA

The project is part of Kenya's National Malaria Control Program that involves an interdisciplinary team in insect ecology, human health and tropical diseases, and ecologists. Noetix Research Inc. was the prime contractor and worked in collaboration with the Kenya Medical Research Institute (KEMRI) and C-CORE. The objectives of this project were to provide detailed maps, monitor changes to mosquito habitats (wetlands), and develop malaria risk maps using data on topography, larva ecology, human health, households, and habitats. This information will be an integral part of an overall larva-control strategy to identify larvicide-application practices that would reduce the mosquito population.

Detailed space-based data on wetlands and mosquito control are not available for the region but can be obtained for reasonable cost by high-resolution Earth-observation sensors. Data acquired over time can monitor the change and provide a more accurate resource map. Continuous cloud cover during wet seasons and the cumulus clouds caused by heat during the dry season means that an all-weather satellite with high spatial resolution modes, like RADARSAT, will provide a reliable source of multi-temporal imagery.

Service Description

The project builds upon the team’s work on establishing appropriate use of EO technology for vector-borne disease mapping in Kenya in close collaboration with African Agencies. The datasets produced included:
- Accumulative runoff map for identifying drainage channels (likely habitats for Mosquitoes) and for input to predictive modeling
- Improved general land cover map
- Mosquito habitat classes
- Mosquito and human distribution information

These datasets are used to establish causal relationships, risk maps, and the latter information to predict where outbreaks are likely to occur. Watershed boundaries and drainage networks are derived from digital elevation models. Each segment of a tributary can be assigned a likelihood of containing a mosquito habitat based upon the size of water basin contributing to the drainage segment (accumulated upstream sub-basins), slope of the surrounding water basin/drainage feature, land cover, and soil drainage properties. Specific mosquito habitats are identified using SAR and optical imagery. Multi-temporal SAR imagery are used to identify ephemeral and permanent water bodies and identify moist soils. Medium spatial resolution optical data are used to identify green vegetation during the dry season.