Down to Earth: Geographical Information for Sustainable Development in Africa

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Down to Earth: Geographic Information for Sustainable Development in Africa

TABLE 6-4 Links Between Disease and Factors That Can Be Remotely Sensed

Factor	Disease	Mapping Opportunity
Vegetation/crop type	Malaria	Breeding/resting/feeding habitats; crop pesticide vector resistance
	Schistosomiasis	Agricultural association with snails; use of human fertilizers
	Trypanosomiasis	Glossina habitat (forests, around villages, depending on species)
	Yellow fever	Reservoir (monkey) habitat
Vegetation green-up	Malaria	Timing of habitat creation
	Rift Valley fever	Rainfall
	Trypanosomiasis	Glossina survival
Deforestation	Malaria	Habitat creation (for vectors requiring sunlit pools); habitat destruction (for vectors requiring shaded pools)
	Yellow fever	Migration of infected human workers into forests where vectors exist; migration of disease reservoirs (monkeys) in search of new habitat
Forest patches	Yellow fever	Reservoir (monkey) habitat; migration routes
Flooding	Malaria	Mosquito habitat
	Rift Valley fever	Breeding habitat for mosquito vector
	Schistosomiasis	Habitat creation for snails
Permanent water and wetlands	Filariasis	Breeding habitat for Mansonia mosquitoes
	Malaria	Breeding habitat for mosquitoes
	Schistosomiasis	Snail habitat
Canals	Malaria	Dry season mosquito-breeding habitat; ponding; leaking water
	Schistosomiasis	Snail habitat
SOURCE: Adapted from Beck et al. (2000).

TABLE 6-5 Research Using Remotely-Sensed Data to Map Disease Vectors

Disease	Vector	Location	Sensor^a	Reference
Dracunculiasis	Cyclops spp.	Benin	Landsat TM	Clarke et al, 1990
	Cyclops spp.	Nigeria	Landsat TM	Ahearn and De Rooy, 1996
Filariasis	Culex pipiens	Egypt	AVHRR	Hassan et al., 1998a
	Culex pipiens	Egypt	Landsat TM	Hassan et al., 1998b; Cross et al., 1996
Malaria	Anoepheles spp.	Gambia	AVHRR, Meteosat	Thomson et al., 1997; Beck et al., 1994
		Kenya	RADARSAT-1	Kaya et al., 2002
Rift Valley fever	Aedes & Culex. spp	Kenya	AVHRR	Linthicum et al., 1990; Pope et al., 1992
	Culex. spp.	Kenya	Landsat TM, Synthetic Aperture Radar	Linthicum et al., 1994
	Culex. spp.	Senegal	SPOT, AVHRR	Malone et al., 1994
Schistosomiasis	Biomphalaria spp.	Egypt	AVHRR	Rogers, 1991
Trypanosomiasis	Glossina spp	Kenya, Uganda	AVHRR	Kitron et al., 1996
	Glossina spp	Kenya	Landsat TM	Rogers and Randolph, 1991
	Glossina spp	West Africa	AVHRR	Rogers and Williams, 1993
	Glossina spp	Africa	AVHRR	Robinson et al., 1997
	Glossina spp	Southern Africa	AVHRR	CEOS, 1995
^aTM = Thematic Mapper; AVHRR = (NOAA’s) Advanced Very High Resolution Radiometer; SPOT = Système Pour l’Observation de la Terre. SOURCE: Adapted from Beck et al. (2000)

For example, the option for users in developing countries to obtain geographic data in processed or raw form from government and private data sources will allow flexibility in the required level of geospatial capacity to use the data.

SUMMARY

Many types of thematic geographic data such as land cover, biophysical data, and some data for managing human health are, with the exception of very high spatial resolution urban land-cover data, available at low cost for addressing Agenda 21 issues in Africa. There are many existing applications of these data. Continuity of the data sources or their equivalents, options for raw and processed data, and coordination among data providers and users are crucial for continued and expanded use of geographic data for sustainable development in Africa.

The next chapter explores how people manage, analyze, and subsequently integrate geographic data into the decision-making process.

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