Satellite Observations and Malaria: New Opportunities for Research and Applications

Abstract

Satellite remote sensing provides a wealth of information about environmental factors that influence malaria transmission cycles and human populations at risk. Long-term observations facilitate analysis of climate-malaria relationships, and high-resolution data can be used to assess the effects of agriculture, urbanization, deforestation, and water management on malaria. New sources of very-high-resolution satellite imagery and synthetic aperture radar data will increase the precision and frequency of observations. Cloud computing platforms for remote sensing data combined with analysis-ready datasets and high-level data products have made satellite remote sensing more accessible to nonspecialists. Further collaboration between the malaria and remote sensing communities is needed to develop and implement useful geospatial data products that will support global efforts toward malaria control, elimination, and eradication.

Keywords: climate; land use and land cover; malaria; mosquito habitat; remote sensing; spatial decision support systems.

Figure 1.. Pathways through Which Satellite Data Provide Information about Malaria.

Satellite data can be used to predict geographic patterns and changes over time in climate factors, mosquito habitats, and human land use. These environmental variables influence malaria transmission through their effects on mosquitoes, parasites, and humans. Abbreviations: LST, land surface temperature; LULC, land use and land cover.

Figure 2.. Spatial and Temporal Resolutions of Satellite Missions and Data Products with Applications to Malaria.

Colors represent the main applications of satellite data at different resolutions. The lighter green color of the Commercial VHR box indicates the variable frequency of image acquisition. In principle, most VHR commercial satellites can collect data on a daily or near-daily repeat cycle. In practice, these satellites image only a fraction of the Earth’s surface each day, acquisition strategies are based on demand from customers, and remeasurement frequency can range from days to years. Abbreviations: AVHRR, advanced very-high-resolution radiometer; CHIRPS/CHIRTS, climate hazards group infrared precipitation/temperature with stations; GLPDR, global land parameter data record; IMERG, integrated multisatellite retrievals for global precipitation measurement; LULC, land use and land cover; MODIS, moderate resolution imaging spectroradiometer; NISAR, NASA-ISRO synthetic aperture radar; SMAP, soil moisture active-passive; SRTM, shuttle radar topography mission; VHR, very high resolution; VIIRS, visible infrared imaging radiometer suite.

Figure 3.. Satellite Data Collected over the Choke Mountains of Northwest Ethiopia in March 2019.

The four images display (A) moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) on 1 March 2019; (B) MODIS daytime land surface temperature (LST) on 1 March 2019; (C) climate hazards group infrared precipitation with station (CHIRPS) total monthly precipitation from 1 March to 31 March 2019; and (D) soil moisture active passive (SMAP) soil moisture on 1 March 2019. The time series charts display 4 years of data collected at the highlighted point on the maps. (E) MODIS NDVI. (F) MODIS LST. (G) CHIRPS precipitation. (H) SMAP soil moisture. The geographic coordinates of the highlighted point are 11.292 N, 36.978 E.

Figure 4.. Satellite Images of a Landscape in the Mecha District of Ethiopia in March 2019.

There is irrigated agriculture in the northwest part of the maps and the town of Merawi is located in the southeast. The zoomed inset map (dark boxes) shows irrigation canals and water storage ponds. (A) Landsat 8 false-color composite (shortwave infrared band displayed as red, near-infrared band displayed as green, and green band displayed as blue). Vegetation appears green, impervious surfaces are pink and purple, wetlands are reddish-brown, and open water is dark blue. (B) PlanetScope false-color composite (near infrared band displayed as red, red band displayed as green, green band displayed as blue). Vegetation appears red, impervious surfaces are white and light blue, wetlands are gray, and open water is light blue. (C) Sentinel-2 false color composite. Band display and interpretation are the same as for Landsat. (D) Sentinel-1 synthetic aperture radar image displaying the strength of signal returned to the sensor. The brightest areas indicate strong returns from buildings. Darker areas indicate open water and wet areas that reflect the signal away from the sensor. The geographic coordinates of Merawi are 11.410 N, 37.154 E.