Emergence and spread of infectious diseases in a changing environment require the development of new methodologies and tools for risk assessment, early warning and policy making. GIS modelling is routinely used to perform risk assessment for the mitigation of these diseases. We use remote sensing technologies to derive ecological indicators from high temporal resolution satellite data time series. Especially the Moderate Resolution Imaging Spectroradiometers (MODIS sensor) which are flown on the Terra and Aqua satellites, deliver an almost complete Earth coverage four times a day at different resolutions (from 250m to 1km pixel resolution). These data are integrated with common GIS data for spatial data analysis. Special focus is on land surface temperatures (LST, daily), snow coverage (weekly), leaf area index (LAI, weekly), and vegetation indices (NDVI, EVI, bi-weekly), all derived from MODIS satellite data. The Enhanced Vegetation Index (EVI) permits to detect seasonal vegetation differences, spring/autumn detection and the length of growing season. Furthermore, the Normalized Difference Water Index (NDWI) can be calculated weekly.

Sensors/data of ecological relevance and low access costs

MODIS

The launches of the NASA satellite systems Terra (December 1999) and Aqua (May 2002) significantly improve the situation of data availability for scientific purposes and predictive epidemiological studies. The Moderate Resolution Imaging Spectroradiometer (MODIS) is a key instrument on both Terra and Aqua satellites. As they deliver daily two global coverages at 250m (Red, NIR), 500m (MIR) and 1000m resolution (TIR), they are most interesting to support epidemiological studies. Usually one week after acquisition the data are available to the public.

For our research, MODIS data are crucial as they help us to derive ecological indicators from MODIS high resolution time series. We are specialised in reconstruction of cloud contaminated LST data which are completely restored using a GIS based methodology. The complex terrain of the Southern Alps is particularly challenging.

Further details

See MODIS LST

See MODIS NDVI/EVI

See MODIS Sensor Specifications

• Carpi G., Cagnacci F., Neteler M., Rizzoli A, 2008: Tick infestation on roe deer in relation to geographic and remotely-sensed climatic variables in a tick-borne encephalitis endemic area. Epidemiology and Infection,136, pp. 1416-1424. (DOI) (ISI 2007: 1.900) [ PubMed ]

• A. Rizzoli, M. Neteler, R. Rosà, W. Versini, A. Cristofolini, M. Bregoli, A. Buckley, and E.A. Gould, 2007: Early detection of TBEv spatial distribution and activity in the Province of Trento assessed using serological and remotely-sensed climatic data. Geospatial Health, 1(2), pp. 169-176. [ PubMed | PDF ]

• M. Neteler, 2005: Time series processing of MODIS satellite data for landscape epidemiological applications. International Journal of Geoinformatics, 1(1), pp. 133-138 (PDF)

FEM-CEA data holdings

MODIS LST data were postprocessed from Terra satellite from 5 mar 2000 - today and from Aqua satellites from 8 jul 2002 - today: more than 11500 maps are in our archive. Aggregation to decades (16 days periods) is performed for epidemiological studies.

LANDSAT

The recent publication of the USGS LANDSAT archive is a great help for long term studies. With a repeat time of 16 days the entire globe is captured.

Further reading

• ASPRS guide to land imaging satellites (2008)
• MODIS Aqua and Terra products table
• MODIS snow & ice products