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Ecological remote sensing

Remote sensing and infectious diseases

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.

Alpine MODIS NDVI example (16 days composite)
Alpine MODIS Vegetation Index example (16 days composite)

Bioclimatic factors from land surface temperature

Over the past decade, a variety of new spatialized climatic data series has become available. While some of these are even provided in daily resolution for some areas of the world (e.g., ECA&D data for Europe; CRU global climate data set), they often suffer from a low spatial resolution in terms of potential application in viticulture. For some continents such data sets are not available at all.
An interesting data source for enriching existing climatic data bases are satellite data, especially selected data products from the Moderate Resolution Imaging Spectroradiometer (MODIS) carried on the two satellites Aqua and Terra.
In order to assess the heat requirements of grapevine from space, the MODIS Land Surface Temperature (LST) and Emissivity product is of major interest. Providing four times per day a global coverage, the Aqua MYD11A1 and Terra MOD11A1 daily MODIS LST products can be used; data are always acquired at approximately the same local time for each site. Daily MODIS LST data from the year 2000 onwards are freely available for download from a NASA FTP server, leading to a total number of 14,000 coverages globally as of today. This permits site characterizations for each day separately over the past decade.

However, despite important preprocessing procedures implemented by NASA, the original LST product is not readily usable for viticulture applications. With regard to the calculation of bioclimatic variables from the MODIS LST time series, the main limitations are missing pixels due to clouds or aerosol contamination. Hence we developed a method to postprocess MODIS LST data which includes outlier detection (originating from undetected clouds) and gap filling procedure for missing pixels. The gap filling procedure includes interpolation in time and space coupled with statistical modelling which in turn uses auxiliary spatialized variables. The resulting LST time series has a map resolution of 250m and is covering Europe with four maps per day. Due to the huge amount of data involved at continental scale, a high performance computing facility and significant storage capabilities are required. The developed method can be easily adapted to further continents. For simplified data dissemination, the new data set  can be integrated into user friendly Web services with easy access.

The potential uses include bioclimatic indicators such as various Growing Degree Day indicators, and Winkler and Huglin indices as commonly used in viticulture.

Sensors/data of ecological relevance and low access costs

SensorPeriodSpatial resolutionTemporal resolutionFormat
  • Land Surface Temperature (LST)
  • Vegetation index (NDVI)
1978-today ~1km Daily L1B
  • Land Surface Temperature (LST)
  • Vegetation indices (NDVI, EVI)
  • Snow extent
  • ...
2000-today 1km
Daily HDF
SPOT Vegetation VGT (NDVI) 1998-today 1km 10 days HDF
LANDSAT-TM1-7 (VIS, NIR, TIR) 1972-today 15/30/60m 16 days GeoTIFF
ASTER (VIS, NIR, TIR) 2000-today 15/30/90m 16 days HDF


MODIS sensor

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 Sensor Specifications

Related publications:
  • °Neteler, M., °Roiz, D., Rocchini, D., Castellani, C. and Rizzoli, A. (2011). Terra and Aqua satellites track tiger mosquito invasion: modeling the potential distribution of Aedes albopictus in north-eastern Italy. International Journal of Health Geographics, 10:49 [ Abstract | DOI | PDF ] (IF: 2.34)
    °The authors contributed equally
  • Tonolli, S., Dalponte, M., Gianelle, D., Neteler, M.,  Rodeghiero, M., and Vescovo, L. (2011). Fusion of airborne  LIDAR and satellite multispectral data for the estimation of timber volume in an Alpine region. Remote Sensing of Environment, 115: 2486-2498.  [DOI | PDF] (IF: 3.951)
  • °Roiz D., °Neteler M., Castellani C., Arnoldi D., Rizzoli A. (2011). Climatic Factors Driving Invasion of the Tiger Mosquito (Aedes albopictus) into New Areas of Trentino, Northern Italy. PLoS ONE. 6(4): e14800. °The authors contributed equally [DOI | PDF] (IF: 4.411)
      - press reactions and
      - featured in European Commission, Directorate-General for the Environment (DG ENV), Science for Environment Policy, Issue 252, "Predicting the spread of the tiger mosquito in Europe"
  • He, K.S., Rocchini, D., Neteler, M., Nagendra, H. (2011). Benefits of hyperspectral remote sensing for tracking plant invasions. Diversity and Distributions, 17: 381-392 [DOI | PDF] (IF: 4.248)
  • Rocchini, D., Metz, M., Frigeri, A., Delucchi, L., Marcantonio, M., Neteler, M. (2011). Robust rectification of aerial photographs in an Open Source environment. Computers & Geosciences, in press [DOI] (IF: 1.416)
  • Tonolli, S., Dalponte, M., Gianelle, D., Neteler, M.,  Rodeghiero, M., and Vescovo, L. (2011). Fusion of airborne  LIDAR and satellite multispectral data for the estimation of timber volume in an Alpine region. Remote Sensing of Environment, in press [DOI | PDF] (IF: 3.951)
  • Rocchini, D., McGlinn, D., Ricotta, C., Neteler, M., Wohlgemuth, T. (2011). Landscape complexity and spatial scale influence the relationship between remotely sensed spectral diversity and survey based plant species richness. Journal of Vegetation Science. 22: 688-698 [DOI| PDF] (IF:  2.457)
  • Neteler, M., 2010: Estimating daily Land Surface Temperatures in mountainous environments by reconstructed MODIS LST data. Remote Sensing 2(1), 333-351. (DOI) [ Abstract | PDF ]
  • 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-CRI 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 13500 maps are in our archive. Aggregation to decades (16 days periods) is performed for epidemiological studies.


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.

Trentino seen by Landsat7 (click to enlarge)

Further reading

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