MODIS time series remote sensing for epidemiological modeling

Markus Neteler
ITC-irst, Trento, Italy

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     Last modified: August 14, 2004
     Presentation date: 09/18/2004 9:00 AM in CH1
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abstracts
In epidemiological modeling, survey data are usually collected at sampling
sites and then regionalized within Geographical Information Systems (GIS).
To enhance the data density, continuous field data such as land surface
temperatures (LST), snow coverage, vegetation indices are commonly derived
from satellite data. The recent launches of the new satellite systems Terra
and Aqua significantly improve the situation of data availability for
scientific purposes and epidemiological studies and predictions. The most
interesting sensor onboard is MODIS which daily delivers two global coverages
at 250m (Red, NIR), 500m (MIR) and 1000m resolution (TIR).

The paper focuses on two of the numerous MODIS data products: Land Surface
Temperatures (LST), and vegetation index 16-day composites.

The integration of MODIS satellite data into a GIS requires several
pre-processing steps, such as the reprojection from MODIS-ISIN or MODIS-SIN
projections to another more common projection (UTM, national coordinate
systems etc.). The resulting maps are filtered pixelwise by applying the
related quality maps which are provided along the data products. Due to
limitations in the official cloud detection algorithm used to create these
land surface temperature quality maps, an outlier detection has been
implemented. Based on the scene statistics, this outlier filter aims at
removing all pixels which contain cloud temperatures instead of the
desired land surface temperatures.

Another set of MODIS time series data are NDVI and EVI vegetation indices.
They can be implemented into epidemiological models to introduce vegetation
dynamics. The 16-day composite product minimizes cloud cover and reflects
at a sufficient temporal resolution the current vegetation status.

The integration of MODIS data into epidemiological research enhances
the spatio-temporal resolution of climatological data in particular in
mountainous regions. The study area, a region of approximately 20000 sqkm,
is of complex terrain with elevation ranging from nearly sea level to
3800 meters with a varying density of meteorological stations.

The recent implementation of general time series processing for GRASS
raster maps supports univariate statistics for a series of MODIS scenes.
By selecting various time ranges and operators, a number of indicators
can be calculated. The comparison of LST with ground truth time series
from climatic stations showed that the LST match quite well with ground
temperatures. While surface and aerial temperatures differ by definition,
it is possible to transform surface to aerial temperatures by a regression
model. Results and comparisons will be presented in the paper.

Keywords: remote sensing, GIS, GRASS, MODIS LST, MODIS NDVI/EVI, time series