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16 2. Remote Sensing with MODIS
In 1970, inspired by spatial photographs taken during Mercury and Gemini’s missions,
and motivated by the possibility to monitor agriculture crop yelds, NASA launched
Landsat 1, the first satellite aiming at observing the earth surface and providing data
for scdientists from different disciplines. Following the success of the Landsat series ( 7
sensors since 1972), today’s intense research in remote sensing technologies is motivated
by deeper questions. Climate change and its potential disastrous impact on human life
urges an improved understanding of earth sciences, ranging from carbon cycle modelling
to weather forecasting. Such achievements are stricly limited by the quality of data collected
by satellite /aerial sensors and require a continous increase of spatial, temporal and
spectral resolution.
2.2 Applications of satellite imagery
Satellite and airborne sensors provide a continuous flow of data exploited in fields as
diverse as telecommunications, agriculture, deforestation, geology, hydrology, oceanography,
urban management and fire monitoring to cite a few. For its abilities to capture wide
portions of the globe, satellite derived data offers a high potential for the surveillance of
both local and global changes occuring in the Earth main systems.
2.2.1 Monitoring land changes
Among many human-induced activites, deforestation is highly responsible for both
weather and climate change. Forests play a crucial role in the climate stability as they
absorb and trap large quantities of carbon dioxide (CO2) present in the atmopshere. The
massive destruction of forests for urbanization and agricultural development, inevitably
releases in the atmopshere all the CO2 and other greenhouse gazes stored in trees for
decades. Assessing the amount and rate of carbon emissions from deforestation or other
activities, remotely sensed data can be used to monitor primary variables of the Earth
land surface such as the spectral reflectance, albedo and temperature. In addition, higher
order variables in the form of vegetation indices can provide valuable information on land
change. A well-known variable is the Normalized Difference Vegetation Index (NDVI)
[Rouse et al., 1973], defined as the ratio between the difference and the sum of reflectances
in the visible (red) and near-infrared spectral bands :
NDVI = ρ NIR − ρ Red
ρ NIR + ρ Red
(2.1)
A major disadvantage of he NDVI index is that it tends to saturate over dense vegetation
and is highly dependent on the background soil composition. In the presence of high
biomass, the Enhanced Vegetation Index (EVI) [Huete, 2002] provides an optimized vegetation
signal able to capture high variability. and minimizes the influence of atmospheric